Renewable Energy from Forest Resources in the United States (Routledge Explorations in Environmental Economics)

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Routledge Explorations in Environmental Economics Edited by Nick Hanley University of Stirling, UK

1. Greenhouse Economics Value and Ethics Clive L. Spash 2. Oil Wealth and the Fate of Tropical Rainforests Sven Wunder 3. The Economics of Climate Change Edited by Anthony D. Owen and Nick Hanley 4. Alternatives for Environmental Valuation Edited by Michael Getzner, Clive Spash and Sigrid Stagl 5. Environmental Sustainability A consumption approach Raghbendra Jha and K.V. Bhanu Murthy 6. Cost-Effective Control of Urban Smog The significance of the Chicago cap-and-trade approach Richard F. Kosobud, Houston H. Stokes, Carol D. Tallarico and Brian L. Scott 7. Ecological Economics and Industrial Ecology Jakub Kronenberg 8. Environmental Economics, Experimental Methods Edited by Todd L. Cherry, Stephan Kroll and Jason F. Shogren 9. Game Theory and Policy Making in Natural Resources and the Environment Edited by Ariel Dinar, José Albiac and Joaquín Sánchez-Soriano 10. Arctic Oil and Gas Sustainability at risk? Edited by Aslaug Mikkelsen and Oluf Langhelle 11. Agrobiodiversity, Conservation and Economic Development Edited by Andreas Kontoleon, Unai Pascual and Melinda Smale 12. Renewable Energy from Forest Resources in the United States Edited by Barry D. Solomon and Valerie A. Luzadis

Renewable Energy from Forest Resources in the United States

Edited by Barry D. Solomon and Valerie A. Luzadis

First published 2009 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN Simultaneously published in the U.S.A. and Canada by Routledge 270 Madison Avenue, New York, NY 10016 This edition published in the Taylor & Francis e-Library, 2008. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” Routledge is an imprint of the Taylor & Francis Group, an informa business © 2009 Editorial matter and selection, Barry D. Solomon and Valerie A. Luzadis; individual chapters, the contributors All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data Renewable energy from forest resources in the United States / edited by Barry D. Solomon and Valerie A. Luzadis. p. cm. Includes bibliographical references and index. 1. Biomass energy industries—United States. 2. Forest products industry—United States. 3. Renewable energy resources—United States. 4. Forests and forestry—Economic aspects—United States. I. Solomon, Barry D. (Barry David), 1955–. II. Luzadis, Valerie A. HD9502.5.B543U665 2009 333.95′397—dc22 2008019429 ISBN 0-203-88842-1 Master e-book ISBN ISBN10: 0–415–77600–7 (hbk) ISBN10: 0–203–88842–1 (ebk) ISBN13: 978–0–415–77600–4 (hbk) ISBN13: 978–0–203–88842–1 (ebk)

Contents

List of contributors Foreword by Christopher Flavin Preface Acknowledgments List of acronyms and abbreviations

viii xi xii xiv xvi

PART I

Overview 1 Introduction

1 3

BARRY D. SOLOMON AND NICHOLAS H. JOHNSON

2 Market analysis and considerations for renewable energy technologies

28

DANA M. JOHNSON

3 From grain to cellulosic ethanol: history, economics and policy

49

BARRY D. SOLOMON, JUSTIN R. BARNES AND KATHLEEN E. HALVORSEN

PART II

Forest biomass energy assessments 4 Resource assessment, economics and technology for collection and harvesting

67

69

ERIN G. WILKERSON AND ROBERT D. PERLACK

5 An integrated supply system for forest biomass TIMOTHY L. JENKINS AND JOHN W. SUTHERLAND

92

vi

Contents

6 Application of biomass-derived fuels for internal combustion engines with a focus on transportation

116

JEFFREY D. NABER AND JEREMY J. WORM

7 Bioenergy, biomass and biodiversity

133

DAVID J. FLASPOHLER, CHRISTOPHER R. WEBSTER AND ROBERT E. FROESE

8 A review of life cycle assessment studies on renewable energy derived from forest resources

163

QIONG ZHANG, KAITLIN R. GOLDSTEIN AND JAMES R. MIHELCIC

9 Using a systems approach to improve bioenergy sustainability assessment

196

VALERIE A. LUZADIS, TIMOTHY A. VOLK AND THOMAS S. BUCHHOLZ

PART III

Regional case studies

211

10 Cost and financial feasibility of two biomass power technologies

213

DANA M. JOHNSON, JAMES H. WHITMARSH AND JILLIAN R. WATERSTRAUT

11 Willow biomass production for bioenergy, biofuels, and bioproducts in New York

238

TIMOTHY A. VOLK AND VALERIE A. LUZADIS

12 Woody biomass feedstock availability, production costs and implications for bioenergy conversion in Mississippi

261

DONALD L. GREBNER, GUSTAVO PEREZ-VERDIN, CHANGYOU SUN, IAN A. MUNN, EMILY B. SCHULTZ AND THOMAS G. MATNEY

13 Regional economic impacts of cellulosic ethanol development in the North Central states BARRY D. SOLOMON

281

Contents 14 Wood methanol as a renewable energy source in the western United States

vii 299

DANIEL J. VOGT, KRISTINA A. VOGT, JOHN C. GORDON, MICHAEL L. MILLER, CALVIN MUKUMOTO, RAVI UPADHYE AND MICHAEL H. MILLER

Afterword by Valerie A. Luzadis Glossary Index

323 325 329

List of contributors

Justin R. Barnes is an energy policy analyst at the North Carolina Solar Center of North Carolina State University. Thomas S. Buchholz is a doctoral candidate in forest and natural resource management at the State University of New York College of Environmental Science and Forestry. David J. Flaspohler is an Associate Professor of Forest Resources and Environmental Science at Michigan Technological University. Christopher Flavin is President of the Worldwatch Institute in Washington, D.C. Robert E. Froese is an Assistant Professor of Forest Resources and Environmental Science at Michigan Technological University. Kaitlin R. Goldstein is a physics student at Brown University. John C. Gordon is the Pinchot Professor of Forestry and Environmental Studies Emeritus, Yale University School of Forestry and Environmental Studies; chairman, Interforest LLC; and chairman, The Candlewood Timber Group. Donald L. Grebner is an Associate Professor of Forestry at Mississippi State University. Kathleen E. Halvorsen is an Associate Professor of Natural Resource Policy at Michigan Technological University. Timothy L. Jenkins is a graduate student in mechanical engineering at Michigan Technological University. Dana M. Johnson is an Associate Professor of Operations Management at Michigan Technological University. Nicholas H. Johnson is a graduate student in environmental policy at Michigan Technological University. Valerie A. Luzadis is an Associate Professor of Natural Resources Policy and


ix

Economics at the State University of New York College of Environmental Science and Forestry. Thomas G. Matney is a Professor of Forestry at Mississippi State University. James R. Mihelcic is a Professor of Civil and Environmental Engineering at University of South Florida. Michael H. Miller is a research associate at the Intellus Corporation. Michael L. Miller is President of the Intellus Corporation. Calvin Mukumoto is a forest management consultant and owner of Mukumoto Associates in Bend, Oregon. He is also a member of the Forest Stewardship Council’s U.S. board of directors. Ian A. Munn is a Professor of Forestry at Mississippi State University. Jeffrey D. Naber is an Associate Professor of Mechanical Engineering at Michigan Technological University. Gustavo Perez-Verdin is a post-doctoral research associate in the Department of Forestry at Mississippi State University. Robert D. Perlack is a resource economist in the Environmental Sciences Division of the Oak Ridge National Laboratory. Emily B. Schultz is an Associate Professor of Forestry at Mississippi State University. Barry D. Solomon is a Professor of Geography and Environmental Policy at Michigan Technological University. He is also the co-founder and ex-president of the U.S. Society for Ecological Economics. Changyou Sun is an Assistant Professor of Forestry at Mississippi State University. John W. Sutherland is the Richard & Elizabeth Henes Chair Professor of Mechanical Engineering at Michigan Technological University where he is also Director of the Sustainable Futures Institute. Ravi Upadhye is a chemical engineer at the Lawrence Livermore National Laboratory. Daniel J. Vogt is an Associate Professor of Forest Resources at the University of Washington. Kristina A. Vogt is a Professor of Forest Resources at the University of Washington. Timothy A. Volk is a research associate at the State University of New York College of Environmental Science and Forestry. Jillian R. Waterstraut is a law student at the University of Iowa.

x


Christopher R. Webster is an Associate Professor of Forest Resources and Environmental Science at Michigan Technological University. James H. Whitmarsh is a production & shipping supervisor at Unimin Minnesota Corp. Erin G. Wilkerson is an agricultural engineer in the Environmental Sciences Division of the Oak Ridge National Laboratory. Jeremy J. Worm is a research engineer in the Department of Mechanical Engineering-Engineering Mechanics at Michigan Technological University. Qiong Zhang is operations manager of the Sustainable Futures Institute at Michigan Technological University, where she is also a senior research engineer and an Adjunct Assistant Professor of Civil and Environmental Engineering.

Foreword Christopher Flavin

The energy economy of the United States has entered a new era. Soaring oil prices and the urgency of stabilizing the climate have led to renaissance of renewable energy. Propelled by a proliferation of new state and federal policies, wind power, solar energy, and bio-ethanol are all growing at double-digit rates, attracting unprecedented investment and accelerating the development of new technologies. At long last, the end of the fossil fuel age may finally be in sight. One would think that the country’s forest resources would be at the leading edge of this energy revolution. Prior to the twentieth century, the abundant forest resources of the United States provided the bulk of the country’s energy – at a time when Great Britain had already shifted to dependence on coal, wood still prevailed in the United States. And even today, the nation’s forests provide almost half of the 7 per cent share of U.S. energy that renewables now provide. But the nation’s forest resources have received much less attention than they deserve. At a time when European countries with a fraction of the U.S. forest resource base have commercialized a host of new heating and electricity generating technologies based on woody biomass, the U.S. has been lagging well behind. This book should help move the United States back into the leading ranks of forest energy pioneers. Barry Solomon, Valerie Luzadis and their colleagues have provided a comprehensive and masterful assessment of the country’s forest resource base and the new technologies available to harness them, demonstrating that the country’s forest resources can provide large amounts of energy while protecting and enhancing the ecological health of our forests. There is no time to waste. Climate scientists believe that unless carbon emissions are reduced dramatically in the next few decades, the world faces a period of catastrophic climate change, with ecological destruction beginning to feed on itself. And if that should happen, the world’s forests would be among the principle casualties, with drought, disease and fire putting vast areas at risk of destruction. Sustainably managing the world’s forests, and using forest biomass to displace fossil fuels, turns out to one of the keys to stabilizing the climate – and thereby saving the country’s forests.

Preface

Recent attention to global climate change, the need to greatly lower greenhouse gas emissions, and to lessen oil-import dependence by rapidly developing renewable energy sources has had policy makers and educators scrambling to respond. One major energy source that seems to fit the bill is one of the oldest: wood and other sources of biomass. Interest in biomass energy resources from forests, farms, and other domestic sources has assuredly been rapidly increasing. Forest resources have a large potential to meet domestic energy needs and can dramatically lower carbon dioxide (CO2) emissions if they are grown sustainably. They should not, however, be considered a panacea given the need to protect natural ecosystems and biodiversity. Even so, policy-makers at both federal and state levels have been developing increasingly ambitious goals and mandates for energy and fuel production from biomass resources. This volume was compiled to provide an up-to-date assessment of the technical, economic, and ecologic ability of U.S. forests to meet future U.S. energy needs, especially for transportation fuels and electric power generation. No existing book comprehensively covers this subject with a U.S. focus. Several older books on the topic exist, but do not reflect the current state of technology of renewable energy from forest resources, as this is a rapidly advancing field. Having said this, we also recognized that while economics is important to the future of biomass energy, only a few of the large number of the technologies now available are likely to be cost-effective in the next decade. Consequently, we emphasize the most promising options, such as cellulosic ethanol, and electric power from wood gasification and co-firing. Even here, the economics are somewhat speculative as the technology is still developing; this is even more the case for potentially promising technologies that have received less support such as bio-butanol. Existing technologies such as wood heating are also recognized, but do not receive much treatment, as they are unlikely to expand in the U.S. The genesis of this book can be traced to two major research projects on forest biomass energy resources – one at Michigan Technological University (MTU) and one at the SUNY College of Environmental Science and Forestry (SUNY-ESF). Both projects are highly interdisciplinary. MTU began a

Preface

xiii

long-term research commitment to forest biomass energy production in 2003, focusing on the development of this industry in the upper Great Lakes region. A research team of scientists and engineers has been assessing a variety of tree species and switchgrass with a primary focus on fuel markets. The MTU team is also working with Michigan State University to help develop a bioenergy economy in the State of Michigan. The parallel effort at SUNY-ESF is much older and currently targets willow, one of several priority shortrotation woody crops. The regional focus is the Northeast U.S. Both projects have been assessing the sustainability of a biomass energy industry. Three additional chapters were commissioned from other bioenergy research projects, which are designed to complement the perspectives of MTU and SUNY-ESF and thereby round out the book. As a result, the uniqueness of this book is its coverage of all major biomass energy markets in the U.S., from both economic and technical perspectives. This book will be of interest to two primary audiences: academics teaching and researching on forest biomass for energy production, and Policy Makers seeking to base decisions to develop alternative energy on a sound scientific foundation and thorough assessments. More and more colleges with environmental and natural resources programs are offering seminars and full courses on the use of forest resources for energy production. This book should serve as a good reader for such courses. It also provides several excellent case studies of current U.S. projects that will be useful to scholarly and industrial researchers undertaking similar projects elsewhere, including abroad.

Acknowledgments

The editors and authors of this volume thank the following organizations for their financial support, without which this work would not have been possible: the Biocomplexity and Sustainability Research Experiences for Undergraduates (REU) programs of the National Science Foundation (Grants No. BE/MUSES-0524872 and No. EEC 0453174); Caterpillar, Inc.; the Sustainable Futures Institute of Michigan Technological University; the Mississippi Institute for Forest Inventory; the Sustainable Energy Research Center at Mississippi State University; and the Biomass Program of the U.S. Department of Energy. We are grateful to the following colleagues for their technical assistance, comments, advice, and/or willingness to review one or more draft chapters of this book: Carl Anderson, Paul Baer, Adam Cooper, Mary Ann Curran, André Dhondt, George Erickcek, Carlos Gavilondo, Brent Haddad, Steve Hoffman, Anwar Hussain, Nick Johnson, Michael Kelleher, Amy Landis, Craig Marriott, Kenny McClevey, Glenn McGrath, Anelia Milbrandt, Shelie Miller, David Morris, Warren Ondras, Terry Reynolds, Patricia Roads, David Shonnard and Frederick Treyz. Chapter 3 is reprinted in revised form from Biomass & Bioenergy, Volume 31, Barry D. Solomon, Justin R. Barnes and Kathleen E. Halvorsen, ‘Grain and cellulosic ethanol: history, economics, and energy policy’, pp. 416–425, copyright © 2007, with permission from Elsevier; Figures 4.6, 4.7, 4.8, 4.9 and 4.11 are courtesy of Deere & Company; Figure 4.10 is courtesy of Montana Community Development Corporation; Figures 5.3, 5.4 and 5.12 are courtesy of VTT, Technical Research Centre of Finland; Figure 5.5 is courtesy of David Shonnard, Timothy L. Jenkins and VTT, Technical Research Centre of Finland; Figure 5.8 is courtesy of Sigurd Falk; Figure 5.11 is courtesy of Wes Rutt of the Colorado Tree Farmers and the National Renewable Energy Laboratory; Figure 7.2 is courtesy of David J. Flaspohler; Figure 7.3 is courtesy of Robert Froese; Figure 7.5 is courtesy of Christopher R. Webster; Figure 7.7 is courtesy of JoAnn Hanowski and Christopher R. Webster; Table 8.1 is adapted from Journal of Cleaner Production, Volume 15, Harro von Blottnitz and Mary Ann Curran, ‘A review of assessments conducted on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and

Acknowledgments xv environmental life cycle perspective’, pp. 607–619, copyright 2007, with permission from Elsevier; Figure 8.7 is reprinted from Energy Conversion and Management, Volume 46, Matteo Carpentieri, Andrea Corti and Lidia Lombardi, ‘Life cycle assessment (LCA) of an integrated biomass gasification combined cycle (IBGCC) with CO2 removal’, pp. 1,790–1,808, copyright © 2005, with permission from Elsevier; Figure 8.8 is reprinted from University of California, Davis UCD-ITS-RR-04–45, Mark Delucchi, ‘Conceptual and methodological issues in lifecycle analyses of transportation fuels’, 2004, with permission from Mark Delucchi; Tables 10.1, 10.7–10.10, 10.13, 10.14, 10.17 and 10.19 are reprinted from International Journal of Environment and Waste Management, forthcoming, Dana M. Johnson, Robert E. Froese, Jillian R. Waterstraut, James H. Whitmarsh, Abraham Martin, and Chris A. Miller, ‘Business viability of biomass co-firing and gasification for electricity generation’, copyright © 2008, with permission from Inderscience; Figure 11.2 is reprinted from Frontiers in Ecology and the Environment, Volume 2, Timothy A. Volk, Theo Verwijst, Pradeep J. Tharakan and Lawrence P. Abrahamson, ‘Growing energy: assessing the sustainability of willow short-rotation woody crops’, pp. 411–418, copyright © 2004, with permission from the Ecological Society of America; and Figures 11.6 and 11.7 are reprinted from Bird Study, Volume 54, André A. Dhondt, Peter H. Wrege, Jacqueline Cerretani, and Keila V. Sydenstricker, ‘Avian species richness and reproduction in shortrotation coppice habitats in central and western New York’, pp. 12–22, copyright 2007, with permission from the British Trust for Ornithology. We would be remiss if we did not recognize the great pleasure that we had to work with Rob Langham and Sarah Hastings at Routledge, whose prompt assistance, advice and good cheer made the production of this book a seamless process from across the Atlantic.

List of acronyms and abbreviations

$ a ABS ac ADM AEO AL APAC ASTM AZ BDC BEA BGCC BIOCOST BLS BMP BTU bu C c Ca CA CAAA CAFE CES cf CFR c g−1 CH4 CHP

United States dollars acceleration anti-lock braking system acres Archer Daniels Midland Company Annual Energy Outlook (Energy Information Administration) Alabama Agricultural Policy Analysis Center (University of Tennessee) American Society for Testing and Materials Arizona bottom dead center Bureau of Economic Analysis (U.S. Department of Commerce) biomass gasification combined cycle Bioenergy Cost (model) Bureau of Labor Statistics (U.S. Department of Labor) best management practice British thermal units bushel Celsius or carbon cents calcium California Clean Air Act Amendments of 1990 Corporate Average Fuel Economy constant elasticity of substitution compare Cooperative Fuel Research (engine) cents per gallon methane combined heat and power

List of acronyms and abbreviations CI cm CNG CNH CO CO2 CP CPI CR CRL CRP CSL CV dbh DDG DDGS DME DNR DOE E E10, E20, E85, E95 ECU e.g. EGR EIA EJ EPA EPAct EPO EPRI ETA ETBE EURO EW F FIA ft FT g gal GHG GIS GJ

xvii

Compression Ignition centimeters compressed natural gas Case New Holland carbon monoxide carbon dioxide constant pressure consumer price index compression ratio composite residue logs Conservation Reserve Program corn steep liquor constant volume diameter at breast height distillers dried grains distillers dried grains with solubles dimethyl ether Department of Natural Resources United States Department of Energy electricity Percentage ethanol in fuel blend, e.g. E20 has 20 per cent ethanol and 80 per cent gasoline electronic control unit for example exhaust gas recirculation Energy Information Administration (U.S. Department of Energy) exajoules, or one quintillion joules United States Environmental Protection Agency United States Energy Policy Act (1992, 2005) electric power only Electric Power Research Institute United States Energy Tax Act of 1978 ethyl tertiary butyl ether Europe energy wood force Forest Inventory Analysis feet Fischer-Tropsch gallon or specific gravity gallon greenhouse gas geographic information system gigajoule

xviii


GRP GWP H or H2 H2O ha HEM HEV HFRA HILD hr IBGCC IBSAL IC ICGCC IGCC ID IEA IL IMPLAN I–O IPCC IRR ISO JEC K kPa kg KLCR km KS kWh L LA LCA LCI lbs LIHD LTL LY m M85

gross regional product global warming potential hydrogen water hectare Human Ecosystem Model hybrid-electric vehicle Healthy Forests Restoration Act of 2003 high input-low diversity hour Integrated Biomass Gasification Combined Cycle Integrated Biomass Supply Analysis and Logistics (model) internal combustion Integrated Coal Gasification Combined Cycle Integrated Gasification Combined Cycle Idaho International Energy Agency (France) Illinois Impact Analysis for Planning (model) input–output Intergovernmental Panel on Climate Change (Switzerland) internal rate of return International Organization for Standardization (Switzerland) Joint Economic Committee of the U.S. Congress potassium kilopascals kilograms knock-limited compression ratio kilometers Kansas kilowatt-hours liters Louisiana life cycle assessment life cycle inventory pounds low input-high diversity less-than-truckload liters per year meter or mass fuel blend with 85 per cent methanol and 15 per cent gasoline

List of acronyms and abbreviations max MF Mg MIFI min MJ ml MMBTU MON MPa MPG MSW MTBE MW MWe MWh N NA NC NGOs NMEP NOx NPV NREL O or O2 O&M odt OECD OEM OPEC ORNL OSB OTR oz P PA Pg PNS POLYSYS PON PPA PPI psi

maximum motor vehicle fuels Megagram or metric tonne; also magnesium Mississippi Institute for Forestry Inventory minimum Megajoule milliliter million British thermal units Motor Octane Number Mega-Pascals miles per gallon municipal solid waste methyl tertiary butyl ether Megawatts Megawatts electric Megawatt-hours nitrogen not available or not applicable North Carolina non-governmental organizations net mean effective pressure nitrogen oxides net present value National Renewable Energy Laboratory oxygen operations and maintenance oven dry tonnes or tons Organization for Economic Co-operation and Development (France) original equipment manufacturer Organization of the Petroleum Exporting Countries Oak Ridge National Laboratory oriented strand board over the road ounce phosphorus Pennsylvania petagrams primary nesting season Policy Analysis System (model) pump octane number power purchase agreement producer price index pounds per square inch

xix

xx


PURPA PW QF R&D RD & D RECs REMI RFS RIMS II

RON RPC RPS s SAM SDI SI SO2 spp SRWC SUNY-ESF SW TDC Tg UK U.S. or U.S.A. USD USDA VEETC VOCs VOTEK VVT WA WUE WY yd yr

United States Public Utility Regulatory Policies Act of 1978 pulpwood Qualifying Facility (United States Public Utility Regulatory Policies Act of 1978) research and development research, development and deployment (or demonstration) renewable energy certificates Regional Economic Models, Inc. Renewable Fuel Standard Regional Input-Output Modeling System, version 2 (Bureau of Economic Analysis, U.S. Department of Commerce) Research Octane Number regional purchase coefficient Renewable Portfolio Standard second social accounting matrix stand density index spark ignition sulphur dioxide species short rotation woody crops State University of New York College of Environmental Science and Forestry solid wood top dead center teragram United Kingdom United States of America United States dollars United Stated Department of Agriculture Volumetric Ethanol Excise Tax Credit volatile organic compounds values, organization, technology, environment, and knowledge variable value timing Washington water use efficiency Wyoming yard year

Renewable Energy from Forest Resources in the United States

Interest in biomass energy resources from forests, farms, and other sources has been rapidly increasing in recent years because of growing concern with reducing carbon dioxide emissions and developing alternatives to increasingly scarce, expensive, and insecure oil supplies. Forest resources have large potential to meet energy needs and can dramatically lower CO2 emissions if they are grown in a sustainable way. They should not, however, be considered a complete solution given the need to protect forest ecosystems and biodiversity. This book is unique because of its coverage of biomass energy markets in the U.S. from an economic as well as technical perspective. Existing books typically focus on single markets or technical aspects at the exclusion of economics, and have given greater coverage to biomass energy outside the U.S. This edited collection has three main parts. Part I provides a historical overview of forest biomass energy use in the U.S., the major technologies, economics, market prospects, and policies. Part II presents forest biomass energy assessments, including life cycle and sustainability perspectives, and Part III includes five sets of regional case studies. After reviewing the history of wood energy use in the U.S. and technology options, the book shows that forests could displace sixteen percent of domestic transportation fuel use in 2030. This book is particularly relevant to areas of study such as energy and forestry economics. It will also appeal to renewable energy specialists, foresters, and ecological economists. Barry D. Solomon is Professor of Geography and Environmental Policy in the Department of Social Sciences at Michigan Technological University, U.S.A. Valerie A. Luzadis is Associate Professor of Ecological Economics and Natural Resources Policy in the Department of Forestry and Natural Resources Management at State University of New York, College of Environmental Science and Forestry, U.S.A.

Part I

Overview

1

Introduction Barry D. Solomon and Nicholas H. Johnson

Background The purpose of this book is to assess the technical, economic, and ecological ability of U.S. forests to meet future U.S. energy needs, especially for transportation fuels and electric power, and to provide detailed case studies from several U.S. regions. Interest in utilizing energy resources from forests, farms, and other biomass for energy has been rapidly increasing in recent years because of growing concern about the need to reduce greenhouse gas emissions and the desire to develop alternatives to increasingly scarce, expensive, and insecure oil supplies. Forests have a large potential to meet domestic energy needs and can dramatically lower carbon dioxide (CO2) emissions if they are grown sustainably, which means their biomass resources are renewable only if they are managed well and not over-harvested. The natural regeneration rate of tree stands to maturity is between 30 and 200 years, although it is quicker with some high yield, short-rotation woody crops such as willow species. Forests should not be considered a panacea to the nation’s energy problems given the need to protect natural ecosystems and biodiversity, which will also be addressed in the text. Thus, forests should be seen as one of several sources of biomass and renewable energy, and should be evaluated against a broad range of alternatives that are best suited for various purposes (e.g. transportation, heating of commercial and residential buildings, manufacturing, electricity generation). The vast forestlands of the U.S. have played a central role in the socioeconomic growth and development of the nation, as they have in other societies. Firewood and charcoal1 were among the initial energy sources of traditional societies, along with sun, wind, water, human and draft animal muscle, crop residues, dried dung, and gunpowder. Indeed, historical evidence indicates that wood in any available form and other combustible resources were the first inanimate source of energy for humanity (Tillman 1978: 1; Smil 1994: 115–119). Forests have come to embody several values and potential uses besides energy that must be considered, such as for lumber, plywood, paper, furniture, watersheds, recreation, wilderness and natural habitats, and aesthetics (Clawson 1979).

4

Overview

Forest land area was at its peak at the time of European settlement of the U.S. in the late fifteenth century at around 384 million hectares, or roughly 50 per cent of the land in the lower 48 states. This compares with around 251 million hectares (almost 303 million hectares if Alaska is included), or 30 per cent of the total land area in 2002 (Lubowski et al. 2006). There was a long and significant decline in forestlands of the nation in the 1800s, as well as in the standing sawtimber volume, as the Industrial Revolution reached North America late in the century. Initially little of the forestlands were used for energy production, given the small population of the country (only 5.3 million non-native residents in 1800) and the much greater value of land when it was cleared for cropping, industry and homebuilding. Other forms of biomass energy are readily available besides wood (e.g. dung, animal fats and tallow, agricultural crops and residues, grasses), so trees are not the only source of renewable biomass energy. Thus the use and benefits of renewable energy resources from forests should be balanced against their other uses as well as the many other sources of energy against which they compete, whether renewable or otherwise. This broader perspective is the goal of this book. Consequently in this introduction we will (1) consider the history of forest energy production in the U.S. over time, (2) assess the current resource base, the variety of technologies available to convert wood into useful heat, gases, liquid fuels and electric power, and (3) address the economic status and market penetration of these technologies where relevant and data permit.

History of forest biomass energy production and use in the U.S. since 1630 Wood was the main energy source used in what is now the U.S. from precolonial times until about 1885. While space considerations do not allow a detailed discussion of wood energy use over such a long sweep of history, this section of the chapter will summarize the relevant highlights into six periods: 1630–1800, 1800–1850, 1850–1870, 1870–1920, 1920–1960, and 1960 to date. The USDA Forest Service has calculated U.S. fuelwood consumption in cords per decade since the 1630s. These very rough estimates are available through 1850, after which the data frequency and quality improves (Reynolds and Pierson 1942; EIA 2000: 349). Based on these federal sources, wood energy was the only official energy source accounted for in the U.S. before 1850, although wind and waterpower were already used in water transportation, farming, and manufacturing. U.S. energy consumption was estimated at less than 0.0005 quintillion joules (exajoules, or EJ) of wood use in 1635, and grew to around 0.5 EJ in 1800. These data are shown in Figure 1.1. The growth in wood consumption accelerated in the late 1700s, but total energy use was still very modest by modern standards. This is not surprising since the country’s population was less than 6 million,2 and the European Industrial Revolution had not yet

Introduction

5

spread to North America. About 80 to 95 per cent of the population worked in agriculture in the 1600s and 1700s, and the predominant use of wood and charcoal was for home heating and cooking. Additional uses of wood in this period included the manufacturing of iron, horseshoes, paper, naval stores, leather goods, other products, and ship transportation. Paper manufacturers, however, used rags as their raw material in the 1700s (Tillman 1978: 4–5). These patterns continued during the Revolutionary War with the United Kingdom from 1775 until 1783, and through the end of the century. From 1800 to 1850 the total energy consumption in the U.S. more than quadrupled, and the population similarly grew to over 23 million. As fuelwood production and use rapidly expanded in line with the nation’s economic growth and westward march, occasional shortages occurred in the first few decades of the nineteenth century that were reminiscent of past experiences in Europe (Perlin 1991). This contributed to rising wood prices and stimulated the demand for alternative energy sources, which included coal and coke for iron blast furnaces, and coal-oil, coal-gas, and town gas for lighting and cooking. Since most markets were local during this period, these transitional forces operated on local and regional bases rather than nationally (Melosi 1982: 59). During this time the country underwent rapid change on several fronts – fewer people worked in farming as manufacturing grew in prominence, more people moved to cities, and the population began shifting westward. While wood continued to be the major and least expensive energy source in the U.S. until the 1880s, textile mills and other early manufacturers relied on other energy sources. Waterpower, wind power, and coal were increasingly used by industry in the 1800s, and wood and charcoal gradually fell out of

Figure 1.1 Wood energy use in the United States, 1660 to date. Source: Reynolds and Pierson (1942: Table 2); and EIA (2000: Appendix F).

6

Overview

favor as the cost of the alternatives fell. Coal was first mined commercially in the U.S. in 1748 around Richmond, Virginia, and as early as the 1300s by the Hopi Indians for cooking, heating, and pottery (DOE 2005). As farms became more commercialized the need grew for multiple energy sources, including small windmills to grind wheat and corn, and for mechanical water pumping. Wood, however, was still commonly used for steam power in manufacturing plants, lumber mills in New England, and in steamboats and locomotives during the first half of the nineteenth century. But coal began to replace wood in steamboats in the East by the 1840s, and in locomotives by the 1870s (Melosi 1982: 59–60). Still, wood accounted for about 91 per cent of the total energy use in the U.S. in 1850, while the dominant uses remained home heating and cooking. Three fourths of the home wood energy supply was consumed in inefficient open fireplaces (Schurr and Netschert 1960: 36–37, 49–52). Overall ~100 million cords of wood were burned in the U.S. in 1850. A major shift began in 1850–1870 from reliance on wood to coal as the primary energy source in the U.S. Wood energy use peaked in 1870 at 3.05 EJ, and per capita consumption began to decline in the 1850s (Schurr and Netschert 1960: 48 and Figure 1.2). Not only did coal production increase dramatically during these two decades, but Colonel Edwin Drake successfully drilled the first U.S. oil well just south of Titusville, Pennsylvania in 1859. Concomitantly, the pulp and paper industry began to modernize with the introduction of soda and mechanical pulping as well as the sulphite process, which allowed wood to replace rags as raw material for paper (Magee 2002). The most significant event of this period was the U.S. Civil War (1861–1865) because this ‘War Between the States’ helped to bring industrial interests to greater prominence. There was a dramatic increase in the demand for iron during the War, which was mostly met by coal-fired blast furnaces but also heavily from charcoal. Ships and especially railroad trains had to keep pace with this economic growth, and increasingly turned to coal for fuel although wood still dominated (Tillman 1978: 9–11). Thus, as the transportation and industrial (especially iron-making) sectors shifted to coal use, the use of fuelwood became even more concentrated in the residential sector by the 1870s (Schurr and Netschert 1960: 49–52). Even so, the industrial sector consumed less than a quarter of the total fuel supply by 1870, as the Industrial Revolution was just beginning in the U.S. (Dewhurst and Associates 1955). The years 1870–1920 marked dramatic and rapid socioeconomic developments in the U.S. The nation industrialized in the last several decades of the 1800s, a period often referred to by some historians as the Second Industrial Revolution. This period also witnessed major changes in transportation and urbanization, with the urban population growing almost by a factor of five to 47 million inhabitants by 1920. These changes had several implications for wood energy consumption, which was already declining and had been falling on a per capita basis even before the 1870s. Changes in two industries were especially important between 1870 and

Introduction

7

Figure 1.2 History of U.S. energy consumption by source, 1635 to 2005. Source: EIA (2007a).

1920. One was the forest products sectors (Magee 2002). The pulp and paper industry in particular experienced very rapid growth, which was aided by the introduction from Germany of the Kraft pulping or sulphate process in the 1880s. Consequently wood pulp production in the U.S. grew from 0.9 × 10 3 tonnes to over 3.4 × 10 6 tonnes from 1869 to 1920. A similar expansion occurred with paper and paperboard manufacture, which exceeded 6.3 × 10 6 tonnes by 1920, and lumber production tripled (Tillman 1978: 12). This resulted in less wood being available for energy consumption as it had higher value uses. In the ancient technology of steel making, the Kelly–Bessemer process revolutionized manufacture from molten pig iron after it was introduced in the U.S. in the late 1860s, which led to mass production, scale economies, and larger production units.3 Under these conditions charcoal was less frequently used in blast furnaces and coking coal was demanded for the steel-making furnaces late in the nineteenth century and beyond (Schurr and Netschert 1960: 67). Coal finally surpassed wood in total energy use by 1885. Oil, natural gas, and hydroelectric power also grew to take more prominent places in the U.S. economy in the early 1900s and new technologies such as the automobile and electric power generation took advantage of the ready availability of these alternative energy sources. Modern energy technologies were advancing

8

Overview

rapidly and were adaptable to national markets, unlike wood, the use of which was more localized. Another factor that would eventually restrain wood and charcoal production was the conservation movement that began in the late 1800s (Stradling 2004), and the eventual expansion of the USDA Forest Service’s system of National Forests and other conserved areas under the jurisdiction of the U.S. Department of the Interior. By the 1920s wood energy use had fallen to levels last seen in the 1840s and since overall U.S. energy consumption was 22.6 EJ in 1920, wood energy’s percentage contribution fell from 73 per cent in 1870 to 7.5 per cent in 1920 (Schurr and Netschert 1960: 35–36). From 1920 until 1960 the U.S. economy experienced advanced industrialization in the manufacturing and transportation sectors, though this was interrupted by the Great Depression (1929–1933 and 1937–1938) and World War II (1939–1945). During this time total wood energy use continued to fall, from 1.7 to 1.4 EJ (about 63 million cords). The rapidly growing sectors of the economy needed highly efficient and large quantities of energy, and wood rarely fitted the bill. Automobile production was booming with its voracious appetite for gasoline, and the chemical manufacturing sector increasingly turned away from wood to produce turpentine, resin, rosin, methanol, acetone, acetate, and other products by the 1930s and 1940s. Petroleum use expanded from 2.85 to 21 EJ and natural gas use grew from 0.84 to 13.1 EJ, respectively, from 1920 to 1960, while coal consumption dropped by more than one-third. In the case of hydroelectric power, production exceeded wood only briefly in the mid-1940s and again by the late 1950s, this time virtually for good (Figure 1.2 and EIA 2000: 349–350). Forestlands were increasingly demanded for other uses during this time, such as for cities, roads, dams, recreation, and even wilderness. Commercial forestlands declined from 202 million hectares in 1920 to 196 million hectares in 1950 (Tillman 1978: 16). While the forest products industries experienced rapid growth, especially for paper production, these sectors increasingly turned to fuel oil and electricity for power generation. These industries also became the largest self-generator of energy based on wood and wood waste, including recovery of weak black liquor, bark and wood residues from the Kraft pulping process. As a consequence, the forest products sectors accounted for about two-thirds of the fuel wood harvested in the 1920s, with almost all of the remainder going to residential fuel use as wood or sawdust (Tillman 1978: 17–19). However, the percentage of homes heating with wood fireplaces was rapidly declining, comprising about 22 per cent in 1940 but only 10 per cent in 1950 (Schurr and Netschert 1960: 57). The last 50 years have seen a modest revival of wood energy consumption in the U.S. on an absolute basis, if not a per capita one (Figure 1.1). While total wood energy use in 1960 was only 1.4 EJ, it more than doubled to a short-term peak in 1983–1985 and 1989 of over 2.8 EJ, then declined to 2.2 EJ in 2006. This figure accounts for just 2 per cent of the 105 EJ of total energy that the U.S. used in 2006 (EIA 2007a).

Introduction

9

What accounted for this temporary reversal of historical trends in wood energy consumption? Tillman (1978: 33) attributes this revival to the onset of strict federal environmental legislation in the 1970s, occasional shortages in oil and gas availability, and price increases in fossil fuels and electric power. To these factors must be added the passage of the Public Utility Regulatory Policies Act of 1978 (PURPA), which encouraged the cogeneration of electric power and heat or steam, and non-utility generation of power at Qualifying Facilities (QF) of generally 80 Megawatts (MW) or less in capacity. A QF is a cogeneration facility or a small power producer that uses a renewable energy source or waste for fuel. Once a QF is certified by the Federal Energy Regulatory Commission, it becomes eligible to sell its excess electricity generation and capacity to the local electric utility at the lucrative ‘avoided cost’ rate, which thus began wholesale competition. This rate is defined as the cost to the utility of expanding its generating capacity, which is determined by the state public utility commission. QFs became increasingly common in the forest products industry, especially in the 1980s, with the main fuel being the spent (black) liquor byproduct from the wood pulping process. Wood electric power grew to over 6,500 MW in nameplate capacity and generated over 30 billion kWh yr−1 in the late 1980s and 1990s (Swezey et al. 1995; Skog and Rosen 1997). About 95 per cent of this capacity is at non-utility generators. The largest users are facilities in Maine, California, Alabama and Georgia. In addition, a comparable amount of electricity in recent years has been generated from the biogenic portion of municipal solid waste (MSW), which includes paper, paperboard and wood, along with other biomass materials. Even so, 70–75 per cent of wood and wood waste used for energy in the last decade has been for industry (with pulp and paper accounting for over 80 per cent of this); 17–20 per cent for home heating; 2–3 per cent for commercial heating; and 6–9 per cent for electricity generation (EIA 2006). Efforts began to deregulate retail electric utility generation at the state level in the early 1990s, which raised concerns about renewable energy commitments. The primary policy instrument that evolved to address those concerns has been the Renewable Portfolio Standard (RPS). As of February 2008, 26 states and the District of Columbia (D.C.) have enacted a mandatory RPS (Table 1.1) (DSIRE 2008). A few additional states have partial or voluntary RPSs. These commitments account for 40 to 50 per cent of the total electric load in the U.S. In general an RPS mandates that a certain portion of electricity generation must come from renewable energy sources and technologies such as wind power, photovoltaic cells, biomass, and MSW gas. Geothermal heat, fuel cells, solar thermal heat, and tidal power are also considered potential sources of power in some states. Thus which electric utilities are included, how much power must be generated, and pricing varies widely from state to state. Typically, the amount of renewable energy generated increases over time – often by 1.0 per cent yr−1. A comparable standard for motor vehicle

10

Overview

fuel, which has been passed at both the state and federal levels, is the Renewable Fuel Standard (RFS). See Chapter 3 for details. Although virtually no RPS specifically mandates that biomass must be used, all of the applicable states allow its use to meet the targeted power generation. A notable exception is Minnesota, which in 1997 passed a law Table 1.1 States with renewable portfolio standards State

Mandated RPS

Arizona California Colorado1,2 Connecticut Delaware District of Columbia Hawaii Illinois Iowa3 Maine Maryland Massachusetts4 Michigan5 Minnesota6 Missouri2 Montana Nevada New Hampshire New Jersey New Mexico1 New York North Carolina1 North Dakota Oregon7 Pennsylvania Rhode Island Texas2 Vermont

• • • • • • • • • • • • • • • • • • • • • • • •

Virginia Washington Wisconsin

• •

Voluntary RPS

% of total state electric generation 15 20 20 23 20 11

•

• • •

20 25 105 MW 10 9.5 4 – 25 11 15 20 23.8 22.5 20 24 10 10 25 18 16 5,880 MW all new electric demands 12 15 10

Target year

Renewable credit trading

2025 2010 2020 2020 2019 2022

yes not at this time yes yes yes yes

2020 2025 fulfilled 2017 2022 2009 2016 2025 2020 2015 2015 2025 2021 2020 2013 2017 2015 2025 2021 2020 2015 2012

no yes yes yes yes yes no yes not at this time yes yes yes yes yes not at this time no yes yes yes yes yes yes

2022 2020 2015

yes yes yes

Source: DSIRE (2008). Notes: 1 The given percentage only pertains to investor owned utilities. Co-ops and municipally owned facilities have a requirement of 10 per cent. 2 The state contains at least one RPS passed by a city. 3 Iowa also has a voluntary 1,000 MW wind power goal. 4 Maine’s RPS goal currently increases 1 per cent per year indefinitely. 5 The city of Lansing has an RPS. 6 Minnesota has also required Excel Energy to produce 110 MW of energy from biomass. 7 The given percentage pertains only to large utilities. Smaller utilities have goals of 5 per cent to 10 per cent.

Introduction

11

requiring Excel Energy, the state’s largest utility, to supply 110 MW of energy from biomass (DSIRE 2008).

Resource assessment Two major biomass energy resource assessments of the U.S. have been made in recent years, one by the National Renewable Energy Laboratory (NREL) on the current resource base, and one by Oak Ridge National Laboratory (ORNL) on the potential resource base in 2030 (Milbrandt 2005; Perlack et al. 2005). The forest resource portions of these assessments are available in detail. While these studies are fairly comprehensive, they need to be supplemented with the paper and paperboard portion of the MSW stream that was left out of the data, which is available from the U.S. Environmental Protection Agency (EPA 2006). In addition, there are a few important differences in coverage, in that only the ORNL assessment considered future fuel treatments to reduce fire hazards and perennial woody crops. The NREL assessment is unique in that it uses geographic information systems to analyze the biomass resource base in the U.S., though many assumptions were required. County-level data were generated and totaled for each of the 50 U.S. states. Both existing and unexploited uses were considered. For purposes of this book, the following biomass energy categories are included (Milbrandt 2005; EPA 2006):

• • • • •

wood residues from logged forests; primary mill residues; secondary mill residues; urban wood residues; paper and paperboard in the MSW stream.

The last category of residues was calculated based on the EPA (2006) data, assuming equivalent waste stream mixes nationwide and an average waste generation rate of 4.5 pounds per person per day (which has been fairly constant since the 1990s). In addition, willow and hybrid poplar trees along with switchgrass could be grown on Conservation Reserve Program (CRP) farmlands as well as on abandoned mine lands. While biomass energy production on these lands may be important in the future, at present this is only a potential (e.g. CRP plantings currently cannot be harvested while registered in the program) and thus we omit these energy crop estimates of Milbrandt (2005) for these lands. As these lands leave the CRP in the future, their land use and biomass energy potential (if any) will change at that time. Forest residues are defined by Milbrandt (2005) as the unused portions of logging operations that are left in the woods, pre-commercial thinnings, weeding, other removals, and land clearings and forest uses that are not associated with round wood product harvests. Reserved forestlands and forests in active commercial production are omitted. Primary mill residues include wood

12

Overview

materials and bark generated at manufacturing plants, including residues recycled for fuel or fiber, or disposed of as waste. Secondary mill residues are wood scraps and sawdust from furniture factories, wood container and pallet mills, and wholesale lumberyards. Secondary mill residues comprise just a few per cent of total mill residues. Urban wood residues are defined as including MSW wood chips, pallets, yard wastes, construction and demolition wood, and utility tree trimmings and/or those from private tree companies. To these categories we have added the paper and paperboard portion of MSW, which accounts for 34.2 per cent of the total waste stream or 76.2 million tonnes yr−1 (EPA 2007). Almost 50 per cent of the U.S. paper and paperboard MSW is recovered for recycling (38 million tonnes), and a significant additional portion is combusted for energy recovery. The current forest-based biomass energy resource base is summarized in Table 1.2, both nationwide and for the 15 leading states. This totals almost 250 million tonnes yr−1, or 167 million tonnes yr−1 without paper and paperboard wastes. The largest categories are mill residues (mostly primary) and paper and paperboard wastes, followed by forest residues. A large concentration of these biomass resources is in the western U.S., owing to the huge MSW and urban wood waste streams generated by California’s 37 million residents and the large forest products industry in the Northwest. The next largest resource base is in the Southeast, where states such as Georgia, Alabama, North Carolina and Mississippi have a large concentration of logging and paper mills. Wisconsin, the largest paper producer in the country, barely makes the list at state number 15. Table 1.2 Current forest-based biomass energy resources in the U.S. and in leading states (103 dry tonnes yr−1) State

Forest residues

Mill residues

Urban wood

Paper/paperboard wastes (MSW)

Total

California Georgia Texas Alabama N. Carolina Florida Mississippi Washington Oregon Louisiana Arkansas Virginia S. Carolina Idaho Wisconsin Total

1,303 3,556 2,060 2,555 2,995 1,778 3,825 1,034 1,041 3,384 2,874 2,403 1,733 873 2,011 56,612

5,019 7,328 2,233 5,914 4,015 2,031 4,581 5,682 6,540 3,610 3,655 2,209 2,506 4,420 1,690 79,740

3,901 924 2,307 483 833 1,678 307 675 382 374 314 813 467 129 548 30,902

9,304 2,336 5,886 1,172 2,236 4,581 752 1,619 937 1,165 717 1,949 1,095 368 1,425 76,231

19,527 14,144 12,486 10,124 10,079 10,068 9,465 9,010 8,900 8,533 7,560 7,374 5,801 5,790 5,674 243,485

Source: Milbrandt (2005) and EPA (2007).

Introduction

13

The Perlack et al. (2005) study investigated the ability of the U.S. land resources to provide a 30 per cent displacement of petroleum consumption by 2030. More specifically, a federal Biomass R&D Technical Advisory Committee set the very challenging goals for biomass to produce 5 per cent of the nation’s electric power, 20 per cent of its transportation fuels, and 25 per cent of its chemicals by 2030. The study concluded that these goals could be met and surpassed. Forestlands in the contiguous U.S. were deemed capable of eventually producing 334 million dry tonnes annually, or double the current forest biomass resource base, on a sustainable basis. However, a large portion of the agricultural resource lands was determined to be capable of supporting short rotation woody crops if land use changes. We assumed that about 50 per cent of the study’s projection for perennial crops includes rapidly-growing, resilient tree species with wide site adaptability (e.g. willow, hybrid poplar, sweetgum, sycamore, maple, eucalyptus, loblolly pine), which would total 167 million dry tonnes yr−1 under the high yield scenario of about 11 to 18 dry tonnes per hectare yr−1. To this figure should be added the projected amount of paper and paperboard wastes from MSW, which this study did not consider. While this latter value is highly uncertain, given unknown future waste management practices, we assumed the current per capita waste generation rate will not change and thus the future quantity of these wastes is a function of population growth. The U.S. Census Bureau (2004) has projected the 2030 population to be 363.6 million, or 23 per cent more than in 2005. This would lead to about 94 million tonnes of paper and paperboard waste yr−1 in 2030, which would increase the grand total to 593.1 million tonnes yr−1. Perlack et al. (2005) also assessed the biomass energy potential from the other agricultural lands, but these figures are excluded here, though. Nevertheless, based on similar assumptions these results indicate that U.S. forest resources could displace ~16 per cent of transportation fuel usage in 2030. The U.S. forest biomass resource projections for 2030 are shown in Table 1.3. The largest resource category is projected to be the perennial woody crops, which accounted for 28.1 per cent of the total estimate. The next largest category is pulping liquors and wood processing mill residues. Overall only 24 per cent of the forest-based biomass energy resource base is currently utilized, primarily for energy production in the pulp and paper industry but also for firewood and energy recovery from MSW. Perlack et al. (2005) made several important assumptions in their forestlands assessment: the authors excluded consideration of forestlands that were not currently accessible by roads or were considered environmentally sensitive; they considered equipment recovery limitations; and they did not include recoverable biomass that was projected to be needed for conventional forest products.

54.4

0

54.4

0

Fuel treatments to reduce fire hazards

46.3

14.5

0

31.8

Fuelwood harvest

58.1

20.9

37.2

0

Logging residues & site clearings

42.6

9.9

25.4

7.3

Urban wood residues

167.0

0

167.02

0

Perennial woody crops

94.0

17.8

61.2

15.01

Paper and paperboard wastes (MSW)

593.1

97.6

352.5

143.0

Total

Notes: 1 We assumed that paper and paperboard comprised about 50 per cent of the MSW stream that is combusted for energy recovery (30.3 million tonnes in 2005). 2 We assumed that 50 per cent of the total potential from perennial crops could be planted in short rotation woody crops, with the remainder planted with grasses. Such production would require a change in land use practices.

Source: Perlack et al. (2005: 17); and calculations based on EPA (2006) and U.S. Census Bureau (2004).

Total 130.7

Growth 34.5

Unexploited uses 7.3

Existing uses 88.9

Pulping liquors & wood processing mill residues

Table 1.3 Potentially available biomass energy resources from U.S. forests in 2030 (106 dry tonnes yr−1)

Introduction

15

Technology assessment Although biomass has historically been used as a source of heat, it is now also utilized in the U.S. for electricity generation and as a source for fuels such as ethanol and biodiesel. Many other organic fuels can be extracted or created as well, although this is less commonly done for economic, than for technical, reasons. Biomass can be processed either thermally or biologically (Table 1.4). When thermochemically processed, biomass is typically combusted, on its own or in cogeneration, to produce heat and/or electricity. Gasification and pyrolysis are additional methods used to process biomass thermochemically. While these technologies also may be used to generate heat, their main purpose is usually to extract gas and liquid fuels. Biochemically, biomass is usually processed for energy by fermentation and hydrolysis. In this section, we will review the major thermochemical and biochemical conversion technologies, as well as biomass fuels. Thermochemical technologies Combustion is the most common commercial method for converting biomass into energy. There are many types of combustors and many types of fuels. Biomass can be converted into logs, charcoal, compressed bricks (briquettes), compressed pellets, woodchips, and sawdust. The processed bricks and pellets are more homogenous in physical characteristics, such as size, density and moisture. They also have a higher energy density, although this is offset by the energy used in the manufacturing process. Some combustors require dry

Table 1.4 Biomass conversion technologies Primary process methods

Resultant products

Secondary process methods

Resultant products

Combustion

Heat and electricity Heat and electricity Syngas

Cogeneration

Heat

Cogeneration

Heat

Sabatier reaction Fisher-Tropsch reaction IGCC Sabatier reaction Fisher-Tropsch reaction

Ethanol, methanol, and butanol Syn-diesel Electricity Ethanol, methanol, and butanol Syn-diesel

Fermentation

Ethanol

Co-combustion Gasification

Plasma gasification

Syngas and electricity

Pyrolysis

Charcoal, organic gasses, and bio-oil Bio-oil Sugar

Flash pyrolysis Hydrolysis

16

Overview

wood, while others can use wood with up to 60 per cent moisture by weight. The conversion efficiency ranges from 60 per cent for wood with high moisture content to 80 per cent for dry wood (Zerbe 2006). Efficiencies of logs in a home fireplace can be as low as 10 per cent (Faaij 2006: 346), but modern U.S. fireplaces are about 70 per cent efficient as a side benefit of a 1988 EPA regulation that controls smoke emissions (EPA 2007). The use of thermal conversion at small, residential scales is usually for direct heat, such as in a campfire. Commercial operations typically use boilers to produce steam, which is then distributed through pipes. However, air and hot water are sometimes used to transfer heat instead of steam. There were 335 electric power plant units (1 MW+) in the U.S. in 2006 that burned wood as their primary or only fuel (EIA 2007b), at about 25 per cent efficiency (much lower than the typical 37–38 per cent efficiency for a new coal-fired power plant). Most of these boilers are in the 5–50 MW range, with the largest ones above 120 MW. Two-thirds of these are at industrial installations, primarily in the paper and pulp industry, which generate heat and electricity for use in-process. The other third of these sell electricity (EIA 1995). These wood boilers were made profitable by PURPA. Wood and wood residues produced a total of 10.6 billion kilowatt-hours of power for the electric grid in 2005, whereas landfill gas and MSW produced 18 billion kilowatt-hours. Industrial facilities produced an additional 28.1 billion kilowatt-hours of electricity from wood and wood residues that was not sold on the grid (EIA 2007c), but avoided landfill disposal costs. Co-combustion, also called co-firing, is the burning of wood residues with other fuels such as coal. While the purpose of doing this is usually to reduce emissions such as nitrogen oxides, sulphur dioxide and mercury (Zerbe 2006), local availability of fuel is needed for it to be economical. Co-combustion increased in popularity in the U.S. after retail deregulation of the electric power market began in many states in the mid 1990s (Heiman and Solomon 2004: 98), and is currently used in at least 20 power plants in boilers of 30–700 MW. It has also been tested or intermittently used in dozens of others. One of the major advantages to co-combustion is the low cost of converting facilities to handle new fuel. It is also a convenient way of disposing of intermittent fuel sources such as railroad ties (IEA 2005). Co-combustion has only minimal effects on boiler output (Shonnard et al. 2006), and has efficiencies of up to 40 per cent (Faaij 2006: 351). Pyrolysis is the heating of a material in the absence (or with a limited amount) of oxygen. Traditionally it has been done to produce charcoal, although more cost-effective methods arose during the 1920s when cheap synthetic options became available for acetic acid and methanol, which are two byproducts. When done with biomass, three products result: liquid, char and gas. Temperatures for pyrolysis are between 400 and 600°C. As temperature is increased the gas yield increases and char yield decreases. Char is a mixture of charcoal and tars, which can be processed further. Depending on the type of biomass, the maximum liquid yield is at approximately

Introduction

17

500°C (Zerbe 2006). The efficiency is 60 to 70 per cent (Faaij 2006: 349) although costs are high due to the amount of energy needed to generate the heat. Flash pyrolysis is a technology that is used to produce bio-oil. Typically, 70 to 75 per cent of the weight of the biomass is converted to oil. Unfortunately, the bio-oil in this form is corrosive and needs further processing to be turned into bio-fuel. In its native form it has a short shelf life and is of a much lower quality. This type of oil can also be used in a boiler (Ringer et al. 2006; Zerbe 2006). Pyrolysis is currently uneconomical for large-scale use, and will not likely be economical in the near future. However, in cases where transportation costs are high (e.g. forest residues that need to be transported a long distance) pyrolysis could be used in pre-treatment to reduce the weight of the product(s) being shipped. Gasification, or the process of converting a carbon-based material into a gas, can either be used for electricity generation or to produce gases from biomass or char (see Chapter 10). The conversion efficiency is around 80 to 90 per cent (Faaij 2006: 348). While pyrolysis takes place in gasification, the higher heat involved also converts the char into gas. Typical temperatures are 800 to 1,000°C, with pressures at 20–30 bar (Ragauskas et al. 2006). The resultant gas is a mixture comprised mainly of carbon monoxide (CO) and hydrogen (H2), and is called syngas (synthetic gas), alternatively known as biogas. It also has small amounts of CO2 and methane (CH4) in it. Gasification technology has been around since it was used to make town gas from coal in the 1800s. It was later used in motor vehicles before and during World War II when petroleum supplies were unavailable. The forest products industries began to be interested in gasification biomass gasification during the 1970s in an effort to better utilize their residues (Shonnard et al. 2006; Weyerhaeuser Co. 2000). There were 161 commercial gasification plants operating in 28 countries in 1999, which mainly used coal and oil as their feedstocks (Rosenberg et al. 2004). Biomass can be gasified either pyrolytically, with air, or with oxygen. With air a typical yield is 51 per cent nitrogen, 22 per cent CO, 18 per cent H2 , 6 per cent CO2, and 3 per cent CH4. With only oxygen present a typical yield is 40 per cent CO, 40 per cent H2 , 17 per cent CO2 and 3 per cent CH4 (BEF 2007), although these results are widely variable. Syngas can be processed further to make methanol, ethanol, ammonia, natural gas, or highly capitalintensive syn-diesel. A new technology in the process of being commercialized is plasma gasification. This form of gasification utilizes a plasma arc of 16,500°C with electricity, biomass and other raw materials such as MSW. A facility would convert wastes into a syngas called Plasma Converted Gas, and a molten glass slag by-product plus excess electricity. The latter can be sold back to the electricity grid for profit. A few of these facilities are planned in the U.S.

18

Overview

(Behar 2007). As with all new energy technologies, of course, comprehensive assessments need to be made. For example, electricity costs may be substantial and emissions of air toxins such as dioxin could be large if chlorine remains in the waste stream (Lemmens et al. 2007). If the gasses produced from gasification or pyrolysis are burned instead of recovered, electric power can be generated. This can be done by combustion in a gas turbine or burning the gas to produce steam that is used in a steam turbine. Combined cycles are energy facilities (heat engines) that use more than one thermodynamic cycle in order to improve overall energy efficiency up to 60 per cent. A typical example combines a Rankine steam turbine with a Brayton gas turbine. The integrated gasification combined cycle (IGCC) is an increasingly commonly used process in which syngas is used to turn a gas turbine, and the exhaust heat is used to produce steam which turns a steam turbine. Small IGCC systems that use biomass have electric conversion efficiencies of around 40 per cent. At this time the increases in efficiency do not make up for the added cost of integrating the two systems, and only a few facilities exist worldwide (Rosenberg et al. 2004). Because syngas is most commonly produced by coal gasification, high natural gas prices make it an attractive technology. Cogeneration technology, or combined heat and power (CHP), is the simultaneous use of recovered, low quality heat along with electric power generation for any of several applications. These include the distribution of energy to homes or factories for space or water heating, for industrial process heating, to dry fuel for combustion, and for use in adsorption chillers – rather than the use of a steam turbine. There are two main types of cogeneration systems: a topping cycle generates electricity first, while a bottoming cycle, which is much less common, is used in energy-intensive manufacturing sectors where very high temperature furnaces are used. A waste heat recovery boiler recaptures waste heat from the manufacturing heating process. Coupling combined cycle turbines with cogeneration systems could significantly increases the efficiency of biomass conversion, up to 60–90 per cent (Faaij 2006: 347). Biochemical technologies The three main components of biomass by weight are the biopolymers cellulose (40–60 per cent), hemicellulose (20–40 per cent) and lignin (10–25 per cent) (DOE 2007). Collectively these are called lignocellulose, and each component can be used to produce energy. Woody biomass is converted to fuel through hydrolysis and fermentation. Anaerobic digestion and aerobic digestion are additional biochemical processes that are used to convert biomass. Neither will be considered here, because anaerobic digestion is primarily used for wet waste treatment of sewage sludge and manure, while aerobic digestion is used to produce soil. One major exception is the production and

Introduction

19

recovery of CH4 biogas from MSW landfills, which can be profitably combusted for heat or electricity production, as noted earlier. Hydrolysis and fermentation are used in series to produce ethanol. Hydrolysis, the process of causing a chemical reaction by adding water, has been used as a step in producing methanol and ethanol from wood for many years. During both World Wars, Europe had many hydrolysis plants in operation (Zerbe 1994). Dilute sulphuric acid (less than 1 per cent sulphuric acid) is often used as a catalyst. This is the most common way to process biomass biochemically. Hydrolysis converts the biomass into a sugar solution, which is then fermented and distilled into ethanol. Lignin is separated out during the hydrolysis and can be converted to electricity through combustion (Shonnard et al. 2006). There are two reasons why hydrolysis is currently challenging as a method for the production of methanol and ethanol. First, the fermentation and distillation process uses about two-thirds of the total amount of energy used in the entire process (Zerbe 2006; Huber et al. 2005). Second, the sugars produced from the cellulose and hemicellulose are different than the sugars produced by corn, and are much more difficult to process (Ragauskas et al. 2006; Sedjo 2007). While it is difficult to assess the economic viability of cellulosic ethanol until commercial facilities gain a track record (Wyman 2007), several plants are being built. This is currently a large area of research in the U.S. and several breakthroughs have recently been made (Chapter 3). Biomass fuels The four primary fuels that can be produced from biomass are ethanol, methanol, biodiesel, and H2, although many others, including gasoline, green diesel, butanol, propanol, isopropyl alcohol, CH4 and CO also can be manufactured. Among these fuels, only ethanol manufactured from corn feedstock has had a large market in the U.S. in recent decades, although biodiesel production is also growing rapidly (and is produced on a larger scale in western Europe, especially in Germany). Ethanol has been used in automobiles since the late 1800s, and initially a much larger role for the fuel was envisioned before the domestic petroleum industry developed. The demand for ethanol from corn and other crops as a vehicle fuel grew during both World Wars in the U.S. and in Russia during and after World War II. Ethanol has an energy density of 24.0 MJ liter−1, which is only two-thirds the density of gasoline (Lide 1992; EIA 2007c). Ethanol is currently used as a gasoline additive in most U.S. states. It has largely replaced methyl tertiary butyl ether (MTBE) in California and the Northeast in the last five years. Although the energy density of ethanol is much lower than gasoline, the octane rating of ethanol-gasoline fuel blends is 15 to 25 per cent higher, and thus can be combusted with higher efficiency when compared with pure gasoline. Corn starch is the current source of over 95 per cent of the ethanol produced for use in automobiles. While the

20

Overview

economics of ethanol are controversial, its production capacity would be nowhere near its current level of 3.6 × 1010 liters (9.4 × 109 g) yr−1 and about 5 per cent of U.S. fuel use if not for large government subsidies (Chapter 3). All car engines in the U.S. that have been produced after 1988 can run with ethanol-alcohol blends of up to 10 per cent ethanol (E10) without problem, and possibly well above that level. Currently about 6 million cars in the U.S. have engines that can use an E85 fuel blend (NEVC 2007), and most engines that don’t can be modified for a few thousand dollars. In many automobiles, however, ethanol use may result in corrosion, deterioration and breakdown of some metal components and rubber and cork gaskets. The U.S. Energy Independence and Security Act of 2007 mandates that 1.36 × 1011 liters (3.6 × 1010 g) of ethanol must be produced annually by 2022. More information on ethanol and its production from lignocellulosic (woody) materials is provided in later chapters. Methanol traditionally has been used as a fuel in racecars due to its high octane rating and safety. It is also used to denature ethanol, as antifreeze, and as a household solvent. More commonly, it is used to make formaldehyde (and thus plastics), dimethyl ether (or DME, a replacement for chlorofluorocarbons in aerosol cans) and acetic acid (Olah et al. 2006). DME can be burned efficiently in diesel engines. The energy density for methanol is even lower than ethanol at 15.9 MJ liter−1 (Lide 1992; EIA 2007c), and is even more corrosive (though as is the case with ethanol it has a higher compression ratio and thus burns more efficiently than gasoline). Methanol is currently made almost exclusively from natural gas in the U.S., as wood alcohol production at chemical plants in the industrial eastern states became uneconomical with the advent of cheap petroleum in the 1930s (Heiman and Solomon 2007: 15). Methanol fuel, however, may be poised for a comeback (see Chapter 14 and Polagye et al. 2007). Methanol is manufactured by combining CO with H2 and a catalyst (Olah et al. 2006). This is sometimes referred to as the Sabatier process or Sabatier reaction. Syn-diesel is also produced using the CO and H2, but through the Fisher– Tropsch reaction. Ammonia is produced by combining H2 and nitrogen with a catalyst. Also, CO2 and H2 can be combined to produce CO and water through the water gas shift reaction. Instead of being used in reactions to make syn-diesel, ammonia or CO, H2 as well as methanol can also be used in fuel cells or in petrochemical processing. To produce biodiesel fuel, alkyl esters must first be extracted from a feedstock such as animal fats or vegetable oils (soybean, rapeseed, palm, waste vegetable oil, etc.) and transesterified to produce fuel. The purpose of transesterification is to lower the oil’s viscosity. This fuel is biodegradable and nontoxic. Germany has been the world’s leading producer of biodiesel, but the U.S. is catching up quickly. There were 171 biodiesel production facilities in the U.S. as of January 2008, with a total capacity of 8.5 × 109 liters (2.24 × 109 g) yr−1 or about 26 per cent of the current U.S. production

Introduction

21

capacity for ethanol (National Biodiesel Board 2008). Actual biodiesel production in 2007 however totaled only 1.7 × 109 liters (4.5 × 108 gallons). Biodiesel development advanced in the 1920s and 1930s through the testing of a variety of feedstocks in several countries. After decades of research the first commercial plant opened in Austria in 1989 (Korbitz 1999). Production from rapeseed oil became popular in Europe in the 1990s. Presently forest resources are not used to manufacture biodiesel in the U.S. However, biodiesel is made from palm oil in some tropical developing countries and jatropha trees have been proposed as a feedstock in Kenya, India and elsewhere (Tiwari et al. 2007). It is possible to produce biodiesel from other woody plants in the U.S., but the technology is not yet commercial. In addition to biodiesel, green diesel is an aromatic and sulphur-free isoparafin-rich fuel product made from plant oils using a conventional hydroprocessing technology. The technology is already widely deployed in petroleum refineries and utilizes the existing refinery infrastructure and fuels distribution system. Green diesel has a very high cetane blending value, and the cold flow properties of the fuel can be adjusted in the process to meet climate-specific cloud point specifications in either the neat or blended fuel (Kalnes et al. 2007). Butanol may be another viable fuel that can be produced from biomass, although currently butanol is manufactured from a syngas created from fossil fuels. It is not available as a fuel today, and is primarily used as a solvent and an intermediate in chemical synthesis. Butanol has several notable advantages over ethanol and methanol: a much higher energy density (29.2 MJ liter−1) and thus higher fuel economy, safe handling, less toxicity, less volatility, lack of corrosiveness, and it can be used in existing pipelines. In addition, it is possible that it could replace gasoline as a fuel in an 85 per cent blend without engine modifications. The fermentation of butanol is inefficient, however, and thus more distillation is required and is costly. Several major companies are conducting research to improve the prospects of using bio-butanol as a fuel. BP Biofuels and DuPont, for example, have been working together on making bio-butanol from sugar beets since 2003 (Chase 2006). In addition, SunOpta, a Canadian food company that is developing cellulosic ethanol, is also actively pursuing bio-butanol. Another advantage of butanol is that an ethanol plant can be converted into a butanol plant or the fuels can be blended (Bullis 2007). It will be at least several years before a bio-butanol fuel plant is built in the U.S., however, as the first commercial facility is planned by BP Biofuels at a British Sugar facility in Wissington, England (Chase 2006). Increasing interest in the U.S. and elsewhere in a ‘hydrogen economy’ (i.e. a nationwide infrastructure that uses H2 to store and transfer energy) may increase the interest in producing H2 from biomass feedstocks as well as other renewable sources of energy (Heiman and Solomon 2007). If fuel cells become more economically viable, H2 (along with methanol) could be produced from biomass to fill the cells. H2 is currently manufactured almost

22

Overview

exclusively from natural gas, although it can also be made from water through electrolysis. However, the high amount of electricity required for electrolysis, and thus its high cost, prohibits this from being done on a large-scale, and the reality of a hydrogen economy appears to be at least a decade away, and is probably much farther off. Consequently, the most economically viable biomass fuels in the U.S. market over the next decade are likely to be ethanol and biodiesel, and perhaps methanol and butanol, all of which can be made from woody feedstocks.

Overview of the rest of the book The rest of Part I has two chapters that address broad challenges for the development of forest biomass energy. In Chapter 2 Dana M. Johnson analyzes the major energy markets for forest biomass energy and fuels, along with policies that may facilitate their commercial deployment. These policies include continuation of government subsidies and support for research and development. Chapter 3, by Barry D. Solomon, Justin R. Barnes and Kathleen E. Halvorsen, considers one of the major future markets for woody biomass, the production of cellulosic ethanol. The authors review the historical development of grain ethanol in the U.S. and Brazil, their economics and policy support, and provide a status report of the shift to cellulosic feedstocks, woody and otherwise. Part II provides several assessments of the forest biomass energy system, which encompass numerous dimensions. A proper assessment must go through three distinct quality checks, related to its scientific analysis and credibility, social legitimacy, and practical usefulness for guiding Policy Makers on a given subject such as renewable energy. While we cover most dimensions, the social aspects of bioenergy and biofuels has been given much less attention, as little research has been done on this to date (though ongoing work at Michigan State and Michigan Technical universities is starting to change this). We begin with two companion chapters on the supply-side of biomass energy from forests. Chapter 4, by Erin G. Wilkerson and Robert D. Perlack, addresses the national resource assessment, economics, and technology for collecting and harvesting the resource. Timothy L. Jenkins and John W. Sutherland provide additional detail in their integrated supply system discussion in Chapter 5. These authors note that despite the large forest biomass energy resource base, only a small fraction of it is economically accessible today. In addition, several logistical changes are required in the value chain to harvest, collect, handle, preprocess, transport and store the biomass in an integrated manner for the system to reach its economic potential. It is readily apparent that the market with the largest growth potential for woody biomass is for fuels. Chapter 6, by Jeffrey D. Naber and Jeremy J. Worm, therefore considers the compatibility of fuels such as ethanol, biodiesel and others with the internal combustion engine. In particular, the authors address fuel consumption, efficiency, and emissions for a variety of fuels as

Introduction

23

well as the development potential for advanced engines optimized to use various biofuels. Assessment of biomass energy would be incomplete without detailed consideration of several ecological and environmental issues. Chapter 7, by David J. Flaspohler, Christopher R. Webster and Robert E. Froese, reviews the known and potential biodiversity effects of these energy systems on both terrestrial and aquatic ecosystems. The growth of monocultures vs. diverse feedstock species, feedstock productivity, competing land use and water requirements receive special attention, as does the sustainability of bioenergy systems. The latter topic is taken up in the final two chapters of Part II. Qiong Zhang, Kaitlin R. Goldstein and James R. Mihelcic review life cycle assessment (LCA) studies on forest biomass energy in Chapter 8. The chapter provides a comprehensive evaluation of these studies. The literature has a wide variety of findings based on inconsistent system boundaries, input assumptions and allocation methods. The authors make several recommendations for improving these studies, such as broadening the environmental impacts considered, incorporating dynamic, technical process improvements, working toward an integrated assessment of forest bioenergy technology. Chapter 9, by Valerie A. Luzadis, Timothy A. Volk and Thomas S. Buchholz, describes a novel approach to assessing bioenergy sustainability. Their proposal effectively meets the criteria for a proper assessment noted earlier by taking a systems approach, incorporating the latest scientific knowledge and social values. A five-step participatory approach that uses Richard Norgaard’s coevolutionary development process is outlined and then demonstrated with a bioenergy example. Part III shifts the focus to regional case studies from different parts of the U.S. Dana M. Johnson, James H. Whitmarsh and Jillian R. Waterstraut begin in Chapter 10 with an analysis of the cost and financial feasibility of biomass co-firing and gasification for electricity generation. The authors calculate the production costs and net present value for five locations in each case, and conclude that the biomass co-firing option is the most financially viable. Chapter 11, by Valerie A. Luzadis and Timothy A. Volk, considers short rotation coppice systems in New York State. In particular, their study addresses willow production for bioenergy, biofuels and bioproducts, research that began two decades ago. In Chapter 12 Donald L. Grebner and colleagues provide a case study of possible production of cellulosic ethanol in Mississippi based on various wood residues, small-diameter trees, and urban wastes. They find through sensitivity analysis that technological efficiency, stumpage prices and procurement distances have the largest effects on production costs. The last two chapters shift to the north central and western regions of the U.S. Chapter 13, by Barry D. Solomon, uses a regional input–output analysis to analyze the economic and demographic impacts of cellulosic ethanol development in the three-state region of Michigan, Wisconsin and Minnesota. Three scenarios are considered. Near-term commercial plant developments will most likely correspond to the scale of the second scenario.

24

Overview

While the analyses make general assumptions, commercial projects have been announced in all three states. Chapter 14, by Daniel J. Vogt and colleagues, discusses the prospects for wood methanol in 11 western U.S. states. Long considered a dead technology, the authors show that the future prospects for woody methanol may be bright for both motor vehicle fuel and to fill fuel cells to generate electricity. With the exception of Nevada, most of the western states have sufficient resources for sustainable biofuels production. Finally, we conclude the book with a short Afterword on the future prospects for renewable energy from U.S. forests.

Notes 1 The making of charcoal began in Europe over 5,000 years ago, and involves the distillation of wood to its carbon content (and often to release chemicals such as methanol) since charcoal burns hotter, cleaner, and much more efficiently than airdried wood. The wood was traditionally cooked in a pit kiln, drum, or another container by pyrolysis. The same basic process is used to turn coal into coke for steelmaking. For details see Rollinson (1999); Smil (1994: 116–117); and Freytag (2007). 2 Note, however, that this figure excludes the population of Native American tribes, which were considered autonomous nations. The population of these tribes probably numbered 3.8 to 4.2 million in North America when Christopher Columbus landed in the New World in 1492, but was already declining rapidly well before the 1600s because of war and disease (Denevan 1992: xxvii). 3 The Bessemer steel-making process was followed by the Siemens–Martin or openhearth furnace in Pennsylvania in 1888, and the electric arc furnace in 1907 (Freytag 2007).

References Behar, M. (2007) ‘The prophet of garbage’, Popular Science, 270(3): 56–61, 86–88. Biomass Energy Foundation (BEF) (2007) ‘Gasification’, available: http://www. woodgas.com/gasification.htm (accessed 15 December 2007). Bullis, K. (2007) ‘BP’s bet on butanol’, Technology Review, 27 March 2007. Available: http://www.technologyreview.com/Energy/18443/ (accessed 17 December 2007). Chase, R. (2006) ‘DuPont, BP join to make butanol’, US Today. 23 June 2006. Available: http://www.usatoday.com/money/industries/energy/2006-06-20butanol_x.htm (accessed 17 December 2007). Clawson, M. (1979) ‘Forests in the long sweep of American history’, Science, 204: 1,168–1,174. Database of State Incentives for Renewables & Efficiency (DSIRE) (2008) ‘Renewable portfolio standards for renewable energy’. Available: http://www.dsireusa.org/ library/includes/seeallincentivetype.cfm?type=RPS¤tpageid=7&back= regtab&EE=0&RE=1 (accessed 1 February 2008). Denevan W.M. (ed) (1992) The Native Population of the Americas in 1492, 2nd edn, Madison: University of Wisconsin Press. Department of Energy, U.S. (DOE) (2005) ‘A brief history of coal use’, available: http://www.fossil.energy.gov/education/energylessons/coal/coal_history.html (accessed 23 September 2007).

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—— (2007) ‘Biomass program technologies’, Office of Energy Efficiency and Renewable Energy, DOE, Washington, D.C. Available: http://www1.eere.energy.gov/ biomass/understanding_biomass.html (accessed 15 December 2007). Dewhurst, J.F. and Associates (1955) American’s Needs and Resources: a new survey, New York: The 20th Century Fund. Energy Information Administration (EIA) (1995) Renewable Energy Annual 1995, DOE/EIA-0603 (95) U.S. Department of Energy, Washington, D.C. Available: http://tonto.eia.doe.gov/FTPROOT/renewables/060395.pdf (accessed 20 December 2007). —— (2000) Annual Energy Review 1999, DOE/EIA-0384 (99) U.S. Department of Energy, Washington, D.C., Appendix F. Available: http://tonto.eia.doe.gov/ FTPROOT/multifuel/038499.pdf (accessed 15 September 2007). —— (2006) Renewable Energy Annual 2005, DOE/ EIA-0603 (2005) U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/cneaf/solar. renewables/page/rea_data/table2.pdf (accessed 28 September 2007). —— (2007a) Annual Energy Review 2006, DOE/EIA-0384 (2006) U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/emeu/aer/eh/ frame.html (accessed 20 December 2007). —— (EIA) (2007b) ‘Annual electric generator report’, Form EIA-860, U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/cneaf/ electricity/page/eia860.html (accessed 19 December 2007). —— (EIA) (2007c) ‘Electricity net generation from renewable energy’ U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/cneaf/ solar.renewables/page/trends/table11.html (accessed 17 December 2007). Environmental Protection Agency (EPA), U.S. (2006) Municipal Solid Waste in the United States: 2005 facts and figures. EPA, Office of Solid Waste. EPA530-R06-011. Available: http://www.epa.gov/epaoswer/non-hw/muncpl/pubs/mswchar05. pdf (accessed 5 October 2007). —— (EPA) (2007) ‘Clean burning wood stoves and fireplaces’. Available: http:// www.epa.gov/woodstoves/technical.html (accessed 15 December 2007). Faaij, A. (2006) ‘Modern biomass conversion technologies’, Mitigation and Adaptation Strategies for Global Change, 11: 343–375. Freytag, D.A. (2007) The History, Making & Modeling of Steel, 2nd edn, Chattanooga, TA: National Model Railroad Association. Heiman, M.K., and Solomon, B.D. (2004) ‘Power to the people: electric utility restructuring and the commitment to renewable energy’, Annals of the Association of American Geographers, 94: 94–116. —— (2007) ‘Fueling U.S. transportation: the hydrogen economy and its alternatives’, Environment, 49(8): 10–25. Huber, G.W., Chheda, J.N., Barret, C.J., and Dumesic, J.A. (2005) ‘Production of liquid alkanes by aqueous-phase processing of biomass-derived carbohydrates’, Science, 308: 1,446–1,450. International Energy Agency (IEA) (2005) ‘Database of biomass co-firing initiatives’. Available: http://www.ieabcc.nl (accessed 15 December 2007). Kalnes, T., Marker, T. and Shonnard, D.R. (2007) ‘Green diesel: a second generation biofuel’, International Journal of Chemical Reaction Engineering, 5 (article A48), http://www.bepress.com/ijcre/vol5/A48. Korbitz, W. (1999) ‘Biodiesel production in Europe and North America: an encouraging prospect’, Renewable Energy, 16: 1,078–1,083.

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Lemmens, B., Elslander, H., Vanderreydt, I., Pays, K., Diels, L., Oosterlinck, M. and Joos, M. (2007) ‘Assessment of plasma gasification of high caloric waste streams’, Waste Management, 27: 1,562–1,569. Lide, D.R. (ed) (1992) CRC Handbook of Chemistry and Physics, 73rd edn, Boca Raton, Florida: CRC Press. Lubowski, R.N., Versterby, M., Bucholtz, S., Baez, A. and Roberts, M.J. (2006) ‘Major uses of land in the United States, 2002’, Economic Research Service, U.S. Department of Agriculture, Washington, D.C. Magee, E.B. (2002) Productivity and Performance in the Paper Industry: labour, capital and technology in Britain and America 1860–1914, new edn, Cambridge: Cambridge University Press. Melosi, M.V. (1982) ‘Energy transitions in the nineteenth-century economy’, in G.H. Daniels and M.H. Rose (eds) Energy in Transport: historical perspectives on policy issues, Beverly Hills: Sage. Milbrandt, A. (2005) A Geographic Perspective on the Current Biomass Resource Availability in the United States, NREL/TP-560-39181, Golden, CO: National Renewable Energy Laboratory. National Biodiesel Board (2008) ‘Commercial biodiesel production plants’, available: http://www.biodiesel.org/buyingbiodiesel/producers_marketers/Producers Map-Existing.pdf (accessed 16 February 2008). National Ethanol Vehicle Coalition (NEVC) (2007) ‘Frequently asked questions’, available: http://www.e85fuel.com/e85101/questions.php (accessed 28 December 2007). Olah, G.A., Goeppert, A. and Surya Prakash, G.K. (2006) Beyond Oil and Gas: the methanol economy, New York, Wiley. Perlack, R.D., Wright, L.L., Turhollow, A.F., Graham, R.L., Stokes, B.J. and Erbach, D.C. (2005) Biomass as Feedstock for a Bioenergy and Bioproducts Industry: the technical feasibility of a billion-ton annual supply, DOE/GO-102005-2135. Prepared by Oak Ridge National Laboratory for U.S. Department of Energy and U.S. Department of Agriculture, Washington, D.C. Perlin, J. (1991) A Forest Journey: the role of wood in the development of civilization, Cambridge, MA: Harvard University Press. Polagye, B.L., Hodgson, K.T. and Malte, P.C. (2007) ‘An economic analysis of bioenergy options using thinnings from overstocked forests’, Biomass & Bioenergy, 31: 105–125. Ragauskas A.J., Williams, C.K., Davison, B.H., Britovsek, G., Cairney, J., Eckert, C.A. et al. (2006) ‘The path forward for biofuels and biomaterials’, Science, 311: 484–489. Reynolds, R.V. and Pierson, A.H. (1942) Fuel Wood Use in the United States, 1630–1930, Circular No. 641, Table 2, Forest Service, U.S. Department of Agriculture, Washington, D.C. Ringer, M., Putsche, V. and Scahill, J. (2006) Large-Scale Pyrolysis Oil Production: a technology assessment and economic analysis, NREL/TP-510-37779, Golden, CO: National Renewable Energy Laboratory. Rollinson, W. (1999) Making Charcoal, Skipton, UK: Dalesman Publishing. Rosenberg, W.G., Alpern, D.C. and Walker, R.W. (2004) ‘Deploying IGCC technology in this decade with 3 party covenant financing: volume I’, ENRP Discussion Paper, 2004–07, Cambridge, MA: Belfer Center for Science and International Affairs, Kennedy School of Government, Harvard University.

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Schurr, S.H. and Netschert, B.C. (1960) Energy in the American Economy, 1850–1975, Baltimore: Resources for the Future. Sedjo, R.A. (2007) ‘From oilfields to energy farms: a brief look at the environmental consequences of biofuels’, Resources, 166: 16–19. Shonnard, D.R., Zhang, Q., Johnson, D.M., Froese R.E., Sutherland, J.W., Solomon, B.D. et al. (2006) Evaluation of Low Greenhouse Gas Bio-Based Energy Technologies. Prepared by Michigan Technological University for Caterpillar Inc. Skog, K.E. and Rosen, H.N. (1997) ‘United States wood biomass for energy and chemicals: possible changes in supply, end uses, and environmental impacts’, Forest Products Journal, 47: 63–69. Stradling, D. (ed) (2004) Conservation in the Progressive Era: classic texts, Seattle: University of Washington Press. Swezey, B.G., Porter, K.L. and Feher, J.S. (1995) ‘The potential of externalities considerations on the market for biomass power technologies’, Biomass & Bioenergy, 8: 207–20. Tillman, D.A. (1978) Wood as an Energy Resource, New York: Academic Press. Tiwari, A.K., Kumar, A. and Raheman, H. (2007) ‘Biodiesel production from jatropha oil (jatropha curcas) with high free fatty acids: an optimized process’, Biomass & Bioenergy, 31: 569–575. U.S. Census Bureau (2004) ‘U.S. interim projections by age, sex, race, and Hispanic origin’, available: http://www.census.gov/ipc/www/usinterimproj/ (accessed 11 October 2007). Weyerhaeuser Co. (2000) Biomass Gasification Combined Cycle: agenda 2020, final report, DE-FC36-96GO1O173, U.S. Department of Energy, Washington, D.C., available: http://www.fischer-tropsch.org/DOE/DOE_reports/10173/10173_ toc.htm (accessed 15 December 2007). Wyman, C.E. (2007) ‘What is (and is not) vital to advancing cellulosic ethanol’, Trends in Biotechnology 25: 153–157. Zerbe, J.I. (1994) ‘Liquid fuels from wood: ethanol, methanol, diesel’, World Resource Review, 3: 405–414. —— (2006) ‘Thermal energy, electricity, and transportation fuels from wood’, Forest Products Journal, 56: 6–14.

2

Market analysis and considerations for renewable energy technologies Dana M. Johnson

Background The market for renewable energy products is determined by several factors and considerations. The entire value chain must be evaluated as well as the external influences, such as government policies and subsidies that play a role in the development of strategic and operational objectives of firms. The research, development, and deployment of new technologies require an understanding of the barriers, the required infrastructure, and how the market will need to be transformed, from a customers’ viewpoint. In this chapter, these issues will be addressed to provide a broad understanding of the introduction of renewable energy technologies into the marketplace. Biofuels and biomass energy technologies for combined heat and power (CHP) and electricity will be addressed in terms of market analysis and market considerations. We will begin with biofuels, followed by biomass energy technologies for CHP and electricity, and close with discussion of the market view of renewable energy technologies. Since government plays a key role in the energy sector, government policies for biofuels markets will be addressed first.

Government policies for biofuels markets Although it is preferable for private industry to invest in biomass energy technologies to hasten their expansion, it also is important for government to play a role that is phased out over time. In a market-driven economy like the U.S., government policy must be carefully crafted so as to not move towards a command economy. Initial government policy can drive the market development at no or little cost. This can include: 1) tax incentives, 2) mandates and enforcement mechanisms, 3) government purchasing power, 4) international fuel quality standards, 5) externality valuation (i.e. local and regional pollution, climate change, other environmental costs, etc.), 6) public–private partnerships, and 7) public awareness (Worldwatch Institute 2006). The most critical of these policies is the federal Renewable Fuel Standard (RFS), to be discussed in Chapter 3.

Market analysis and considerations for renewable energy technologies

29

The ethanol market has experienced rapid expansion in the last six years through increased blending of ethyl tertiarybutyl ether (ETBE) as a replacement additive for methyl tertiarybutyl ether (MTBE) in gasoline at levels of 5 to 10 per cent, and production of E85 (see Chapter 3). Even so, the ability of ethanol to displace petroleum is uncertain. Displacement of petroleum There are many opinions regarding the amount of petroleum used in transportation that can be displaced by biofuels, which requires a life cycle assessment (Chapter 8). Sims (2003) notes that the benefits of oil displacement include the positive contribution to a country’s balance of trade and domestic economic activity. The full benefits are difficult to measure, requiring general equilibrium modeling and assumptions regarding the costs and risks of foreign oil dependence, such as the risk of supply disruption and sudden price spikes (Sims 2003). With a forecasted growth of U.S. transportation fuel demand of 32 per cent from 2000 to 2020, displacing higher percentage shares of transport fuel in the future will be more difficult (IEA 2002). High oil prices have an effect on all oil-importing countries but an even further detrimental effect on developing countries that are less able to afford these continuing increases (Owen 2004). Because of the growing demand for transportation fuels and pressure to increase the availability of biofuels, more and more companies are interested in introducing new products and pursuing new technologies to expand their market. Market introduction and technology strategies There are several variables to take into account when considering the introduction of new products. Once the product is developed, a strategy to implement the most cost-effective technology is critical to the success of the new operation. The technology strategy needs to be integral to the overall business strategy. Among the considerations are plant infrastructure, vehicle and engine technologies, fuel distribution, and technology transfer. Plant infrastructure Economies of scale in production are the norm when a single or few products are produced in a given plant. There are cost and economic advantages to larger-scale operations. Traditional ethanol production plants, closed-loop CHP, and electricity provided from bio-based resources generally have been smaller scale; they cannot provide comparable volumes as petroleum refineries or traditional electricity plants fueled by coal. The principle of economies of scale applies to commercial biofuel systems. ‘It is the reason why feedstock requirements for biorefineries are often

30

Overview

projected to be at least 2,000 dry tons per day. The capital cost per unit of output (e.g. $ MWh−1, $ gallon−1) for a large (10 ×) system is about half that of a small (1 ×) system’ (Wimberly 2005). There are significant economyof-scale advantages, especially in the reduction of production costs. The relatively dispersed nature of agricultural crops and the high cost of transporting solid biomass will put upper limits on the future scale of biofuel plants (Worldwatch Institute 2006). Vehicle and engine technology In the value chain, the development of vehicle and engine technologies needs to parallel closely the developments for alternative fuels using biomass (see Chapter 6). Several areas of consideration include advanced fuel and powertrain development, optimized vehicles, new materials for use in vehicle parts, and fuel additives (Worldwatch Institute 2006). There are many challenges and implications associated with advanced technology flexible fuel vehicles. One of those challenges is the higher cost typically found in the introductory stage of any new product. This can best be addressed by allowing either producer or consumer tax credits to offset the higher incremental costs associated with new technology (Beazant 2005). Fuel distribution For E85 fuel sales, only two states, Minnesota and Illinois, thus far have at least 180 retail stations (DOE 2008). The co-offering of traditional and alternative fuel products at the same stations will require that the distribution channels adapt to a major change in how business is conducted. It clearly makes sense to use the petroleum distribution infrastructure as opposed to developing a new infrastructure to support a single or few alternative biofuel products. Technology transfer Brazil has the most experience in developing new biofuel programs. This expertise in cultivating crops and utilizing new technologies for converting these into fuels can expedite both the displacement of petroleum and global economic development by sharing this knowledge with the U.S. (Worldwatch Institute 2006). Effective technology transfer requires: capacity building and knowledge transfer – including strengthening of governmental and financial institutions and it labor force; private sector engagement; and effective diffusion mechanisms (IEA 2002). Summary of biofuels policy Biofuels may be easier to commercialize than other alternative fuels because there is already an existing infrastructure in place for fuel distribution. Biofuels


31

can play a significant role in climate change policy and in measures to reduce greenhouse gas (GHG) emissions (IEA 2004a). Because the reduction of GHG emissions is affected by forest management and agricultural practices, a better understanding of how biofuels production affects crop and food markets is needed. Biofuels are only one of several options for reducing GHGs. The use of alternative energy technologies for CHP and electricity generation represents other opportunities.

Market deployment policies Government and nongovernmental organizations (NGOs) are involved in promoting and facilitating new technologies that will have a perceived societal value. Initially the government may develop public policy to support market deployment. Ultimately, it is the responsibility of the market players to make progress and be competitively self-sufficient. Market deployment policies seek to:

• • • • •

Increase technical performance and differentiation of product or service; Improve technology cost-competitiveness; Achieve a sustainable level of production and market share; Enhance public awareness and social acceptance; and Move towards a sustainable energy system (IEA 2004b).

There are several associated market deployment policies. Policy instruments can be categorized into four key areas. Two areas are economically based, with emphasis on demand (consumer) side and supply (producer) side issues. From a production perspective, further categorization occurs along the capacity and generation policy deployment aspects. This is shown in Figure 2.1. Policies play a key role in market growth and expansion for biofuels and energy products from biomass. Furthermore, these policies that will need to be adjusted and refined as the state of knowledge advances and as the risk and opportunities of biofuel development becomes clearer (Worldwatch Institute 2006). Although the policies may be in place to drive the market forward, supply and capacity are necessary variables to ensure a company is the optimum size to ultimately achieve profitability. Policies addressing supply and power generating capacity There are many policies addressing the supply and capacity factors associated with the deployment of renewable electric technologies. These include investment incentives, tax measures, and government purchases. Each of these policies is described in more detail.

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Overview

Figure 2.1 Market deployment policy instruments. Source: IEA (2003).

Investment incentives are often available during the introduction of renewable electric technologies to provide for possible reductions in capital costs. Several government agencies at the national (federal) and state levels offer grant programs and incentives for private investors to take advantage of during the inception and introduction of new technologies (Anonymous 2002: 87–97). These incentives make it more appealing for private investors to proceed. Without these incentives private investors may avoid investing in technology that has not been commercialized or that has a high potential risk (Feldman 2003). Capital grants can be an important tool to promote renewable electric power programs, including biomass energy among other technologies (IEA 2004b). Another popular investment incentive allows for private investors to reduce their business risk. This occurs through thirdparty finance arrangements where the government assumes risk or provides low interest loans (IEA 2004b). Most government incentives bridge the gap between the market price of traditional energy and the cost of renewable electricity. Incentives are typically phased out as the technology becomes more efficient through process improvements, resulting in lower production and generation costs and better utilization of capacity. On the production side, typical tax measures available to private investors include investment tax credits and property tax exemptions. These will vary from country to country, region to region, and state to state. Like most investment incentives, tax incentives are available for a limited time period (Brasher and Kraske 2003). The benefit of government purchases is typically to provide a renewable power source for government-owned facilities. These on-site renewable energy systems provide renewable electricity to government offices, schools, or other public buildings (IEA 2004b). Policies addressing supply and capacity will then lead to policies applying to supply and power generation.


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Policies addressing supply and power generation Once capacity is available, the utilization of capacity to generate renewable energy is subject to several potential market deployment policies. The market deployment policies associated with supply and generation can be categorized as incentive tariffs, tax measures, obligations, or tradable certificates (IEA 2004b). An issue facing many renewable electricity suppliers is above-market rates causing prices to be higher than those for traditional energy supplies. Where the government has primary control over the energy supply, it is able to set an incentive tariff allowing for a premium price to be paid for power generated from renewable energy sources; this occurs in many countries (IEA 2004b; del Rio and Gual 2004). Whether this would be feasible in the U.S. is another issue, subject to the ability to charge a price differential. This is most commonly available in the form of voluntary ‘green pricing’ programs offered by electric utilities to their customers (see below). Tax measures for U.S. production tax credits have not been as successful as initially anticipated. As an example, although tax credits spurred a substantial number of new installations of wind power since 1999, there has been a significant decline in the development rate of the U.S. wind power industry compared to Europe (Perdue 2004). The success of a tax policy in influencing investment decisions is dependent on the level of incentives available to cover the additional costs associated with renewable energy systems compared to market alternatives, as well as their longevity (Hutchinson 2000). Obligations are a quota system that requires a specific percentage or quantity of electric power supply to be from renewable energy sources. ‘An effective obligation system takes into account renewable resource availability, the ability of the renewable energy industries to respond with technology and systems and the lead times required to bring new projects into operation. Obligations should also be in place long enough to ensure that investors recover their investments’ (IEA 2004b). The primary U.S. example of this is the renewable portfolio standard (RPS), as discussed in Chapter 1. From an electricity supply and generation perspective, tradable certificates provide a mechanism to track and register renewable electricity production, and can be also used to document compliance with quota systems or can be sold to end-use consumers in a voluntary green power market. The establishment of a renewable energy certificates (RECs) system does not by itself constitute a supply requirement, but rather RECs provide greater market flexibility in achieving the goals of other policy instruments (IEA 2004b). Policies addressing power generation and demand There are two primary market deployment policies associated with power generation and demand: voluntary programs and tax measures. Voluntary

34

Overview

programs emphasize green pricing and net metering arrangements. A green pricing tariff typically is higher than the market price for nonrenewable electricity. Participating customers generally pay a surcharge on their electric bill to cover the incremental cost of the renewable energy (Jacobsson and Bergek 2004). Net metering programs are a form of guaranteed pricing. For consumer-owned systems, net metering is a practice that allows customers to ‘bank’ at the utility any excess electricity generated from qualifying systems for later use and the customer pays only for the electricity used ‘net’ of the electricity generated over the entire billing cycle at the retail rate (Shoock 2007). Policies addressing power demand and capacity Market deployment policies associated with power demand and capacity are very similar to those for addressing supply and capacity. These include investment incentives and tax measures as described earlier. Additional tax measures for customer-owned systems may include tax credits or sales tax rebates (Ralls 2006). Summary of renewable electricity market deployment policies In summary, renewable electric power market deployment policies have resulted in several favorable outcomes (IEA 2004b):

• • •

•

• •

significant market growth has resulted from combinations of policies, rather than single policies; longevity and predictability of policy support is important to overall market success. The challenge is how to incorporate strong incentives for cost-reduction and competition while ensuring longevity and predictability of policy support; national policies are strengthened when state and local governments have the authority to act independently of the federal government. In the U.S., although the federal government has established a renewable energy deployment program, over half the states have their own RPS, and many more offer their own set of financial incentives (Chapter 1); market liberalization offers new challenges for renewable technologies still in the early deployment stage. If energy prices fall, the price targets that renewable energy must meet become challenging. Policies and systems such as quotas and RECs can be compatible with more competitive market structures; individual policy mechanisms are evolving as countries gain experience; and it is too soon to assess fully the impacts of many renewable energy policies, as most have been established only since 2000.


35

Renewable energy market deployment policies have led to the reform in specific energy sectors, which will be discussed next.

Market reform in electricity and gas markets Another aspect for consideration is the impact of reform on markets for electricity and gas. Energy market reform refers to the processes underway throughout the world that transfer decision-making in energy industries from governments to private enterprises, and to the gradual substitution of more open and competitive markets for publicly regulated companies. This is important from a market development point of view. Countries implementing such reforms can benefit as their energy sectors become more dynamic through improved transparency and accountability, accelerated development of technology and more entrepreneurial approaches to energy exploration, production, distribution and supply. Increased competition is designed to improve quality of service, reduce prices to final consumers and contribute to economic growth (Scheman 1997), although U.S. experience in the last decade has been mixed. The different components of market reform in the electricity and gas markets include sustainable development; energy subsidy reform; economic, social, and environmental effects of subsidies; and social implications. Sustainable development Sustainability is one of the major thrusts behind renewable energy market development. Market reform that focuses on cost minimization and the search for greater efficiencies can have profound impacts on the magnitude and structure of energy supply. This has important implications for sustainability in the energy sector, as different supply structures imply different impacts on the environment and on the security of supply. The effects of market reform are felt through reduced prices that lead to increased consumption, increased competition and entrepreneurial dynamism lead to greater efficiency, and the creation of new energy markets and services such as green electricity. (IEA 2004b) Energy subsidy reform For energy subsidy reform to be effective in achieving social and environmental objectives and to fix problems in the way energy markets operate, some common justifications for subsidies are necessary. Energy subsidies stimulate regional economic development with emphasis on national and social cohesion while simultaneously improving the living standards in rural

36

Overview

communities. They can help to reduce oil imports for energy-security purposes. From a sustainability standpoint, renewable energy subsidies can help to protect the environment (Solomon and Georgianna 1987; Scheman 1997).

Economic, social and environmental effects of subsidies Economic costs that cannot be overlooked are associated with subsidies to conventional energy sources. The economic efficiency loss is evident in one or more of the following ways (IEA 2004b: 9): 1

2

3

4 5 6

7

subsidies to consumption and/or production, by lowering end-use prices, lead to higher energy use and reduced incentives to conserve or use energy more efficiently; by reducing the price received by producers, a subsidy may undermine energy providers’ return on investment and, consequently, their ability and incentive to invest in new infrastructure; subsidies to producers, by sheltering them from competitive market pressures, reduce incentives to cut costs, resulting in less efficient plant operation and investments that may otherwise not be economic; direct subsidies in the form of grants or tax exemptions drain government finances; price caps or ceilings below market clearing levels may lead to physical shortages and a need for rationing that is costly to administer; consumption subsidies, by increasing energy use, boost demand for imports or reduce the amount of energy available for export. This harms the balance of payments and supply security by increasing the country’s dependence on imports; and subsidies to specific energy technologies inevitably undermine the development and commercialization of other technologies that might become more economically and environmentally attractive. In this way, subsidies can lock-in technologies to the exclusion of other, more promising ones (Brown et al. 2008).

Social implications Typically, some energy subsidies are aimed at improving the living conditions of individuals in low-income households. They may be aimed at improving the air quality and reducing indoor pollution for poor communities. But instead of benefiting the intended constituents, more usually the energy companies, suppliers, and better-off households are the beneficiaries. Many energy-subsidy programs intended to boost poor households’ purchasing power or rural communities’ access to modern energy through lower prices can actually leave the poor worse off, since the costs are shared by the entire


37

population and by individuals at different income levels (Wong 2005; Holzer 2005; Owen 2004).

Marketing – the research, development and deployment (RD&D) perspective Investment in cleaner energy technologies in competitive markets has positive effects. First there is the direct effect on the production and use of energy, making energy systems more efficient and cleaner. This effect is the direct target of governments. The deployment of new technologies also leads market actors to learn how to produce and use them more cheaply and more effectively (Wong 2005). As a part of the RD&D process, learning effects play a significant role and benefit the entire value chain. The learning effect manifests itself in succeeding generations of the technology, with associated reductions in product price, better technical performance and improved or innovative methods of marketing and application (IEA 2003). The public sector alone normally cannot directly bring about the cost reduction that will make a technology competitive in a market economy with other competing products and substitutes being offered at lower prices. The learning process does not have a virtuous cycle and no substantial cost reductions can be made without market interactions. Learning in energy technology deployment programs There are two types of learning that need to be addressed in relation to marketing with a RD&D perspective. These include technology learning, and institutional or organizational learning. The technology learning primarily focuses on achieving the improvements in the technology that will drive the production costs down, but also yield improvements in output yields and performance (Rafaj et. al. 2006). This technology learning needs to be driven by market forces and private business owners. Initial subsidies and government policies may provide preliminary support, but ultimately it is the responsibility of the private sector to achieve necessary reductions through technology learning experiences. Although cost reductions may be achieved through technology learning, not realizing the importance of organizational learning can result in a failure to achieve the desired long-term results. Institutional or organizational learning refers to an increase in the organization’s capability for effective action (Espejo et al. 1996). Market deployment leads to organizational learning for the company by developing and promoting technology, as it learns how to overcome barriers that are not directly related to the cost or performance of a technology (Ellis 1996). Most organizational learning has focused primarily on internal organization, but it should also include external constituents such as consumers, intermediaries and government. External entities and forces play a major role in implementing an organizational strategy. A key

38

Overview

aspect in organizational change is the cultural implications associated with the change. Cultural implications In deploying new technologies and their associated products, by-products and services, the final consumer’s use of new products and the timing associated with their adoption cannot be overlooked. The supply may be readily achievable but without the associated demand for the alternative fuel technologies and energy products, the market will fail. Transforming energy systems and markets to facilitate these changes may require major changes in the way market actors conduct their business, changes in their relationships, and in some cases the emergence of new actors. Technology learning remains important because sustainable markets for new technologies ultimately depend on cost reductions (Rafaj et. al. 2006). Technology learning also must reach out to the end-use consumer of alternative energy technologies and products. This educational process needs to be jointly launched by public and private sector organizations for it to have the greatest reach and impact on developing market potential. Because the U.S. fleet of over 230 million vehicles turns over only about every 15 years, even major technologies take a while to make a difference (Welch and Aston 2006). Consumers are also interested in a quicker payback than can often be realized. Between the initial vehicle purchase price, the lower fuel economy of ethanol, and an approximate payback of 10 years (assuming gasoline prices of about $3 per gallon), consumers may not be willing to wait so long to see a return on their investments in a biofuels vehicle (Welch and Aston 2006).

Market barrier perspective Technology and R&D alone do not create an energy market, and the markets alone may provide insufficient incentives for RD&D. There needs to be a support mechanism to drive innovation and commercialization of new technology to support renewable energy production. This is because there are impediments or barriers to the introduction of new technology because society’s infrastructure changes slowly. For instance, the long useful life of capital stock sometimes results in missed opportunities to put more efficient and cleaner capital stock into place when opportunities arise, and can perpetuate excessive energy use and associated effects on the environment for decades (IEA 2002). Another common barrier is that customers acquire imperfect information regarding the benefits of new technology. The strategic management of new technology also will play a role in market deployment. The more common strategies are the niche market or market leader, or a combination of the two. The niche market strategy focuses on a relatively small share of the overall market, thereby focusing on small-scale


39

operations. The market leaders may use a niche strategy in developing a challenger position to incumbent technology with a view towards the mass market. The market leader strategy generally focuses on larger-scale operations and is willing to take on more risk. Radically new technologies will require developing market infrastructure and networks from the ground up. In contrast, adaptations and improvements of existing technologies and system reorganization may require the transformation of well-established markets (Neuhoff 2005). Ingrained consumer attitudes regarding existing technologies represent a significant barrier in the expansion and growth of alternative energy supply markets. An associated barrier is the unwillingness of some companies to invest in new technology because of past costly capital investments that are now finally reaping benefits for the company (Hornstein and Gebhart Stroemer 2006). This will slow progress in the diffusion of new energy technologies.

Market transformation perspective Market transformation refers to ‘a significant or even radical change in the distribution of products in a given market, in which the most efficient products substantially displace the least efficient ones’ (IEA 2003: 81). This view of efficiency is based from an intermediary as well as the end use consumer of energy products. From a market transformation policy perspective, government can facilitate market development along the entire value chain. However, to clarify, this does not mean that government support of the new energy technology constitutes a market transformation program (Neuhoff 2005). The importance of market transformation focuses on people involved in technology deployment policy to encourage the adoption of new products in the same way that private sector suppliers think about it (Neuhoff 2005). The market transformation perspective places primary focus on gaining an understanding of the buyer-relevant characteristics. Part of the evaluation includes looking at both positive and negative aspects of the market transformation process. The desire is to boost positive attributes, including lower emissions and high-energy efficiency, and overcome negative attributes such as higher purchase costs. Adoption of renewable energy products is dependent on the buyers and consumers. From a market transformation perspective, it can be used to build a structured view of consumer attitudes that are helpful in developing marketing strategies and in understanding deployment policies. This is illustrated in Figure 2.2 and demonstrates the time it will take to develop a sufficient market based on consumers’ attitudes. The number of innovators and early adopters represents approximately 16 per cent of the market for any given product. The majority are categorized into early and late based on the ability to accept the risk. The remainder will adopt when there is an incentive to do so (i.e. lower prices).

Figure 2.2 Who will buy and why? Source: Rogers (1995); Moore (1991).


41

Market infrastructure Markets need to operate within a framework that facilitates the goods and services being traded to promote the exchange between sellers and buyers effectively and efficiently. The value chain approach to developing market infrastructure will focus on the entire chain of custody from raw materials through final end-user consumption. There is an existing market infrastructure for energy products that can serve as a basis for the development of infrastructure for renewable energy products. It is likely that the current infrastructure can be utilized with modifications, as opposed to developing new infrastructure. Market stakeholders There are several key stakeholders throughout a product’s value chain. Each plays a role in the markets associated with bio-based energy products. The major stakeholders in the value chain for biomass energy are:

• • • • •

the agricultural industry; the forestry products industry; companies that harvest and transport the feedstock; equipment vendors; and energy suppliers/producers.

To transform the market, multiple stakeholders are necessary to develop the market infrastructure. The infrastructure is an important element in the market transformation process. Figure 2.3 graphically depicts what this network may look like. Technology procurement Procurement processes are a natural mechanism for encouraging technology market development by offering an entry point for influencing industry decisions. Technology procurement can be viewed as a tool that can influence the entire chain of innovation and commercialization (IEA 2003). This is an important element because often the new technology is replacing existing technology. The existing technology may have some useful life remaining and companies may be hesitant to switch to a new technology. If the technology vendors are in a position to promote the new equipment, then they can also have a significant impact on the renewable energy market. The technology procurement process is graphically depicted in Figure 2.4.

42

Overview

Figure 2.3 Developing market transformation networks for renewable energy. Source: Nilsson (1996).

Figure 2.4 The technology procurement process. Source: Lund (2001).

Market view of renewable energy There are important considerations related to renewable energy from the consumer’s perspective. These include consumption of biofuels (e.g. ethanol or biodiesel), consumption of electricity from renewable energy sources, and power generation.


43

Biofuels When determining the potential market, the consumers’ views of renewable fuels and reliability are critical. Most individuals are willing to make very simple life changes without some amount of evaluation and analysis. The market views renewable fuels as less dependable, more awkward, and more expensive than gas delivered on a long-term contract by pipeline to power an electric power plant (Brown and Yuen 1994). Another contributing factor in biomass resources is the contracts for wood supply tend to be short-term (Brown and Yuen 1994). A major culture shift would need to occur to make sure there is a secure supply of biomass resource necessary to provide the renewable energy demand and needs of the consumers. Electricity There are a large number of choices of electricity in the current power market. Such markets allow consumer preferences for improved performance to be taken into consideration. There are currently no clear standards to allow truly informed consumer choice. Claims about ‘green electricity’ reflect marketing gimmicks as much as true product differentiation. An important role for government is the setting of clear and transparent performance standards that will allow consumers to make comparisons and quality competition between different providers (del Rio and Gual 2004). The majority of electricity production from biomass is used for base load power in the existing electrical distribution system. Biopower also includes industrial process heat and steam. More than 200 companies outside the wood products and food industries generation biomass power in the U.S. Where power producers have access to very low cost biomass supplies, the choice to use biomass in the fuel mix enhances the competitiveness in the marketplace. This is particularly true in the near term for power companies choosing to co-fire biomass with coal to save fuel costs and earn emissions credits. An increasing number of power marketers are starting to offer environmentally-friendly electricity, including biomass power, in response to consumer demand and regulatory requirements (DOE 2006). Power generation Electric power generation from biomass by advanced combustion technology and co-firing schemes is a potential growth market worldwide. In various markets the average scale of biomass combustion schemes rapidly increases due to improved availability of biomass resources and the economic advantages of economies of scale of conversion technology. It is also in this field that competitive performance compared to fossil fuels is possible where lower cost residues are available. This is in

44

Overview particular true for co-firing schemes, where investment costs can be minimal. Gasification technology (integrated with gas turbines/combined cycles) offers even better perspectives for power generation from biomass in the medium term and can make power generation from energy crops competitive in many areas in the world once this technology has been proven on a commercial scale. (Faaij 2006: 368)

Product markets There are a few primary bioenergy product markets: CHP, electricity and biofuels. For commercial-scale operations producing electricity, long-term power purchase agreement should be secured for 100 per cent of the output, which reduces market risk to nearly zero (Wimberly 2005). For biofuels, longterm contracts are typically unavailable. However, potential biofuels demand is substantial, provided that product prices are competitive with other fuels and are compatible in use. A comprehensive market perspective The comprehensive market perspective focuses on three key characteristics: customer relations, business and market organization, and rules and institutions. Each area has associated operational objectives, characteristic application, and example measures (Figure 2.5). We will briefly discuss each of the characteristics (IEA 2003):

• • •

Customer relations may need attention, meaning that the customer needs to be better served in making choices, presented with price and other incentives that will lead to clever choices, or perhaps needs to be protected from making risky choices. Business and market organization may need adjustment. The potential demand from the public for services of better energy technologies may have to be made manifest to business interests and the supply structure may need vitalization. Rules and institutions governing market behavior may have to be adjusted to allow competition to function better, to avoid favoring or disfavoring alternative technologies for extraneous reasons, or to facilitate better optimization of an overall energy system.

All market actors play a role in the entire value chain from concept to market. The performance measurements are a way to monitor the progress made at each step of the process.


Figure 2.5 Operational objectives applications.

in

market

deployment

and

45

characteristic

Source: IEA (2004).

Conclusions The three market perspectives of RD&D, market barriers, and market transformation are all necessary for successful renewable energy market developments, i.e. (STCI and MNP 2004: vii):

• •

‘the RD&D perspective, which focuses on the innovation process, industry strategies and the learning that is associated with new technologies; the market barrier perspective, which characterizes the adoption of a new technology as a market process, focuses on decisions made by

46

Overview

•

investors and consumers, and applies the analytical tools of the economist; and the market transformation perspective, which considers the distribution chain from producer to user, focuses on the role of the actors in this chain in developing markets for new energy technologies, and applies the tools of the management sciences.’

The adoption of clean energy technologies may not succeed unless all three perspectives are considered, and it is necessary to: . . . invest in niche markets and learning in order to improve technology cost and performance; remove or reduce barriers to market development that are based on instances of market failure; and use market transformation techniques to address stakeholders’ concerns in adopting new technologies and help to overcome market inertia that can unduly prolong the use of less effective technologies. (IEA 2003)

References Anonymous (2002) ‘Incentives in the energy industry’, International Tax Review: industry guide energy, Deloitte Touche. Available: http://www.deloitte.com/dtt/research/ 0,1015,sid%253D19663%2526cid%253D27024,00.html (accessed 21 March 2008). Beazant, G. (2005) ‘U.S. wants to go it alone’, Professional Engineering, 18(10): 39. Brasher, L.T. and Kraske, P. (2003) ‘Renewable power purchase agreements: a reflection of the carrot-and-stick approach to renewable energy legislation’, Journal of Structured Finance, 9: 44–46. Brown, M.A., Chandler, J., Lapsa, M.V. and Sovacool, B.K. (2008) Carbon Lock-In: barriers to deploying climate change mitigation technologies, ORNL/TM-2007/124, prepared by the Oak Ridge National Laboratory, Oak Ridge, TN. Brown, M.H. and Yuen, M. (1994) ‘Changing a historical perspective’, Independent Energy, 24(7): 64–67. del Rio, P. and Gual, M. (2004) ‘The promotion of green electricity in Europe: present and future’, European Environment, 14: 219–234. Department of Energy, U.S. (DOE) (2006) U.S. Department of Energy, Energy Efficiency and Renewable Energy, Biomass Program Biomass FAQs. Available: http:// www1.eere.energy.gov/biomass/biomass_basics_faqs.html (accessed 27 April 2006). —— (DOE) (2008) ‘E85 fueling station locations’. Available: http://www.eere. energy.gov/afdc/fuels/ethanol_locations.html (accessed 16 July 2008). Ellis, J. (1996) ‘Why promote renewable energy?’, Organization for Economic Cooperation and Development, OECD Observer, 201: 17–20. Espejo, R., Schuhmann, W., Schwaninger, M. and Bilello, U. (1996) Organizational Transformation and Learning: a cybernetic approach to management, Chichester: Wiley. Faaij, A. (2006) ‘Modern biomass conversion technologies’, Mitigation and Adaptation Strategies for Global Change, 11: 343–375. Feldman, R.D. (2003) ‘Renewing renewables’, Journal of Structured Finance, 9(2): 37–39.


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Holzer, V.L. (2005) ‘The promotion of renewable energies and sustainability: a critical assessment of the German Renewable Energies Act’, Intereconomics, 40: 36–45. Hornstein, M.D. and Gebhart Stroemer, J.S. (2006) ‘The Energy Policy Act of 2005: PURPA reforms, the amendments and their implications’, Energy Law Journal, 27(3): 25–38. Hutchinson, H. (2000) ‘Small-scale power’, Mechanical Engineering, 122 (10): 76–80. International Energy Agency (IEA) (2002) World Energy Outlook 2002, OECD Publication Services, Paris. —— (2003) Creating Markets for Energy Technologies, OECD Publication Services, Paris. —— (2004a) Biofuels for Transport: an international perspective, OECD Publication Services, Paris. —— (2004b) Renewable Energy: market & policy trend in IEA countries, OECD Publication Services, Paris. Jacobsson, S. and Bergek, A. (2004) ‘Transforming the energy sector: the evolution of technological systems in renewable energy technology’, Industrial and Corporate Change, 13: 815–850. Lund, P. (2001) ‘Market transformation perspective and involvement of market actors and stakeholders in the IEA case studies’, paper presented to the IEA Workshop, Technologies Require Markets: Best Practices and Lessons Learned in Energy Technology Deployment Policies, Paris, 28–29 November, 2001. Moore, G.A. (1991) Crossing the Chasm: marketing and selling technology products to mainstream customers, revised edn, New York: Collins. Neuhoff, K. (2005) ‘Large-scale deployment of renewables for electricity generation’, Oxford Review of Economic Policy, 21: 88–111. Nilsson, H. (1996) ‘Looking inside the box of market transformation’. Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings, 5.181. Owen, A.D. (2004) ‘Environmental externalities, market distortions and the economics of renewable energy technologies’, Energy Journal, 25: 127–156. Perdue, J.C. (2004) ‘Energy venture capital sector sees renewed interest’, Journal of Structured Finance, 10(4): 56–59. Rafaj, P., Barreto, L. and Kypreos, S. (2006) ‘Combining policy instruments for sustainable energy systems: an assessment with the GMM model’, Environmental Modeling & Assessment, 11: 277–296. Ralls, M.A. (2006) ‘Congress got it right: there’s no need to mandate renewable portfolio standards’, Energy Law Journal, 27: 451–472. Rogers, E.M. (1995) Diffusion of Innovations, New York: Free Press. (S&T)2 Consultants Inc. and Meyers Norris Penny LLP (STCI and MNP) (2004) ‘Economic, financial, social analysis and public policies for fuel ethanol’, Natural Resources Canada, Office of Energy Efficiency, Ottawa. Scheman, L.R. (1997) ‘Energy, efficient and renewable, in the Americas: an agenda for progress’, Journal of Structured Finance, 3(4): 59–63. Shoock, C.S. (2007) ‘Blowing in the wind: how a two-tiered national renewable portfolio standard, a system benefits fund, and other programs will reshape American energy investment and reduce fossil fuel externalities’, Fordham Journal of Corporate & Finance Law, 12: 1,011–1,077. Sims, R. (2003) ‘The triple bottom line benefits of bioenergy for the community’, OECD workshop on Biomass and Agriculture, Vienna, 10–13 June.

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Solomon, B.D. and Georgianna, T.D. (1987) ‘Optimal subsidies to new energy sources’, Energy Economics, 9: 183–189. Welch, D. and Aston, A. (2006) ‘Fill ’er up – but with what?’, Business Week, 22 May, 3985: 60–63. Wimberly, J. (2005) ‘Pursuing realistic opportunities in home-grown energy’, BioCycle, 46(6): 53–57. Wong, S.F. (2005) ‘Obliging institutions and industry evolution: a comparative study of the German and UK wind energy industries’, Industry and Innovation, 12: 117–145. Worldwatch Institute (2006) Biofuels for Transportation: global potential and implications for sustainable agriculture and energy in the 21st century – extended summary, Washington, D.C.

3

From grain to cellulosic ethanol: history, economics and policy Barry D. Solomon, Justin R. Barnes and Kathleen E. Halvorsen

Introduction While U.S. interest in fuel ethanol has grown since the oil crises of the 1970s, its use in gasoline blends accounted for only about 5 per cent of total fuel use in motor vehicles in 2007 (RFA 2008; EIA 2007). Although ethanol (i.e. ethyl alcohol) has the advantage of being derived from domestic resources, its use for fuel has often been criticized as technically, economically and environmentally undesirable (e.g. Pimental and Patzek 2005). Even so, interest in alternative transportation fuels is growing for several reasons: oil supply insecurity, price increases and its impending peak, and the imperative to lower carbon dioxide (CO2) emissions from fossil fuel use in order to help stave off adverse global climatic change (Farrell et al. 2006). Several alternative fuels and engines for the transport sector have been assessed in detail in recent years (Ogden et al. 2004). These include electric and hybrid-electric vehicles, compressed natural gas (CNG), hydrogen-fuel cells, and biomass fuels. While electric and CNG vehicles are available on a small scale their driving range is limited, severely restricting their consumer appeal. Hydrogen-fuel cell vehicles exist as prototypes, but they are extremely expensive and will be impractical for a decade or more (Solomon and Banerjee 2006). This leaves hybrid-electric vehicles (HEVs) and biomass fuels as the most cost-effective alternatives to oil in the near term. HEVs are attractive, as they increase fuel use efficiency and thus help to stretch petroleum resources and lower CO2 emissions. Only sustainable biomass fuels however, such as ethanol and biodiesel, can directly decrease oil reliance (see Chapter 6). There are several ways to make biomass fuels, as well as alternative alcohol products. For example, in the 1970s methyl alcohol (methanol) received as much consideration as ethanol. Both fuels can be produced from food crops and biomass, as well as from fossil fuels (Solomon 1980a). While methanol can be made at a lower cost than ethanol, some refiners over-blended or used improper blending and handling techniques. This led to consumer and media problems and the eventual phase-out of almost all methanol/gasoline blends, with its use largely restricted to several auto races. Even here, the Indy Racing

50

Overview

League switched its cars from methanol to 100 per cent ethanol fuel for its 2007 races (National Corn Growers Association 2007). Similarly, methanol caught on as a feedstock for production of methyl tertiary butyl ether (MTBE) under Clean Air Act requirements for 2.0 to 2.7 per cent oxygen blends in ozone and carbon monoxide non-attainment areas. However, as noted in a later section MTBE has been at least partially banned in 25 states since 2000, although over half of these states never used it (EIA 2003; EPA 2007). Nonetheless, the main markets for methanol are for formaldehyde, acetic acid and other chemicals. Another promising option is biodiesel (methyl esters), which is made from vegetable oil or animal fats. Biodiesel has similar benefits as cellulosic ethanol, as noted below, but is limited to diesel engines (Chapter 6). There are two primary technologies to make ethanol. The first option, in wide use today, is to convert the starchy part of foods such as corn or wheat into ethanol through seven steps: milling, liquefaction, saccharification, fermentation, distillation, dehydration and denaturing. When sugar cane is used (e.g. in Brazil) or sugar beets (e.g. in France), only four or five steps are required: milling, pressing, fermentation and distillation, plus dehydration in the case of alcohol blends. The other option is lignocellulosic or cellulosic (biomass) ethanol, which is currently being commercialized. This process converts the woody part of trees, plants, grasses or residues into sugars and then ferments the sugars into ethanol. Over 95 per cent of U.S. ethanol production comes from cornstarch, with the rest made from milo, wheat, barley, beverage residues, cheese whey, potatoes, and wood (RFA 2008). This path to ethanol production has been criticized, often erroneously, for having an unfavorable net energy balance and significant arable land and water requirements (Giampietro et al. 1997). While corn-based ethanol has several important environmental impacts, including soil erosion, water pollution, loss of biodiversity, and higher nitrogen oxide and volatile organic compound air pollution, it does result in a positive energy return on investment and a modest reduction in CO2 emissions (cf. Pimental and Patzek 2005: Farrell et al. 2006; Giampietro et al. 1997; Solomon 1980b; Hammerschlag 2006). These results are more favorable for sugarcane-based ethanol in Brazil (De Oliveira et al. 2005). Given land use concerns it is unlikely that grain ethanol can grow from its current U.S. production capacity of 3.6 × 1010 liters (9.4 × 109 g) yr−1 to even twice that level, even with increased agricultural productivity (RFA 2008; Urbanchuk 2006). Firstly, over half of the U.S. corn crop is needed for feed grain for livestock as compared to 20 per cent for ethanol (Westcott 2007). Secondly, as discussed later, ethanol output from cornstarch was capped at a little over twice the 2007 production level by energy legislation passed in December 2007. Fortunately cellulosic ethanol has the potential to be superior on all these dimensions except for conventional air pollution. Its advantages are that it can reduce net CO2 emissions by over 90 per cent, and that it can be derived from a diverse, widespread resource base (see for example Wyman 1999;

From grain to cellulosic ethanol

51

Berndes et al. 2001). For instance, it can be made from tree species such as hybrid poplar, willow, silver maple and black locust; wood residues including chips and sawdust; construction site residues, municipal garbage, paper and sewage sludge; corn stover, corn and sugarcane processing residues; cereal straws such as wheat, oat, barley and rice; and grasses such as switchgrass, sorghum, reed canary grass, and Miscanthus. This chapter will assess the progress and evolution of the ethanol industry from one based largely on corn and sugarcane to one that we expect will be increasingly based on cellulosic materials, and track ethanol’s costcompetitiveness in the U.S. relative to gasoline. The next section traces the development of ethanol fuel from its consideration in the early stages of the automobile industry to its use as a substitute liquid fuel today in the U.S., Brazil and elsewhere. This will be followed by a review of the simple economics of ethanol fuel production. The next section considers several federal and state policy instruments that have been used in the ethanol industry, including a variety of tax credits and Renewable Fuel Standards. The chapter will close with some preliminary conclusions about the future of ethanol development and use and the efficacy of public policies.

Historical development Ethanol and ethanol-gasoline blends have a long history as automotive fuels (Hunt 1981; Kovarik 1998). In the late 1800s for example, Henry Ford, Nicholas Otto and others built engines and cars that could run on ethanol. Ford equipped his Model T in 1908 as a flexible fuel vehicle, with carburetors that could be adjusted to use alcohol, gasoline, or a ‘gasohol’ mix. The need for fuel during World War I increased the demand for ethanol in the U.S. to 1.9–2.3 × 108 liters (5–6 × 107 g) yr−1. Demand decreased after the war because gasoline became the motor fuel of choice, but there was a continued interest (e.g. from General Motors and DuPont) in ethanol as both an antiknock agent (i.e. octane enhancer) and as a possible replacement for petroleum fuels. The discovery of the anti-knock properties of tetraethyl lead in 1921 dampened some of the enthusiasm for ethanol, and despite persistent health concerns, sales of leaded gasoline increased dramatically in later years. Alcohol blended fuels enjoyed a brief resurgence in the mid 1930s as falling corn prices prompted Midwestern states to seek alternative uses for their farm products. During this period, various alcohol–gasoline blends were marketed under trademarked names such as Alcolene and Agrol. The latter brand, with blends ranging from 5–17.5 per cent alcohol, was sold in over 2,000 retail outlets from Indiana to South Dakota during the late 1930s. Demand also grew during World War II. After that war, however, interest in ethanol waned because leaded gasoline proved cheaper and easier to produce, while new oil discoveries reduced the perceived urgency of finding petroleum substitutes (Kovarik 1998). The fuel ethanol market was revived in the 1970s. First, Brazil developed a

52

Overview

crash ‘Proalcool’ program in 1975 based on sugarcane, in response to the 1973 Arab oil embargo by the Organization of Petroleum Exporting Countries (OPEC). Over half of the cars in Brazil ran on 95 per cent anhydrous ethanol (E95) in the late 1980s, though a late 1980s sugar shortage and price hikes have reduced that figure to where it is today, at 20 per cent of flex fuel cars. Still, all of the gasoline sold in Brazil today must have at least a 20 per cent anhydrous alcohol blend (E20). Ethanol currently comprises about 40 per cent of the total vehicle fuel used within the country (Knight 2006). Brazil also exported over 1.7 × 109 liters (4.5 × 108 g) of ethanol to the U.S. in 2007 (FO Licht’s 2008). Although the U.S. rebuilt its fuel ethanol industry more gradually than Brazil, the two nations are today the world leaders in its production and usage (Table 3.1). The U.S. Energy Tax Act of 1978 (ETA) officially defined gasohol as a blend of gasoline with at least 10 per cent non-fossil fuel based ethanol by volume (E10). The ETA exempted ethanol from the 1.05 c liter−1 (4.0 c g−1) excise tax on gasoline, which equalled a 10.5 c liter−1 (40.0 c g−1) subsidy for ethanol (Solomon 1980a). After peaking at 15.8 c liter−1 (60.0 c g−1) in the mid to late 1980s, this excise tax exemption was reduced to 13.4 c liter−1 (51.0 c g−1) of ethanol in 2005 (California Energy Commission 2004). After the 1980s leaded gasoline phase-out by the U.S. Environmental Protection Agency (EPA), interest increased in using ethanol as an octane booster and volume extender. However, MTBE dominated most oxygenated gasoline markets over ethyl tertiary butyl ether (ETBE) throughout the 1990s. While the commercial ethanol industry was small at this time, in 1980 Congress approved several more tax benefits, as well as loan and price guarantees, to support ethanol producers and blenders. The growth of this industry was again stymied by low gasoline prices following the oil price collapse that began in 1986. The Energy Policy Act of 1992 (EPAct) contributed to increased usage of ethanol blends by requiring specified (primarily government-owned) car fleets Table 3.1 Top ten ethanol producing nations in 2006, capacity in liters (g) yr−1 Nation

2006

U.S. Brazil China India France Germany Russia Canada Spain South Africa

1.8 × 1010 (4.9 × 109) 1.7 × 1010 (4.4 × 109) 3.8 × 109 (1.0 × 109) 1.9 × 109 (5.0 × 108) 9.5 × 108 (2.5 × 108) 7.7 × 108 (2.0 × 108) 6.4 × 108 (1.7 × 108) 5.8 × 108 (1.5 × 108) 4.6 × 108 (1.2 × 108) 3.8 × 108 (1.0 × 108)

Source: RFA (2008).


53

to begin purchasing alternative fuel and flex-fuel vehicles. Such vehicles had to be capable of operating on E85, which is a blend of 85 per cent ethanol and 15 per cent gasoline. In the private sector, the production of alternative fuel vehicles was promoted by the Alternative Motor Fuels of Act of 1988, which provided auto companies with credits against their compliance requirements under the Corporate Average Fuel Economy (CAFE) standards for each flexfuel or alternative fuel vehicle they produced (California Energy Commission 2004). In reality, the initiative had little effect on the use of alternative fuels because at the time few fuel retailers offered E85. For this reason, the program was frequently criticized as a mechanism for automakers to avoid CAFE requirements while being ineffective at supporting purchases of E85 (Duffield and Collins 2006). Even today, the estimated five million of such vehicles on the road rely primarily on gasoline alone because only 1,519 U.S. retail outlets sell E85 (DOE 2008). Even so, U.S. annual ethanol production passed the 3.8 × 109 liter (1.0 × 109 g) mark in 1992 (Figure 3.1). Continued low gasoline prices in the early 1990s, coupled with weak corn harvests and the doubling of corn prices, led several Midwestern states to approve new subsidies to keep the struggling ethanol industry solvent. In 1996 total ethanol production nonetheless declined by 1.1 × 109 liters (3.0 × 108 g) from the 1995 level, reducing output back to the 1992 level (Warren and Ryan 2001). For the U.S. ethanol industry the last decade has been far different. Ethanol production recovered, consolidated, and grew rapidly with total 2005 output triple that of 1997, and 2007 output more than triple that of 2002 (Figure 3.1). The industry today still has a high four-firm concentration ratio at 40 per cent with the two largest firms, Poet Biorefining (formerly Broin) and VeraSun Energy, accounting for 26 per cent of total production

Figure 3.1 U.S. grain ethanol production, 1980–2007. Source: RFA (2008).

54

Overview

capacity. Archers Daniel Midland alone had accounted for 75 per cent in 1990. These companies operate distilleries in several Great Plains and North Central states. The facilities are close to large corn farms, as well as most of the major ethanol consuming states in the region. Conversely, 28 per cent of the industry’s 168 mills are owned by family-farm cooperatives (RFA 2008). The rapid growth in U.S. ethanol production and use, especially since 2002, can be directly attributed to increasing restrictions on MTBE as a fuel oxygenate (Figure 3.1). For example, MTBE bans in California, New York, and Connecticut, states that had accounted for a total of 42 per cent of national MTBE consumption, took effect in January 2004 (EIA 2003). In addition, the oxygen requirement for reformulated gasoline was repealed under the EPAct of 2005. Accelerated growth in ethanol production will continue at least through 2012 because of the Renewable Fuel Standard approved under EPAct, to be discussed later in this chapter (U.S. House of Representatives 2006).

The economics of ethanol production Existing ethanol plants have varied in size from 1.5 × 106 to 1.0 × 109 liters (4.0 × 105 to 2.7 × 108 g) yr−1 of production capacity (ADM owns the largest plants, in Illinois and Iowa) and are highly capital-intensive. Over 80 per cent of production, including most recent plants, uses anhydrous (dry grind) mills, with the rest made in wet mills (Urbanchuk 2006). The main cost components are capital and the feedstock supply. Given the proprietary nature of much ethanol corporate cost data, it is difficult to model the production technology precisely. This uncertainty extends to the degree of or lack of substitutability among factor inputs (i.e. capital, labor, energy, materials, water) and economies of scale, with the latter having been found to be highly variable in the dry mill industry (Gallagher et al. 2005). Thus rather than modeling grain ethanol production technology with a Cobb-Douglas, Leontiff or CES format, we will posit a simpler equation 3.1 (Solomon 1980a): CA = CC/2.75 + CK + CL + CE + CM + CO − (PDDGS (0.0005)(6.5)),

(3.1)

where CA = cost of ethyl alcohol production ($3.8 liter−1 or $1 g−1); CC = cost of corn ($35.2 liter−1 or $bushel−1); CK = cost of capital investment; CL = cost of labor; CE = cost of energy; CM = cost of raw materials; CO = other costs, including maintenance, overhead, water, residue disposal, insurance, taxes, regulatory compliance; and PDDGS = price of distillers dried grains with solubles (DDGS) co-product to be sold ($0.9 Mg−1 or $1 ton−1); and assuming 1.1 × 102 liters (2.75 g) of alcohol 3.5 dekaliter−1 (1 bushel−1) of corn for an anhydrous grain ethanol plant, and 3.0 kg (6.5 lbs) DDGS 3.8 liter−1 (1 g−1) alcohol output. An average size, dry mill ethanol plant of 1.9 × 108 liters (5.0 × 107 g) yr−1


55

in the U.S. requires about $70–$100 million in capital costs, employs 25–40 people and has $45–60 millions in annual operating costs (Urbanchuk 2006). The average wholesale, rack market price for grain ethanol was only about $0.53 liter−1 ($2.00 g−1) in October 2007 (Ethanol Market 2007). While this was below the October 2007 average oil refiner sales price of $0.58 liter−1 ($2.20 g−1) for gasoline in the U.S. (EIA 2008) and has risen since that time, it should be noted that over 90 per cent of ethanol production is sold under low priced long-term contracts (RFA 2008). Ethanol production in Brazil has been generally less expensive than in the U.S. since the 1970s given the more simplified processing of sugarcane vs. grain, and the availability of free fuel in the form of bagasse (Moreira 2000). The importation of cheap ethanol from Brazil into the U.S. has been officially restricted since 1980, however, by a 14 c liter−1 (54 c g−1) tariff on imports of foreign-produced ethanol. Even so, Brazil accounted for two-thirds of the ethanol imported into the U.S. in 2006, and the rest arrives indirectly through Central American and Caribbean nations (RFA 2008). The potential supply of lignocellulosic biomass sources for ethanol is far greater than that of food crops, but development has been impeded by the greater recalcitrance of biomass materials to be hydrolyzed into sugars. However, recent developments by Genencor International and Novozymes Biotech have resulted in up to a 30-fold drop in the cost of enzymes for hydrolysis, to 2.6–5.3 c liter−1 (10–20 c g−1) of ethanol (Greer 2005: 62). Thus, cellulosic ethanol has the potential to compete on a large scale with gasoline without subsidies in the next decade. Testing has occurred in 18 pilot plants, and one dozen demonstration and 15 commercial plants are being developed. While most of these facilities are in the U.S., several are being sited abroad (Tables 3.2–3.4). Several technology configurations are being actively researched and developed to produce ethanol from cellulosic biomass. These include diluted sulfuric acid and enzymatic hydrolysis, gasification, fast pyrolysis, and concentrated acid processes (So and Brown 1999). Pretreatment is also needed to break apart the biomass structure to allow for efficient hydrolysis of cellulosic sugars, and several technologies can be employed. Based on Wyman (1999) and Hamelinck et al. (2005) the reference technology assumed is dilute acid pretreatment and enzymatic hydrolysis of cellulose, since it offers the best near-term potential for commercialization competitive with fuel ethanol from grain. A revision of equation 3.1 for cellulosic ethanol can be expressed as: CA = CB/91 + CK + CL + CE + CM + CO − PP,

(3.2)

where CA = cost of ethyl alcohol production ($3.8 liter−1 or $1 g−1); CB = cost of the biomass feedstock ($0.9 Mg−1 or $1 dry ton−1); CK = cost of capital investment; CL = cost of labor; CE = cost of energy; CM = cost of raw materials; CO = other costs, including maintenance, overhead, water, residue disposal, insurance, property taxes, regulatory compliance; and PP = price of excess

56

Overview

Table 3.2 Cellulosic ethanol pilot plants Company

Location

Feedstock

Capacity or Feed rate

Start date

Iogen Iogen Masada/TVA

Ottawa, Canada Ottawa, Canada Muscle Shoals, AL Norval, Canada

wood chips wheat straw wood

9.0 × 102 kg day−1 9.0 × 102 kg day−1 NA

1985 1993 1993

various (nonwoody) various

4.5 × 102 kg hour−1

1995

9.0 × 102 kg day−1

1995

softwood & bark wood, bagasse corn stover, others wood residues, rice straw wood chips bagasse

NA 1.8 × 103 kg day−1 9.0 × 102 kg day−1 2.7 × 104 kg day−1

1998 1999 2001 2001

wood residues

9.0 × 102 kg day−1

SunOpta Arkenol Bioengineering Resources Verenium NREL/DOE Pearson Technologies NEDO Dedini SA Tsukishima Kikai Co. Etek Etanolteknik PureVision Universal ClearFuels Entech Sicco A/S Abengoa Bioenergy

Coskata Dupont Danisco

Orange, CA Fayetteville, AR Jennings, LA Golden, CO Aberdeen, MS Izumi, Japan Pirassununga, Brazil Ichikawa, Chiba, Japan Ornskoldsvik, Sweden Ft. Lupton, CO

spruce sawdust & wood chips corn stover, bagasse Honolulu, HI bagasse Phoenix, AZ municipal garbage Odense, Denmark wheat straw York, NE corn stover, wheat straw (co-located) with grain ethanol plant) Madison, PA wood chips, other Vonore, TN corn stover, switchgrass

3.0 × 102 liters day−1 2002 1.8 × 106 liters yr−1 2002 2003

5.0 × 102 liters day−1 2004 9.0 × 10 kg day−1

2004

4.5 × 103 kg day−1 1.0 × 102 liters day−1 1.0 × 102 kg hour−1 7.6 × 106 liters yr−1

2004 2004 2005 2007

1.5 × 105 liters yr−1 0.9 × 106 liters yr−1

2009 2009

Source: Ethanol Producer Magazine, 1996–2008.

electric power byproduct that can be sold to the electric grid (c kWh−1); and assuming a process yield of 314 liters 0.9 Mg−1 (83 g alcohol dry ton−1) of biomass feedstock for an anhydrous cellulosic ethanol plant. While only twothirds this conversion efficiency has thus far been achieved, this assumption is reasonable with mature technology (Lynd et al. 2007: 84, 90).


57

Table 3.3 Cellulosic ethanol demonstration plants, capacity in liters yr−1 Company

Location

Iogen

Ottawa, Canada wheat, oat and barley straw Zhaodong City, corn stover China

China Resources Alcohol Bioethanol Japan Kansai Abengoa Bioenergy & SunOpta Verenium

Mascoma ClearFuels

Sakai, Japan Salamanca, Spain

Capacity

Start Date

3.0 × 106

2004

6.4 × 106

2006

construction wood 1.4 × 106 residues wheat straw (co-located w/ 5.0 × 106 grain ethanol plant)

2007

5.3 × 106

2008

1.9 × 106

2008

3.5 × 106

2008

6

3.8 × 10

2009

1.0 × 107

2009

3.0 × 107 5.0 × 106

2010 2010

7.6 × 106

2012

Jennings, LA

bagasse, rice hulls (colocated with grain ethanol plant) Rome, NY wood chips, paper sludge, switchgrass, corn stover Kaumakani, HI bagasse

Florida Crystals

Okeelanta, FL

Pacific Ethanol

Boardman, OR

Xethanol Sekab ETechnology Lignol Innovations

Feedstock

bagasse, urban wood wastes

wheat straw, corn stover, poplar residues Auburndale, FL orange peels Ornskoldsvik, softwood residues Sweden (spruce and pine) Commerce wood chips, corn stover, City, CO switchgrass

2008

Source: Ethanol Producer Magazine, 2004–2008.

Earlier cost estimates and projections for cellulosic ethanol production have been modified and updated to 2007 dollar in Table 3.5 based on the $250 million capital investment projections of Iogen Corporation of Canada, which plans to build a 6.8 × 107 liters (1.8 × 107 g) yr−1 commercial plant by 2010 in Saskatchewan. An additional construction expense of over $50 millions will be incurred, since 500 construction workers will be needed to build the plant over two years. An economic lifetime of 15 years is assumed for the reference cellulosic ethanol mill. Given the lack of commercial experience thus far the capital cost may decrease over time, as scale economies are expected but yet unknown (So and Brown 1999; Hamelinck et al. 2005). The largest capital cost components are for feedstock pretreatment, at 17 per cent; simultaneous saccharification and fermentation, at 15 per cent (which can

58

Overview

Table 3.4 Near-term cellulosic ethanol commercial plants, capacity in liters yr−1 Company

Location

Feedstock

Capacity

Start Date

Bioethanol Japan Kansai Colusa

Sakai, Japan

construction wood residues rice straw and hulls MSW, wood and agricultural residues wood residues MSW, wood residues spent pulping liquor MSW, wastewater sludge wheat straw

3.8 × 106

2008

4.7 × 107

2008

1.2 × 107

2009

7.6 × 107 7.2 × 107

2009 2010

2.3 × 107

2010

3.4 × 107

2010

6.8 × 107

2010

wood chips, other corn stover, wheat straw & switchgrass corn stover

3.8 × 108

2011

4.9 × 107

2011

6.8 × 107

2011

bagasse wood chips/ residues wood chips

1.1 × 108 1.5 × 108

2011 2012

3.8 × 107

2012

bagasse

1.8 × 107

2012

Colusa, CA

BlueFire Ethanol

Lancaster, CA

Range Fuels BlueFire Ethanol Flambeau River Biorefinery a Masada

Soperton, GA Corona, CA

Iogen Coskata

Park Falls, WI Middletown, NY Saskatchewan, Canada TBD

Abengoa Bioenergy

Hugoton, KS

Poet & Voyager Ethanol Verenium Mascoma

Emmetsburg, IA TBD Jault Ste, Marie, MI Little Falls, MN Brazil

SunOpta Dedini SA

Source: Ethanol Producer Magazine 2005–2008. Note: This product will provide diesel fuel.

a

also be done in separate vessels); and energy utilities, at 36 per cent (for boilers and turbogenerators). For cellulosic ethanol production the major wholesale cost components are the annualized capital charge, at 40 per cent of the total; and the feedstock and other raw materials, at 46 per cent. A total production cost virtually identical to the gasoline price is calculated, at $0.58 liter−1 ($2.20 g−1) (Table 3.6). While this cost estimate has not been confirmed with commercial experience and product markup, this finding is encouraging.


59

Table 3.5 Estimated capital investment cost for a 2.2 × 108 liters (5.8 × 107 g) yr−1 cellulosic ethanol plant Cost category

Million $ (2007)

Feedstock handling (wood or switchgrass) Pretreatment Xylose fermentation Cellulase production Simulataneous saccharification and fermentation Ethanol recovery Off-site tankage Environmental systems Utilities (steam, electricity, water) Miscellaneous Fixed capital investment Start-up costs Working capital Total capital investment

12.7 41.9 10.9 5.0 37.0 7.1 7.2 7.0 90.0 8.5 227.3 11.4 11.3 250.0

Source: adapted and updated from (Wyman 1999: 199).a Note: a The original study assumed an ethanol production rate of 314 liters (83 g) 0.91 Mg−1 (1 dry ton−1) of wood based on a plant feed rate of 1.7 103 Mg (1.9 103 tons) day−1.

Table 3.6 Estimated cost of cellulosic ethanol production Cost category

Million $ yr−1 (2007)

Cents liter−1(g−1) (2007)

Feedstock (wood or switchgrass)a Enzymes Other raw materials (sulfuric acid, lime, glucose, nutrients) Gypsum disposal Electricity Water Labor/supervision Maintenance Direct overhead General overhead Insurance & property taxes

41.20 11.88 5.86

18.8 (71.4) 5.4 (20.7) 2.7 (10.1)

0.61 (5.04) 0.22 2.41 7.96 1.47 7.27 3.99

0.3 (1.0) −2.3 (−8.6) 0.1 (0.4) 1.1 (4.1) 3.6 (13.70) 0.7 (2.5) 3.3 (12.4) 1.8 (6.8)

Total cash costs Annualized capital chargeb

77.83 50.00

35.7 (134.4) 22.6 (85.5)

127.83

58.1 (220.0)

Total production cost

Source: adapted and updated from (Wyman 1999: 200). Note: a The costs of the feedstock, other raw materials (except for enzymes), residue disposal, energy, water, and labor have been updated based on growth in the producer price index for pulp, paper and allied products between 1990–2007, calculated from (Bureau of Labor Statistics 2008); all other cost categories have been updated based on the total capital investment requirements. b 20 per cent of total capital investment, and assuming a 10 per cent after-tax rate of return on capital investment.

60

Overview

Several factors could lower the production cost of cellulosic ethanol. These include the use of cheap residues for biomass feedstocks lacking other markets, low-cost debt financing, and integration into a biorefinery platform to increase the product mix to include higher-value chemical co-products (Ragauskas et al. 2006). The latter option could potentially increase ethanol yields and further enhance economic competitiveness.

Federal and state energy policy instruments Given the marginal economics but potentially large social benefits of ethanol development, government subsidies and other support mechanisms have been a consistent and essential part of the U.S. ethanol industry for 30 years. Subsidies have taken several forms at the federal and state government levels, stimulating both supply and demand for the product, and sometimes prompting considerable criticism (Pimental and Patzek 2005; Libecap 2003; Taylor and Becker 2003). Because of the numerous support mechanisms that have been in effect since 1979 it is difficult to tease out the impact of any single policy instrument. The following discussion provides a brief history of U.S. government support for ethanol, a more detailed look at some recent changes and, when possible, an assessment of the relative importance of these instruments. Federal support Three basic government initiatives fueled the early years of the modern fuel (i.e. corn) ethanol industry. The first and most important of these was a partial exemption from the federal gasoline excise tax for gasohol (a fuel containing at least a 10 per cent component of biomass-derived ethanol). This exemption was instituted by the U.S. Energy Tax Act of 1978, and implemented in 1979 (Solomon 1980a). A fuel blender’s tax credit and a pure alcohol fuel credit were added to the mix in 1980. These new initiatives were in essence the same subsidy as the fuel excise tax exemption, but recouped through a different system and available to a small number of companies who were unable to claim the fuel tax exemption. Through subsequent years, all three of the tax provisions were periodically renewed and altered in terms of the benefit magnitude, with changes in one being mirrored by changes in the others. For a variety of reasons, most notably its ease of use, the excise tax exemption has been by far the most widely used incentive (double crediting with the fuel blender’s tax credit is not permitted) with total government revenue impacts estimated at between 16 and 56 times those of the other two tax credits combined (U.S. General Accounting Office 2000). Thus it was the most important of early ethanol support mechanisms and it remains of great importance to this industry. Further federal support came in 1990 with passage of the Small Ethanol Producer Tax Credit, which provided small plants (less than 1.1 × 108 liters


61

(3.07 g) yr−1 production capacity) with an additional 2.6 c liter−1 (10 c g−1) income tax credit for volumes up to 5.7 × 107 liters (1.5 × 107 g) yr−1 (California Energy Commission 2004). The U.S. Energy Policy Act of 2005 (U.S. House of Representatives 2006), discussed below, redefined small producers as those producing up to 2.3 × 108 liters (6.0 × 107 g) yr−1. In recent years the total combined federal support for ethanol has equaled a taxpayer subsidy of $3.8 billion yr−1 (U.S. General Accounting Office 2000), although this subsidy offsets an even larger subsidy to U.S. farmers and the cost of oil imports (Anonymous 2008). From 1978 until 2004 there was little change to the main component of federal support, the excise tax exemption. Benefit levels changed several times, culminating in a progressive reduction from $0.14 liter−1 ($0.54 g−1) to $0.13 liter−1 ($0.51 g−1) of ethanol during 1998–2005 as a result of the Transportation Equity Act of 1998. In 2004 however, the basic mechanics of the subsidy were changed by the introduction of the Volumetric Ethanol Excise Tax Credit (VEETC). The VEETC streamlined the system by making it volume based rather than limited to specific blends, eliminated negative impacts on the Highway Trust Fund by taking the credit from general government revenues, and renewed the subsidy until 2010 at $0.13 liter−1 ($0.51 g−1) of ethanol (RFA 2008). The Energy Policy Act (EPAct) of 2005 approved several major incentives to usher in a new era of renewable fuels, where corn ethanol is no longer synonymous with ethanol (U.S. House of Representatives 2006). Cellulosic ethanol, although presently still in the pre-commercial stage, received considerable attention from EPAct, garnering subsidies over and above that for traditional ethanol production. Even so, the most widely publicized provision of EPAct, the Renewable Fuel Standard (RFS), applies to both corn and cellulosic ethanol, and will operate in the place of the now eliminated oxygenate requirement for reformulated gasoline. Implementation of the RFS by the EPA began in 2006 at 1.5 × 1010 liters (4.0 × 109 g) yr−1 (which was almost met in 2005), and was scheduled to increase to 2.8 × 1010 liters (7.5 × 109 g) yr−1 in 2012. In light of the current market for ethanol EPAct provides only a modest boost to production. Additional provisions of EPAct were designed to improve commercialization prospects for the new technology through increased R&D funding in all aspects of the industry, including feedstock development, processing technology, co-product production and systems optimization (Wyman 2003). Project financing and funding, considered by many to be a major bottleneck (Hamelinck et al. 2005; National Conference of State Legislators 2007), received attention as well through a series of grants and loan guarantees for biorefinery development and commercialization (U.S. House of Representatives 2006). Overall, EPAct provides an essential short-term boost to accelerate commercialization and technological development, while also attempting to cement a place for the new technology in the longer-term ethanol market. Implicit in this is the assumption that cellulosic ethanol is capable

62

Overview

of providing larger societal benefits than corn ethanol, although in the near future, corn ethanol will still dominate the market. The prospects for cellulosic ethanol received a much greater boost through passage of the U.S. Energy Independence and Security Act in December 2007. This law revises and extends the RFS, beginning in 2008 at 3.4 × 1010 liters (9 × 109 g) yr−1, up to a rather ambitious 1.36 × 1011 liters (3.6 × 1010 g) yr−1 by 2022. Of this total, no more than 5.7 × 1010 liters (1.5 × 1010 g) yr−1 will come from cornstarch, with the remaining 7.9 × 1010 liters (2.1 × 1010 g) yr−1 to come from advanced biofuels with greatly reduced greenhouse gas emissions (including biodiesel). Over three fourths of the advanced biofuels total will eventually come from cellulosic materials, and this part of the mandate could be met with any combination of ethanol and other alcohols (Sissine 2007). State support During the revival of the U.S. ethanol industry in the late 1970s over a dozen state governments were quick to approve partial or total gasohol exemptions from state road use taxes. These included producer states in the Midwest such as Iowa, but also southern states such as Louisiana, Arkansas and Oklahoma (Louisiana repealed its tax exemption in 1989). These state programs are generally similar to the federal programs, and as of 2004, 36 states were supporting ethanol development (California Energy Commission 2004). By 2005, nine states had some level of excise tax exemption (including Minnesota, which only offers the exemption for 85 per cent fuel blends) (National Conference of State Legislators 2007). Producer credits were offered in 11 additional states: a $2 million payment per plant is offered in Montana if state grains are used; and several other states offered grants, loan programs, or tax exemptions (California Energy Commission 2004). The policy environments in Minnesota and Iowa have been heralded as especially effective, combining measures that support both production and consumption of ethanol. Instrumental to this effort in Minnesota has been a 1997 state requirement that all gasoline sold in the State must have a 10 per cent ethanol content. An increase to a 20 per cent mandate was approved in Minnesota in 2005 (which would take effect in 2013), pending EPA approval (National Conference of State Legislators 2007). Similar laws that mandate a 10 per cent ethanol-blended fuel were passed in Hawaii and Montana in 2005, and Missouri, Washington, and Oregon in 2006–2007 (RFA 2008). The requirements have varying phase-in dates. Kansas has a 10 per cent ethanol blend mandate that applies to state fleet vehicles only. The State of Iowa joined Minnesota in 2006 by approving a comprehensive state RFS – a 10 per cent mandate for 2009 that will increase to 25 per cent by 2020. In addition, a retail tax credit on E85 of $0.07 liter−1 ($0.25 g−1) was approved, though it will be lowered starting in 2009. This compares with Minnesota, which has a state fuel tax exemption on E85 of $0.015 l−1 ($0.058 g−1) and an ethanol production payment of $0.05 liter−1 ($0.20 g−1). Minnesota also boasts the most


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extensive E85 infrastructure in the country, with 339 retail outlets. Illinois has the second largest number with 180 (DOE 2008). Additionally (as noted earlier), the 1990 Clean Air Act Amendments stimulated demand for ethanol by mandating the use of oxygenated fuels in many carbon monoxide and ozone non-attainment areas. In the several Midwestern states where ethanol production is concentrated, ethanol has become the primary oxygenate used for this purpose, while others such as California gravitated towards the more readily available MTBE (California Energy Commission 2004). Recent revelations about the toxic effects of MTBE and its accumulation in groundwater have led to it being partially or completely banned in half the states, including California and New York, further cementing the place of ethanol as a gasoline additive (EPA 2007). Moreover, the 2005 EPAct did not include liability protection for MTBE manufacturers. Thus, the future of ethanol production and use will depend upon a mix of federal and state support as well as technical and economic developments.

Conclusions Ethanol production has a long history. During this time, production has had many peaks and valleys, although it is currently at the highest ever production levels. Each time production rose or fell it responded to complex combinations of changes in demand for competing products, incentive programs, and government mandated production levels. Current production is highest in Brazil and the U.S. Brazil’s experience illustrates that it is possible to successfully mandate large-scale shifts to ethanol use. MTBE bans in half the U.S. states, including the major markets of California, New York and Connecticut, are contributing significantly to record demand for ethanol (Table 3.3 and Figure 3.1). The fuel is also experiencing unprecedented levels of attention due to its value as an alternative to gasoline, with its problematic links to climate change, peak oil supply, rising oil prices, and Middle Eastern political instability. Cellulosic ethanol production, in particular, can result in a fuel with a high net energy yield and close to CO2 neutrality (Hammerschlag 2006; Farrell et al. 2006). This makes it increasingly desirable as a gasoline alternative. We therefore expect demand for ethanol to substantially grow in future years, but do not expect corn alone to meet this demand. Corn remains the largest source of U.S. ethanol production, however this is likely to change as demand for this feedstock is expected to exceed supply and technological improvements in processing converge to lower the cost of cellulosic ethanol production (Perlack et al. 2005). In some ways, the growth in grain ethanol production has laid the groundwork for a shift into cellulosic ethanol production. For instance, the political power of U.S. farm interests has built support for ongoing state and federal subsidies of grain ethanol. These supports are currently in place for all feedstocks, and

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there will be additional federal support for emergent cellulosic ethanol production. Our analysis estimates that cellulosic ethanol production costs could conceivably match the price of gasoline, with an optimistic assumption on conversion process yield. Thus while cellulosic ethanol production is not yet commercial due to higher capital costs and immature technology, its potential price could be competitive with further cost reductions and reasonable product markup. The technology may become especially attractive in the coastal states that produce only small corn or other grain crops. Moreover, cellulosic ethanol may experience further cost decreases due to the use of inexpensive farm and forestry residue feedstocks. Even so, it is important to emphasize that price supports remain critical in the short-run. Subsidies that recognize the social value of grain and cellulosic ethanol as alternatives to gasoline and as a domestic product will be essential to market success, along with the need to substitute for MTBE and meet the RFS. Additional policy solutions aimed at discouraging reliance on gasoline might similarly increase the competitiveness of both corn and cellulosic ethanol.

References Anonymous (2008) ‘Soberly weighing advantages of higher ethanol consumption’, Tampa Tribune, 18 March. Available: http://www2.tbo.com/content/2008/mar/ 18/na-soberly-weighing-advantages-of-higher-ethanol-c/ (accessed 24 March 2008). Berndes, G., Azar, C., Kaberger, T. and Abrahamson, D. (2001) ‘The feasibility of large-scale lignocellulose-based bioenergy production’, Biomass & Bioenergy, 20: 371–383. Bureau of Labor Statistics, U.S. Department of Labor (2008) ‘Producer price indexes’. Available: http://www.bls.gov/ppi (accessed 27 February 2008). California Energy Commission (2004) ‘Ethanol fuel incentives applied in the U.S., reviewed from California’s perspective’, CEC Staff Report P600-04-001, Sacramento. De Oliveira, M.E.D., Vaughan, B.E. and Rykiel, E.J. (2005) ‘Ethanol as fuels: energy, carbon dioxide balances, and ecological footprint’, BioScience, 55: 593–602. Department of Energy, U.S. (DOE) (2008) Alternative Fuels Data Center. Available: http://www.eere.energy.gov/afdc/fuels/stations_counts.html (accessed 22 July 2008). Duffield, J.A. and Collins, K. (2006) ‘Evolution of renewable energy policy’, Choices, 21: 9–14. Energy Information Administration (EIA) (2003) ‘Status and impact of state MTBE ban’. Available: http://www.eia.doe.gov/oiaf/servicerpt/mtbeban/ (accessed 20 December 2007). —— (2007) Monthly Energy Review 2007, DOE/EIA-0384, U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/emeu/mer/contents. html (accessed 16 February 2008). —— (2008) ‘Wholesale price of motor gasoline’. Available: http://tonto.eia.doe.gov/ steo_query/app/priceresult.asp (accessed 27 February 2008).


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Environmental Protection Agency, U.S. (EPA) (2007) ‘State actions banning MTBE (statewide)’, EPA420-B-07-013. Available: http://www.epa.gov/mtbe/ 420b07013.pdf (accessed 15 December 2007). Ethanol Market.Com, LLC. (2007) ‘Rack ethanol prices’, 28 October. Available: http://www.ethanolmarket.com/20071028EthanolMarket.pdf (accessed 27 February 2008). Ethanol Producer Magazine (1996–2008, various issues) Bryan & Bryan International, Grand Forks, North Dakota. Available: http://www.ethanolproducer.com/ (accessed 8 February 2008). Farrell, A.E., Plevin, R.J., Turner, B.T., Jones, A.D., O’Hare, M. and Kammen, D.M. (2006) ‘Ethanol can contribute to energy and environmental goals’, Science, 311: 506–508. FO Licht’s. FO (2008) World Ethanol & Biofuels Report, 6(10): 181. Gallagher, P.W., Brubaker, H. and Shapouri, H. (2005) ‘Plant size: capital cost relationships in the dry mill ethanol industry’, Biomass & Bioenergy, 28: 565–571. Giampietro, M., Ulgiati, S. and Pimental, D. (1997) ‘Feasibility of large-scale biofuel production: does an enlargement of scale change the picture?’, BioScience, 47: 587–600. Greer D. (2005) ‘Creating cellulosic ethanol: spinning straw into fuel’, BioCycle, 46(4): 61–65. Hamelinck, C.N., van Hooijdonk, G. and Faaij, A.P.C. (2005) ‘Ethanol from lignocellulosic biomass: techno-economic performance in short-, middle- and long-term’, Biomass & Bioenergy, 28: 384–410. Hammerschlag, R. (2006) ‘Ethanol’s return on investment: a survey of the literature 1990–present’, Environmental Science and Technology, 40: 1,744–1,750. Hunt, D.V. (1981) The Gasohol Handbook, New York: Industrial Press. Knight, P. (2006) ‘Sugar and ethanol in Brazil and South America’, International Sugar Journal, 108: 472 ff. Kovarik, B. (1998) ‘Henry Ford, Charles Kettering, and the “fuel of the future” ’, Automotive History Review, 32: 7–27. Libecap, G.D. (2003) ‘Who really benefits from ethanol?’, Consumers’ Research Magazine, 86(8): 20–21. Lynd, L.R., Laser., M.S., McBride, J., Podkaminer, K. and Hamilton, J. (2007) ‘Energy myth three – high land requirements and an unfavorable energy balance preclude biomass ethanol from playing a large role in providing energy services’, in B.K. Sovacool and M.A. Brown (eds.) Energy and American Society: Thirteen Myths, Dordrecht: Springer, pp. 75–101. Moreira, J.R. (2000) ‘Sugarcane for energy – recent results and progress in Brazil’, Energy for Sustainable Development, 4(3): 43–54. National Conference of State Legislators (2007) ‘Ethanol production incentives’. Available: http://www.ncsl.org/programs/energy/ethinc.htm (accessed 25 August 2007). National Corn Growers Association (2007) ‘Indy 500 drivers praise ethanol, NCGA notes’, press release, 1 June. Available: http://www.ncga.com/news/notd/2007/june/ 060107.asp (accessed 1 June 2007). Ogden, J.M., Williams, R.H. and Larson, E.D. (2004) ‘Societal lifecycle costs of cars with alternative fuels/engines’, Energy Policy, 32: 7–27. Perlack, R.D., Wright, L.L., Turhollow, A.F., Graham, R.L., Stokes, B.J. and Erbach, D.C. (2005) Biomass as Feedstock for a Bioenergy and Bioproducts Industry: the

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technical feasibility of a billion-ton annual supply, DOE/GO-102005-2135. Prepared by Oak Ridge National Laboratory for the U.S. Department of Energy and U.S. Department of Agriculture, Washington, D.C. Pimental, D. and Patzek, T.W. (2005) ‘Ethanol production using corn, switchgrass and wood’, Natural Resources Research, 14: 65–76. Ragauskas, A.J., Williams, C.K., Davison, B.H., Britovsek, G., Cairney, J., Eckert, C.A., et al. (2006) ‘The path forward for biofuels and biomaterials’, Science, 311: 484–489. Renewable Fuels Association (RFA) (2008). Available: http://www.ethanolrfa.org/ (accessed 11 September 2008). Sissine, F. (2007) ‘Energy Independence and Security Act of 2007: a summary of major provisions’, Congressional Research Service Report for Congress, Order Code RL34294, Washington, D.C. So, K.S. and Brown, R.C. (1999) ‘Economic analysis of selected lignocellulosic-toethanol conversion technologies’, Applied Biochemistry and Biotechnology, 79: 633–640. Solomon, B.D. (1980a) ‘Gasohol, economics, and passenger transportation policy’, Transportation Journal, 20: 57–64. —— (1980b) ‘Agricultural energy: debunking the net energy loss myth’, The Environmental Professional, 2: 292–295. —— and Banerjee, A. (2006) ‘A global survey of hydrogen energy research, development and policy’, Energy Policy, 34: 781–792. Taylor, J. and Becker, D. (2003) ‘A complete waste of energy’, Los Angeles Times, 29 October 2003. Available: http://www.cato.org/pub_display.php?pub_id=5592> (accessed 25 September 2006). Urbanchuk, J.M. (2006) ‘Contribution of the ethanol industry to the economy of the United States’, Prepared by LECG LLC for the Renewable Fuels Association, Washington, D.C. U.S. General Accounting Office (2000) ‘Petroleum and ethanol fuels: tax incentives and related GAO work’, GAO, Resources, Community, and Economic Development Division. RCED-00-301R, Washington, D.C. U.S. House of Representatives, Committee Print of the Energy Policy Act of 2005 (2006). Available: http://energycommerce.house.gov/108/ energy_pdfs_2.htm (accessed 20 October 2006). Warren, W.D. and Ryan, S. (2001) ‘Temporal patterns of ethanol use in the United States: 1981–1998’, Transportation Quarterly, 55(4): 25–39. Westcott, P. (2007) ‘Ethanol expansion in the United States: how will the agricultural sector adjust?’, Outlook Report No. FDS-07D-01, U.S. Department of Agriculture, Economic Research Service, Washington, D.C. Wyman, C.E. (1999) ‘Biomass ethanol: technical progress, opportunities and commercial challenges’, Annual Review of Energy and the Environment, 24: 189–226. —— (2003) ‘Potential synergies and challenges in refining cellulosic biomass to fuels, chemicals, and power’, Biotechnology Progress, 19: 254–262.

Part II

Forest biomass energy assessments

4

Resource assessment, economics and technology for collection and harvesting Erin G. Wilkerson and Robert D. Perlack

Biomass production The timber resources in the U.S. can produce 334 million dry tonnes annually (368 million dry tons) by 2030 (Perlack et al. 2005). Of this volume, 58 million tonnes (64 million tons) are residues from logging operations and site clearing. Another 54 million tonnes (60 million tons) are from fuel treatment operations involved in reducing fire hazards. The availability estimates for these two key primary forestland resources take into account environmental concerns by assuming sufficient biomass is left on-site for nutrient recycling purposes, avoiding steep-sloped and inaccessible areas (i.e. roadless areas), and accounting for collection frequency. The forestland potential also considers the allocation of recovered resources to both biomass and highervalued forest products. The harvest and collection operations associated with these resources are the subject of this chapter as they are currently underutilized, and represent a significant fraction of the lignocellulosic materials available for conversion in biofuel facilities.

Forest residues from commercial logging and other removal operations Logging residues are defined as the unused portions of growing-stock and non-growing-stock trees cut or killed by logging and left in the woods. Other removal residue is the unutilized wood cut or killed due to cultural operations such as pre-commercial thinnings or from timberland clearing. A recent analysis shows that annual removals from the forest inventory totaled nearly 18.3 billion m3 (20.2 billion ft3). Of this volume, 78 per cent was for roundwood products, 16 per cent was logging residue, and slightly more than 6 per cent was classified as ‘other removals’ (Smith et al. 2004). The total annual removals constitute about 2.2 per cent of the forest inventory on timberland and are less than the net annual forest growth. The logging residue fraction is biomass removed from the forest inventory as a direct result of conventional forest harvesting operations. This biomass material is largely tree-tops and small branches left on site because these materials are currently

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uneconomical to recover either for product or energy uses. The remaining fraction, other removals, consists of timber cut and burned in the process of land conversion or cut as a result of cultural operations such as precommercial thinning and timberland clearing. Because the material is of low value, of low demand, and has high extraction cost, the remaining residue is left on the land. This is undesirable for aesthetic and fire control reasons, so loggers often place the residue or ‘slash’ into piles for burning. This can contribute to air quality issues in some areas. The amount of logging and other removal residue was estimated using the U.S. Department of Agriculture Forestry Inventory and Analysis program’s Timber Product Output Database Retrieval System. For the U.S., total logging residue and other removals currently amount to nearly 61 million tonnes (67 million dry tons) annually: 44.4 million dry tonnes (49 million dry tons) of logging residue and 16.3 million dry tonnes (18 million dry tons) of other removal residue. Not all of this resource is potentially available for bioenergy and biobased products. Generally, these residues tend to be relatively small pieces consisting of tops, limbs, small branches, and leaves. Stokes (1992) reported a wide range of recovery percentages, with an average of about 60 per cent potential recovery behind conventional forest harvesting systems. With newer technology, it is estimated that current recovery is about 65 per cent. Other removals, especially from land-clearing operations, usually produce different forms of residues, and are not generally as feasible or as economical to recover. It is expected that only half of the residues from other removals can be recovered. The amount of biomass that can be sustainably harvested varies by soil and vegetation type and is the subject of some debate among soil scientists and foresters. Some portion of this material, especially the leaves and parts of tree crown mass, may be needed on site to replenish nutrients and maintain soil productivity. Because many forest operations involve the construction of roads that provide only temporary access to the forest, it is assumed that these residues are removed at the same time as the harvest or land clearing operations that generate the residues. Limiting the recoverability of logging and other removal residue reduces the size of this forest resource from about 61 million to 37.2 million dry tonnes (67 million to 41 million dry tons). About threefourths of this material would come from the logging residue. Further, because of ownership patterns, most of the logging residue and nearly all residues from other sources (such as land clearing operations) would come from privately owned land. The spatial distribution of these resources is shown in Figures 4.1 and 4.2. Forest fuel treatment thinnings Currently, there are vast areas of U.S. forestland that are overstocked with relatively large amounts of woody materials. This excess material has built up

Figure 4.1 Spatial distribution of logging residues.

Figure 4.2 Spatial distribution of other removal residues.

Resource assessment, economics and technology

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over years as a result of forest growth and alterations in natural fire cycles. Over the last ten years, federal agencies have spent more than $8.2 billion fighting forest fires, which have consumed over 20 million hectares (49 million acres). The cost of fighting fires does not include the costs of personal property losses, ecological damage, loss of valuable forest products, or loss of human life. The USDA Forest Service and other land management agencies are currently addressing the issue of hazardous fuels buildup and looking at ways to restore ecosystems to more fire-adaptive conditions. The removal of excess woody material would also improve forest health and productivity (Graham et al. 2004). In August 2000, the National Fire Plan was developed to help respond to severe wild-land fires and their impacts on local communities while ensuring sufficient firefighting capacity for future fires. The National Fire Plan specifically addresses firefighting capabilities, forest rehabilitation, hazardous fuels reduction, community assistance, and accountability. The Healthy Forests Restoration Act (HFRA) of 2003 was enacted to encourage the removal of hazardous fuels, encourage utilization of the material, and protect, restore, and enhance forest ecosystem components (U.S. Forest Service 2007). HFRA is also intended to support R&D to overcome both technical and market barriers to greater utilization of this resource for bioenergy and other commercial uses from both public and private lands. Removing excess woody material has the potential to make relatively large volumes of forest residues and small-diameter trees available for bioenergy and biobased product uses. Fuel treatment thinnings are classified as standing and downed trees in overstocked stands that, if removed, would leave the stand healthier, more productive, and less susceptible to fire hazard. The overstocking of many forest stands has resulted from years of forest growth without harvesting and from alteration of natural fire cycles. The amount and location of potential fuel treatment wood in timberlands was generated by the USDA Forest Service using a model called the Fuel Treatment Evaluator (Miles 2004). This assessment tool identifies, evaluates, and prioritizes fuel treatment opportunities. Timberland fuel treatment data, retrieved by state and county and in collaboration with USDA Forest Service staff, were modified based on several assumptions. Thinnings were assumed to be 60 per cent accessible on public lands and 80 per cent accessible on private lands. Only 80 per cent of the accessible material was assumed to be collectable in a given stand. Of the collected material, 70 per cent was assumed to be larger pieces usable for high-value products; thus, only 30 per cent was assumed to be available for energy. Next, a 30-year harvest cycle was assumed. Estimation of potential fuel treatment thinnings from ‘other forestland’ (forested areas not categorized as ‘timberland’) was based on the Forest Inventory Analysis database using similar assumptions (Miles 2004). In total, there are about 7.6 billion dry tonnes (8.4 billion dry tons) of treatable biomass in inventory that is potentially available for bioenergy and biobased products. However, only a fraction is removable in any year given

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the combination of recoverability, accessibility, and harvest cycle factors noted previously. These factors reduced the amount of fuel treatment biomass that can be sustainably removed on an annual basis to about 44.4 million dry tonnes (49 million dry tons) from timberlands and about 10 million dry tonnes (11 million dry tons) from other forestlands. Most of the fuel treatment biomass from timberlands would come from privately owned lands; slightly less than 20 per cent of the material would come from national forests. In contrast, proportionately more of the fuel treatment biomass allocated to bioenergy and biobased products on other forestland land would come from publicly held lands, located mostly in the western regions of the country. The 54.4 million dry tonnes (60 million dry tons) of fuel treatment biomass assumes that a relatively large percentage (70 per cent) goes to higher-valued products. If feedstock prices for biomass were to increase relative to conventional forest products, the amount of biomass available for bioenergy and biobased products could increase substantially. The spatial distribution of these resources is shown in Figures 4.3 and 4.4.

Woody harvest, collection, and handling systems Current logging technologies are designed for felling, extracting, and transporting high quality saw logs or pulpwood from forests. If convenient, logging residues (limbs and small diameter tree tops, also called slash) and small, non-merchantable trees are occasionally recovered for use as energy wood by paper mills or power plants. More often than not, logging residues are left in the woods, as the costs of removal have traditionally exceeded the market value of the material. However, as the demand for biomass feedstocks to produce power, heat, fuel, and biobased products increases, a new market for logging residues will develop. Because biomass for energy and bio-based products is, and will likely remain, a low-value product relative to roundwood, additional harvest and handling costs must be minimized in order for collection to be economically feasible. Assembling a biomass supply chain to meet the U.S. Department of Energy feedstock cost targets (DOE Office of the Biomass Program 2007) requires consideration of the benefits and drawbacks of technologies available for each component of the chain. The following section describes existing logging technologies that have been adapted for collection and transport of forest residues and some new technologies currently under development. Harvest and collection Biomass can be harvested along with timber in a two-pass system or a onepass, integrated system. In the two-pass system, energy wood is cut and piled in the forest to dry while timber is extracted. Biomass is then collected and removed in a later operation. The one-pass system is an integrated approach in which all products are harvested in a single operation. One-pass systems

Figure 4.3 Spatial distribution of fuel treatment thinnings from timberlands.

Figure 4.4 Spatial distribution of fuel treatment thinnings from other forestlands.


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include whole-tree harvesting and in-forest delimbing in which biomass is collected at the same time as timber. Increasing the degree of integration in logging operations with one-pass systems has been shown to be cost-effective. However, other factors, such as the value of drying biomass in the forest, may make a two-pass system more desirable. In typical U.S. mechanized logging operations, timber is harvested as whole trees or, less frequently, as cut-to-length logs. In whole-tree logging operations, felled trees are extracted to the landing where limbs and tops are removed before logs are transported to the mill yard or pulp plant. Cut-tolength systems in which limbs and tops are removed and logs are cut to the desired length while still in the woods are also available. Harvesting logging residues is possible in either system (see Figure 4.5), and feasibility varies with how the operation is executed. Choice of technologies should account for scale of the logging operation, nature of the forest site, infrastructure, and integration (if necessary) into the existing logging operation. Integrated residue collection with whole-tree logging In conventional whole-tree harvesting systems, a feller buncher (Figure 4.6) can be used to fell and stack trees. The feller buncher head grips the tree, saws

Figure 4.5 Integrated operations for collection of forest residues. Forest residues can be collected in whole-tree or cut-to-length harvest operations. The schematic shows current options for residue harvest and collection.

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Figure 4.6 Feller buncher harvesting saw timber. Feller bunchers are used in conventional whole-tree logging operations to cut and accumulate trees for extraction from the forest (photo courtesy of Deere & Company).

it through at the base, and gathers it into the accumulator pocket. The feller buncher then carries the bunch of trees to the roadside and stacks them up. In a conventional, two-pass system trees are delimbed on site. Logs are then cut into sections (bucked) or dragged whole with a tractor (skidded; see Figure 4.7) to the yard or landing. The slash is left in piles in the forest to dry. Logs are loaded onto specialized trucks and transported to a saw mill to be turned into lumber or to a pulp and paper mill. In integrated whole-tree logging operations, trees are felled and transported to the yard with top and limbs intact. They are then delimbed, topped, and bucked at the landing. This method results in slash accumulation at the landing. In a recent study by Adebayo et al. (2007), a whole-tree harvesting approach was compared with cut-to-length harvesting (see next section). The whole-tree harvesting system had a slightly higher hourly machine rate, but its higher productivity resulted in lower production costs compared with the cut-to-length system. Although these results were based on log costs, the conclusions can be extended to include biomass because the only additional costs for biomass collection in an integrated system are transport costs. Whole-tree harvesting also includes utilization of entire trees, including branches and tops. Harvesting small diameter trees for bioenergy uses can be integrated with timber harvest operations. A study by Watson et al. (1986) compared one-pass and two-pass harvesting methods for small diameter trees and larger timber. In the one-pass approach, a feller buncher separated trees


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Figure 4.7 Grapple skidder used to drag whole trees from forest to landing. Skidders grasp tree bunches and drag them from the forest to the landing (photo courtesy of Deere & Company).

into piles of energy wood and piles of roundwood. Both types of piles were skidded to a landing and processed simultaneously. In the two-pass method, the feller buncher maneuvered around merchantable trees to cut the energy wood first. The energy wood was piled in the forest and allowed to dry for several weeks while the merchantable timber was harvested. They found that the one-pass method resulted in better utilization of the wood and lower costs. However, a disadvantage of this method was that biomass was delivered at higher moisture contents because it was not allowed to dry before extraction. Felling costs were highest for energy wood, indicting that advances in felling machines offer the best opportunity to improve feasibility of biomass harvesting. Integrated residue collection with cut-to-length logging Cut-to-length harvesting is a highly integrated option for collecting timber and slash. Cut-to-length harvesters, such as the John Deere 1270D shown in Figure 4.8, are equipped with a versatile head that grips the tree while cutting it at the base. The harvester head then rotates so that the tree is turned parallel to the ground and spiked rollers feed the tree through the delimbing device. As the tree is fed through, a saw embedded in the harvester head cuts the log into specified lengths. Typically, residues are piled in front of the harvester’s tracks to serve as a mat for the harvester as it progresses through

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Figure 4.8 Cut-to-length harvesting. A John Deere 1270D harvester falls a tree, delimbs it and cuts logs to a specified length with the same head (photo courtesy of Deere & Company).

the forest. In operations where biomass is harvested, residues can be stacked alongside the machine’s track for later collection. Cut-to-length systems are likely most suited for logging larger trees (Huyler and LeDoux 1999; Klepac et al. 2006). In a recent study evaluating the Timberjack 1270 harvester for fuel reduction treatments, Klepac et al. (2006) found that harvest costs for wood (biomass collection not included) was dramatically affected by tree size. Harvesting trees 7.6 to 12.7 cm (approximately 3–5 inches) in diameter was cost prohibitive. Costs for trees larger than 12.7 cm (5 inches) in diameter decreased dramatically so that the cost of harvesting 36-cm (14-inch) trees was essentially negligible compared to other components of the supply chain. With regard to harvest time, the harvester is the limiting machine (Klepac et al. 2006) and was outperformed by the forwarder (Figure 4.9) by about two to one for producing sawlogs and five to one for producing biomass. The efficiency of cut-to-length harvesting systems can be increased by employing appropriate techniques. A study by Nurmi (2007), compared harvester operations where residues were piled in front of the machine (typical method), to one side of the road, or to both sides of the road. Stacking residues to one or both sides of the road significantly improved collection times and yield of residue recovery compared with stacking residues on the road in front of the harvester. It should be noted, however, that stacking residues in the path of the harvester is believed to minimize soil disturbance and compaction.


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Figure 4.9 Forwarder stacking logs in a bunk. Forwarders are specialized machines that load logs, haul them from the forest, and unload them at the landing. Unlike skidders, forwarders carry logs clear of the ground (photo courtesy of Deere & Company).

Pre-processing Before woody biomass can be fed into a reactor at the biorefinery, two important pre-processing steps must occur: comminution and drying. Comminution (size reduction) is an energy intensive process. The characteristics of the ground material determine the equipment required in all subsequent steps of the supply chain. Therefore, when and how size reduction occurs has a significant impact on the overall efficiency of the operation. Drying increases the stability of biomass during storage and increases its value. Comminution Comminution of woody biomass is a prerequisite step for all bioenergy technologies. Grinders come in assorted configurations for introducing and reducing feedstocks. The material is cut (knife milling) or chopped by blunt impact (hammer milling). Many models of grinders are available, with some belt fed and others top fed. Large-diameter, large-scale grinding operations typically employ a hammer-mill, top-fed tub grinder. Energy production systems require particles less than 2.5 cm (1 inch) in diameter that can be obtained using a hammer mill. In a review of size reduction technologies for woody biomass, Naimi et al. (2006) concluded that

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two stages of size reduction, a coarse grinding (to particles greater than 2.5 cm in diameter) followed by a fine grinding (less than 2.5 cm.), are needed in most energy production applications. Size reduction can occur at any point in the supply chain, from the stump to just prior to feeding into a reactor. Where residue grinding occurs is largely dependent on the logging operation equipment and site. Grinders can be mounted on a truck, trailer, or on tracks enabling them to access nearly any site. Horizontal belt fed knife mill chippers are usually trailer mounted and used to grind smaller diameter materials on smaller sites. Comminution drastically changes the properties of woody biomass, which determines the equipment needed for each subsequent step of the supply chain. Wood chips require very different handling and transportation equipment than do loose residues or logs. Solid containers or chip vans are needed to haul wood chips. Although this additional equipment increases costs, the significant increase in material bulk density typically offsets these costs compared to hauling loose residues. The size reduction process can also change the moisture content of the material. Fresh green material typically has a moisture content of about 55 per cent. Grinding can reduce moisture content by 5 to 20 per cent. Drying Reducing the moisture content of biomass increases its energy density and makes it more friable, lowering the energy cost of grinding. Although mechanical drying of biomass is typically not feasible, transpirational drying in the forest or landing may be beneficial. As previously mentioned, in a twopass harvesting system residues can be stacked in the forest and allowed to dry before extraction. A study by Nurmi (1999) found that while biomass is drying in the forest, defoliation occurs. The leaves and needles fall to the ground replenishing the soil with vital nutrients. This also eliminates a material that degrades quickly in storage compared to the woody portions of the tree. Defoliation also occurred in material stored at the landing, but to a lesser degree. Drying increases the stability of wood chips in storage. Whole tree wood chips stored in piles have been known to self-heat if the moisture level is greater than 30 per cent (note that self-heating is also dependent on factors such as tree species and pile size). Dry biomass has a reduced amount of biological activity and, thus, lower dry matter loss during storage. Transportation For low bulk density material such as wood residues, transportation can account for as much as 30 per cent of the total collection costs (Andersson et al. 2002). Therefore, selecting a transportation mode has a large impact on the costs and efficiency of the biomass supply chain. As woody biomass


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progresses along the bioenergy supply chain, changes in terrain, road conditions, and format of the material necessitates transfer to different transportation modes. Handling biomass to transfer between transportation methods is an additional cost that should be avoided when possible. The first stage of transportation is movement of the slash from stump to the grinder. This segment of the transportation network requires specialized off-road equipment, as it covers rough and often steep terrain. In conventional logging operations, moving timber from the stump to the landing is typically accomplished with a skidder or forwarder. If residues are collected with timber in an integrated whole-tree harvesting system, the tree with both timber and biomass remains intact while being dragged from the forest with a skidder. In fuel reduction operations or two-pass harvesting systems, biomass is collected and transported independently of timber in a modified forwarder (sides added to prevent loss of material) or off-road dump truck. New methods of bundling biomass packages the material in a form that can utilize typical logging equipment such as forwarders (see later section for more information on biomass bundling). From the grinder, biomass is transferred to the end user or another mode of long-distance transportation. If a chip van can reach the landing, chipping directly into the chip van and hauling the biomass to the end user is the least expensive option (Rawlings et al. 2004). Often, large chip vans (92 or 113 m3; 120 or 148 yd3) cannot reach grinders on remote landings. In this case, dump trucks or roll on/off containers can be used as in Figure 4.10. Several roll on/ off containers can be hauled to remote sites and dropped off. Leaving containers on site improves system efficiency since fewer trucks are needed and those trucks that are being used can operate continuously rather than spend time waiting to be filled. Slash is fed into a grinder and then deposited directly into the containers. Full containers are picked up and brought to an area accessible by the chip van. If the end-user facility is nearby, roll on/off containers can economically be used to transport chips directly to the plant. Transportation of more than 16–24 km (10–15 miles) on improved roads is more economical with large capacity vehicles such as 92 or 113 m3 (120 or 148 yd3) chip vans. Wood chips can be loaded into these large vehicles from small chip vans that carry it from the grinder at the landing. However, it is most economical to centrally locate the grinder in a place that is accessible to the large vehicles, transport whole slash to the grinder, and deposit wood chips directly into the large van (Rawlings et al. 2004). Handling comminuted wood Handling systems are too often overlooked in planning woody biomass collection operations and biorefineries. However, Hakkila (2004) states that handling systems can be problematic, particularly when the receiving plant is not prepared for the special properties of wood chips. The capacity of the receiving station to unload trucks must be synchronized with the rate of

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Figure 4.10 Secondary transportation of woody biomass. When chip vans cannot access remote landings, roll on/off containers can be used to haul wood chips to the utilization facility or to a larger truck (photo courtesy of Montana Community Development Corporation).

utilization. Improperly sized handling systems slow down and sometimes halt operations, adding undue cost and frustration. Selection of handling systems must take into account the particle size distribution and moisture content, making this step in the supply chain highly dependent on techniques used for grinding and storage. To unload wood chips at a user facility, large volume facilities use hydraulic dumpers that lift and tilt whole trucks (see Figure 4.11). These systems can empty a semi-trailer in three to five minutes (Badger 2002). Intermediate scale facilities may use semi-trailer dumping systems that require the trailer to be uncoupled from the truck. This process is more time consuming than whole truck dumpers. Smaller-scale facilities may utilize walking bed trailers that can unload in about 10 minutes. These facilities may also use small dump trucks for short hauls or unload a standard semi-trailer with a small, skid-steer-type, front-end loader (Badger 2002). Comminuted wood handling systems inside a plant are highly variable and include screw augers, bucket conveyors, and pneumatic systems (Badger 2002). Storage and queuing Many areas of the country can harvest woody biomass year around, and long-term storage is not necessary. However, in some areas the supply and demand of woody biomass for energy and bio-based product production will be, at times, in an unbalanced state due to seasonal variations. In these areas of the country, storage for a one- to six-month supply of biomass will be


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Figure 4.11 Bundling logging residue. A John Deere 1490D bundles logging residue, which can then be handled with conventional logging equipment (photo courtesy of Deere & Company).

required. Storage of woody biomass when production exceeds demand is desirable for biorefineries, in that it provides a constant feedstock supply. Storage also offers an important advantage for suppliers. Adding this material buffer minimizes the strain on harvesting and transportation systems (Andersson et al. 2002). This eliminates any need for overtime labor charges and reduces the risks associated with production halts due to equipment problems or severe weather events. Also, if stored properly, biomass dries while in storage, which may increase its value. A significant disadvantage for suppliers in developing storage systems is the high costs of building storage structures. DOE estimates that full-size lignocellulosic plants will process 635 tonnes (700 tons) per day of dry material. A minimum 10-day supply in a queuing pile at such a refinery would contain 6,300 tonnes (7,000 tons). This would require a storage yard of at least 4,400 m2 (47,361 ft2), assuming a maximum height of 10 m (32.8 ft) (Wilkerson et al. 2007). Also, depending on the condition of the chips and the ambient environment, there is significant potential for material degradation during storage. Nurmi (1999) observed dry matter losses and increases in moisture content of wood chips stored for one year. This led to a recommendation that comminuted material be utilized as quickly as possible after grinding to minimize dry matter losses (Nurmi 1999). Furthermore, whole tree chips (chips containing a large proportion of leaves and bark) have a propensity to self-heat.

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Piles of such chips at industrial facilities have been known to cause fires (Springer 1979). Chips with a moisture content of greater than about 30 per cent will self-heat when placed in large piles, but this can be mitigated by keeping pile heights under 9 m (30 ft) and/or limiting the amount of time the chips are in the pile to less than 10 days (Garstang et al. 2002). Fungi and bacteria begin to grow as soon as a pile of wood chips is formed (Andersson et al. 2002). The microbial growth rate depends on temperature, moisture content, particle size, and composition. External factors such as the size of the pile and duration of storage also affect the growth rate. The particular microbes that tend to colonize wood chip piles do not have a significant effect on dry matter loss. However, they do produce microspores that can cause respiratory problems when inhaled (Andersson et al. 2002). Microbial growth is typically slow in freshly harvested biomass because the temperature is low enough to inhibit growth. But, higher temperatures caused by heating in large wood chip piles favor rapid growth of fungi and bacteria. Handling uncomminuted material poses less health risk because fungal growth is much less than in chipped fuel. Bundling and baling The costs of transporting low-density forest residues generated during integrated harvesting and forest thinning operations can inhibit their use as an energy feedstock. Bundling technologies have been developed in recent years by several forestry equipment manufacturers as alternatives to hauling loose forest residues or in-woods chipping. Slash bundles (also called composite residue logs, or CRLs) are considerably denser than loose residues, making them less expensive to transport. Also, slash bundles are not as susceptible to dry matter loss and self-heating; thus, can be stored until needed. Slash bundles are shaped and sized like typical logs and can be handled as such with existing equipment. Only simple modifications are needed to integrate slash bundles into existing logging operations. The primary consideration should be to cover loaded trucks to prevent loose material from coming dislodged during road travel (Cuchet et al. 2004). John Deere is currently the only U.S. manufacturer of slash bundling machines (John Deere 1490D, formerly TimberJack 1490D). The John Deere 1490D is shown in Figure 4.11. Other manufacturers include World Wood Pac (Sweden) and Pinox Oy (Finland). The John Deere 1490D was first introduced in the U.S. in 2003 by TimberJack (acquired by John Deere in 2005). In 2004, it was estimated that 25 TimberJack 1490D slash bundlers were in operation in Finland and another 10 units in Sweden, the Czech Republic, Switzerland, France, Italy, Spain, and the U.S. (Karha and Vartiamaki 2006). The 1490D, based on an eight-wheeled TimberJack forwarder, features a bundling unit mounted on the rear frame that can be rotated right to left to pick up slash from either side of the road (Rummer et al. 2004). The operator loads residue into the infeed deck with the crane. A


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pair of hydraulic rollers pulls the residue into the bundler cage where the material is compressed, and slid through. At the outlet end, the bundle is wrapped and tied with sisal or polypropylene twine. The continuous procedure is halted momentarily while a ‘log’ of specified length is cut. Slash logs are typically ~3 m (10 ft) long and about 60–80 cm. (24–31.5 inches) in diameter (Packalen 2006). Each slash log can produce 1 MWh of power, about the same as a half-barrel of oil. In a clear-cutting operation, it is estimated that 148 slash logs per hectare (60 per acre) can be produced. Bundling has been shown in a number of studies to be a promising technology for collecting forest residues (Johansson et al. 2006; Karha and Vartiamaki 2006; Rummer et al. 2004; Saarenmaa 2005). However, several areas of additional research and development are needed to improve its efficiency before widespread adoption of this technology will occur. Rummer et al. (2004) predicts that the additional cost of the bundler machine would make collecting forest residues cost prohibitive. However, they point out that if the value of bundling the residue for removal to aid forest management is considered (as opposed to burning, for example), bundling may be cost effective. Similarly, Karha and Vartiamaki (2006) concluded that although the costs of bundling collection systems currently exceed collection of loose residues or roadside chipping, if the costs of bundling can be optimized, bundling supply chains would be extremely cost competitive. Factors that have been shown to affect bundling performance are operator skill, slash density, and layout. As might be expected, the operator work method and experience are the primary factors in bundling productivity. In an efficient operation, over 50 per cent of the total bundler work time should be spent loading residues (Karha and Vartiamaki 2006). As bundling technology develops and becomes more widely adopted, worker competencies in operating bundlers will also increase. Another critical factor in bundling performance is the slash density and layout. Slash should be piled to one side of the road (Karha and Vartiamaki 2006) in large piles containing at least a full grapple load (approximately 136 kg bone dry, or 300 lbs) (Rummer et al. 2004). Bundling is most cost effective in areas with large amounts of logging residues. There is concern among some that removing forest residues is not environmentally sustainable. In cut-to-length operations, residues are left in the path of the harvester to minimize soil disturbance and add nutrients to the soil. Slash bundling may not negate these beneficial effects as much as might be expected. In the study performed by Cuchet et al. (2004), only a portion of the logging residues were removed by bundling operations, leaving 50 per cent or more for soil protection. As the demand for energy wood increases, new residue packaging technologies, in addition to the slash bundlers currently on the market, will likely be developed. Forest Concepts, LLC of Auburn, Washington, is developing a prototype square baler, essentially a modified recycling baler, for woody biomass (Dooley et al. 2006). Preliminary tests show that the square bale concept can significantly reduce transportation costs for woody residues from forest

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thinnings and urban tree waste. The square bales can be hauled on typical flatbed trailers making them easy to transport via highway from the landing to the end user. A small, portable baler is especially promising for small logging or foresting thinning operations and for urban or residential areas that do not produce enough wood residues to justify purchase of a mobile slash bundler. Potential environmental impacts of forest residue collection Slash removal has been shown to have significant impacts on forest soils. Belleau et al. (2006) found that the amount of slash left on the forest floor was the main factor in determining soil nutrient dynamics. They found that slash increased soil acidity and improved cation availability. The presence of slash also affects soil compaction. McDonald and Seixas (1997) compared soil compaction caused by a forwarder when the slash density was 0, 10, and 20 kg m−2 (0, 0.62, and 1.25 lb ft−3) in dry and wet soils. They found that the presence of slash did reduce soil compaction, particularly in drier soils, but the density of the slash had little to no effect. This seems to indicate that management practices could be developed in which a portion of the slash is left in the forest to improve soil quality while the rest is recovered for energy. Environmental impacts of removing forest residues will influence landowner decisions, policy development, and industry sustainability. There is debate amongst foresters and environmental scientists regarding the magnitude of the environmental impacts of slash removal. More research is needed to quantify the long-term effects of removing slash on soil nutrient dynamics, soil compaction, wildlife habitats, and water quality for various topographies and soil types across the U.S.

Future directions This chapter provides estimates of the quantity and spatial distribution of the major sources of primary forest residues. These include the unused portions of trees cut or killed in commercial harvesting operations, unutilized residues from silvicultural or land clearing operations, and overstocked volumes of trees that require thinning or treatment to reduce fire hazards and/or to improve forest health. Although the current availability of this combined resource exceeds 89.3 million dry tonnes (100 million dry tons) annually after accounting for environmental sustainability, only a fraction is deemed economically accessible today. In addition to discussing the forest residue resource potential, this chapter reviews residue collection and harvesting systems and identifies what adaptations or changes in collection and harvesting technology and infrastructure are required to make a significant fraction of these resources economically accessible. Currently, costs for collecting forest residues using existing technologies often exceed their value as fuel. Developments in technology and supply chain logistics are needed to see the realization of a large-scale biomass supply


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system. One important area of development will be harvest and collection systems that are optimized for small-diameter trees and suitable for rough, steep terrain. Further development of new machines suitable for densification of woody biomass in the forest, either by comminution or packaging, would significantly reduce transportation and handling expenses. Additional research is also needed to better understand storage of comminuted and bundled biomass, particularly with regard to air quality and environmental health concerns. New and improved tools for analysis of biomass supply chain logistics are needed to aid managers in making decisions regarding landing locations, transportation methods and routes, and efficient equipment selection. The USDA Forest Service developed the Forest Residue Trucking Simulator (Rummer et al. 2004). Work is also currently underway to expand the Integrated Biomass Supply Analysis and Logistics Model (IBSAL) (Sokhansanj et al. 2006) developed at Oak Ridge National Laboratory to include forest residue collection. Development and application of these and similar models require data of equipment performance for harvesting and handling woody biomass. Since most data regarding performance of forestry machines is for roundwood, more data is needed to quantify capacities of machines for harvesting biomass. This data should include production rates for residue collection equipment over a range of moisture contents, tree species, and the biomass composition (ratio of wood to foliage).

References Adebayo, A.B., Han, H.S. and Johnson, L. (2007) ‘Productivity and cost of cutto-length and whole-tree harvesting in a mixed-conifer stand’, Forest Products Journal, 57 (6): 59–69. Andersson, G., Asikainen, A., Bjorheden, R., Hall, P.W., Hudson, J.B., Jiris, R. et al. (2002) ‘Production of forest energy’, in J. Richardson, R. Bjorheden, P. Hakkila, A.T. Lowe and C.T. Smith (eds) Bioenergy from Sustainable Forestry: Guiding principles and practice, Dordrecht, The Netherlands: Kluwer. Badger, P.C. (2002) Processing cost analysis for biomass feedstocks. ORNL/TM-2002/ 199. Prepared by Oak Ridge National Laboratory, Oak Ridge, TN. Belleau, A., Brais, S. and Pare, D. (2006) ‘Soil nutrient dynamics after harvesting and slash treatments in boreal aspen stands’, Soil Science Society of America Journal, 70(4): 1,189–1,199. Cuchet, E., Roux, P. and Spinelli, R. (2004) ‘Performance of a logging residue bundler in the temperate forests of France’, Biomass & Bioenergy, 27: 31–39. DOE Office of the Biomass Program (2007) Biomass Multi-Year Program Plan. Available: http://www1.eere.energy.gov/biomass/pdfs/biomass_program_mypp.pdf (accessed on 17 December 2007). Dooley, J.H., Fridley, J.L., DeTray, M.S. and Lanning, D.N. (2006). ‘Large rectangular bales for woody biomass’, ASABE Paper No. 068054, paper presented at the 2006 American Society of Agricultural and Biological Engineers Annual International Meeting, Portland, OR.

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Garstang, J., Weekes, A., Poulter, R. and Bartlett, D. (2002) Identification and characterization of factors affecting losses in the large-scale, non-ventilated bulk storage of wood chips and development of best storage practices. FES B/W2/00716/ REP. Prepared by First Renewables Ltd. for DTI, London. Graham, R.T., McCaffrey, S. and Jain, T.B. (2004) Science basis for changing forest structure to modify wildfire behavior and severity. Prepared by U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, Colorado. Hakkila, P. (2004) Developing technology for large-scale production of forest chips: Wood Energy Technology Programme 1999–2003. Prepared by VTT Processes, Helsinki. Huyler, N.K. and LeDoux, C.B. (1999) Performance of a cut-to-length harvester in a single tree and group selection cut. Prepared by USDA Forest Service, Northeastern Research Station, Radnor, PA. Johansson, J., Liss, J.-E., Gullberg, T. and Bjorheden, R. (2006) ‘Transport and handling of forest energy bundles – advantages and problems’, Biomass & Bioenergy, 30: 334–341. Karha, K. and Vartiamaki, T. (2006) ‘Productivity and costs of slash bundling in Nordic conditions’, Biomass & Bioenergy, 30: 1,043–1,052. Klepac, J., Rummer, B. and Thompson, J. (2006). ‘Evaluation of a cut-to-length system implementing fuel reduction treatments on the Coconino National Forest in Arizona’, paper presented at the 29th Council on Forest Engineering Conference, Coer d’Alene, ID. McDonald, T.P. and Seixas, F. (1997) ‘Effect of slash on forwarder soil compaction’, Journal of Forest Engineering, 8(2): 15–26. Miles, P.D. (2004) Fuel treatment evaluator: web-application version 3.0. Available: http://ncrs2.fs.fed.us/4801/fiadb/fire_tabler_us/rpa_fuel_reduction_treatment_opp. htm (accessed 19 December 2007). Naimi, L.J., Sokhansanj, S., Mani, S., Hoque, M., Tony, B., Womac, A.R. et al. (2006). ‘Cost and performance of woody biomass size reduction for energy production.’ CSBE Paper No. 06–107, paper presented at the Canadian Society of Bioengineering Conference, Edmonton, Alberta. Nurmi, J. (1999) ‘The storage of logging residue for fuel’, Biomass & Bioenergy, 17(1): 41–47. Nurmi, J. (2007) ‘Recovery of logging residues for energy from spruce (Pices abies) dominated stands’, Biomass & Bioenergy, 31: 375–380. Packalen, A. (2006) ‘Turning logging residues into bioenergy and biofuel’, In the Forest: International Forestry Magazine, 2(2): 4. Perlack, R.D., Wright, L.L., Turhollow, A.F., Graham, R.L., Stokes, B.J. and Erbach, D.C. (2005) Biomass as feedstock for a bioenergy and bioproducts industry: the technical feasibility of a billion-ton annual supply. DOE/GO-102005-2135. Prepared by Oak Ridge National Laboratory for the U.S. Department of Energy and the U.S. Department of Agriculture, Washington, D.C. Rawlings, C., Rummer, B., Seeley, C., Thomas, C., Morrison, D., Han, H.-S. et al. (2004) A study of how to decrease the costs of collecting, processing, and transporting slash. Prepared by Montana Community Development Corporation (MCDC), Missoula, MT. Rummer, B., Len, D. and O’Brien, O. (2004) Forest residues bundling project: new technology for residue removal. Prepared by Forest Service Forest Operations Research Unit, Southern Research Station, Auburn, AL.


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Saarenmaa, A. (2005). ‘A novel forest biomass production system for the world’s biggest biofuel plants.’ ASAE Paper No. 058026, paper presented at the ASAE Tampa, FL. Smith, W.B., Miles, P.D., Vissage, J.S. and Pugh, S.A. (2004) Forest Resources of the United States Gen. Tech. Rep. NC-241. Prepared by U.S. Department of Agriculture, Forest Service, North Central Research Station, St. Paul, Minnesota. Sokhansanj, S., Kumar, A. and Turhollow, A.F. (2006) ‘Development and implementation of integrated biomass supply analysis and logistics model (IBSAL)’, Biomass & Bioenergy, 30(10): 838–847. Springer, E.L. (1979) Should whole-tree chips for fuel be dried before storage? Fpl0241. Prepared by U.S. Department of Agriculture, Forest Products Laboratory, Madison, WI. Stokes, B.J. (1992) ‘Harvesting small trees and forest residues’, Biomass & Bioenergy, 2: 131–147. U.S. Forest Service (2007) Healthy Forests Initiative. Available: http://www.fs.fed.us/ projects/hfi/ (accessed 19 December 2007). Watson, W.F., Stokes, B.J. and Savelle, I.W. (1986) ‘Comparisons of two methods of harvesting biomass for energy’, Forest Products Journal, 39(4): 63–68. Wilkerson, E.G., Blackwelder, D.B., Perlack, R.D., Muth, D.J. and Hess, J.R. (2007) A Preliminary Assessment of the State of Harvest and Collection Technology for Forest Residues. ORNL/TM-2007/195. Prepared by Oak Ridge National Laboratory, Oak Ridge, TN.

5

An integrated supply system for forest biomass Timothy L. Jenkins and John W. Sutherland

Introduction As noted in previous chapters the U.S. has a large wealth of forest resources. The proposition of displacing petroleum through biofuels or creating renewable electricity derived from forest-based biomass resources has tremendous appeal. It offers opportunities for improved energy independence, a more favorable trade balance, job creation in rural areas, decreased demand for petroleum, and lower fossil-derived CO2 emissions. To seize on the opportunity to use forest biomass in a manner that is economically viable, environmentally sound, and acceptable to society requires that careful consideration be given to all stages of the supply system or ‘value chain’. Forest resources can be placed into two categories: industrial wood and fuelwood (USDA Forest Service 1989). Industrial wood is all merchantable wood, also known as roundwood, which is utilized for lumber, pulp and paper, and other commercial products. Fuelwood is roundwood that is used for fuel plus forest residues. Forest residues can in turn be broken down into three groupings: slash from final fellings, slash and small trees from thinnings and cleanings, and un-merchantable wood (EUBIA 2007). Figure 5.1 shows the uses of forest resources for industrial and fuelwood purposes. In considering the environmentally, economically, and societally sustainable use of forest resources for the creation of renewable energy and biofuels, a variety of factors must be considered. Many of these factors are discussed in greater detail in other chapters. Fundamentally, all of these factors should be simultaneously considered, in an integrated manner, to select technologies and make decisions that are the best from a life-cycle sustainability perspective. Extracting forest resources, pre-processing, storage, and movement to a processing facility are critical steps in the conversion of biomass to a useful form. These steps often have significant costs and do not add value to the biomass. Thus, the decisions associated with these steps require careful consideration. The technology and practices for harvesting and collection of forest resources were addressed in Chapter 4. Chapter 4 also provided a brief overview of pre-processing, transportation, and storage of forest biomass

An integrated supply system for forest biomass

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Figure 5.1 Uses of forest resources.

residues. This chapter builds upon those discussions by offering additional insights into the logistical (pre-processing, transportation, and storage) systems needed prior to and upon arrival of forest biomass resources at a processing facility site. Several important factors, which impact the supply chain and key activities necessary to ensure a viable and sustainable system of supply, will be identified and discussed. Finally, a review of the specific needs of a processing facility will be discussed in order to bring together the complete forest resource supply system from the forest to the processing facility in an integrated way. Clearly, these demands will influence the size and type of storage facility, the required preprocessing steps, and the ideal mode for biomass transportation from forest to processing facility.

Pre-processing As previously noted low-density forest residues or biomass have moisture contents around 55 per cent and are not desirable from a transportation standpoint. Densification techniques such as comminution and drying have been discussed as a means of increasing the mass of cellulosic material that can be transported per load. We will discuss three strategies for comminution

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of forest residues, all of which are used in Finland and Sweden and some of which are used in the U.S. These strategies are:

• • •

field site chipping (terrain or at a landing, generally roadside chipping); terminal (sometimes described as a storage facility) chipping; chipping at the plant.

In Sweden, the first two have been practiced since the late 1970s (EUBIA 2007), while the latter is still under testing in Sweden, although in Finland it is now well established. The most predominant comminution method is roadside chipping, where the material is brought out from the harvest site to a landing or roadside location and ground or chipped. Details associated with these strategies will now be described, followed with some thoughts on drying. Field (forest) site chipping There are two basic ways to transport forest materials, as whole logs or bundles, and as ground or chipped pieces. A third means of transportation, discussed in Chapter 4 and later in the section on transportation, is to bail the stems and other small wood, which is similar to bundling. From purely a transportation standpoint, grinding or chipping of forest residues is preferred over carrying bundles/bales since it allows for higher material packing density. This pre-processing can be accomplished either in the woods (terrain chipping) or at the landing or roadside (Suadicani 2003). Terrain chipping Comminution in the terrain, or at the source, requires a highly mobile chipper suitable for cross-country operations and possibly equipped with a tippable 15–20 m3 (20–26 yd3) chip container. If the mobile chipper and container are separate pieces of equipment then the container carrier must also be highly mobile. The chipper moves in the terrain on the paths created by felling trees or on strip roads and transfers the biomass with its grapple loader to the feeder of the chipping device (Figure 5.2). The chip load is hauled to the roadside and tipped into a larger container, which may be on the ground or on a truck trailer. One advantage of terrain chipping is that less machinery is required for harvesting, which makes the organization of the work easier. Additionally, less landing space is required for terrain chipping than for roadside chipping (Alakangas et al. 1999). One drawback to this method is that when large volumes of forest biomass are processed, terrain chipping may become difficult to manage.


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Figure 5.2 Terrain chipping of forest residues, Boise National Forest. Source: USDA Forest Service (2003).

Roadside chipping Comminution at a landing is the traditional option of forest chip production (Hakkila 2004). The biomass is hauled by forwarders to the landing and bunched into piles or windrows. Comminution is performed at the landing using tractor driven or towed stand-alone chippers in smaller operations and heavy truck or trailer-mounted chippers or grinders in largescale operations. Landing chippers or grinders do not operate off-road, and can therefore be heavier, stronger, and more efficient than terrain chippers (Figure 5.3). A method used in Scandinavia is to employ a single chipper truck that replaces a truck-mounted chipper and separate chip truck. A chipper truck blows the chips directly into a container and then hauls the load to a processing facility. One disadvantage of this equipment is that it generally has a smaller load capacity, which tends to reduce the operation radius, thus potentially limiting the size of the energy production plant. On the other hand, as only one single unit is needed the costs tend to be lower, thus making the chipper truck suitable for small work sites and for delivering chips to smaller nearby processing facilities.

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Figure 5.3 Roadside chipping of forest residues. Source: Hakkila (2004).

Terminal (storage) site chipping Comminution at a terminal is a compromise between comminution at a landing and at the plant, and can be effective when the need for storage is advisable due to seasonality of forest resource availability. Biomass is hauled uncomminuted to the terminal for size reduction, and then transported to the plant as chips in large chip vans, suitable railcars or on barges. If the network of terminals is dense, the distance from the logging site to the terminal remains short. For this case, the system does not differ much from the traditional option where comminution is carried out at a landing. A terminal is a tool for controlling the procurement process and managing the flow of biomass. Biomass can be stored at the terminal uncomminuted and processed when the demand for fuel is high and working conditions at the forest end of the supply chain are difficult (Andersson et al. 2002). Facility/plant chipping When comminution is performed at the plant, truck transportation of biomass takes place in the form of loose logging residues, that is, whole trees or pieces of unmerchantable wood. The low bulk density of the biomass is the


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weak link in the plant chipping system. It is therefore necessary to increase the bulk density of residues. A new system has been developed where logging residues are compressed and tied into 70 cm (2.3 ft) diameter, 3 m (10 ft) long bales or composite residue logs (CRLs) (Figure 5.4). These bales or CRLs are transported to the roadside using a conventional forwarder and on to the terminal or processing facility using a conventional timber truck. Comminution at a plant, unlike the other two methods, eliminates the need for chip vans and makes the chipper or grinder an independent process. As a result, the technical and operating availability of the equipment increases, control of the procurement process is facilitated, demand for labor is decreased, and control of fuel quality is improved. Mobile or towed chippers can be replaced by heavy stationary crushers that are suitable for comminuting all kinds of biomass, including stump and root wood, and recycled wood. The larger the fuel flow, the more obvious become the advantages of such a stationary system. Since the investment cost is high, typically only large plants can afford a stationary chipper or grinder. Drying The methods for drying whole residues or chips include open (transpirational) air, thermal, and mechanical compression (Bowyer et al. 2007: 469–470). The

Figure 5.4 Terrain collection of forest residues. Source: Hakkila (2004).

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simplest and cheapest method is open-air drying. Mechanical drying will not be discussed, as this method is best suited for applications where water has been added to the biomass during processing. Thermal drying is used to predry wood before combustion. This process is widely used in Scandinavian countries and has begun to see use in the U.S. (Bowyer et al. 2007). Air drying can take place in all four areas where chipping or grinding occur: in the forest, at the landing, while in storage at a terminal, and at the energy processing facility. As noted in Chapter 4 and the following two sections, reducing the moisture content of forest biomass can increase bulk density, which reduces transportation costs and improves stability during storage. Additionally, drying has a direct impact on energy costs and actually adds value to the biomass by increasing its energy density and improving its combustibility (Hakkila 2004).

Transportation There are many ways of moving forest resource material from the harvesting site to an energy facility. At least two steps are required: 1) a mustering activity to assemble biomass in the field, and 2) a long hauling activity directed at moving the assembled biomass to the facility. Mustering options include carrying the material by hand, the use of human-powered implements like wheelbarrows or carts, and forest harvesting equipment like forwarders and skidders; as is evident, these options suggest moving materials only over short distances. For long hauling, several options are available: road, railway, water, and air. Air will not be considered since the cost, as noted in Table 5.1, is excessive. Furthermore, most commodity transportation in the U.S. is done by road, rail, and water at 78, 16, and 6 per cent, respectively (AASHTO 2003). Figure 5.5 shows the movement of forest resources using truck, train, and barge. As shown in Figure 5.6, movement from the field location to the final end user can take several paths (Andersson et al. 2002). Three of these pathways in the figure include movement to a terminal for storage or for additional pre-processing. Storage will be discussed later in the chapter. The other two pathways are direct transport from the field to the plant as either comminuted or uncomminuted residues. It is likely that movement from the field to the terminal will be by truck. This is due to the nature of the resource: loose, bundled, or comminuted, and the available infrastructure. From the terminal, any of three modes are possible depending on location, cost, and volume of material needed by the energy producer. Road transport Trucking is often the most expensive option for shipping the harvested biomass to the facility supply chain and can account for as much as half the costs (McDonald et al. 2001). Its advantages include flexibility in terms of

100

2.04 96

122

100

35.6 146

3.9 146

1.11 100

2.13 100

119

Trucka Index (1990 = 100)

Class | rail Index (1990 = 100)

Barge Index (1990 = 100)

Oil pipeline Index (1990 = 100)

Producer Price Index (1982 = 100)b

123

2.12 100

1.11 100

3.8 142

33.7 138

99

93.6

1995

1996

1997

Source: DOT (2007).

1998

1999

2000

2001

110

125

2.09 98

1.11 100

3.7 138

36.4 149

112

126

2.15 101

1.08 97

3.6 137

36.5 150

118

128

2.20 104

1.07 97

3.5 132

36.6 150

126

131

2.04 96

1.07 96

3.4 129

38.0 107

123

132

2.04 96

1.08 97

3.5 132

38.1 107

128

125

1.08 98

3.3 125

38.3 107

131

133

2.01 2.13 95 100

1.08 98

3.4 128

38.2 107

121

138

2.12 100

1.07 97

3.3 124

39.5 111

124

141

2.15 101

1.05 95

3.3 123

38.8 109

94

139

U U

U U

3.3 124

U U

143

U U

U U

3.3 125

U U

82

77.7

149

U U

U U

3.4 129

U U

92

86.7

2006

156

U U

U U

3.8 144

U U

118

160

U U

U U

U U

U U

127

111.2 120.0

2002 2003 2004 2005

104.2 105.5 111.7 119.0 116.5 120.7 118.1 113.9 117.4 88.8

1994

KEY: U = data are not available. a General freight common carriers, most of which are LTL (less-than-truckload) carriers. b Total finished goods.

1.14 103

3.8 142

36.2 149

94.6

94.4

Air carrier, domestic, sched service Index (1990 = 100)

1990 1991 1992 1993

Table 5.1 Average freight revenue per tonne km (current ¢)

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Figure 5.5 Transportation modes for forest resources. Photos courtesy of David Shonnard, upper left; Hakkila (2004), upper right; Timothy Jenkins, lower two.

Figure 5.6 Transportation pathways from source to energy producer.

accessibility to locations not reachable by other transportation modes, and ability to handle smaller loads in relation to competing modes. The map displayed in Figure 5.7 shows the expanse and intricacy of the U.S. highway system. Over the road (OTR) infrastructure and networks are very well established in the U.S. In assessing potential future changes to the roadway system


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Figure 5.7 Truck freight flows in the United States – 1998. Source: DOT (2003).

that could influence biomass transport, it is not likely that new systems will be developed in the short- (three to five years) or mid-term (five to 10 years) (Carter et al. 2002). Additional roadway challenges include limitations to movement (e.g. road weight restrictions) during seasonal thawing in the upper half of the U.S. and bridge/underpass restrictions established based on weight/height limits. Most commodities hauled on roadways use semi-truck tractors and trailers. A wide variety of configurations of truck and trailer can be achieved with the best selection dependent on the intended use and cargo. Traditionally, forest resources such as roundwood have been carried on a flat bed trailer capable of carrying logs up to 2.7 m (9 ft) in length cross-wise and as long as 22.9 m (75 ft) along the length of the trailer. Generally such logs are not intended for energy production. However, such logging trailers can be used to haul bundled forest residues assuming they have been configured into CRLs using a slash bundler as described in Chapter 4 and shown in Figure 5.4 or in bales.

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Alternatively, this residual material can be ground or chipped using mechanized equipment with the resulting wood particles or chips carried in a panel trailer or chip van. As described previously, these chip vans are used to carry chipped or ground biomass from the forest landing or terminal to the processing facility. The chip vans can vary in size from 40 m3 (52 yd3) to 113 m3 (148 yd3). There are several challenges with these options, one of which is depicted in Figure 5.8 with trucks hauling unprocessed logging residues, stems and other tree sections, chips, and roundwood (logs for pulping). For each load depicted in the figure, the same amount of solid wood is being carried (Nilsson 1983). As shown in this figure, bundling or haphazard arrangement of forest residues produces air pockets within the load (lower material density), which increases the volume of the load for a given mass. Roundwood has the highest material density, and grinding/chipping of forest residues allows this form of biomass to begin to approach that of roundwood. In almost all cases, a higher density load is preferred from a transportation standpoint. For the cases shown on the left side of the figure, weight will not be the issue; rather, the loads will be volume constrained meaning that the trailer is not carrying as much weight as it could. In addition, it should be remembered that freshly recovered biomass contains high moisture content, up to 55 per cent water. Since this water content has no value to the energy facility, its transportation is a costly, non-value added activity. Beyond these concerns, after the truck/ trailer has transported the biomass to the energy facility, it must return for

Figure 5.8 Proportion of solids in un-comminuted residues, tree sections, chips and pulpwood. Source: Andersson et al. (2002), courtesy of Sigurd Falk.


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another load empty; again, this represents a cost that adds no value to the supply chain. Railway and water transport As with road systems, railway and water transport systems are well developed and provide a significant network for movement of commodities within the U.S. Like roadways, it is not expected that additional infrastructure will be developed to accommodate any increase in demand needed by those harvesting forest residues for energy production. The maps of Figures 5.9 and 5.10 show the infrastructure of the rail and inland waterway transport systems for the U.S. The primary asset of rail is its ability to move large quantities of material over long distances. It is also a relatively ‘green’ system, in that its consumption of energy per unit load per mile is lower than OTR modes (AASHTO 2003). One of its major disadvantages is the cost of capital improvements. Unlike highways, railway beds are owned and operated by the railroads that travel on them. Maintenance of rail beds, exchanges, and track is at the discretion of the railroad (not the government), although established guidelines are in place for safety and security. In several parts of the U.S. the rail systems are more extensive than roadways and are the preferred method for

Figure 5.9 Rail freight flows in the United States – 1999. Source: DOT (2003).

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Figure 5.10 Waterborne freight flows in the United States – 1998. Source: DOT (2003).

moving commodities. Transportation by rail is cheaper than by truck (see Table 5.1) and more gross weight can be transported over longer distances. Regarding transportation of forest residues, many energy-producing plants already have rail infrastructure in place to support deliveries of coal and other fossil fuels. Switching to or increasing the use of forest biomass and train transportation seems a logical step towards a more sustainable form of energy. Waterborne commerce is one of the oldest forms of transportation and an important element in the movement of commodities such as grain and coal (Gibbons 1986: 74–75). Even industrial wood and forest residues are transported on water (U.S. Army Corps of Engineers 1980; Stenzel et al. 1985: 314–320). However, drawbacks to water transportation include its slowness, lack of direct routes, and sensitivity of this mode to weather factors that can cause delays. Although forest biomass can be hauled by large barges, this is

An integrated supply system for forest biomass 105 generally impractical. First, the quantities for movement would have to be substantial in order to be economical, with amounts in excess of 5,450 tonnes (6,000 tons) per load (Stenzel et al. 1985). In addition, most facilities using these materials would need to be near a port or use transshipment via truck or train to complete the trip. The rail and inland waterway transport industries are presently stable, productive, and competitive, with enough business and profit to operate but not to update their infrastructure quickly or expand rapidly (AASHTO 2003). Several aspects of the railway and water systems will need to be improved in order to add more capacity and expand to accommodate more biomass shipments. Two such improvement areas are new carriers and improved logistics planning.

Storage At some point during the movement of biomass from the field site to a processing facility it is likely that the biomass will be stored. The processing facility will also maintain a certain amount of storage as noted in Chapter 4 and by Wilkerson et al. (2007). The need to store forest resources is driven by such issues as the need to keep a processing facility running year-round, seasonal availability of biomass, processing facility size, and type of energy being produced. The storage of forest resources is dependent upon several factors. These include the availability of year-round harvesting, the types and species of forest resources being harvested, and the operational requirements of the end user facilities. Balancing these supply and demand issues can be eased by storage sites (log yards) (Andersson et al. 2002). For industrial wood, a log yard can serve many purposes, as is evident from the specific descriptors often assigned to them: mill yard, reload yard, sort yard, etc. A log yard can range in size from 0.20 ha (0.5 acre) to 4 ha (10 acres) with an average size between 2 and 4 ha (5 to 10 acres); these facilities can be operated by the end user, a logging company, or a third party who operates the yard for profit (Dramm et al. 2002). The challenge of log yards is in cost control and management. Simply building and using them for collection, storage and processing of forest residues would probably result in failure. Utilizing them in conjunction with industrial wood storage and sorting would likely improve long-term economic viability and provide outlets for any sorted forest material not considered merchantable to the industrial sector. The value added by integrating the storage and processing of forest residues with merchantable wood storage would reduce costs and provide additional outlets from other yard operations like debarking and peeling (Dramm et al. 2002). The integrated storage and operational system described may suggest that storage of forest residues will take place only at purpose-built facilities intermediate to the forest and production facility or at the production facility

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itself. However, it is possible to store the residues at the forest or field site as well. This section will describe two methods for storage operations as well as several types of material storage. Storage locations – centralized and decentralized The size of an energy processing facility, the availability of forest biomass, and the cost to transport biomass dictate the need and size for storage locations and facilities. Storage facilities may be needed to support the required rate of biomass delivery to meet operational specifications of the energy plant. Though it is likely that there will be some storage capacity onsite at the energy processing facility to maintain operations for a minimum time period, additional storage may be needed at a centralized nearby facility or decentralized facilities closer to the source of the forest resources. Whether centralized or decentralized storage is used will be determined by several factors: location of biomass resources in relation to the plant site, seasonal availability of biomass for processing, transportation equipment requirements and costs, need for intermediate pre-processing before arrival at the plant site, and available onsite storage at the facility. Since in many parts of the U.S. harvesting takes place year-round, the need for external storage may be unnecessary. However, this does not preclude the need for terminal sites to provide for centralized comminution of residues. Storage types Depending on the size of the energy processing facility, offsite storage may be necessary in order to maintain a sufficient and continuous supply of biomass feedstock to sustain plant operation. Several types of options are available for the storage of forest resource biomass. Badger (2002) noted that these options include open air or canopy covered, enclosed structure, and underground silo or bin (Figure 5.11). Each of these types will be described in detail and additional considerations identified for future requirements and relating storage to both pre-treatment needs and transportation. Open air storage yards are the simplest and cheapest to build (EPA 2007). This system has a concrete or crushed rock floor and the forest residues are formed into piles or windrows. These can be made either by using a mechanical conveyor system or by using front-end loaders or similar handling equipment. The residues are unloaded from the truck using a truck tipper, walking bed trailer, or small front-end loaders (Chapter 4). Once the piles are formed they can be left exposed to the air or covered with tarps to reduce exposure to rain. Covered piles are typically lower in height in order to reduce heat buildup. If these systems are built at the processing facility site, additional equipment such as a radial screw active reclaim feeder can be used to move the residues from the piles into the plant (EPA 2007). Otherwise, the


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Figure 5.11 Storage facility types. Source: NREL, top and bottom right; Colorado Tree Farmers courtesy of Wes Rutt, bottom left.

residues can be reloaded onto trucks, rail cars, or barges for transport to the processing facility from a storage terminal. Enclosed structure This storage system, unlike the bin or silo to be described below, has a floor similar to the open-air system but also includes a large roof. It can be used to house chips as noted in Figure 5.11 and to store bundled or baled uncomminuted residues. These bundles or bales are then stacked out of the weather until needed either for delivery to the energy plant or comminuted and then transported. This storage system is the most expensive option. Bin or silo Bins or silos are smaller structures and would likely be used at the site of the processing facility (Nurmi 1999; Badger 2002). A bin, which can be used with both green and dry residues, is a rectangular shaped concrete structure buried in the ground so that the top of the structure is at ground level. The top of the bin (or bunker) may be covered or a shed constructed over it. Burial in the ground minimizes biomass-freezing problems and facilitates truck unloading directly into the bunker (GLRBEP 1986). Augers are used to move the material out of the bin and into the plant (Figure 5.11).

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Silos provide ease of biomass retrieval and require minimal space. The structures are typically vertical cylinders constructed out of metal or concrete (Badger 2002). Silos, unless carefully designed, are subject to blockages caused by irregular shaped or large forest residue pieces. This is referred to as bridging (Schmidt 1991). Since most forest residue chips have poor flow characteristics, silos are usually equipped with agitators or screw augers to prevent bridging. Wet or green biomass stored in silos is subject to freezing and adhering to the silo wall. One method to minimize freezing problems is to construct the silo so that its lower six meters are within a heated building (GLRBEP 1986). Regardless of the method used to store forest residues, there are some inherent advantages and risks (see Chapter 4). The biggest advantage is the reduced strain on harvesting and transportation systems (Andersson et al. 2002) – storage provides a biomass inventory buffer, which can be relied on if problems occur in harvesting or transportation. In addition, the material can dry while in storage, thus reducing its water content and increasing its value. The risks include the high cost of building storage structures, dry matter degradation and potential increases in moisture content (Nurmi 1999), selfheating and potential flammability (Springer 1979), and fungal and bacterial growth (Andersson et al. 2002). Garstang et al. (2002) indicates that maintaining suitable pile heights and properly rotating and queuing biomass can mitigate many of these concerns, thus providing an option for sustainable supplies of forest residues for energy production. Impact on transportation and pre-processing The need for any pre-processing and the modes of transportation being used to move the biomass from the field to the energy processing plant must be considered when planning the design, size, and location of any storage facility or facilities. Storage yards will likely be multipurpose facilities that integrate storage of multiple species and grades of industrial wood, preprocessed roundwood for utility poles or log structures, and uncomminuted and chipped or ground material residues (Dramm et al. 2002). The yards can be located near multiple modes of transportation, and be used for staging and reloading to cost effective transport modes such as rail and water.

Integrated supply system Up to this point we have provided an overview of the supply system necessary to support the emergence and viability of energy production from forest resources. In Chapter 4 the authors described the harvesting and collection of forest residues in an integrated way with logging operations and forest management. Along with operations in the forest, the remaining segments of the supply chain must be developed in an integrated fashion as well. There are several unique challenges confronting the development of an


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integrated supply system for forest resources to produce energy products. These include the limitations associated with existing technologies for harvesting and material collection as noted previously, composition of the forest material itself, requirements from the processing facility, and economical modes of transportation and storage. Additionally, many of the following factors impact in unpredictable ways the orderly flow of biomass from field to energy conversion facility (Lindley and Backer 1994):

• • • • • • • • •

low bulk density of forest biomass; spoilage due to high moisture content or water absorption during storage; variability in physical and chemical characteristics of forest biomass; geographical and seasonal variations in biomass; conflicting demands for labor and machines; combustible nature of biomass; changes in harvesting strategy based on soil fertility issues; local regulations on storage and transport; and changes in biomass demand owing to sensitivity of prices for co-products.

In combination, these factors add levels of uncertainty and complexity that need to be addressed across the entire supply system. The uniqueness of woody biomass The integration of fuelwood collection (e.g. forest residues) with other forest resource harvesting operations is complex, mainly due to the unique nature of forest biomass. Biomass properties can vary by species, size, age, and season (Andersson et al. 2002). Though it is unlikely that energy producers need to utilize single species or specific types, other industries do have requirements for diameter, stem length, shape, and other characteristics that might preclude harvesting in certain areas at certain times. Without an integrated effort for resource harvesting and recovery, the feasibility of collecting the fuelwood may be cost prohibitive. The logistics system is not a simple supply chain from the forest to the energy producer. The biomass to energy supply chain must be integrated with other existing supply chains; these integrated chains provide increased efficiencies and reduced costs (Hektor 1998). Such nested supply chains form a complex network of parallel and interconnected linkages that requires careful development; during operation, such a complex system can offer cost improvements through reduced transportation and specialty equipment needs, and improved utilization of labor. Current strategies/systems In Chapter 4 the current harvesting techniques for roundwood and forest residues were described and how the use of integrated processes would benefit

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collection of residual forest resources. To illustrate further the use of integrated practices across the whole supply chain, Figure 5.12 links all the key functional areas: harvesting, transportation, storage, and pre-processing together with the final end user. The integration of forest residue collection and processing along with logging operations is already a well-established practice in Scandinavia. Figure 5.12 depicts three ways to comminute residues: in the woods, at the landing, and at the energy production facility. As a means of increasing bulk density of residues for transport in lieu of onsite comminution, bundling or baling can be employed. Bundling is a promising technology for collecting residues (Chapter 4). Additionally, baling into large rectangular bales offers increased bulk density and easier handling for local and long-distance transportation (Dooley et al. 2006). The next area where integration is critical is in storage. The merits of storing forest residues for use later by the energy processing facility have been described. However, simply storing whole residues and comminuted material alone would not be profitable (Dramm et al. 2002). One solution is to combine the storage of forest residues along with industrial wood in log yards. Such an addition gives the log yard owners another value-added operation that improves financial health and viability. Moreover, since many of these yards are located near the forest resource, transportation of uncomminuted residues is minimized. It is also typical for these storage facilities, while

Figure 5.12 Forest chips harvesting methods integrated into wood raw material harvesting. Source: Hakkila (2004).

An integrated supply system for forest biomass 111 located near the forest material source, to have additional transport modes linked to them on site, namely railroad spurs. Depending on location there may also be a river or other waterway nearby for barge hauling. The inclusion of rail or water transport at this point provides less costly means of transport with the assumption that large quantities of material need to be moved. This is dependent on the requirements of the energy production facility. Energy facility requirements Up to this point we have discussed the supply chain for forest resources used in energy production from an integrated perspective at the forest and in storage. It is worth noting that the user of the biomass material has certain requirements that need to be considered in order for this system to be viable. These concerns include size of the residues, quality of the material, contamination, moisture content, and whether the resource is a single or mixed species (Simpkins et al. 2006). The size of any energy processing facility will also impact the size and type of onsite storage and equipment requirements, availability of pre-processing equipment for comminuting residues, and the transport systems that can be used (Badger 2002). Yet another consideration in terms of the needs of the energy processing facility is the likelihood that such a facility will be co-located with other forest product operations. Andersson et al. (2002) describe the integration of forest residue use or energy wood (EW) industries with other operations including solid wood (SW) industries and pulpwood (PW) industries. Thus, in addition to possible stand-alone production facilities making use of economy of size and other factors to reduce costs, combining two or more different facilities could simplify transportation planning and costs, provide for more storage options, and allow for optimization across the whole supply chain. Production costs While fossil fuels occur in large deposits and can therefore be recovered and hauled with relatively low handling/transportations costs, forest fuels are spatially distributed and must be collected and transported from a large number of locations. The costs of these residues depend on many steps within the logistics chain – such as harvesting, comminuting, storage, and transport – as well as the scale of operation, the biomass source, and the quality requirements placed upon the biomass. The largest fraction of the procurement cost is associated with biomass mustering and long haul road transport. Therefore, the core of forest chip logistics is the control of transportation costs. Converting the biomass into transportable form with a chipper, crusher, bundler, or baler is also an essential part of the logistics system. Factors such as technological progress, up-scaling, refinement of procurement logistics, and learning through experience have led to significant reductions in supply costs of forest resources

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in recent decades. However, as the demand increases, the operations have to be extended to more and more difficult stand conditions and remote locations, potentially putting pressure on the supply and costs of forest resources.

Summary A facility focused on converting forest biomass into energy resources relies heavily on a stable, predictable feedstock stream in order to be successful. This requires a reliable, well-established supply chain for delivering forest resources to the facility in a form that can be used effectively by the processes being employed. In general, the supply chain requires steps for: 1) harvesting forest resources, 2) transporting forest resources, and 3) storage and preprocessing of forest resources prior to facility usage. As has become evident, a variety of options are available for each of these steps in the supply chain. Ultimately, the choices made at each step should be jointly determined, since the decision at one step can influence the performance at other steps; thus, the supply chain must be established in an integrated manner (Ertogral et al. 1998). The supply chain from forest to processing facility is complex and interconnected. The environmental, economical, and societally sustainable use of forest resources for the creation of renewable energy and biofuels requires the consideration of a variety of factors: biomass composition, economical transport and storage, and processing facility requirements. Biomass characteristics that impact supply chain decisions include low bulk density of forest biomass, variability in physical and chemical characteristics of forest biomass, biomass combustibility, and potential spoilage due to high moisture during storage. The infrastructures for transporting forest fuelwood are well developed in the U.S., and many equipment systems already exist to move forest biomass from field to plant. There are issues to be aware of in order to plan and make decisions about transportation as well as storage. These include geographical and seasonal variations in biomass, local regulations on storage and transport, and the interconnected nature of forest product companies within the market that can impact wood prices. Ultimately a facility focused on converting forest biomass into energy resources relies heavily on a reliable feedstock stream in order to be successful. This requires a dependable, well-established supply chain for delivering forest resources to the facility in a form that can be used effectively by the processes being employed. We have discussed modes of transportation, storage facilities, and pre-processing. Storage facilities, though capital intensive, may be needed to support the possible seasonality of forest harvesting and collection of residues. These intermediate facilities would provide staging areas for material during low demand periods, and opportunities for drying and preprocessing before forwarding to the energy processing facility.


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Many of the operations and technologies that have been discussed in this chapter are being tested or are currently in use in countries such as Finland and Sweden. Given the dramatic changes that appear to be underway in terms of the use of forest resources for renewable energy and biofuel production in the U.S., it appears that efforts should be directed to implementing many of the operations, technologies, and ‘lessons learned’ elsewhere to rapidly and successfully establishing a robust energy production industry based on forest resources.

References Alakangas, E., Sauranen, T. and Vesisenaho, T. (1999) ‘Production techniques of logging residue chips in Finland’, VTT Energy, AFB-NET IV and Benet, ENE39/ T0039/99, Helsinki, 83 pp. American Association of State Highway and Transportation Officials (AASHTO), (2003) Freight Rail Bottom Line Report. Available: http://freight.transportation.org /doc/FreightRailReport.pdf (accessed 20 October 2007). Andersson, G., Asikainen, A., Björheden, R., Hall, P.W., Hudson, J.B., Jirjis, R. et al. (2002) ‘Production of forest energy’, in J. Richardson, R. Björheden, P. Hakkila, A.T. Lowe and C.T. Smith (eds), Bioenergy from Sustainable Forestry: guiding principles and practice, Dordrecht, The Netherlands: Kluwer Academic Publishers, pp. 49–123. Badger, P.C. (2002) Processing Cost Analysis for Biomass Feedstocks, Oak Ridge National Laboratory, Environmental Sciences Division, Report ORNL/TM-2002/ 199, Oak Ridge, TN. Bowyer, J.L., Shmulsky, R. and Haygreen, J.G. (2007) Forest Products and Wood Science: an Introduction, 5th edn, Oxford: Wiley-Blackwell. Carter, T. (Capt.), Kasky, P.C. (Capt.), Needham, P., and Plehal, J.V. (Captain) (2002) ‘Transportation’, Industrial College of the Armed Forces, Washington, D.C. Department of Transportation, U.S. (DOT) (2003) Bureau of Transportation Statistics, Federal Highway Administration, and Office of Intermodalism (Office of the Secretary), ‘GeoFreight’, CD, Washington, D.C. Department of Transportation, U.S. (DOT) (2007) Bureau of Transportation Statistics, Research and Innovative Technology Administration, ‘National transportation statistics’, Washington, D.C. Available: http://www.bts.gov/publications/ national_transportation_statistics/ (accessed 12 April 2007). Dooley, J.H., Fridley, J.L., DeTray, M.S. and Lanning, D.N. (2006) ‘Large rectangular bales for woody biomass’, ASABE Paper No. 068054, paper presented at the American Society of Agricultural and Biological Engineers, Annual International Meeting, Portland, OR. Dramm, J.R., Jackson, G.L. and Wong, J. (2002). Review of log sort yards. Gen. Tech. Rep. FPL-GTR-132. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 39 pp. Environmental Protection Agency, U.S. (EPA) (2007). Biomass Combined Heat and Power Catalog of Technologies, prepared by Energy and Environmental Analysis, Inc. and Eastern Research Group, Inc. (ERG) for the U.S. Environmental Protection Agency, Washington, D.C., Combined Heat and Power Partnership. Ertogral, K., Wu, S.D. and Burke, L.I. (1998) ‘Coordination, production, and

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transportation scheduling in the supply chain’, Technical Report #98T-010, Department of Industrial & Mfg. Systems Engineering, Lehigh University, Bethlehem, PA. European Biomass Industry Association (EUBIA) (2007) ‘There are four main supply chains of forest residues’. Available: http://www.eubia.org/191.0.html (accessed 10 December 2007). Garstang, J., Weekes, A., Poulter, R., and Bartlett, D. (2002) Identification and Characterisation of Factors Affecting Losses in the Large-Scale, Non-Ventilated Bulk Storage of Wood Chips and the Development of Best Storage Practices’, Publication URN 02/1535, London: Department of Trade and Industry. Gibbons, D. (1986) The Economic Value of Water, Washington, D.C.: Resources for the Future. GLRBEP (1986) Industrial/Commercial Wood Energy Conversion: a guide to wood burning, fuel storage & handling systems. Chicago: Great Lakes Regional Biomass Energy Program, Council of Great Lakes Governors. Hakkila, P. (2004) ‘Developing technology for large-scale production of forest chips: Wood Energy Technology Programme 1999–2003’. Prepared by VTT Processes, Helsinki. Hektor, B. (1998) ‘Assessment of wood fuel prices in integrated operation’, Proceedings of the lEA Conference on Wood Fuels in Integrated Forestry, Nokia, Finland. Lindley, J.A. and Backer, L.F. (1994) Agricultural residue harvest and collection, Prepared by the Agricultural Engineering Department, North Dakota State University, for Western Regional Biomass Energy Program, Denver, CO, under PO#AA-PO-111671-12134. McDonald, T., Rummer, B., Taylor, S. and Valenziela, J. (2001) ‘Potential for shared log transport services’, Proceedings of the 24th Annual Council on Forest Engineering, J. Wang et al. (eds), Covallis, OR, pp. 115–120. Nilsson, P.O. (1983) ‘Energy from the forest’, Research Results NE 1983:9, Swedish Board of Energy Production, Stockholm. Nurmi, J. (1999) ‘The storage of logging residue for fuel’, Biomass and Bioenergy 17(1): 41–47. Schmidt, Katherine & Associates (1991) Biomass Design Manual Industrial Size Systems, U.S. Department of Energy, Southeastern Regional Biomass Energy Program, Tennessee Valley Authority, Muscle Shoals, AL. Simpkins, D., Allard, N. and Patrick, J. (2006) ‘Clean energy from wood residues in Michigan’, Michigan Biomass Energy Program, Department of Labor & Economic Growth, Lansing, MI. Springer, E.L. (1979) ‘Should whole-tree chips for fuel be dried before storage?’, Research Note FPL-0241, Washington, D.C.: USDA Forest Service. Stenzel, G., Pearce, J.K. and Walbridge, T.A. (1985) Logging and Pulpwood Production, 2nd edn, New York: Wiley-Interscience. Suadicani, K. (2003) ‘Production of fuel chips in a 50-year old Norway spruce stand’, Biomass & Bioenergy, 25: 35–43. U.S. Army Corps of Engineers (1980) National Waterways Roundtable Papers: Proceedings on the History and Evolution of U.S. Waterways and Ports, 22–24, IWR-80-2, Norfolk, VA: Institute for Water Resources. USDA Forest Service (1989) ‘Interim resource inventory glossary’. File 1900. Washington, DC: USDA Forest Service, 96 pp.


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—— (2003) A Strategic Assessment of Forest Biomass and Fuel Reduction Treatments in Western States, Washington, D.C.: USDA Forest Service, Research and Development. Wilkerson, E.G., Blackwelder, D.B., Perlack, R.D., Muth, D.J. and Hess, J.R. (2007) A Preliminary Assessment of the State of Harvest and Collection Technology for Forest Residues. Oak Ridge National Laboratory, ORNL/TM-2007/195, Oak Ridge, TN.

6

Application of biomass derived fuels for internal combustion engines with a focus on transportation Jeffrey D. Naber and Jeremy J. Worm

Introduction For both personal and commercial vehicles, internal combustion (IC) engines are expected to remain the principle mode of power conversion and generation for many decades (Pischinger et al. 2006). This will require the continued supply of high quality fuels in the face of increasing concern with the use of and availability of petroleum-based fuels, stimulating the development of renewable and low environmental impact transportation fuels from biomass. Vehicle and engine original equipment manufacturers (OEMs) have developed powertrains to utilize ethanol and methylester biodiesel, two biofuels that have received significant attention in the U.S. As of 2007 more than 5 million ethanol flex-fuel vehicles were estimated to be on the road in the U.S. (DOE 2007), and automotive manufactures have committed to making one-half of their vehicles ethanol flex-fuel capable by 2012 (GM 2007). Likewise, use of biodiesel is approved at levels of 5 to 20 per cent concentrations by many of the diesel engine and vehicle OEMs (Cummins 2007). However, other fuels including Fisher–Tropsch (green or synthetic) diesel (Goodger 1975), dimethyl ether (DME) (Silva-Petrobras 2006), methanol, butanol, biogas, and hydrogen are also notable alternative fuels in IC engines (SAE 2007) and other power conversion systems such as fuel cells. In this chapter, biofuels are reviewed and compared to fossil-based fuels with respect to their specific energy content, application to engine technologies and efficiencies, CO2 emissions, and toxic emissions. In addition, comparison of CO2-related vehicle specific emissions standards and targets are discussed including their relationship to U.S. CAFE standards. Specific examples of vehicles with flex-fuel powertrains are reviewed, and finally the potential of advanced concept IC engines optimized on biomass fuels is discussed.

Combustion and fuels IC engines are inherently linked to the specific properties of the fuel and combustion initiation and completion processes. Throughout history,

Biomass derived fuels for internal combustion engines

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different engine technologies have been utilized to operate with different fuels (Cummins 1989) including the 1908 Ford Model T that operated on gasoline, ethanol, and their combinations. Fuels in use today are comprised primarily of the atomic elements hydrogen, carbon, and oxygen, although trace amounts of other elements including sulfur are also found (Goodger 1975). The basic fuel reaction can be illustrated by the combustion of the ideal fuel–air reaction in the following simplified equation, where fuel is burned with the precise or stoichiometric amount of air for complete reaction to CO2 and H2O: CHX OY + (1 + X/4 − Y/2)·(O2 + 3.773 · N2) → CO2 + X/2 H2O + . . .

(6.1)

where X is the atomic hydrogen-to-carbon ratio and Y is the oxygen-tocarbon ratio of the fuel. Equation 6.1 shows that the primary products of combustion are CO2 and H2O. Also it is notable that for every atom of carbon combusted in the fuel, one molecule of CO2 is formed. It is through this reaction of fuel with air to produce CO2 and H2O that chemical energy is converted to sensible energy that elevates the product gas temperature and enables a heat engine such as the IC engine to extract energy from the working fluid, that being the combusted gases. It is clear, therefore, that CO2 is an unavoidable by-product of combustion when fuel contains carbon, and for a given fuel CO2 production is directly proportional to fuel consumption (every carbon atom in the fuel produces one CO2 molecule). In most combustion applications, including IC engines, the combustion process is very efficient if done with the ideal amount of oxygen to react the fuel completely to CO2 and H2O. For nearly all modern IC engines that are regulated by toxic emission standards, and operating under normal conditions, the conversion of the chemical energy to sensible energy is typically greater than 95 per cent (Heywood 1988) and for on-road vehicles, over 99.9 per cent of the carbon in the fuel is converted to CO2 through combustion and after-treatment devices such as catalytic converters (Heck and Farrauto 2002). However, due to several losses and limitations, which will be discussed throughout this chapter, not all of that released chemical energy results in usable work transmitted to the output shaft of the engine. Fuels, energy and CO2 emissions There are many types of fuels and all, by definition, carry useful energy that is released during combustion, but certain types of fuels are better suited for specific applications and engine technologies. Key properties with respect to specific energies and carbon content of petroleum, bio-based and other fuels are given in Table 6.1. One way to categorize these fuels is whether they are pure molecules or blends of components. Methanol, ethanol, butanol, dimethyl ether (DME), methane and hydrogen are all molecules and thus their characteristics are

2

F CO2/Fuel (gco gFuel−1) 2

G CO2/Energy (gco MJ−1)

a Soy based methylester biodiesel. b Pressurized to 25.0 MPa. c Pressurized to 34.5 MPa.

F G

E

760 792 785 810 827 885 761 668 165 29

44.0 20.0 26.9 33.0 43.2 37.3 44.6 28.4 50.0 120.0

1.00 0.47 0.63 0.80 1.07 0.99 1.01 0.57 0.25 0.10

3.17 1.37 1.91 2.37 3.17 2.83 3.14 1.91 2.74 0.00

72 69 71 72 73 76 70 67 55 0

H/C ratio = X in equation 6.1 is the molar ratio of hydrogen to carbon atoms. O/C ratio = Y in equation 6.1 is the molar ratio of oxygen to carbon atoms. Density is the mass of fuel per unit volume. Energy content on a mass basis. Known as the heating value (e.g., lower heating value, LHV) and is the quantity of energy contained per unit mass of fuel. Energy content on a volume basis. The product of the energy per unit mass times the density normalized to the value for gasoline. CO2 production in terms of grams of produced from each gram of fuel. CO2 production in terms of grams of produced per unit energy of the fuel.

E Energy Unit Vol (−)

A B C D

0.00 1.00 0.50 0.25 0.00 0.11 0.01 0.50 0.00 –

D Energy LHV (MJ kg−1)

1.87 4.00 3.00 2.50 1.86 1.83 1.74 3.00 4.00 Inf

C Density (kg m−3)

Gasoline Methanol Ethanol Butanol Diesel Biodiesela FT diesel DME Methaneb Hydrogenc

B O/C (−)

A H/C (−)

Fuel

Table 6.1 Transportation fuel properties including specific energies and CO2 production


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independent of the source whether that is from different biomass feedstocks or from synthetic sources. The other fuels including biodiesel, gasoline and diesel are blends composed a number of different molecules in various concentrations and thus these fuels can have different properties. Examining Table 6.1 and comparing ethanol to gasoline we see that the energy content of ethanol on a volume basis (e.g. per liter) is only 63 per cent that of gasoline. For engines and vehicles operating at the same efficiencies, this indicates that there will be a direct increase in fuel consumption when measured on a km/liter or mile/gallon (MPG) or other consumption basis when using ethanol, and a proportional increase in fuel consumption when using ethanol blends such as E85. Further, methanol has less than half the energy content on a volume basis while butanol has an energy content closer to that of gasoline at 80 per cent. In comparison, biodiesel also has a lower energy content then that of diesel, but only by 8 per cent. This lower energy density of biofuels is partly the result of their having oxygen in the fuel. The specific energy content of fuels on both a mass and volume basis can vary significantly. This becomes apparent when we compare liquid fuels to gaseous fuels such as methane and hydrogen. Even at high storage pressures of 25 Mega-Pascals (MPa) for methane and 34.5 MPa for hydrogen as given in Table 6.1, the energy densities of methane and hydrogen are only fractions of those of the liquid fuels. This does not mean that they are poor fuels, just that the economic value placed on them should factor in their energy content as it is with other energies. For example, the price of electrical energy is measured and priced in energy units such as kWh and natural gas in energy units including therms and BTUs (1 therm = 100,000 BTU = 29.3 kWh), while transportation fuels are priced in somewhat arbitrary units of volume. Focusing next on the specific CO2 emissions from fuels, factors relating to this are listed in the last two columns of Table 6.1. We see from the values given in the table that a fuel’s carbon content directly determines the quantity of CO2 produced per unit of fuel combusted (recall the discussion on the ideal combustion of fuels from equation 6.1). Fuels such as gasoline and diesel with low hydrogen-to-carbon (H/C) ratios and no oxygen have high CO2-to-fuel-mass ratios, while higher H/C ratios such as in ethanol and methane result in a decrease in the CO2-to-fuel-mass ratio. However, this measure of CO2 released per fuel mass is not the best measure of specific CO2 emissions resulting from fuel combustion. A better measure is to normalize the CO2 production based on energy content of the fuel, as shown in column G (CO2/Energy) of the table. This is the ratio of the CO2/fuel, column F, divided by the energy per unit mass, column D, multiplied by 1,000 for conversion of kg to grams. It is seen that all the liquid fuels are within 7 per cent of gasoline, indicating that when operating at the same efficiency, IC engines will be producing nearly the same amount of CO2 for a given power level. To reduce the CO2 emissions from this portion of the life cycle when using either petroleum or biofuels, we must look at fuel consumption and fuel efficiency metrics.

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There are several measures of fuel consumption, efficiencies, and CO2 specific emissions such as g(CO2) km−1 or g(CO2) kWh−1, but one should note that given a specific fuel and with complete combustion these are all interrelated. Figure 6.1 illustrates this for gasoline based upon the properties in Table 6.1 for the metrics typically used in the U.S., including MPG and CAFE standards, liters per 100 kilometers (liters 100 km−1) as used in Europe and other countries, and CO2 specific emission in g(CO2) km−1. Considering these, MPG is a measure of efficiency in that it is the usable output in miles divided by the input in gallons of fuel, while liters 100 km−1 is a specific consumption metric (input over output). It is then clear that they are inversely proportional (liters 100 km−1 = 234.2 MPG). Then following through, the g(CO2) km−1 specific emissions metric is proportional to L/100 km and is dependent upon the fuel properties (density and CO2 Fuel−1 columns C and F in Table 6.1). Here we note that because of the inverse relationship between MPG and liters 100 km−1 or g(CO2) km−1, when changes are discussed, a specific change in one does not correspond to the same change in the other, on a percentage basis. In addition to the specific emission scale on Figure 6.1 also shown are the related standards and targets for CO2 and CO2 equivalent GHGs. With this figure comparisons can be made between U.S. CAFE standards including the newly adopted 35 MPG standard (Sissine 2007) and European CO2 specific emissions targets (Brink et al. 2005), and California CO2 equivalent GHG specific emissions standards (California Legislation 2002; ARB 2005). From

Figure 6.1 Relationships between fuel efficency metric (MPG) and fuel consumption metric (liters 100 km−1) with U.S. CAFE standards (䊊), European g(CO2) km−1 specific emissions levels achived (䉭) and targets (䉮), and California (CA) proposed CO2 equivalent greenhouse gas emissions standards (ⵧ).


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this figure we can see that these standards are all regulating CO2 specific emissions. Additionally, although the measured emissions are determined on different vehicle test cycles, it can be seen that the U.S. 35 MPG CAFE regulation is at a higher CO2 specific emission level than the European targets and California limits.

Impact of vehicle characteristics and fuel energy density on vehicle range In addition to the IC engines themselves, the vehicle application has a major effect on the resulting energy consumption required for transportation. Basic physical relations dictate that as the vehicle becomes larger and heavier, the aerodynamic resistance increases, the rolling resistance of the tires increases, and the energy required to accelerate the vehicle (F = ma) and move it up inclines in the road surface increases. However, size and weight correlate with utility, creature comforts, and safety, which are factors that most motorists have been reluctant to sacrifice for improved fuel economy. Additionally, as we drive faster, the power wasted to overcome losses increases proportional to the vehicle speed cubed.1 Another important factor driving the overall energy consumption of the vehicle is performance, which is the ‘a’ term in F = ma. In order to supply the acceleration performance expected by the customer, the power plants integrated in vehicles are sized to provide significantly more power than is required to move the vehicle and its maximum payload down the road. As a result, the IC engines operate at part load most of the time, which is not typically maximum efficiency. Fuel energy density is a major factor in transportation applications where considerations such as physical space available to package a fuel tank, and range between refueling events, all favor a fuel with a higher energy content per volume (refer to column E of Table 6.1, which is normalized to the energy content of gasoline). If we assume all fuels are converted to mechanical work with the same overall efficiency, and accept that the type of vehicle defines the amount of energy required to move down the road, the distance we can drive between refueling events is defined by how much energy is stored on board the vehicle. For conventional and non-hybrid vehicles, this energy is stored as fuel in a tank. Figure 6.2 illustrates a hypothetical comparison of the potential driving range attainable for different types of fuels assuming the vehicle type, speed, elevation, storage tank size, and overall vehicle fuel conversion efficiency remains constant.2 One may note that although some gaseous fuels, and in particular hydrogen, have a high energy content per unit mass (120 MJ kg−1 for H2 compared to just 44 MJ kg−1 for gasoline), they still cannot deliver the same vehicle range as liquid fuels such as gasoline or diesel, due to the low energy per unit volume in the gas state. The technical challenge for these fuels becomes packing as many kilograms of fuel on board the vehicle as possible. Technologies do exist to store hydrogen at very low temperatures and extremely high pressures

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Figure 6.2 Hypothetical comparison of maximum driving range between fueling stops for different types of fuels, assuming no changes to the vehicle, including constant fuel tank volume and constant overall fuel conversion efficiency. Notes: (1) Fisher–Tropsch (synthetic) Diesel. (2) Soy based methylester biodiesel. (3) Dimethyl ether. (4) High-pressure Methane. Pressurized to 25 MPa absolute @ 20°C and completely consumed during drive. (5) Pressurized to 34.5 MPa absolute @ 20°C and completely consumed during drive. (6) Wood-based (producer) syngas with typical constituent compositions from Borman and Ragland (1998). (7) Low-pressure Methane. Barometric pressure (101.3 kPa) @ 20°C and completely consumed during drive.

in a liquid state. However, in addition to adding considerable cost, these solutions require a significant amount of additional energy to compress and cool the hydrogen, and this extra energy must be taken into account when evaluating the overall energy requirement through the life cycle of hydrogen. It should also be noted that electrical energy storage, can be utilized, but considering that it would take a nickel metal hydride battery about the size of a six-pack of 12 oz (355 ml) beverage cans to store the amount of energy contained in a ‘shot’ (1.5 oz 44 ml−1) of gasoline, we realize even with electric vehicles, the issue still comes down to the ability to store enough energy on board the vehicle. It is factors such as these that led to 93.6 per cent of light duty vehicles sold in the U.S. in 2005 being designed to run on only gasoline or diesel, and when considering hybrids and flex-fuel vehicles that use gasoline or diesel as one of the on-board energy sources, this number increases to


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99.96 per cent (EIA 2007). However, with current increases in alternative energy mandates, this percentage is likely to decrease in the coming years.

Impact of fuel ignitability on engine type – octane vs. cetane There are two different IC engine technologies in use today for transportation; Spark Ignition (SI) and Compression Ignition (CI). These technologies are interdependent on differences in the ignitability of the fuel at high temperatures and pressures. Inside the combustion chamber of the engine, a sufficient amount of energy must be present with the appropriate fuel-air mixture at the appropriate phase of the engine cycle to initiate the combustion process in a controlled manner. In an SI engine this energy is provided by a spark, while in a CI engine the energy comes from the compression of gases in the cylinder. The compression process heats the gases and when the fuel, which is injected near the end of compression, mixes with the hot gases, it undergoes rapid exothermic reactions (ignition and combustion). The temperature at which this occurs is the auto-ignition temperature. The auto-ignition temperature of gasoline is higher than diesel (Borman and Ragland 1998). As a result, engines operating on gasoline require a highenergy electrical discharge (a spark) to initiate combustion. This causes the fuel-air mixture locally to ignite. Once the fuel–air mixture starts to burn, the flame propagates through the combustion chamber, consuming the fuel in a controlled manner. Because diesel fuel ignites at a lower temperature than gasoline, engines operating on diesel fuel utilize the CI combustion process with the ignition energy coming from the compression process. As a result, although not technically correct, the terms diesel and compression ignition are often used synonymously, as are gasoline and spark ignition. Although the auto-ignition temperature provides information about the ignitability of a fuel, metrics specific to SI and CI engines have been developed that correlate better to the ignition characteristics of fuels under these operational strategies. These metrics are the octane and cetane ratings, and are typically applied to fuels used in spark ignition and compression ignition engines respectively. Octane and cetane ratings of standard and biofuels are given in Table 6.2. The octane rating is a measure of the fuel’s resistance to auto-ignition; the higher the octane, the lower the tendency to auto-ignite in an engine. In an SI engine, if the octane rating is not high enough, combustion knock (unwanted auto-ignition) will occur, potentially damaging the engine. Fuels with high octane ratings enable an SI engine’s compression ratio to be raised, which, as will be discussed shortly, increases efficiency. The cetane rating used in CI (diesel) engines can be thought of as the inverse of the octane rating. The higher the cetane number, the lower the temperature necessary for auto-ignition, making ignition via compression (i.e. CI) easier to achieve.

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Forest biomass energy assessments Table 6.2 Fuel octane and cetane ratings Octanea

Fuel Gasoline Methanol Ethanol Butanol Diesel Biodiesel DME Methane Hydrogen

87–93 99 98 96 33–54 15–31 – 120 106

Cetane 14–20 12 14 15 40–50 52–58 55–60 – –

Note: a (R+M)/2.

As can be seen in Table 6.2, fuels with a high octane number (indicating a high resistance to auto-ignition and a good SI fuel) have a low cetane number (poor CI fuel) and vice versa. Methanol and ethanol are thus good fuels for SI engines, while biodiesel and DME are good fuels for CI engines. In fact, methanol and ethanol have higher octane ratings than gasoline, and biodiesel and DME have higher cetane ratings than diesel. This indicates, at least from an auto-ignition standpoint, that they are better fuels for their respective engines than the base petroleum fuels. Although the cetane and octane numbers indicate whether a fuel should be used in an SI or CI engine, there are other properties of the fuel that influence important operating characteristics, such as the efficiency, maximum power, and emissions. Impact of fuel octane and cetane on efficiency In the operation of both SI and CI engines the mechanical work is extracted from the combusted fuel–air mixture during the expansion stroke, which occurs during and following combustion. Therefore, it is desirable to expand the combusted gases as much as possible to extract the maximum work. Since most engines have a stroke that is the same during both directions of piston travel, additional expansion is obtained by increasing the ratio of cylinder volume when the piston is at the bottom of its travel (BDC) to the cylinder volume when the piston is at the top of its travel (TDC). This ratio of volume is commonly known as the geometric compression ratio (CR), and is equal to the geometric expansion ratio (Equation 6.2). CR =

Maximum Cylinder Volume Minimum Cylinder Volume

(6.2)

It can be shown that the ideal fuel conversion efficiency of the engine is dependent upon the CR of the engine (see, e.g. Wark 1988) and based upon


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the above discussion, the larger the expansion, the higher the ideal efficiency. Additionally as the rate of combustion is increased (providing more expansion potential), the efficiency also increases. Utilizing the relationships developed from thermodyamic cycle analysis (again, see Wark 1988), the ideal efficiency is plotted in Figure 6.3 over the range of CRs for ‘slow’ combustion and ‘fast’ combustion. In the thermodyamic analysis ‘slow’ combustion refers to constant pressure (CP) combustion often associated with the CI combustion process and ‘fast’ combustion process refers to constant volume (CV) combustion often asssociated with SI combustion. As can be seen in Figure 6.3, the fast or constant volume combustion has a higher efficiency. This can be reasoned intuitively by considering that in the constant volume case all the fuel’s energy is released at the minimum cylinder volume, allowing for full expansion. In actuality, neither SI or CI combustion processes follow these rates, but are typically somewhere in the middle of ‘slow’ and ‘fast’. Also observed in Figure 6.3 is that increasing the CR increases efficiency. Here is where the fuel’s octane number impacts the potential efficiency. Unlike CI engines where the heat of compression is required to ignite the fuel once it is injected, in SI engines, excessive heat of compression can lead to unwanted combustion knock at high loads. Thus, as shown in the figure,

Figure 6.3 Compression ratio effect on ideal efficiency and ranges of current gasoline and diesel engine operation.

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the upper limit for the CR for SI gasoline engines is around 13:1, while the CR for CI diesel engines is higher and typically in the range of 16:1–22:1. This is the primary reason why CI engines are more efficient than SI gasoline engines. Referring back to Table 6.2, although the octane number of unleaded gasoline can be as high as 93, most IC engines used in vehicles are designed for 87 octane fuel. The alcohol fuels have significantly higher octane numbers (98–9) than gasoline, while methane (the major component in natural gas) has an octane number of 120. The high octane rating of these alternative fuels makes them an enabler for higher CRs and thus higher engine efficiency. However, current flex-fuel vehicles must operate on multiple fuels, which can lead to limitations. For example, because ethanol/gasoline flex-fuel vehicles must operate within a range from pure gasoline to ethanol-gasoline blends up to 85 per cent ethanol, they are limited in CR by gasoline’s lower octane. This effect is shown in Figure 6.4. Here, a SI Cooperative Fuels Research (CFR) engine run at a constant part load condition and the most efficient spark timing began knocking (auto-ignition) at a CR of 10:1 with pure gasoline. As ethanol was added to the gasoline, the knock-limited CR (as well as efficiency) increased, until a maximum CR of 16:1 was achieved at an

Figure 6.4 Operating CR range of ethanol/gasoline blended fuels in terms of knocklimited compression ratio (KLCR) for a SI engine operating at part load (900 RPM/330 kPa Net Mean Effective Pressure). Base gasoline was 87 octane research fuel. Tests were conducted on a CFR (flat top piston with valves in block) engine modified with electronic port fuel injection.


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ethanol/gasoline mixture containing 60 percent ethanol. Therefore, for a simplified example, if the engine in Figure 6.4 were installed in a flex-fuel vehicle capable of running on gasoline and E85, the CR could not be set at 16:1, because the engine would still need to run acceptably on pure gasoline (E0), thus the CR would be limited to 10:1, giving up much of the benefits of ethanol. If an engine were able to change the CR as the fuel changed some of the potential benefit of the higher octane rating fuels could be recovered. Due primarily to mechanical complexity, truly variable geometric CR systems are rarely used in production, although several have been designed and developed (Drangel et al. 2002; Moteki et al. 2003; Rosso et al. 2006) and the concept has been shown to enable SI engine efficiency to surpass that of a CI engine over the speeds and loads most often encountered in engine operation (Ribeiro and Martins 2007). However, a popular technology, especially in SI engines, Variable Valve Timing (VVT) offers many benefits in terms of efficiency, emissions reduction, and full load torque (Roth et al. 2007; Schafer and Balko 2007; Bonello et al. 2003). By closing the intake valve later than normal, the ‘effective’ CR can be reduced, thus reducing the autoignition tendencies and enabling higher geometric compression ratios. However, a limitation is reached under full load operation where it is desirable to advance the intake valve closing in order to trap the maximum amount of air and fuel possible. The compromise then becomes reducing geometric CR and sacrificing efficiency or reducing effective CR and sacrificing full load air and fuel trapping efficiency and power. Maintaining the exhaust valve opening as late as possible in the expansion stroke maximizes the ‘effective’ expansion ratio and thus cycle efficiency. VVT can also be used to control the amount of residual exhaust gas inside the engine, which reduces emissions and also improves efficiency. Fuels with a faster burn rate, such as ethanol, can enable higher residual gas fractions, and thus efficiency improvements, although some of the efficiency improvement can be offset by increased pumping work due to ethanol’s heat of vaporization being higher than that of gasoline. Although there are many more valve timing scenarios that could be discussed, the advantages and implementation of VVT is extensive, and as such, outside of the scope of this text.

Vehicle technologies In a simpler world, we would only need to supply energy to get our vehicles moving (remember F = ma), and once moving, we would only supply a force if we wanted to accelerate. Unfortunately, that is not the case, and there are many losses that must be overcome as we drive. These losses are shown in Figure 6.5 (DOE and EPA 2008). Here we see that for each unit of fuel we put in our tanks approximately 62.4 per cent is used to overcome losses in the engine. Examples of these losses include: the work required to bring air and fuel into the engine and expel exhaust gas out; the work to circulate coolant and oil through the

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Figure 6.5 Typical allocation of fuel energy in an automobile. Source: OTA (1995: Appendix A), assuming a Spark Ignition engine.

engine; the windage and friction associated with the many moving parts; the heat that is lost to the cooling and exhaust systems that was not retained to do useful work; the losses due to non-instantaneous combustion; dissociation of combustion products; and even the losses inherent in the ideal cycle (recall, Figure 6.3 illustrates that even the ideal engine cycles do not achieve 100 per cent efficiency). There are many other losses shown in Figure 6.5. The idling losses simply represent the fuel that is consumed just keeping the engine running when power from the engine is not needed. Examples include stopping at stoplights, and in heavy stop and go traffic. The braking losses are not as intuitively obvious. Under normal driving, the engine supplies power to accelerate the vehicle to some cruising speed. Then, when the drivers wish to stop or slow the vehicle, they apply the brakes, and in doing so, convert the kinetic energy of motion to heat, which is wasted to the surroundings. The drivetrain losses and rolling resistance result from inefficiencies in the drivetrain and tires respectively. The aerodynamic drag is a result of the work that is done to move the vehicle through the air. Finally, the impact accessory and auxiliary power requirements have on vehicle efficiencies is significant in modern automobiles. This includes safety system such anti-lock braking system (ABS) and electronic stability control, and auxiliary and comfort systems such as air conditioning, electrically-operated windows, seats, and mirrors, heated seats, radios, and the multiplex of computers, sensors, and actuators


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(Jurgen 1999). On high-end vehicles with significant electrical content, the auxiliary power consumption can be as high as the power required to move down the road at moderate speeds. When considering accessories, we must simply remember, any feature that requires energy must get that energy from the combustion of fuel. Clearly the single largest area for losses that occur is in the engine itself. However, idling and braking account for the second and third largest loss categories respectively. Because these two areas account for a combined 23 per cent of the fuel utilization, it makes sense to invest effort into reducing them. In fact, reducing these losses is the primary impetus for hybrid vehicles. A typical hybrid vehicle utilizes a powertrain that is a combination of two power systems.3 By doing so, the two systems can work synergistically with each other, partially offsetting the shortcomings of each system. One of the biggest advantages of a hybrid vehicle is that the electric motors can also be used as generators, allowing kinetic energy to be recovered from regenerative braking, which is how hybrid vehicles reduce the amount of braking losses (Figure 6.5). The other big advantage is that the alternative drive system can be used to power the vehicle, instead of the engine, when the engine would otherwise be at low inefficient loads including idle, reducing the idling losses (Figure 6.5). It is for these two reasons that hybrid vehicles show their biggest benefit in city driving when the vehicle spends a lot of time decelerating and sitting still. Electric vehicles, and even ‘plug-in’ hybrid vehicles, can utilize electrical energy from the municipal power grid for vehicle propulsion. However, despite potentially high MPG ratings of fuel from the tank, we must consider the overall energy conversion process from source to wheels. When analyzing the electrical generation process, we find that nearly 70 per cent of the source energy is irrecoverably lost (EIA 2006a: 221) (greater than the engine losses shown in Figure 6.5), and furthermore, 71 per cent of the U.S. electrical power production is from fossil fuels (EIA 2006b: 16). From this, it becomes clear that even when driving on electricity, these vehicles are still contributing to the depletion of fossil fuels, and the increase of atmospheric CO2. However, if we can increase the penetration of renewable sources of electricity production, such as raw biomass, biomass based syngas, hydro-electricity, solar, wind, etc., these vehicles will show more benefit.

Implementation cost The implementation cost for some of these technologies varies significantly. For example, from the manufacturers’ point of view, enabling a vehicle originally designed for gasoline to run on either gasoline or E85 requires little or no additional hardware. Although the fuel system materials must be ethanolcompatible and the flow rates must be increased, all of the components required to run E85 are essentially already present in the gasoline-only fuel system and ‘sensing’ of the ethanol concentration can sometimes be done

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through various methods inside the engine’s electronic control module, while other times an additional sensor is required. Piston rings and valve seats are often upgraded to be compliant with the alternative fuel, which can also add some cost. Some cost must be factored in for increased engineering effort because of the additional calibration and federal certification required. However, if production volumes are sufficiently high, this increased cost is low on a per vehicle basis. From the consumers’ point of view, assuming the increase in initial vehicle cost is negligible and no tax incentives come into play, the only economic factor to consider is the difference in fuel costs. As previously discussed, ethanol contains less energy than gasoline, therefore E85 contains about 30.6 per cent less energy than pure gasoline on a volumetric basis. Neglecting any changes in vehicle efficiency, the fuel consumption of a vehicle will increase by 30.6 per cent when E85 is used. Therefore, E85 is only economical to the driver if it can be purchased for no more than 69.4 per cent of the cost of gasoline. A very similar cost analysis is applicable to vehicles designed to run on blends of diesel and biodiesel.

Conclusions In this chapter we have discussed the impact and interdependencies of fuels and engine and vehicle technologies with a focus on biofuels and where they fit into current and future technologies. Comparing the liquid fuels, the energy densities on a volumetric basis for the oxygenated biofuels were lower than their comparable petroleum fuels. Anytime we combust a carbon-based fuel, be it gasoline, ethanol, diesel, or biodiesel in the presence of oxygen, we produce CO2. When normalized, the CO2 produced from combustion on an energy basis for the petroleum and bio-based liquid fuels are within ±7 per cent of gasoline. Significant differences exist with respect to the biofuels energy density in comparison to gasoline and diesel. Biodiesel has the closest energy density and butanol the second for the fuels compared. For the gaseous fuels (hydrogen and methane), although high in energy on a gravimetric basis, the volumetric energy density is low even at relatively high pressures and significant advancement in storage will be required to match the energy density of the liquid fuels and thus provide the driving range motorists desire in transportation applications. Furthermore, fuels such as diesel or DME are better suited to CI engines, while other fuels such as gasoline, ethanol, and gaseous fuels are better suited to SI engines. Even so, it is difficult to take full advantage of biofuels such as ethanol with a high octane rating when designing a flex-fuel vehicle since many design parameters have to be specified for the ‘worse-case’ fuel. We also examined the various vehicle technologies and sources of losses throughout vehicle. By minimizing the system losses, including engine losses, as well as reducing vehicle size, mass, and driving speeds significant fuel


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consumption improvements can be obtained resulting in lower specific CO2 emissions.

Notes 1 In actuality the power to overcome losses increases at a rate even greater than the speed cubed, because rolling resistance has also been shown to be a function of speed and, for a given transmission gear, ‘spin losses’ associated with moving parts in the engine, transmission, axles, and accessories will also increase with increases in vehicle speed. 2 As used here, overall vehicle fuel conversion efficiency is the efficiency of the entire system in converting chemical energy to tractive force. As such, it includes axle, driveline, transmission, and engine efficiency. As we will see later, engine efficiency is not necessarily constant among different types of fuels, partly due to the design differences in the engine when implementing the fuels, however, for this example it can be assumed that the differences in engine efficiency for each fuel type are negligible when compared to the difference in the volumetric energy content of each fuel. 3 Most modern hybrid vehicles utilize a combination of an IC engine and one or more electric motors and can be arranged in either series or parallel. Other systems, including pneumatic, mechanical, and hydraulic, which is increasing in popularity have been proposed. However, due to their greater market penetration, any discussion in this chapter pertaining to hybrid vehicles will assume an IC engine/electric hybrid.

References Air Resources Board (ARB) (2005) California Code of Regulations Section 1961.1, Title 13 with Amendments to Sections 1900 and 1961, 15 September. Bonello, M.J., Cremonesi, J.D., Davis, R.S., Prior, G.P. and Zinser, J.C. (2003) ‘The Next-generation Northstar DOHC 4.6L V8 engine with four-cam continuously variable valve timing for Cadillac’, Progress in Technology, 100: 23–46. Borman, G.L. and Ragland, K.W. (1998) Combustion Engineering, New York: McGraw-Hill. Brink, P., Skinner, I., Fergusson, M., Haines, D., Smokers, R., van der Burgwal, E. et al. (2005) ‘Service contract to carry out economic analysis and business impact assessment of CO2 emissions reduction measures in the automotive sector’, Brussels: Institute for European Environmental Policy, IEEP/TNO/CAIR. California Legislation (2002) Assembly Bill No. 1493, ‘Vehicular emissions: greenhouse gases’. Available: http://www.calcleancars.org/ab1493.pdf (accessed 20 March 2008). Cummins, C.L. (1989) Internal Fire: the internal-combustion engine 1673–1900, 2nd edn, Wilsonville, OR: Carnot Press. Cummins Inc. (2007) ‘Cummins announces approval of B20 biodiesel blends’, Cummins Press Release, 21 March, Available: http://www.biodiesel.org/resources/ PR_supporting_docs/20070321_cummins_b20.pdf (accessed 20 March 2008). Department of Energy, U.S. (DOE) (2007) ‘Flexible fuel vehicles: providing a renewable fuel choice’, DOE Fact Sheet, DOE/GO-102007–2431, Washington, D.C. —— and Environmental Protection Agency, U.S. (EPA) (2008) ‘Advanced technologies & energy efficiency, Available: http://www.fueleconomy.gov/feg/atv.shtml (accessed 28 February 2008).

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Drangel, H., Reinmann, R. and Olofsson, E. (2002) ‘The variable compression (Svc) and the combustion control (Scc) – two ways to improve fuel economy and still comply with world-wide emission requirements’, SAE Technical Paper 2002–01– 0996, Warrendale, PA. Energy Information Administration (2006a) Annual Energy Review 2006, DOE/EIA0384, U.S. Department of Energy, Washington, D.C. Available: http:// www.eia.doe.gov/emeu/aer/contents.html (accessed 20 March 2008). —— (2006b) Electric Power Annual 2006, DOE/EIA-0348, U.S. Department of Energy, Washington, D.C. Available http://www.eia.doe.gov/cneaf/electricity/epa/ epa_sum.html (accessed 20 March 2008). —— (2007) ‘Regional and other detailed tables’, a supplement to Annual Energy Outlook 2007 with Projections to 2030, DOE/EIA-0383, U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/oiaf/aeo/ supplement/index. html (accessed 20 March 2008). General Motors Corporation (GM) (2007) 2006 Annual Report, Report 002CX13758, Detroit. Goodger, E.M. (1975) Hydrocarbon Fuels – production, properties and performance of liquids and gases, New York: Wiley. Heck R.M. and Farrauto, R.J. (2002) Catalytic Air Pollution Control, New York: Wiley. Heywood, J.B. (1988) Internal Combustion Engine Fundamentals, New York: McGraw Hill. Jurgen, R.K. (1999) Automotive Electronics Handbook, New York: Mc-Graw Hill. Moteki, K., Aoyama, S., Ushijima,K., Hiyoshi, R., Takemura, S., Fujimoto, H. and Arai, T. (2003) ‘A study of a variable compression ratio system with a multi-link mechanism’, SAE Technical Paper 2003–01–0921, Warrendale, PA. Office of Technology Assessment (OTA) (1995) Advanced Automative Technology: visions of a super-efficient family car, OTA-ETI-638, Government Printing Office stock #052–003–01440–8, Washington, D.C. Pischinger, S., Rottmann, M. and Fricke, F. (2006) ‘Future of combustion engines’, SAE Technical Paper 2006–21–0024, Warrendale, PA. Ribeiro, B. and Martins, J. (2007) ‘Direct comparison of an engine working under Otto, Miller and diesel Cycles: thermodynamic analysis and real engine performance’, SAE Technical Paper 2007–01–0261, Warrendale, PA. Rosso, P., Beard, J. and Blough, J.R. (2006), ‘A variable displacement engine with independently controllable stroke length and compression ratio’, SAE Technical Paper 2006–01–0741, Warrendale, PA. Roth, D.R., Sisson, J., Gardner, M. and Wing, B. (2007) ‘Valve-event duration reduction through ultra-fast phaser actuation’, SAE Technical Paper 2007–01–1281, Warrendale, PA. SAE Fuel and Lubricants Tc7 Committee (SAE) (2007) ‘Alternative automotive fuels’, SAE Technical Report, Warrendale, PA. Schafer, J. and Balko, J.S. (2007) ‘High performance electric camshaft phasing system’, SAE Technical Paper 2007–01–1294, Warrendale, PA. Silva-Petrobras, D.F. (2006) ‘DME as alternative diesel fuel: overview’, SAE Technical Paper 2006–01–2916, Warrendale, PA. Sissine, F. (2007) ‘Energy Independence and Security Act of 2007: a summary of major provisions’, Congressional Research Service Report for Congress, Order Code RL34294, Washington, DC. Wark, K., Jr. (1988) Thermodynamics, New York: McGraw-Hill.

7

Bioenergy, biomass and biodiversity David J. Flaspohler, Christopher R. Webster and Robert E. Froese

Overview This chapter provides an overview of some of the known and potential impacts of biomass energy production on ecosystems and species. We focus on how an expanding bioenergy economy in the U.S. is likely to affect land use and habitat quality for forest and grassland species. We use evidence from key feedstocks such as corn to examine how current monoculture systems for feedstock production are likely to shape future landscape patterns. Then, we explore how the composition and patterning of habitats on the landscape will dictate which species are likely to be harmed by a growth in bioenergy feedstock production in the U.S. Alternatives to spatially extensive monocultures exist. Some of these are competitive with monocultures in terms of biomass produced per hectare and many are superior in terms of other metrics of land health such as wildlife habitat, soil conservation, ground and surface water pollution, and landscape aesthetics.

Biodiversity enters the bioenergy debate As the environmental costs of human activities have become more widely recognized, scientists and Policy Makers have grown accustomed to measuring or estimating the economic and ecological costs of such things as increased discharge of sewage into waterways or CO2 into the atmosphere. The rapid expansion of biofuels in the U.S. that began in 2002 has happened largely without a careful accounting of short- or long-term costs and benefits of new systems of feedstock production or processing (c.f. Pimentel and Patzek 2005; Hill et al. 2006; Tilman et al. 2006). Without such information, the political and market forces that shape the short-term patterns of feedstock production are unlikely to create a system that meets current demand while minimizing long-term ecological and economic costs. This is partly because many of the costs associated with biofuel production are externalities (such as soil erosion, aquifer overdraw) not explicitly included in near-term decision-making (Cook et al. 1991; Worldwatch Institute 2007). Forest and grassland ecosystems and the natural communities they support will be the

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first to feel the impact of this new surge in biofuels. Virtually absent from discussions of biofuels and their potential to replace a portion of U.S. transportation fuels has been consideration of how biodiversity will be affected. Renewable energy accounted for just 7 per cent of the 100 quads of BTUs used in the U.S. in 2006 (DOE 2006). The largest share (6.8 1015 BTUs) came from biomass. Biomass energy use included wood (65 per cent), biofuels (23 per cent) and biowaste (12 per cent) (DOE 2006). Perlack et al. (2005) estimated that although some capacity exists for expanding secondary sources of biofuels such as mill residues and manure, at least 70 per cent of future bioenergy capacity will have to come from primary sources such as perennial grasses, and forestry and crop residues (Perlack et al. 2005). Currently more than 90 per cent of biofuel produced in the U.S. comes from corn (Zea mays L.), a crop that demands large inputs of fossil fuel, fertilizer, herbicides, pesticides, and water (NASS/USDA 2007). Corn-based ethanol is also likely to increase greenhouse gas emissions worldwide, as land is converted from native forest and grassland cover (Searchinger et al. 2008). Given these broad impacts, corn-based biofuel is arguably not renewable energy. Even among today’s corn ethanol promoters, few would suggest that reliance on corn is a good strategy for ensuring healthy soils, waters, and air and stabilizing atmospheric CO2 into the future. Even so, corn continues to expand in aerial coverage in the U.S., and ethanol-processing plants designed for corn feedstock continue to be built, particularly in the Midwest (Chapter 3). Cornfields provide habitat for few species, and are associated with some of the highest soil erosion rates of any crop and high input rates of fossil fuel, herbicides, and pesticides (NAS 2003). Although conversion of soybean (Glycine max (L.) Merr.) or other row crops to corn has environmental consequences, of greater concern to ecologists and conservation biologists is the trend toward conversion of high quality, species-rich grasslands currently enrolled in the federal Conservation Reserve Program (CRP) to corn. Similarly, where woody species such as hybrid poplar (Populus spp.) and willow (Salix spp.) could replace diverse native forests, concern over loss of associated wildlife is also high. Calls for increased U.S. production of ethanol over the next decade (Figure 7.1) suggest that the pressure to convert other row crops and currently uncultivated land will increase. The central U.S. has the largest potential for biomass-to-energy production (Milbrandt 2005) and most current land use change associated with biofuels is occurring in this region; Wisconsin planted 160,000 more hectares of corn in 2007 than in 2006 (NASS/USDA 2007). Researchers from Iowa State University estimated that 50 to 55 per cent of the land enrolled in the CRP in Iowa could be converted back to cropland if corn prices stay at $3.00 to $3.50 per bushel (Secchi and Babcock 2007). In autumn 2006, USDA estimated that 2.8 million hectares of land enrolled in the CRP would be suitable for producing corn and soybean (Collins 2006). Although forestlands in the U.S. have not yet experienced a

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Figure 7.1 U.S. historic ethanol production and the announced goal for future production. Source: RFA (2008).

parallel conversion related to expanding markets for biofuel, this potential exists. The native tree species and relatively diverse forest tree communities that characterize the northern Great Lakes region support rich plant and animal communities (Dickmann and Leefers 2003). For this reason, any widespread land use change that would tend to homogenize these forests would threaten much of the region’s biodiversity. Recent optimistic predictions about the potential for biofuels to replace up to 30 per cent of global demand without harming food production (Koonin 2007) obscure what it might take to achieve such output: massive conversion of diverse native ecosystems into species-poor monocultures. What has been missing from the biofuel boosting that was sparked by dramatic gasoline price increases was a healthy debate on the relative merits of different biofuel production and ethanol synthesis systems. That debate has largely caught up with the leading feedstock production systems, at least in academic and policy circles (Sample 2007; Groom et al. 2008), if less so among the wider public (Bourne 2007; New York Times 2007). However, for this debate to serve society by clarifying the relative costs and benefits of competing choices, it must be informed by sound science and a thorough assessment of how every stage of bioenergy production is likely to impact the terrestrial and aquatic ecosystems that sustain life. In this chapter, we review the current understanding of bioenergy feedstock production systems and their known and predicted effects on terrestrial and aquatic wildlife and the ecosystems that support them.

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Feedstock productivity and production systems Dedicated production of biomass crops for energy is not new. For example, cultivation of poplars for fuelwood dates back to at least the fifteenth century (Dickmann 2006). In the U.S., switchgrass (Panicum virgatum L.) has been the focus of biofuel research for several decades, stimulated in part by the energy crisis in the 1970s. In this section we discuss production systems and typical inputs, grouping systems loosely into the three categories of intensive agriculture, extensive agriculture, and managed ecosystems. Intensive agricultural systems Agricultural biomass production systems are currently focused on cereal crops in the U.S., almost exclusively corn grain used for ethanol production as vehicle fuel. Corn stover and wheat straw have substantial potential as feedstocks as well (Perlack et al. 2005), though retaining this material on site also plays an important role in soil and nutrient conservation. Cultivated originally as food, cereal crops have undergone intense breeding to optimize yield under defined production systems. Yields are very high with the average 2007 U.S. corn production over 383 bu ha−1 today (NASS/USDA 2007). Production systems that achieve such yields are intensive; corn grain and stover contain about 6.7 and 16.1 kg N dry Mg−1, respectively (Kuepper 2002; Lang 2002), which must be replaced when the crop is removed. Thus, corn production consumes 40 per cent of all commercial fertilizer applied to U.S. croplands (Christensen 2002). Herbicide and pesticide applications are widely utilized as insect, disease, and weed controls, because proliferation of these pests can be major limiting factors on corn. New genetically engineered crops, biological weed controls, non-traditional cultivation methods and new cropping systems have helped address resistance issues, but chemical use will remain important in yield management (Wheeler 2002). Extensive agricultural systems Perennial grasses, such as switchgrass and Miscanthus (Miscanthus spp.) have shown tremendous promise as bioenergy feedstocks, due to high yields and suitability for many U.S. regions (Figure 7.2). Reported yields for both crops range from 9–25 dry Mg ha−1 yr−1 (Lewandowski et al. 2000, 2003). Cultivation follows a typical agricultural model, with careful site preparation, weed control, and irrigation essential for high first-year survival (Lewandowski et al. 2003). Similarly, short-rotation woody crops (SRWCs) such as poplar or willow, have also received considerable attention (Dickmann 2006; Keoleian and Volk 2005). Typically, these plants are produced in a semi-agricultural framework and with lower inputs than cereals over multi-year rotations. However, though several are native forest species, they are still normally established as monocultures on cultivated lands (Dickmann 2006). Notably,


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Figure 7.2 Switchgrass monoculture in October near Madison, Wisconsin (photo courtesy of David Flaspohler).

grasses are usually harvested only once annually, in the fall or winter, after senescence. This means that they have to be paired with other sources available at different times or stored in substantial quantities if used in bioenergy systems that require continuous feedstock inputs. More flexibility is available for SRWCs that accumulate and effectively store biomass in stems. Willow is advocated as an energy crop because of its ease of propagation, high yield potentials, broad genetic base, and its ability to re-sprout after many harvests (Keoleian and Volk 2005). Willow is well suited for establishment in the northern U.S. (Walsh et al. 2000), and yields from test plots have ranged as high as 22 to 29 dry Mg ha−1 yr−1. Typically, commercial-scale trials produce much smaller yields, closer to 6.7 dry Mg ha−1 yr−1 in trials in New York (Keoleian and Volk 2005). Substantial variability in yield is reported among clones (Figure 7.3), and matching the clone to site is critical for optimizing productivity (Dickmann 2006). Establishment of willow occurs by planting cuttings harvested from one year-old shoots, which exhibit rapid growth characteristics. Planting rates exceeding 15,000 trees per hectare are not unusual (Walsh et al. 2000). Site preparation follows methods used for agricultural crops and complete weed control is required for successful establishment. Willow re-sprouts profusely after cutting and yields may increase

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Figure 7.3 These willow clones planted on abandoned agricultural land in northern Michigan illustrate the importance of matching clone to site. Note deep shade prevents any understory development (photo courtesy of Robert Froese).

20 to 90 per cent between early harvests. The first harvest is made about the fourth year, followed by harvests every three years, for a total of about seven harvests before plants need to be re-established. Nutrient inputs, especially nitrogen, are required at stand establishment and after each harvest with the type and amount determined by site conditions (Volk et al. 2004). Seed and pollen are wind dispersed so gene migration into native populations is likely. Poplar also has a substantial history of development as a fiber crop. Eight native species are found in North America, including cottonwoods and two species of aspen. Stands are typically grown with wide spacing resulting in a density range of 700 to 1,700 trees per hectare. Yields of 9–22 dry Mg ha−1 yr−1 are typical (Tuskan 2000). Rotations in the U.S. range from 10 years in the north, eight in the south, and six in the Pacific Northwest. Proper site preparation and weed control are very important for successful establishment. Weed control is essential and may be required during the first three years. After three years, the canopy closes and weeds cannot establish in the deep shade beneath. Fertilizer requirements are minimal over the rotation period and mainly involve nitrogen additions (Tuskan 2000). Because of high growth rates and cultivation as monocultures, hybrid poplars may often suffer more insect damage than parent species (Coyle et al. 2006). Like willow, seeds and pollen are spread mainly by wind, but the lower pollen production


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and seed viability often observed in hybrid poplars may reduce gene flow into native populations (EPA 1999). Managed ecosystems In contrast to agriculture, managed ecosystems are forest and non-forest systems managed at low intensity and comprised of largely native plants, typically in polycultures. As bioenergy production systems, these include native and naturalized grasslands (Florine et al. 2006), brushlands, low-input highdiversity (LIHD) perennials (Tilman et al. 2006), woodlands and forests not reserved from management (Smith et al. 2004). Managed ecosystems can be competitive with agricultural systems in terms of productivity in some cases, particularly outside of the most fertile agricultural lands, and have compelling attributes from many environmental perspectives (Figure 7.4). Harvesting of grasslands can use conventional agricultural equipment. A review of harvesting technology for forestry resources is provided in Chapter 4. Among biomass production systems, naturalized grasslands and LIHD perennials have received recent attention. These systems have few inputs; little or no fertilizer is used, and herbicides or irrigation are used only during establishment. For example, despite low inputs, Florine et al. (2006) found late June yields from typical pasturelands in southern Iowa averaging 4.2 dry Mg ha−1. Assuming an energy content of 15 MJ Mg−1, Tilman et al. (2006) found yields of about 4.5 dry Mg ha−1 yr−1 from LIHD perennials planted on degraded agricultural land in Minnesota. Notably, yields from LIHD plots were 238 per cent greater than monocultures at the same location.

Figure 7.4 Relationship between feedstock plant community diversity and associated species richness of other taxa in the ecosystem.

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Biomass production from forests is often indirectly related to the production of conventional forest products. Biomass may be collected from harvest residues, such as tops, branches, rough and rotten logs, and the non-commercial tree species cut in conventional harvest operations. Typically, these residues are left behind to decay or are processed (burned or masticated) on site to reduce fire hazard. An alternative source is residues from stand tending operations, which are activities where improving forest condition is a priority, and revenue generation is secondary. Estimating productivity for indirectly produced forestry feedstocks is difficult because they are residues – they are residual to a primary activity. Hence, the rate of production depends on the rate of harvest of conventional forest products, as well as underlying biological potential, stand structure, and market potential for alternative uses. Contemporary estimates (e.g., Perlack et al. 2005; Walsh 2006) usually rely on survey-based estimates of forest industrial activity maintained by the USDA Forest Service (Smith et al. 2004), scaled down to reflect operational recovery rates (e.g. Stokes 1992). For example, using a recovery rate of 65 per cent in the U.S., 42.8 million dry Mg of harvest and tending residues (not including fuel treatments) are potentially available annually (Perlack et al. 2005). Dividing these across the estimated 203.8 million ha of U.S. timberland (Smith et al. 2004) is equivalent to an annual rate of about 0.21 dry Mg ha−1 yr−1. This number seems small, but these residues constitute only about 16 per cent of total removals and about 5 per cent of gross productivity on U.S. timberland (Smith et al. 2004). Few estimates are available for the amount of residues associated with typical forest operations. McMinn et al. (1987) reported about 28–73 dry Mg ha−1 of residues, including un-merchantable trees, from mature stands in central Georgia. Scott and Dean (2006) found standing crown mass to be relatively constant at about 20.5 oven dry Mg ha−1 for 27–57-year-old loblolly pine stands in the southern states. Thinning treatments in a young ponderosa pine (Pinus ponderosa Douglas ex C. Lawson) plantation and mature mixedconifer stand reported by Hartsough et al. (1997) both produced about 55.2 dry Mg ha−1 of residues. In Minnesota, Sorensen (2007) reported average post-harvest logging residues ranged from about 13.6 Mg ha−1 for lowland conifer types up to 23.1 Mg ha−1 for upland hardwoods. Notably, in most of these examples little or no inputs were required. Fertilizer and herbicide use is increasing in many U.S. forest types (e.g. Albaugh et al. 2007) but remains small compared to agricultural uses. Biomass may also be produced directly from forests, following the energy crop model but in the context of managed ecosystems. Candidate forests include those with low value for traditional uses (Weetman 2000), such as previously forested bare land, cutover land regenerating to low value species, or forests degraded through exploitive management. For example, quaking aspen (Populus tremuloides Michx) reproduces vigorously from suckers, and aspen regeneration can dominate after logging of stands with relatively low pre-harvest aspen components. Reported yields for aspen exceed 3.1 dry Mg


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ha−1 yr−1 in unmanaged stands in Minnesota (Edgar and Burk 2001) and 10.4 dry Mg ha−1 yr−1 in Wisconsin (Crow 1987). Davis and Trettin (2006) report yields for young plantations of sycamore (Platanus occidentalis L.) and sweetgum (Liquidambar styraciflua L.), established on abandoned agricultural land in South Carolina, of 4–6 dry Mg ha−1 yr−1. Yields of the same species under management (e.g. with fertilizer, herbicides or irrigation) are likely to approach double the unmanaged yields (Davis and Trettin 2006). Notably, these values compete favorably with monoculture grasses and short-rotation wood crops (SRWC) in all but ideal conditions for the latter alternatives.

Terrestrial ecosystems The long-term impact of bioenergy production on the stability and productivity of terrestrial ecosystems is not well understood (Mann and Tolbert 2000). The impacts of a given system are influenced by attributes of the production system, site and species. For the purposes of this discussion, we will focus on management of established native vegetation and conversion of open land to biomass plantations. Primary concerns regarding the sustainability of feedstock yields from native forests are depletion of soil fertility, degradation of soil structure including carbon losses, erosion, and habitat alteration for wildlife. Although some authors have suggested that up to a quarter of current U.S. energy needs could be met by using organic wastes and residues from agriculture and logging (Junginger et al. 2006), such removals would almost certainly affect long-term soil fertility, increase erosion (Lal 2006), and deplete soil organic carbon (Aguilar et al. 1988). Whole-tree harvesting typically removes more nutrients from a site than traditional saw timber and pulp wood harvesting, since leaves and fine branches contain relatively high concentrations of nutrients (Mann et al. 1988; Rytter 2002). The level of nutrient export depends on the species being removed, the timing of the harvest (c.f. deciduous vs. evergreen species), and the time interval between harvests. For example, Johnson and Todd (1987) found that whole-tree harvesting of oak (Quercus spp.) and hickory (Carya spp.) removed more nutrients from a site than whole-tree harvesting of loblolly pine (Pinus taeda L.), which has lower nutrient concentrations in its tissues. However, since loblolly pine is harvested on a shorter rotation than oak and hickory, average annual export of most nutrients is actually greater in the pine system (Johnson and Todd 1987). An important exception was calcium, which occurs in much higher concentrations in hardwoods than softwoods like loblolly pine. Calcium is critical to growth and disease resistance in plants, and its loss from ecosystems may have severe long-term effects on the structure and function of forest ecosystems (McLaughlin and Wimmer 1999). For example, low levels of soil calcium have been linked to overstory tree dieback (Horsely et al. 2002), reduced seedling survival (Juice et al. 2006), and increased virulence of some exotic fungal diseases (Holzmueller et al. 2007). Consequently, effects of

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alterations to the nutrient cycle need to be considered when developing best management practices (BMPs) for bioenergy feedstock production. One way to return calcium to harvest sites is through the application of ash as a liming agent. Depending on the process used to convert biomass to bioenergy, large quantities of ash may be created as waste. Ash application may also reduce the need for inorganic fertilizer, since it is typically rich in potassium and phosphorous, though nutrient analysis varies depending on the species burned (Sander and Andrén 1997). Because trees and plants tend to accumulate heavy metals, the resulting ash may contain high levels of heavy metals such as cadmium (Sander and Andrén 1997; Reijnders 2006). Concerns over heavy metal accumulation have prompted bans on ash disposal in some European countries (Reijnders 2006). Whole-tree harvesting could also increase leaching of nutrients and runoff since harvest residues (i.e., logging slash) are an important source of soil organic matter and help stabilize the soil surface. In some cases, major differences in leaching of nutrients and runoff have not been observed between more traditional harvesting systems and whole-tree harvesting in native forests (Mann et al. 1988). Results would likely be different on marginal sites or steep terrain and in intensively managed plantations with little or no understory vegetation present to stabilize the site after harvest (Patzek and Pimentel 2005; Reijnders 2006). Adherence to BMPs may help reduce sedimentation and erosion from biomass harvests, but more research is needed on this subject. The conversion of land currently in row crop agriculture to perennial bioenergy systems has a high potential for enhancing ecosystem stability and agro-ecological sustainability. In addition to reducing erosion and sedimentation, establishing perennial crops can increase carbon sequestration (Hansen 1993; Sartori et al. 2007), improve soil structure (Blanco-Canqui et al. 2005), and provide habitat to grassland and early successional wildlife (Figure 7.5). As with intensive cropping of native forest, intensive cropping of SRWCs may also necessitate soil amendments to avoid depleting soil calcium and other nutrients. The need for fertilization and liming may affect the balance sheet for some crops, but some pitfalls may be avoided by carefully matching cultivars to specific sites (Fike et al. 2006). Furthermore, harvest frequency for most crops will likely need to be adjusted based on site fertility and environmental conditions (Fike et al. 2006). An advantage of using polycultures (e.g., native prairie species [Tilman et al. 2006] and species-rich woodlands [Vilà et al. 2007]) is that high levels of productivity may be attained with fewer inputs. Furthermore, mixtures of warm season native prairie grasses have also been shown to sequester more carbon belowground than traditional row crops (Ma et al. 2000; Omonode and Vyn 2006). In a five-year study using perennial grasses in the Midwest, 1.1 Mg ha−1 yr−1 of carbon was added to the upper 300 cm of the soil (Gebhart et al. 1994). The deep (330 cm) root systems of native warm season grasses like switchgrass may also reduce leaching of nutrients (Ma et al. 2000); erosion losses from corn fields in Iowa were

Figure 7.5a

Figure 7.5b

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Figure 7.5c Three grasslands in southern Wisconsin representing a gradient of plant species richness: (a) switchgrass monoculture, (b) mixed warm season grass CRP planting, (c) highly diverse prairie remnant dominated by warm season grasses and forbs (photos courtesy of Christopher Webster).

70 times greater than otherwise similar fields planted in grasses (Shifflet and Darby 1985). Diverse plantings are also better buffered against climatic variations since a climate related decline in one species is typically offset by an increase in the abundance of its competitors (Tilman 1996).

Aquatic ecosystems Humans currently use approximately 26 per cent of all water available for terrestrial evapotranspiration and 54 per cent of accessible runoff (Postel et al. 1996). Much of the world faces regular scarcity of water and current trends suggest that ecological, economic and human demographic patterns will increasingly be shaped by water availability. As recently as 2002, the issue of how the large-scale cultivation of crops or woody plants for biomass would affect water supplies had not been investigated (Berndes 2002). Consequently, all future projections of increased water use are related to expansion of the food sector to meet the increasing needs of a growing population; any increases related to demands for biomass will place greater pressure on fresh-water resources. Although data on groundwater depletion in the Corn Belt of the central


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U.S. are poor, it is clear that an expanded biofuel economy will affect aquatic ecosystems in at least two ways (Keeney 2006): increased use of water for feedstock irrigation and ethanol processing, and increased runoff and erosion into proximate and distant water bodies from expanded row crops and other intensive agroecosystems used to grow feedstocks. Fresh water can be a renewable resource if the rate of use is below the rate of replenishment. However, in many parts of the U.S., aquifers are being drained at unsustainable rates. This trend will likely be exacerbated in ethanol production since ethanol plants are large water users. Intensive cultivation of row crops with fertilization contributes to environmental problems near planted areas such as ground and surface water contamination, and problems distant from cultivation such as fertilizer build up in the Gulf of Mexico hypoxia zone (NAS 2003). In addition to chemical water pollution, soil erosion and associated sedimentation are a significant byproduct of many feedstock production systems. Many parts of the U.S. experience erosion rates that are ten times faster than are sustainable (NAS 2003), and agriculture is blamed for the majority of sedimentation in aquatic ecosystems. Sedimentation of rivers, streams, and lakes remains one of the most widespread forms of pollution, affecting a large proportion of U.S. waterways. Many aquatic species are affected by sedimentation including fish, their eggs, and the invertebrates that comprise much of their diet. Runoff into water bodies adjacent to row crops can been greatly slowed by not disturbing intact native vegetation in riparian zones or by restoring denuded riparian areas with native plant communities. Switchgrass is one species that has been shown to improve water quality when it is cultivated along streams (King et al. 1998).

Wildlife The wildlife effects of using land for bioenergy depend upon the type of land being converted, the type of biomass being produced (Murray et al. 2003; Tolbert and Wright 2000), and the wildlife species being considered. From a wildlife conservation perspective, recent trends in land use suggest that new strategies are needed to prevent large areas of land from being converted from perennial grasslands to intensive corn production and from diverse native forests to plantations. In a nutshell: biomass production from diverse polycultures such as native forests and perennial grasslands is more beneficial to wildlife than bioenergy production from monoculture feedstocks such as hybrid poplar or corn. We suggest that a move of bioenergy options away from food crops and towards other cheaper or more sustainable forms of plant-based energy would be beneficial from many perspectives. For example, though the economic viability of lignocellulosic sources of ethanol awaits significant technological advances (Himmel et al. 2007), lignocellulosics remain the best feedstock option for reducing liquid fuel-related GHG emissions.

146 Forest biomass energy assessments

The importance of size and structure Every student of ecology learns that species richness increases with habitat patch size (species–area relationship) and with habitat structural diversity at many scales (habitat heterogeneity–species richness relationship). These two fundamental patterns in ecology can be used as basic but robust starting points in evaluating the biodiversity implications of natural and modified systems for biomass production. All else being equal (e.g., latitude), larger, and more heterogeneous (at the appropriate scale for the organism of interest) landscapes tend to contain more species than smaller and more homogeneous landscapes. These rather intuitive observations, when supported as they are by an enormous body of empirical research (see Brown and Lomolino 1998 for review of species-area, and Tews et al. 2004 for review of habitat structure), provide guidance for evaluating the potential impacts of different feedstock production systems. Replacing diverse grassland with a single species (e.g. corn or soybean) tends to remove available niches for all but a few generalists (e.g. red-winged blackbirds, Agelaius phoeniceus). For instance, many species that were common in Illinois’s tallgrass prairie 200 years ago no longer exist or are extremely rare in the state today (Samson and Knopf 1994). Similarly, when single species plantations replace diverse native forests in the Great Lakes region, one again sees this pattern of loss or decline in many species of arthropods, mammals, herptiles, and birds (Christian et al. 1997; Hagan et al. 1997). Anthropogenic ecosystem simplification is often coupled with a general reduction in size of remaining patches of natural habitat. Species that require larger areas (e.g. badgers [Taxidea taxus] in grassland, cerulean warblers [Dendroica cerulea] in forests or species that are sensitive to the amount of habitat edge in their home range then tend to decline. Recommendations for wildlife If biofuel production systems are ever to work in harmony with the long-term health of land and waters, such first generation feedstocks should be replaced by polyculture grasslands and forests that provide multiple benefits including biomass production and diverse habitat (Figure 7.6). For such a transition to be successful, BMPs need to be developed. These BMPs should apply to specific production systems that yield a reliable source of feedstock, while also providing habitat for viable populations of grassland or forest dependent wildlife. Management of trees or grasslands solely for biomass production will benefit a few species of wildlife but will negatively impact many others. For this reason, BMPs that consider the entire suite of forest and grassland dependent wildlife need to be considered. Such BMPs must include: 1) the species of plant being grown for biomass; 2) the production system, including the timing, frequency, and physical method of harvest across a


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Figure 7.6 Hypothetical future trends in the relative contribution of first- and secondgeneration feedstocks to U.S. ethanol output. Rapid transitions (steep slopes) are desirable to minimize negative environmental effects of firstgeneration feedstocks.

variety of latitudes; and 3) the spatial configuration of fields, stands and surrounding cover types. A basic outline for such BMPs for several current feedstocks is presented below. The recommendations presented are general in nature with the hope that they will promote further discussion and research. Specific recommendations will likely vary due to different wildlife habitat requirements in different regions of the country. Biofuel plant species or community The substitution of the native North American grass, switchgrass, is often portrayed as an improvement over corn from the perspective of GHG reduction, soil protection, and wildlife habitat. In the first two, the belowground carbon storage and perennial nature of switchgrass clearly gives it an advantage over corn (Gebhart et al. 1994). However, when grown as a monoculture, the advantages of switchgrass over corn as wildlife habitat may not be that great and require further study. As we have noted, diverse plant communities are better for diverse communities of wildlife than are monocultures. Greater plant species richness generally supports greater species diversity at higher trophic levels in both grasslands (Siemann 1998) and forests (Roth 1976). Native plant species are generally preferred over non-natives because they are not prone to become invasive, act to support local genotypes and associated

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genetic diversity, and are often more valued by the public than non-native species (Raghu et al. 2006). A major obstacle to second-generation biofuel feedstocks such as LIHD grasslands and woody species is the lack of conversion processes that are cost competitive with corn. The U.S. government and bioenergy industry can encourage progress toward polyculture feedstocks by establishing cost-share programs, and working with the conservation community to share in the cost of establishing fields with measurable benefit to wildlife and outdoor recreation. Most states have enormous experience establishing polyculture grasslands as a result of two decades of CRP plantings, and a wealth of knowledge on maintaining forest diversity and productivity over many cutting cycles. Harvesting scheme Grasslands and forests used for biomass production will be harvested and the timing, method (e.g. vegetation height in grasslands, retention or no retention of legacy trees), and proportion harvested will influence habitat structure and ultimately, the species likely to be found in a landscape. Grassland bird management guidelines derived from research in the Midwest make it clear that each grassland bird species is adapted to a particular range of habitat conditions (Herkert et al. 1993; Sample and Mossman 1997). In productive aspen forests, retention of legacy conifers or hardwoods (as opposed to uniform aspen) (Figure 7.7) would encourage use by species such as the yellow-rumped warbler (Dendroica coronata) and scarlet tanager (Pirana olivacea). Such practices that seek to maintain compositional complexity within managed forests are sometimes referred to as ecological forestry (Halpern et al. 2005). When coupled with landscape–scale considerations, ecological forestry can provide robust science-based guidance for the management of forests to meet multiple objectives (Palik et al. 2003). For both grasslands and forests, the best harvest scheme would preserve a mosaic of harvested and un-harvested fields/stands over time across a large landscape. Migratory bird species that return to find a field or stand altered since the previous summer should always be able to find desirable habitat nearby. For less vagile species such as amphibians, habitat linkages at smaller scales should be maintained. Because different bird species have diverse habitat needs and grassland field conditions in the spring can determine suitability for territory establishment, Roth et al. (2005) recommended that fields be harvested in a mosaic to preserve a diversity of thatch and stubble heights during the spring and summer breeding season. Harvest timing must consider the phenology of species of management concern. The timing of breeding and migration and the overwintering habits of any species of concern should be carefully considered. For example, harvest should not occur when most birds are at peak breeding activity in the region, also known as the primary nesting season (PNS). The PNS varies by geographic region. If it is desirable to retain grassland vegetation as winter


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Figure 7.7a

cover for resident species, early-spring harvest must occur just prior to the established PNS for each state to minimize impacts on grassland birds. For grasslands, fall harvests are typically recommended to occur after the first killing frost, well after the ending dates for the PNS for all grassland birds. Spring harvests have the added advantage of allowing storage of biomass in the field. In forests, winter harvest minimizes impacts on wildlife and soils. Some research has examined how birds utilize primary short-rotation woody biomass species including willow (Dhondt and Sydenstricker 2001; Dhondt et al. 2004) and hybrid poplar (Christian et al. 1997), but much more work is needed not only for birds, but for other taxa as well. Spatial configuration A common question for landowners concerned with conserving wildlife habitat is how much of a field or forest stand needs to be retained as a refuge to support populations of wildlife. The answer to this and related spatial questions depends upon characteristics of the surrounding landscape. In a landscape that is 80 per cent early successional aspen forest, the landowner

Figure 7.7b

Figure 7.7c


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Figure 7.7d

probably needs to do little to retain ruffed grouse (Bonasa umbellus) on the landscape. For area-sensitive grassland birds on the other hand, even the retention of 20 acres of uncut switchgrass may not be enough to attract Henslow’s sparrows (Ammodramus henslowii). Therefore, the size of plantings and the make up of the surrounding landscape, are important considerations for wildlife-loving landowners and agencies charged with maintaining biodiversity on the landscape. Because most individual landowners have smaller holdings and do not influence how their neighbors manage their lands, it often falls to land management agencies to see the big picture and work to maintain sufficient habitat on the landscape through time. When making choices between woody and grass-based lignocellulosic feedstocks, the cover type that characterized the region for the last several eons should be carefully considered. For example, much of the northern Great Lakes region has been forested for at least the last 6,000 years (Cole

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Figure 7.7e Four management scenarios for growing Populus spp. along a general gradient of low to higher plant community diversity and structural heterogeneity: (a) and (b), a structurally and compositionally simple hybrid poplar plantation in Minnesota (photo: JoAnn Hanowski), (c) a clonal aspen stand with somewhat more structural and compositional diversity, (d) clonal aspen stand regenerating with mature conifer (mostly eastern white pine [Pinus strobus]) legacy trees, (e) a clonal aspen stand regenerating under a hardwood (Acer and Quercus spp.) overstory (all other photos courtesy of Christopher Webster).

et al. 1998), while areas just to the south were dominated by prairie or savanna for a similar period. Human disturbance or the suppression of natural disturbance such as fire has altered the proportion of forest and grassland in the region so that today, we see large areas of forest in the south and much open agricultural land in portions of the northern Lake States. Species that have utilized these general cover types for thousands of years can be best supported by at least keeping trees where there have been trees and open land where it once dominated; exceptions to this might be justified for the conservation of certain rare species whose primary habitat has greatly declined. Targeting regions with existing cover that aligns with the transportation and processing facilities is one way to benefit both industrial planners and regional biodiversity.


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Sustainability of bioenergy systems Currently, the dominant transportation biofuel feedstocks are food crops, while in the direct-fire bioenergy sector the dominant feedstocks are residues from timber processing (e.g. bark, sawdust, black liquor). Both use wellestablished systems for producing, collecting, storing and processing. In bioenergy parlance, systems that depend on such sources are sometimes referred to as ‘first generation’ feedstocks. It has been clearly demonstrated that biofuel can be produced with intensive agricultural practices using annual food crops and much is known about the impact of intensive agriculture on biodiversity (Tilman et al. 2001; Raghu et al. 2006). On the other end of the environmental impact spectrum, it may be possible to produce bioenergy by sustainably harvesting biomass from low-input highdiversity systems (Tilman et al. 2006) that are similar to natural ecosystems and that support a large percentage of native biodiversity. For example, diverse polycultures have been grown on a large scale for decades as part of the CRP. Such polycultures, whether grasslands or forest, typically provide a host of ecosystem services that monoculture systems do not. Bioenergy harvest from CRP-like grasslands and other woody ecosystems could mimic natural removal of biomass by grazing or burning and would support a wide range of wildlife species. Several factors tend to change across this spectrum from intensive agriculture to relatively natural ecosystems. External inputs decrease, the longevity of the crop increases (from annual to short lived perennial, to a self-sustaining ecosystem with a mixture of annuals and perennials), the diversity of the system increases, the value of associated ecosystem services increases (e.g. water purification, wildlife habitat, carbon sequestration, inter-annual stability, pollination services, and increased soil fertility), and due to decreased inputs, the short-term yield in some areas may decrease. However, the long-term sustainability of such polyculture systems would compare favorably to the monoculture alternative (Hill et al. 2007). In nature, aspen, switchgrass and other species targeted as feedstocks grow among other species, reducing their vulnerability to pathogens compared to monocultures (Tilman et al. 2006). Additionally, if the income to landowners decreases across the spectrum, conservation payments would likely need to increase. Such payments would amount to compensation for landowners providing ecosystem services for surrounding natural and human communities (Sanchez-Azofeifa et al. 2007).

Conclusions Several clear areas of consensus emerge from this review. While most feedstock systems can be highly productive, particularly in ideal locations, they have comparatively different attributes, input requirements and impacts (Table 7.1). Overwhelming evidence suggests that to conserve biodiversity and soils, and to achieve the greatest ecosystem resiliency and GHG reductions

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Table 7.1 Comparison of biomass feedstocks in terms of productivity, availability, inputs and impacts The Ideal biomass crop?

Forest residues

LIHD perennials

Short-rotation woody crops

Monoculture grasses

Cereal grain or stover

Highly productive Widely available Site impact

no

yes

yes

yes

very

yes and unutilized low

somewhat

near none

near none

limited

low

moderate

moderate

Biodiversity impact Energy inputs Noninvasive

neutral to restorative very low

neutral to restorative low

high

high

very high high

moderate

moderate

yes

yes

usually

Few pests or disease Uses existing technology Need storage facilities

usually

yes

geneticallymodified sometimes

very high yes

usually

no

yes

yes

yes

yes

harvest year-round

yes

sometimes inefficiently harvest yearround

yes

yes

compared to fossil fuels, biomass for bioenergy should focus on lowering cultivation inputs (e.g. fertilizers, pesticides and energy), and using native forest species or perennial plant species in polycultures. Any expansion of tree plantations at the expense of diverse native forests will harm regional biodiversity. Likewise, as long as corn acres continue to expand at the expense of perennial grasslands such as CRP, this will have negative effects on almost all species of wildlife. Such an expansion of corn also contributes to increased worldwide greenhouse gas emissions and loss of biodiversity as habitats outside of the U.S. are converted to profit from higher corn prices (Fargione et al. 2008). About 20 per cent of current U.S. corn production is used to make 19 billion liters of ethanol, which replaces about 1 per cent of U.S. fossil fuel consumption (Pimentel 2005). Replacing 10 per cent of fossil fuels in the U.S. and Europe would require that 43 per cent and 38 per cent of current cropland be allocated to biomass production, respectively (IEA 2004). Existing arable lands cannot meet even this modest goal. Where then, will expansion of energy crops take place? Existing fertile grasslands and forests will undoubtedly be targeted for conversion to bioenergy crops. This is, in fact, already happening in Malaysia where tropical rainforest has been cleared for palm oil plantations for biodiesel (Hensen 2005; Dennis and Colfer 2006), and in the


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Midwestern U.S., where CRP grasslands have been plowed and planted to corn (EIA 2006; Marshall 2007). First generation biofuels have already increased conflicts between society’s need for food and fuel from the same crop, and will increase the ecological footprint of agriculture worldwide (Marshall 2007). The land needed for food production to support a larger and wealthier human population was expected to increase even before the recent push for bioenergy growth; e.g., 109 ha, an area larger than the U.S., is expected to be converted to agriculture between 2000 and 2050 (Tilman et al. 2001). For the above reasons we believe that urgent and dramatic action is needed to steer the bioenergy industry away from the monoculture paradigm and toward a sustainable system that provides a range of ecosystem values and services. What is clear is that as complex ecosystems are simplified for the benefit of one or a few species (e.g. corn, hybrid poplar, willow), ecosystem services suffer and species are lost from the ecosystem. Where do these species go? One scenario, that now seems increasingly optimistic, is that these species go to the remnant habitats spared by the expanding needs of humans for land to provide food, shelter, and now energy. Today, what seems more likely is that those species that do not thrive in a human altered landscape, and cannot even get by in remnant natural areas, will be lost or will become increasingly rare. There are several important research needs that are required for bioenergy to fulfill its promise without accelerating the depletion of ecosystem services. In addition to resolution of the biochemical portion of converting cellulosic feedstocks, research is needed on the regional and global effects of bioenergy production on the conversion of natural habitat to bioenergy production. In places where bioenergy crops expand into uncultivated lands or native forest, less land will be available for other uses, such as natural habitat for wildlife. For grasslands, research is needed to determine the habitat suitability of crop or plant community composition, harvest frequencies, refugia, stubble height, and minimal fertilization on sustainable yield and wildlife and plant diversity. In forests, similar research is needed to understand the trade-offs that may exist between production systems driven by feedstock demand and production systems informed by the broader needs of wildlife for habitat and humans for ecosystem services such as clean drinking water, and amenities such as aesthetically pleasing landscapes.

An afterword As society debates the merits of major land use and energy issues, we expect and usually eventually get an airing of important environmental considerations. In the case of bioenergy, following an exuberant period where some portrayed it as a panacea for many of our energy problems, we now appear to be entering a period of greater reflection and deliberation. Science has caught up to the boosters, and has tempered early predictions of quick

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‘green’ solutions to our energy and environmental problems. Simple solutions to complex problems such as growing U.S. dependence on foreign-source or non-renewable energy usually fall into the category of: ‘if it sounds too good to be true, it probably is’. In the words of H.L. Mencken, ‘For every complex problem there is an answer that is clear, simple, and wrong.’ Citizens comfortable with the status quo and their political representatives are usually happy to endorse solutions that require few or no lifestyle changes. The current rush to bioenergy has been driven more by provincial politics and venture capital than by deliberative science and policy. Those who care about native biodiversity and who have followed recent events in the bioenergy realm can be excused for growing cynicism. However, we would like to offer a perspective that may provide a partial antidote to such cynicism and may even give reason for optimism. Consider the Illinois tall grass prairie. Although for eons it was burned by fire and grazed by bison, it remained a sea of grass from horizon to horizon. Ecologists now recognize that ecosystems are dynamic through time. Occasionally, the typically slow change seen in ecosystems is punctuated by sudden catastrophic change as when Euro-Americans busted the prairie sod of central North America. In three short decades, the vast prairies of Illinois disappeared beneath the moldboard plow and were replaced by crops to satisfy a nation’s hunger for food, feed for livestock and the land on which to grow them. Can we imagine what the debate might have sounded like 150 years ago, as settlers were poised to convert 9 million hectares of diverse prairie and oak savanna into a highly simplified system for producing a few favored species? Although some may have recognized what was being lost in this process, there was no real debate, no deliberations on the trade-offs between land use and wildlife habitat, no conferences on the environmental impacts of replacing 10,000-year-old sod with row crops. Some have suggested that today’s push to expand bioenergy feedstocks has the potential to drive a similarly vast land use change. Yet we find reason for optimism in the following observation: nearly every other major land use change in North America has occurred without a serious consideration of its long-term ecological, economic, and societal wisdom. We suggest that the many meetings, talks, papers and books relating to biomass and bioenergy are part of something new in the history of land use policy in the U.S., a true consideration of the potential environmental and economic costs and benefits of a major land use change before it runs its course. We trust that this chapter and this volume will play a small role in this growing democratization of land and water use policy.

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8

A review of life cycle assessment studies on renewable energy derived from forest resources Qiong Zhang, Kaitlin R. Goldstein and James R. Mihelcic

Overview The U.S. government promotes the use of bio-based products and bioenergy1 with the intent to reduce foreign oil dependence, strengthen energy security, increase environmental quality and stimulate economic growth. Many life cycle assessment case studies, and several review studies, have been conducted on biomass as a source of renewable energy in the past decade, to understand the environmental impacts associated with this form of energy. However, the majority of these works have looked exclusively at the benefits of agricultural biomass as an energy source to replace fossil fuels used in transportation and have focused on the environmental impact of greenhouse gas (GHG) emissions. In this chapter we focus on the use of forest resources as an energy feedstock, investigate broader end products beyond transportation fuels, and discuss additional environmental impacts beyond GHG emissions. We provide a brief review of U.S. energy consumption, an introduction to life cycle assessment (LCA), and an overview of lignocellulosic feedstocks, conversion technologies, and bioenergy end products. Previous LCAs performed for renewable energy derived from forest resources are discussed in depth and findings and recommendations are provided for future LCA studies related to deriving bioenergy from forest resources.

Introduction U.S. energy consumption With only 4.6 per cent of the world’s population, the U.S. accounted for 22.5 per cent of the world’s total primary energy2 consumption and 22 per cent of the world’s carbon dioxide (CO2) emissions in 2004 (EIA 2006: 304, 336). The U.S. energy supply is primarily derived from fossil fuels with petroleum comprising 40 per cent, natural gas 23 per cent and coal 23 per cent. Renewable energy accounts for only 7 per cent (EIA 2007a). U.S. energy supply is clearly dependent on fossil fuels, especially petroleum; however, world oil production is predicted to peak within the next 10 to 20 years, if not

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sooner (Cavallo 2003), and developing countries are competing for fossil fuels because of continued population growth and economic expansion. For example, energy demand in China and India is projected to grow at an average annual rate of 3.2 per cent from 2004 to 2030 compared with 0.8 per cent for OECD (Organisation for Economic Co-operation and Development) countries (EIA 2007b). While important to us, the extraction, transportation, processing and combustion of fossil fuels impacts the environment in many ways (e.g. global warming, air pollution, water pollution, land pollution) and across large geographical and temporal scales. Renewable energy resources can help conserve fossil fuels, avoid some negative environmental impacts associated with use of fossil fuels, promote energy diversity and security, and provide regional economic benefits. Biomass today accounts for almost half of the U.S. renewable energy supply (Figure 8.1). This includes wood and wood-derived fuels, biofuels derived from other biomass, and waste (e.g. landfill gas, MSW conversion, and other biomass). Wood and wood-derived fuels comprise 65 per cent of the current biomass energy supply (Figure 8.1). Accordingly, it is believed they can play an important role in achieving a U.S. goal of reducing gasoline usage by 20 per cent over the next decade (White House 2007). While bioenergy holds promise for alleviating oil dependency through diversifying energy resources and mitigating global warming by lowering emissions of fossil fuel-derived CO2, a useful evaluation will require that environmental impacts be minimized over the entire life of a product, process, or service. Fossil fuel replacements must thus be examined using a cradle-to-grave approach, and energy and material input/outputs must be analyzed at all stages of the bioenergy life cycle. Consequently, LCA is a vital tool in evaluating any transition to bioenergy.

Life cycle assessment Life cycle assessment is a systematic framework optimized for the analysis or comparison of the environmental impact of products, processes, and systems

Figure 8.1 The role of renewable energy consumption in the overall U.S. energy supply. Source: adapted from EIA (2007a).

Life cycle assessment studies on renewable energy

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throughout their life cycle. The life cycle includes: 1) raw material extraction from the environment, 2) manufacturing/processing the raw material(s) to end products, 3) transportation, 4) product use or consumption, and 5) reuse/ recycle/disposal. The inherent value of this method lies in its holistic and systematic perspective. LCA comprises four basic components (ISO 14040 2006; ISO 14044 2006) as shown in Figure 8.2: 1 2

3 4

Goal definition and scope: defining the goal and scope of the study; Inventory: quantifying energy and material inputs and associated environmental releases from cradle to grave (raw material extraction to final disposal); Impact assessment: characterizing and assessing ecological and human health impacts and other effects associated with a product/process; Interpretation: analyzing the areas wherein changes should/could be implemented.

Biomass energy system For this discussion, the energy products derived from biomass will refer to heat, electricity and transportation fuels. Examples of transportation fuels are ethanol, hydrogen, methanol and Fisher–Tropsch liquids,3 which may be formed from intermediate energy carriers (e.g. syngas) produced in the conversion process(es). In general, biomass resources applied to energy production can be classified as dedicated energy crops (trees, grasses, oil plants and other crops) and biomass residues (forest, agricultural, industrial and municipal residues and wastes). Dedicated energy crops are biomass specifically cultivated for energy products and biomass residues are by-products of other activities. The biomass feedstock can also be classified based on the composition of biomass as starch based (e.g. corn, wheat), sugar based (e.g. sugarcane, sugar beet), and cellulosic (e.g. trees, grasses). Cellulosic biomass, also called lignocellulosic biomass, is composed of cellulose, hemicellulose, and lignin. Lignocellulosic biomass can be obtained from forest resources that include short rotation woody crops, paper mill residues from milling and pulping operations, forest residues from forest harvesting, thinning, clearing and fuel reduction treatments, and urban wood wastes (Chapter 1). Technologies that process lignocellulosic biomass to energy products include thermal conversion (pyrolysis, gasification, combustion, liquefaction) and biological conversion (fermentation, anaerobic digestion, hydrolysis, aerobic digestion). Figure 8.3 summarizes the various lignocellulosic biomass feedstocks, conversion technologies and resulting energy products. Figure 8.4 shows the general life cycle stages involved in a biofuel or biomass-derived electricity system.

Figure 8.2 Detailed life cycle assessment methodology. The four components of the LCA are goal definition and scoping, life cycle inventory, impact assessment, and interpretation.


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Figure 8.3 Summary of biomass feedstocks, conversion technologies, intermediate products and energy products.

Figure 8.4 General life cycle stages in a biofuel or biomass-derived electricity system.

Study features There are several high-quality reviews of the life cycle environmental performance of bioenergy. One paper (von Blottnitz and Curran 2007) incorporated 47 studies analyzing bio-ethanol for use as a transportation fuel compared with traditional fuels on a life cycle basis; however, it is restricted only to bioethanol. Other studies (Quirin et al. 2004; Larson 2005) analyzed more energy products (i.e. biogas, ethanol, biodiesel, vegetable oil, DME, MTBE, biogas, hydrogen) but are still focused on biofuels. These review studies thus ignore other energy products, which can include heat and electricity, and some studies ignore different biofuels. Assessing the life cycle impacts for different bioenergy products is important if we are to determine the most effective option for replacing fossil energy and mitigating adverse environmental impacts. Lignocellulosic biomass is abundant both domestically and internationally; its development could thus address some aspect of U.S. foreign oil dependency. The Natural Resources Defense Council (2005) estimates that with the proper pubic policy in place, lignocellulosic biomass could produce

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the equivalent of 7.9 million barrels of oil a day by 2050. This is over 50 per cent of current U.S. demand in the transportation sector (~ 14 million barrels per day in 20064) and almost four times the amount of Persian Gulf imports (~ 2 million barrels per day in 20065).

Approach The underlying objectives for reviewing LCA methodologies are to: 1) understand the environmental impacts of energy products derived from forest resources compared with energy products derived from fossil resources, and 2) provide insight into future LCA studies on lignocellulosic bioenergy in terms of inventory assumptions, result reporting, impact assessment and interpretation, and methodologies. The approach included conducting a thorough literature search, documenting LCA methods and results from reviewed LCA studies, and analyzing the documented materials to provide conclusions on environmental performance of bioenergy derived from forest resources and recommendations for future LCA studies in this area. The literature review was confined to bioenergy LCA studies with preference towards lignocellulosic biomass used either as the primary feedstock, or as one of several feedstocks. Literature was also limited to the last ten years (1997–2007). This time frame was selected because bioenergy systems, especially technologies involved in biofuel systems, are still primarily in the developmental stages and higher quality data are thus available in a more recent timeframe. Publications in peer-reviewed journals and those notable in the field of LCA were favored and the review was limited to publications in English. Three review studies were found: one on bio-ethanol (von Blottnitz and Curran 2007) and two on biofuel systems (Larson 2005; Quirin et al. 2004). Twenty-eight individual LCAs were reviewed in great detail. The feedstocks, conversion processes, end products, system boundaries, allocation methods and impact metrics of those studies are summarized in Table 8.1. The feedstocks from forest resources included poplar, willow, eucalyptus, mixed timber, spruce wood residues, fuel wood, demolition wood, sawdust, bark, and wood chips.

Results and discussion Environmental impacts of bioenergy products derived from forest resources The following challenges exist when comparing results:

• •

System boundaries differ between studies, and many studies ignore the distribution and consumption life stages that were depicted in Figure 8.4. The functional unit that was selected to compare equivalent performance

review study not specified (reviewed 40 LCA studies on liquid biofuel systems)

animal grease, canola, coconuts, cooking grease and oil, corn, lignocellulose, maize, molasses, potatoes, rapeseed, soy beans, sugar beet, sugar cane, sunflowers, wheat, wood

Biomass Feed Stock

Larson 2005

Technology corn, wheat, potatoes, cassava, lignocellulose, sugarcane, and sugar beet

Type

von Blottnitz and review study not specified Curran 2007 (reviewed 47 LCA studies on bio-ethanol systems)

Source cradle to grave (production of inputs, agriculture & harvesting, transport, conversion process, and combustion of biofuel)

ethanol

not specified biodiesel, biogas, dimethyl ether (DME), ethanol, ethyl tertiary butyl ether (ETBE), Fischer-Tropsch liquids, hydrogen, methanol, rape methyl ester (RME), soy methyl ester (RME)

System Boundary

End Product

(Continued Overleaf)

focused on energy requirement and greenhouse gas emissions

replaced fossil energy, energy yield ratios, avoided CO2 equivalent emissions, acidification, eutrophication, human toxicity, ecological toxicity, photochemical smog, solid waste, land use, water use, ozone depletion, odor

not specified

discussed six approaches to allocating coproduct credits

Metrics

Allocation Method

Table 8.1 Summary of feedstocks, conversion processes, end products, system boundaries, allocation methods and impact metrics in the reviewed studies

not specified

Kaltschmitt et al. LCA study on 1997 biofuels and biopower

Technology

extraction, fermentation, gasification, pyrolysis, Hydro Thermal Upgrading (HTU)

Type

Quirin et al. 2004 review study (reviewed 109 LCA studies on liquid biofuel systems)

Source

Table 8.1 Continued

mentioned that pollution is divided between various coupled products

cradle to grave, biogenic and fossil energy life cycles were compared

heat, electricity, RME, biodiesel, ethanol

winter wheat, triticale, barley, rye, Miscanthus, reed, cocksfoot grass, poplar, willow, rapeseed, potato, sugar beet, wheat, wheat straw, rape straw, spruce wood residues, beech wood residues, and cut grass

discussed variation of allocations

cradle to grave biodiesel, biogas, DME, ethanol, ETBE, Fischer-Tropsch liquids, HTUdiesel, hydrogen, methanol, methyl tertiary butyl ether (MTBE), pyrolysis oil, vegetable oil

Allocation Method

animal grease, canola, coconuts, cooking grease and oil, corn, lignocellulose, maize, molasses, potatoes, rapeseed, soy beans, sugar beet, sugar cane, sunflowers, wheat, wood

System Boundary

End Product

Biomass Feed Stock

energy use, CO2, NOx, SO2, N2O, acidification potential

saved primary energy, saved CO2 equivalent emissions, acidification, eutrophication, smog, ozone depletion, toxicity

Metrics

LCA study on biofuels and biopower

LCA study on biofuels and biopower

Hanegraaf et al. 1998

Elsayed et al. 2003

combustion, gasification, pyrolysis

extraction, distillation, gasification, combined heat and power, cofiring

oilseed rape, recycled vegetable oil, wood chip from forest residues, wood chip from short rotation coppice, Miscanthus, straw, wheat straw, sugar beet, wheat

biodiesel, combined heat and power, electricity, ethanol, heat, rapeseed oil

cradle to grave

heat, electricity, cradle to gate raper seed, liquid fuels sugar beet, winter wheat, sweet sorghum, silage maize, hemp, Miscanthus, poplar, willow, eucalyptus, and grass fallow

energy requirement, CO2, CH4, N2O and total greenhouse gas emissions

net energy budget, greenhouse gas emissions, acidification, ozone depletion, minerals, pesticides, erosion, water use, fertilizer use, waste, biodiversity, costs, and employment


based on the relative economic value of the primary product and coproducts

not specified

Type

LCA study on biobased polymer, biofuel and biopower

LCA study on automobile fuels

An environmental evaluation of biofuel and how LCA can be used to assess the environmental impacts

Source

Dornburg et al. 2004

MacLean et al. 2000

Puppan 2002

Table 8.1 Continued

biodiesel, ethanol

rapeseed, sugar beet, winter wheat, potato

base catalyzed transesterification, fermentation

not specified

cradle to grave

not specified

not specified

not specified

cradle to gate

biobase polymers, natural fiber composites, ethanol, electricity

potato, corn, sugar beet, flax, hemp, Miscanthus, short rotation woody crops

corn, ethanol, herbaceous biodiesel biomass, woody biomass, soybean

Allocation Method

System Boundary

End Product

Biomass Feed Stock

dry milling for corn ethanol

fermentation, pretreatment + fermentation, combustion + steam cycle, biomass integrated gasification with combined cycle

Technology

depletion of abiotic resources, climate change, acidification, eutrophication, photochemical oxidants formation, ozone depletion, human and ecotoxicity, wastes

global warming potential, energy use, fertilizer use, fuel expenditure

energy and greenhouse gas emissions

Metrics

LCA study on ethanol

LCA study on ethanol

LCA study on transportation fuels

Sheehan et al. 2004

Hu et al. 2004

Delucchi 2005

not specified

not specified

fermentation

wood, grass, soybeans, corn

cassava

corn stover

hydrogen, methanol, ethanol, methane, propane, electricity

ethanol

ethanol

not specified

cradle to grave

energy use, criteria pollutant emissions including VOCs, NO, NOx, PM10, and SOx, greenhouse gas emissions

life cycle cost, emissions of CO2, CO, HC, NOx, PM, energy consumption, the combined economic, environment and energy indicator

energy use, soil sustainability, climate change, air quality, cost


not specified

allocation of life-cycle flows between corn grain and corn stover production

cradle to grave

cradle to grave

corn, woody biomass, herbaceous biomass

LCA study on advanced fuels/ vehicle systems

Brinkman et al. 2005

electrolysis of water, fermentation, Fischer-Tropsch process

wet milling, dry corn stover, milling, soybean corn grain, milling soybean

LCA study on bioethanol and biodiesel

virgin timber, recycled newspaper

Biomass Feed Stock

Kim and Dale 2005

Technology

fermentation

Type

Kemppainen and LCA study on Shonnard 2005 ethanol

Source

Table 8.1 Continued

cradle to grave

cradle to gate

System Boundary

cradle to grave methanol, ethanol, Fischer-Tropsch (FT) diesel, hydrogen, and electricity

ethanol, biodiesel

ethanol

End Product

among products at the level of individual refining processes

allocate environmental burdens to biofuels and their coproducts using system expansion approach

not specified

Allocation Method

energy consumption, greenhouse gas emissions, criteria pollutant emissions, including VOCs, NO, NOx, PM10, and SOx

nonrenewable energy consumption, global warming impact, acidification, eutrophication

energy consumption, fish toxicity, human toxicity, global warming, smog formation, acidification

Metrics

LCA study on biogas

anaerobic digestion

cradle to grave

cradle to gate

biogas manure-pig, manure-cow, grease separator sludge, ley crops, municipal organic waste, slaughterhouse waste, tops and leaves of sugar beet, straw

ethanol

Berglund and Börjesson 2006

wheat

cradle to gate

cradle to gate

not specified

corn, ethanol, switchgrass, biodiesel wood, soybean, sunflower

native grassland ethanol, perennials synfuel, electricity

LCA study to compare large and small scale production of ethanol

Bernesson et al. 2006

not specified

not specified Tilman et al. 2006 Evaluation of biofuels from low-input highdiversity grassland biomass

LCA study on ethanol and biodiesel

Pimentel and Patzek 2005

energy balance

energy balance, greenhouse gas reduction, fertilizer use, pesticide use

energy requirements, global warming potential, acidification, eutrophication, photochemical ozone creation potential

energy outputs and fossil energy inputs


looked at different mass allocation methods

not specified

looked at physical and economic allocations

not specified

LCA study on biogas

Börjesson and Berglund 2006

biomass residue, hybrid poplar

vegetable oil, soybean

Bain et al. 2003

LCA study on biomass cobiomass derived firing, biomass electricity gasification, combustion

End Product

electricity

electricity

green diesel, biodiesel

biogas municipal organic waste (sorted), food industry waste, ley crops, straw, tops and leaves of sugar beet, liquid manure (pig)

Biomass Feed Stock

switchgrass

UOP/Eni EcofiningTM process

anaerobic digestion

Technology

Ney and Schnoor Incremental combustion 2002 LCA on biomass derived electricity

Kalnes et al. 2007 LCA study on biodiesel and green diesel

Type

Source

Table 8.1 Continued

cradle to gate

cradle to gate

cradle to grave

cradle to grave

System Boundary

not specified

not specified

allocate based on relative mass production rates of various products

different allocation methods based on mass

Allocation Method

fossil energy consumption, CO2 emissions

bioenergy emission, bioenergy sequestration, fossil emission avoided

total energy consumption, fossil energy consumption, greenhouse gas emissions

emissions of CO2, CO, NOx, SO2, HC, CH4, particles

Metrics

LCA study on a not specified willow bioenergy cropping system

Evaluation of biomass derived electricity technology

Heller et al. 2004

Corti and Lombardi 2004

integrated gasification combined cycle with CO2 chemical absorption process

LCA study on not specified biomass derived electricity

Mälkki and Virtanen 2003

electricity

electricity

willow

dry poplar

logging and electricity sawmill residues

cradle to gate

cradle to gate

cradle to gate

greenhouse effects

primary energy consumption, energy ratio, CO2, global warming potential, acidification potential, eutrophication potential

greenhouse gases emissions including CO2, N2O, CH4, acidic emissions including NOx, SO2, particulate matter, oil releases, energy efficiency indicators


not specified

not specified

not specified

Type

LCA study on biomass derived electricity technology

LCA study on biomass heat

Source

Carpentieri et al. 2005

Raymer 2006

Table 8.1 Continued

combustion

integrated gasification combined cycle with CO2 chemical absorption process

Technology electricity

poplar

heat fuel wood, sawdust, pellets, briquettes, demolition wood, and bark

End Product

Biomass Feed Stock

cradle to grave

cradle to gate

System Boundary

not specified

not specified

Allocation Method

avoided greenhouse gas emissions

greenhouse effect, ozone layer depletion, acidification, eutrophication, heavy metal, winter/ summer smog, carcinogenic substances, pesticides, energy, solid waste

Metrics

LCA study on biomass cobiomass derived firing, biomass electricity gasification, combustion

Spath and Mann 2004

steam turbine power generation, straw-fired heating generation

LCA methods for renewable energy technologies

Pehnt 2006

not applicable

LCA methods for renewable energy technologies

Delucchi 2004

electricity, heat

electricity, heat

wood, straw

urban biomass, biomass energy crop

not applicable

not applicable

cradle to gate

not specified

not applicable

not specified

not specified

not applicable

CO2 emissions, energy balance, cost associated with CO2 sequestration or storage and transport

greenhouse gas emitted, iron ore requirements, finite energy resources, acidification

compared ideal LCA model (policy, prices, environmental systems is included) with conventional LCA model for transportation and climate change

180

•

•

Forest biomass energy assessments between product systems varied from ‘per unit of energy produced’ to ‘per kilometer driven’ or ‘per land area used’. Because different products are investigated and the energy produced is the common basis for comparison, our results are reported relative to ‘units of energy produced’ or ‘land area used’ to provide a land use perspective. Most studies conduct impact assessments that are restricted to an energy balance and GHG emissions. Therefore, quantitative results were presented for these two categories and qualitative analysis was documented for other impact categories, such as acidification, eutrophication, human toxicity, and ecotoxicity. Other issues that differ include: 1) allocation methods (allocating environmental burdens based on mass stoichiometry or energy content or market value, system expansion to include co-product functions), 2) temporal variation of data (averaging data over certain period or current data), 3) geographic coverage of data collection (local, regional or global), and 4) technological properties of data (averaging different technologies or using best available technology).

Energy balance The replaced fossil energy is used to compare the life cycle energy requirement for different energy products. It is calculated as: Replaced fossil energy = EFF + Ebyproduct − EFB,

(8.1)

where EFF is the total fossil energy required to provide an equivalent performance of fossil-based products, Ebyproduct is the energy output from co-products during the biomass conversion, and EFB is the total fossil energy required to produce the bioenergy products. A positive value for replaced fossil energy means that from a life cycle perspective, bioenergy systems consume less fossil energy compared with their fossil energy counterparts. Bioenergy products and their fossil energy counterparts are listed in Table 8.2. For studies that do not report their energy balance as the replaced fossil energy, the results are converted based on equation 8.1. The range of the replaced fossil energy for bioenergy products derived from forest resources is shown on a per energy produced basis in Figure 8.5a and a per hectare land use basis in Figure 8.5b. The positive values shown in Figure 8.5 indicate that energy balances are favorable for bioenergy derived from forest resources when compared to their fossil energy counterparts. In Figure 8.5, values for biofuels (ethanol, hydrogen, methanol, Fischer–Tropsch liquids and dimethyl ether) are based on lignocellulosic biomass. This is because some biofuel studies use the term ‘lignocellulose’ without specifying the feedstocks, and some studies perform their analysis based on a mixture of herbaceous biomass and woody biomass (e.g. 50 per cent herbaceous and 50 per cent woody biomass in Brinkman et al. 2005).

Figure 8.5a

Figure 8.5b Replaced fossil energy for different bioenergy products derived from forest resources: (a) energy basis, (b) land use basis.

182

Forest biomass energy assessments Table 8.2 Bioenergy product and fossil energy counterparts Bioenergy

Fossil energy counterpart

Heat Electricity Ethanol Hydrogen Methanol Fischer-Tropsch liquids Dimethyl ether

Heating oil Coal electricity Gasoline Gasoline Gasoline Fossil diesel fuel Fossil diesel fuel

The results from the study of Pimentel and Patzek (2005) are not included in Figure 8.5 because they differed greatly from other studies, for example by incorporating energy input from human labor, using dated information for fertilizer, herbicides and insecticides, including high farm machinery energy values, and importantly, ignoring the energy credit obtained from co-products. Co-products are important because they can displace other products (e.g. fossil-based electricity, animal feed) and thereby save the energy required to manufacture or distribute those products. In fact, a sensitivity analysis conducted by Farrell et al. (2006) showed that net energy calculations are most sensitive to assumptions about co-product allocation. For example, for corn ethanol, the net energy for the studies with correct coproduct allocation is approximately 4 MJ to 9 MJ per liter ethanol higher than studies that ignore co-products. The allocation in developing the life cycle inventory is an absolute requirement to comply with the ISO standards. From both an energy (Figure 8.5a) and land use (Figure 8.5b) perspective, the biomass-to-electricity route replaces the largest amount of fossil energy: combustion: 2.5–3.46 MJ per MJ energy produced; gasification: 2.9–3.8 MJ per MJ energy produced and 118–200 GJ per hectare; and pyrolysis: 2.7–2.8 MJ per MJ energy produced. Biomass co-firing is not compared with other electricity generation technologies because the results are highly dependent on the percentage of co-firing obtained with biomass feedstock. Combined heat and power (electricity) also replace a large amount of fossil energy (2.9–3.0 MJ per MJ energy produced). Coal is the fossil-based counterpart for biomass derived electricity (Table 8.2). Coal is the most popular fossil source for electricity production in the U.S. The fossil energy requirement for a coal fired power plant ranges from 3.08 MJ (Elsayed et al. 2003) to 4.05 MJ (Carpentieri et al. 2005) per MJ of electricity. From an energy perspective for biofuels, ethanol obtained from forest resources consumes less fossil resources and replaces more fossil energy (0.6–1.4 MJ per MJ energy produced) compared with other biofuels. Using biomass for heat to replace heating oil is comparable to ethanol (0.8–1.4 MJ


183

per MJ energy produced). However, Figure 8.5b shows that from a land use perspective, methanol is more efficient than ethanol in replacing fossil energy (30–260 GJ ha−1). This is because the number for methanol is from the study of Quirin et al. (2004) and they specify lignocellulose as cultivated biomass for methanol production. Greenhouse gas emissions Figure 8.6 compares the avoided GHG emissions over the life cycle for energy products derived from forest resources. GHG emissions are reported in units of CO2 equivalents – a measure used to compare the emissions from different greenhouse gases based upon their global warming potential (GWP). For a given mass of GHG emissions, the CO2 equivalent is calculated by multiplying the mass of emissions by the GWP of the specific gas. The results from the study of Delucchi (2005) are not included in Figure 8.6 because the method he used to estimate the emissions for land conversion is different than the method used by the Intergovernmental Panel on Climate Change (IPCC). The uncertainty in estimating N2O emissions associated with land conversion can have a large impact on the results. This is because 1 g N2O is equivalent to 310 g CO2. The calculation of the avoided GHG emissions is determined as: Avoided GHG Emissions = GHGFF + GHGbyproduct − GHGFB

(8.2)

where GHGFF is the life cycle GHG emissions of a fossil-based product with equivalent performance, GHGbyproduct is the total GHG emissions associated with the production of the co-products from a fossil-based resource, and GHGFB is the GHG emissions associated with the life cycle of a bioenergy product. Comparing Figure 8.6 with Figure 8.5 shows that the avoided emissions of GHGs correlate very well with the energy balances. Similar conclusions that were drawn from the Figure 8.5 energy balances can be applied to the avoided GHG emissions summarized in Figure 8.6. For example, from an energy perspective, the biomass to electricity pathway saves the largest amount of equivalent CO2 emissions (combustion: 137–394.4 g CO2 equivalent per MJ energy produced; gasification: 154–269.4 g CO2 equivalent per MJ energy produced and 8,000–22,500 kg CO2 equivalent per hectare) and the route of biomass to heat is comparable to biomass to transportation fuels. Figure 8.6 shows that bio-ethanol is again more favorable over other biofuels derived from lignocellulosic biomass. However, from a land use perspective, methanol has higher avoided GHG emissions (6,000–15,000 kg CO2 equivalent per hectare) than ethanol.

Figure 8.6a

Figure 8.6b Avoided GHG emissions for different bioenergy products derived from forest resources: (a) energy basis, (b) land use basis.


185

Other impact categories Other life cycle impact categories that have been considered are resource depletion, acidification, eutrophication, ozone depletion, photochemical oxidants/smog, particulate matter, air quality, human toxicity, ecological toxicity, soil erosion, biodiversity, land use, and water use. For producing heat from biomass, one study (Kaltschmitt et al. 1997) evaluated the impact of acidification in addition to energy and greenhouse gas emissions. This study indicated an advantage for biomass compared with heating oil. For production of bio-electricity, one study (Hanegraaf et al. 1998) quantified the acidification potential for different energy crops to electricity through gasification. The results showed that the avoided SO2 equivalent per hectare is negative for the tree species of poplar and willow. This means the bio-route is not favourable from an acidification standpoint compared with fossilbased electricity. Another study (Carpentieri et al. 2005) assessed ozone depletion, acidification, eutrophication, smog formation, solid wastes, heavy metals and carcinogenic substances. In all categories except solid waste, the fossil counterpart (coal) held advantages compared with biomass (Figure 8.7). Table 8.3 shows that for converting biomass to a transportation fuel such as bio-ethanol, the results are mixed. In general, the impacts of acidification, eutrophication, and ozone depletion are worse for the biofuels derived from cultivated biomass than for their fossil counterpart (gasoline). Nitrogen and phosphorus emissions during the cultivation phase of biomass are the primary contributors to soil acidification and eutrophication (Sheehan et al. 2004; Bernesson et al. 2006; Kim and Dale 2005). Within cultivation, the steps that contribute the most to emissions were fertilizer application and soil emissions. Fertilizer usage contributed 51–68 per cent of the environmental impacts (Bernesson et al. 2006) and was the dominant impact in six of ten environmental categories analyzed. Fertilizer production is also extremely energy intensive. For example, in the case of using willow as a source of biomass, the production of inorganic fertilizer was responsible for 40 per cent of the energy production cost (Heller et al. 2003). Air emissions were also primarily attributed to crop farming and fertilization. Heller et al. stated that the usage of treated wastewater sewage sludge (biosolids) as nutrients in cultivation can increase the net energy ratio (harvested biomass energy at the farm gate divided by fossil energy consumed during production) by 40 per cent. For biofuels derived from agricultural, forest and paper mill residues or wastes, the impacts need to be examined on a case-by-case basis. In particular for ethanol derived from forest resources, where biological processes are used for biomass conversion (as was shown in Figure 8.4), the biomass must be pretreated to liberate the cellulose from the crystalline structure of the lignin. The impacts associated with use of pretreatment chemicals, such as ammonia and sulfuric acid, must be carefully evaluated. Ammonia can be harmful to aquatic ecosystems at low aqueous concentrations and

186


Figure 8.7a

Figure 8.7b Other environmental impacts for biomass-derived electricity compared with coal electricity: (a) solid waste (kg MJ−1), summer smog (kg C2H4 eq. MJ−1), winter smog (kg SPM eq. MJ−1), eutrophication (kg SO2 eq. MJ−1), acidification (kg SO4 eq. MJ−1), (b) carcinogenic substances (kg B(a)P eq. MJ−1), heavy metals (kg Pb eq. MJ−1), ozone depletion (kg CFC11 eq. MJ−1). Source: Carpentieri et al. (2005).

using sulfuric acid increases sulphur oxide emissions associated with burning residues from the process. Life cycle assessment methodology Variability of LCA results LCA results vary significantly among studies and thus require examination of details of each individual study with respect to the system boundary, input assumptions and allocation method so studies can be compared in a meaningful way. As pointed out by von Blottnitz and Curran (2007), many studies on bio-ethanol were just ‘cradle-to-gate’ analyses and did not include the biofuel’s entire life cycle. This is especially true for studies on electricity,

NA ↑

NA

↑

↑ ↓

Photochemical smog

Ozone depletion

NA : Not assessed – No significant change

NA

NA

Source: von Blottnitz and Curran (2007).

NA

NA

↓

NA

↑ NA

NA

↑ NA

↓

China

Cassava

↓

Europe

Sugar beet, wheat, potato

: Increased impact for bio-ethanol : Decreased impact for bio-ethanol

–

NA

–

–

Acidification

NA

↓

↓

Resource depletion

Eutrophication

Germany

Germany

Human toxicity

Sugar beet, winter wheat, potato

Sugar beet, wheat, potato

Impact category

Agricultural crops

Feedstock

NA

NA

↓

↑

NA NA

↓

↑

↓

U.S.A.

Corn stover

↓

↓

↓

India

Waste bagasse

Residues/wastes

Table 8.3 Life cycle impact categories for bio-ethanol compared to conventional fuel in seven studies

NA

↓

↑

↑

↑

↓

Philippines

Agricultural cellulosic wastes

188


where few studies extended electricity generation to the life stages of distribution and use. Various assumptions applied to the data collection, process technology and allocation of co-products may also lead to the different conclusions. For example, Pimentel and Patzek (2005) concluded that ethanol produced from corn, switchgrass, and woody biomass required 29 per cent, 50 per cent and 57 per cent more fossil energy, respectively, than the energy ethanol fuel produced. This was because assumptions on corn yields, ethanol conversion technology efficiency, fertilizer application rates, and co-product evaluation contributed to high fossil energy inputs. Furthermore, Börjesson and Berglund (2006) investigated three approaches for allocating the diluting requirements and energy input at a biogas plant and found the energy input in a biogas system based on straw decreased 75 per cent just by using a different allocation method. It is important thus for all LCA studies to use state-of-the-art data and technology, clearly state all assumptions on data input, and provide a justification for the allocation method used. Reporting of LCA results As discussed previously, the functional unit differs for LCA studies and results are thus expressed differently. This impacts the interpretation of the results and the resulting conclusions. Fortunately, because all LCA studies have bioenergy production included in their system boundaries, nearly all these studies report results on the basis of per MJ of bioenergy produced. Several studies on biofuels extend the analysis to the consumption stage and express the results on a per vehicle km basis. Even so, only a few studies (Kaltschmitt et al. 1997; Hanegraaf et al. 1998; Dornburg et al. 2004; Quirin et al. 2004; von Blottnitz and Curran 2007) reported the results on a per hectare basis to examine land use efficiency. The results of energy balances and GHG emissions are also reported in different ways. For instance, energy results can be expressed as a net energy balance (sum of all energy outputs minus the sum of fossil energy inputs) or net energy balance ratio (sum of energy outputs divided by the sum of fossil energy inputs) (Tilman et al. 2006). However, because the primary goal of bioenergy is to replace fossil energy and mitigate impacts of global warming, it is of great interest to report the results in terms of replaced fossil energy and avoided GHG emissions (Quirin et al. 2004; von Blottnitz and Curran 2007). The findings based on this type of reporting are dependent on the type of fossil fuel counterparts. For example, the maximum replaced fossil energy for electricity via gasification is 3.5 MJ per MJ energy produced (Bain et al. 2003) if coal is considered as the fossil counterpart. This value will be much lower (2.3 MJ per MJ energy produced) if natural gas is the fossil counterpart. Some of the assessments also do not include a benchmark fossil fuel comparison in the reporting emissions, energy yield, and efficiency.


189

Impact and improvement assessment Few LCA studies evaluate environmental impacts beyond GHG emissions and nonrenewable fossil fuel usage; only two out of ten studies on biomassderived electricity, and 12 out of 109 on biofuels (reviewed in Quirin et al. 2004). As previously noted, the route to bioenergy appears to be unfavorable in terms of acidification, ozone depletion and eutrophication. To understand the environmental performance and trade-offs of bioenergy in a more complete manner, it is critical to evaluate broad environmental impacts. Unfortunately, while there is a lack of studies that assess broad environmental impacts, there are even fewer studies that investigate the effects of materials or process improvements over different life stages. For example, Kemppainen and Shonnard (2005) incorporated process improvement strategies and found that heat integration has the potential to reduce fossil energy consumption to an extent that the fossil energy required over the life cycle is negative per unit of ethanol produced. For bioenergy derived from forest resources, it is thus important not only to compare alternative products, but also to investigate the potential process or technology improvements that will minimize environmental impacts. Integrated assessment The utilization of bioenergy, especially biofuels derived from lignocellulosic biomass, also requires environmental, economic, and social analyses to understand economic feasibility and impact, environmental impact, and social desirability of the large-scale development of such energy resources. Among all studies reviewed, only four (Hanegraaf et al. 1998; Hu et al. 2004; Quirin et al. 2004; Sheehan et al. 2004) out of 34 evaluated the costs of biofuels and impact of their particular feedstock(s). As the concept of sustainable development (Mihelcic et al. 2003) has emerged and received increased attention by the global community, a balanced concept of sustainability should be introduced into LCAs that carefully integrates economic and social issues with environmental impacts. Delucchi (2004) proposed a conceptual LCA of transportation fuels (Figure 8.8). In the ideal model, the impact of policy actions, such as 10 per cent ethanol mandates, should be evaluated and dynamic economic linkages among all sectors should be included. This is because when a policy action is implemented, it will have direct impact on production, consumption of energy and materials, land use, economics, and emissions. Dynamic approach Current LCA studies evaluate environmental burdens over the life cycle using a static approach. This approach is not appropriate for lignocellulosicderived biofuels, which are in the developmental stage. In this case, the

Source: Delucchi (2007).

Figure 8.8 Conceptual LCA model compared with conventional LCA for evaluating biofuels.


191

efficiency, materials, and technologies involved over the life cycle will change and invariably improve as time goes on. For example, advances in bioprocessing and engine/vehicle technologies should be considered in the LCA. Collaboration between technology, social science, and economic researchers is thus critical to incorporate new information in the analysis. For example, chemical engineering researchers could predict the evolution of process efficiency for biofuels obtained from forest biomass. This information could then be used to develop a future scenario for the inventory of materials and emissions. Pehnt (2006) points out that an LCA should include a time-line with an impact assessment. Predictions for technological improvements within this time line can then be provided and integrated in the LCA. For this type of assessment, the time-dependent parameters that are environmentally relevant should also be identified and extrapolated into the future. The assessment is then conducted for the future scenarios.

Conclusions Environmental impact of lignocellulosic bioenergy In almost every study, on a life cycle basis, fossil energy demand and GHG emissions were significantly lower for bioenergy. This finding has caused many authors to claim that it is unnecessary to perform detailed GHG assessments, stating that it is appropriate to rely on previous work. In contrast, examining environmental stressors such as acidification and eutrophication generally favour fossil fuel counterparts. No broad conclusions can be made on the impact of smog formation and there is a large knowledge gap concerning the release of toxic substances over the life cycle of bioenergy. The impacts of acidification, eutrophication and air quality due to the use of fertilizers and pretreatment chemicals (e.g. ammonia and sulphuric acid) also emerge as a consistent finding that should be taken into consideration. Life cycle assessment As discussed previously, the existing LCA studies present a wide range of results because of differences in the system boundary, input assumptions and allocation methods that were used. The most representative studies are those that cover the complete life cycle, use a transparent allocation method, and include co-products and state-of-the-art data. Most LCA studies report the results on an energy basis and few studies use a per hectare basis to examine the efficiency of land use. In terms of impact and improvement assessment, very few studies have thoroughly accounted for broad environmental impacts and incorporated process or technology improvement in the LCA. Although conceptual LCA modeling, with incorporation of policy action and prices has been proposed, no integrated assessment has yet to be conducted.

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Recommendations Existing LCA studies have done well analyzing energy balances and GHG emissions. The results in these categories thus need not be repeated. Impact assessment should, however, go beyond energy and GHG emissions. As fertilizer use and associated issues of water quality impairment have emerged as significant issues, future LCAs should focus on eutrophication, soil erosion and impact on the land and water supply in addition to acidification, ozone depletion, smog formation, human toxicity and eco-toxicity. Researchers should also include emerging environmental stressors (e.g. water scarcity, biodiversity, introduction of invasive plant species). The assessment should also be a balanced approach that integrates social and economic implications of a bio-energy choice to the environmental impact. The different cultivation and harvesting practices, process, and engine/vehicle configurations need to be incorporated into the assessment as well to identify the opportunities to make improvement. Finally, a dynamic approach should be applied to predict the impact of future scenarios.

Notes 1 Bioenergy is energy in the form of heat, electricity and fuels generated from biomass. 2 World primary energy consumption includes consumption of petroleum products (including natural gas plant liquids, and crude oil burned as fuel), dry natural gas, and coal (including net imports of coal coke); and the consumption of net electricity generated from nuclear electric power, hydroelectric power, wood, waste, geothermal, solar, and wind (EIA 2006). 3 Fisher–Tropsch liquids are liquid hydrocarbons produced from a catalyzed chemical reaction in which carbon monoxide and hydrogen are converted into aliphatic straight-chain hydrocarbons plus water. 4 From EIA energy data: U.S. consumption of petroleum in 2006 was 20.6 million barrels per day, the share of consumption for transportation was 68 per cent. 5 From EIA energy data: U.S. consumption of petroleum in 2006 was 20.6 million barrels per day, net import as share of consumption was 60 per cent and the share of U.S. imports from the Persian Gulf was 16 per cent.

References Bain, R.L., Amos, W.A., Downing, M. and Perlack, R.D. (2003) Highlights of Biopower Technical Assessment: State of the industry and the technology, National Renewable Energy Laboratory. NREL/TP-210-33502, Golden, CO. Berglund M. and Börjesson, P. (2006) ‘Assessment of energy performance in the life-cycle of biogas production’, Biomass & Bioenergy, 30: 254–266. Bernesson, S., Nilsson, D. and Hansson, P. (2006) ‘A limited LCA comparing largeand small-scale production of ethanol for heavy engines under Swedish conditions’, Biomass & Bioenergy, 30: 46–57. von Blottnitz, H. and Curran, M.A. (2007) ‘A review of assessments conducted on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and environmental life cycle perspective’, Journal of Cleaner Production, 15: 607–619.


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Börjesson, P. and Berglund, M. (2006) ‘Environmental systems analysis of biogas systems, part I: fuel-cycle emissions’, Biomass & Bioenergy, 30: 469–485. Brinkman, N., Wang, M., Weber, T. and Darlington, T. (2005) Well-to-Wheels Analysis of Advanced Fuel/Vehicle Systems – A North American study of energy use, greenhouse gas emissions, and criteria pollutant emissions, General Motors Corporation/ Argonne National Laboratory/Air Improvement Resources, Inc. Carpentieri, M., Corti, A. and Lombardi, L. (2005) ‘Life cycle assessment (LCA) of an integrated biomass gasification combined cycle (IBGCC) with CO2 removal’, Energy Conversion and Management, 46: 1,790–1,808. Cavallo, A.J. (2003) ‘Predicting the peak in world oil production’, Natural Resources Research, 11: 187–195. Corti, A. and Lombardi, L. (2004) ‘Biomass integrated gasification combined cycle with reduced CO2 emissions: performance analysis and life cycle assessment (LCA)’, Energy, 29: 2,109–2,124. Delucchi, M.A. (2004) ‘Conceptual and methodological issues in lifecycle analyses of transportation fuels’, Institute of Transportation Studies, University of California, Davis, UCD-ITS-RR-04-45. —— (2005) ‘A multi-country analysis of lifecycle emissions from transportation fuels and motor vehicles’, Institute of Transportation Studies, University of California, Davis UCD-ITS-RR-05-10. —— (2007) ‘Lifecycle analysis of CO2-equivalent greenhouse-gas emissions from biofuels’, Powerpoint slides used for Transportation Technology and Policy (TTP) class at University of California, Davis. Dornburg, V., Lewandowski, I. and Patel, M. (2004) ‘Comparing the land requirements, energy savings, and greenhouse gas emissions reduction of biobased polymers and bioenergy: an analysis and system extension of life-cycle assessment studies’, Journal of Industrial Ecology, 7: 93–116. Elsayed, M.A., Matthews, R. and Mortimer, N.D. (2003) ‘Carbon and energy balances for a range of biofuel options’, B/B6/00784/REP URN 03/836, Resources Research Unit, Sheffield Hallam University, UK. Energy Information Administration (EIA) (2006) Annual energy review 2006, DOE/ EIA-0384 (2006). U.S. Department of Energy, Washington, D.C. Available: http:// www.eia.doe.gov/cneaf/solar.renewables/page/prelim_trends/rea_prereport.html (accessed 3 March 2008). —— (2007a) Renewable energy consumption and electricity, preliminary 2006 statistics. U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/ cneaf/solar.renewables/page/prelim_trends/rea_prereport.html (accessed 3 October 2007). —— (2007b) International energy outlook 2007, DOE/EIA-0484 (2007). U.S. Department of Energy, Washington, D.C. Available: http://www.eia.doe.gov/oiaf/ ieo/index.html (accessed 25 February 2008). Farrell, A.E., Plevin, R.J., Turner, B.T., Jones, A.D., O’Hare, M. and Kammen, D.M. (2006) ‘Ethanol can contribute to energy and environmental goals’, Science, 311: 506–508. Hanegraaf, M., Biewinga, E.E. and van der Bijl, G. (1998) ‘Assessing the ecological and economic sustainability of energy crops’, Biomass & Bioenergy, 15: 345–355. Heller, M. C., Keoleian G.A. and Volk T.A. (2004) ‘Life cycle assessment of a willow bioenergy cropping system’, Biomass & Bioenergy, 25: 147–165. Hu, Z., Pu, G., Fang, F. and Wang, C. (2004) ‘Economics, environment, and energy

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life cycle assessment of automobiles fueled by bio-ethanol blends in China’, Renewable Energy, 29: 2,183–2,192. ISO 14040 (2006) Environmental Management – life cycle assessment -principles and framework, International Standard Organization (ISO), Geneva. ISO 14044 (2006) Environmental Management – life cycle assessment -requirements and guidelines, International Standard Organization (ISO), Geneva. Kalnes, T., Marker, T. and Shonnard, D.R. (2007) ‘Green diesel: a second generation biofuel’, International Journal of Chemical Reaction Engineering, 5 (article A48). Kaltschmitt, M., Reinhardt, G.A. and Stelzer, T. (1997) ‘Life cycle analysis of biofuels under different environmental aspects’, Biomass & Bioenergy, 12: 121–134. Kemppainen, A.J. and Shonnard, D.R. (2005) ‘Comparative life-cycle assessments for biomass-to-ethanol production from different regional feedstocks’, Biotechnology Progress, 21: 1,075–1,084. Kim, S. and Dale, B.E. (2005) ‘Life cycle assessment of various cropping systems utilized for producing biofuels: bioethanol and biodiesel’, Biomass & Bioenergy, 29: 426–439. Larson, E.D. (2005) ‘A review of LCA studies on liquid biofuel systems for the transport sector’, presented at the GEF/STAP Workshop on Liquid Biofuels for the Transport Sector, 29 August–1 September, New Delhi. MacLean, H.L., Lave, L.B., Lankey, R. and Joshi, S. (2000) ‘Life-cycle comparison of alternative automobile fuels’, Journal of the Air and Waste Management Association, 50: 1,769–1,779. Mälkki, H. and Virtanen, Y. (2003) ‘Selected emissions and efficiencies of energy systems based on logging and sawmill residues’, Biomass & Bioenergy, 24: 321–327. Mihelcic, J.R., Crittenden, J.C., Small, M.J., Shonnard, D.R., Hokanson, D.R., Zhang, Q., et al. (2003) ‘Sustainability science and engineering: emergence of a new metadiscipline’, Environmental Science and Technology, 37: 5,314–5,324. Natural Resources Defense Council (2005) ‘A responsible energy plan for America’, New York. Ney, R.A. and Schnoor, J.L. (2002) ‘Incremental life cycle analysis: using uncertainty analysis to frame greenhouse gas balances from bioenergy systems for emission trading’, Biomass & Bioenergy, 20: 257–269. Pehnt, M. (2006) ‘Dynamic life cycle assessment (LCA) of renewable energy technologies’, Renewable Energy, 31: 55–71. Pimentel, D. and Patzek, T.W. (2005) ‘Ethanol production using corn, switchgrass, and wood; biodiesel production using soybean and sunflower’, Natural Resources Research, 14: 65–76. Puppán, D. (2002) ‘Environmental evaluation of biofuels’, Periodica Polytechnica, 10: 95–116. Quirin, M., Gartner, S.O., Pehnt, M. and Reinhardt, G.A. (2004) CO2 Mitigation through Biofuels in the Transport Sector: Status and Perspectives – Main Report, Institute for Energy and Environmental Research (IFEU), Heidelberg. Raymer, A.K.P. (2006) ‘A comparison of avoided greenhouse gas emissions when using different kinds of wood energy’, Biomass & Bioenergy, 30: 605–617. Sheehan, J., Aden, A., Paustian, K., Killian, K., Brenner, J., Walsh, M. and Nelson, R. (2004) ‘Energy and environmental aspects of using corn stover for fuel ethanol’, Journal of Industrial Ecology, 7: 117–146. Spath, P.L. and Mann, M.K. (2004) Biomass Power and Conventional Fossil Systems with and without CO2 Sequestration – comparing the energy balance, greenhouse gas


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emissions and economics. National Renewable Energy Laboratory, NREL/TP-51027637, Golden, CO. Tilman, D., Hill, J. and Lehman, C. (2006). ‘Carbon-negative biofuels from low-input high-diversity grassland biomass’, Science, 314: 1,598–1,600. White House, The (2007). ‘President Bush delivers State of the Union address’. Available: http://www.whitehouse.gov/news/releases/2007/01/20070123-2.html (accessed 15 December 2007).

9

Using a systems approach to improve bioenergy sustainability assessment Valerie A. Luzadis, Timothy A. Volk and Thomas S. Buchholz

Introduction Dwindling oil supplies in the face of growing demand have accelerated interest in developing biomass-based systems for the production of bioenergy, biofuels and bioproducts. The current global focus on sustainable development and the goal to transition from a fossil fuel-based to a renewable-based economy brings with it the challenge of assessing the sustainability of the wide array of different potential systems. Not only are we asked to assess the sustainability of existing systems, we must also assess the sustainability of proposals for individual bioenergy plants through entire systems that encompass production of biomass though its conversion to energy and product delivery to consumers. Current concerns about the impact of growing biomass for energy on food security in the poorest regions of the world intensifies the need for reliable, manageable, comprehensive approaches to assessing the sustainability of biomass systems at all scales (UN-Energy 2007). The long-term focused effort to develop, implement, and revise criteria and indicators that assess the sustainability of forest management provides a framework from which to build strong bioenergy sustainability assessment approaches. However, the forest management effort only encompasses one type of feedstock, namely woody biomass, from one source, naturally occurring forests. It also focuses on one only portion of bioenergy systems, namely biomass production. To fully assess the sustainability of biomass from its production through to useful energy products, the assessment must focus on all components of the system and encompass social and economic values in addition to the latest scientific knowledge and understanding of ecosystems (Buchholz et al. 2007). The new field of sustainability science provides a strong foundation for this work. Sustainability science ‘seeks to understand the fundamental character of interactions between nature and society’ suggesting the need to span the range of spatial scales and to account for complexity (Kates et al. 2001). Sustainability science transcends disciplinary biases to focus on understanding the complex interactions between human and environmental systems

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(Clark 2007). We now benefit from a greater understanding of complex systems afforded us through research using multiple methodologies as suggested by Norgaard (1989). This leaves the question of how to put together the many useful insights gained from these multiple approaches. To that end, we recommend employing a systems approach to synthesize the many insights from multiple methodologies and perspectives in sustainability assessment efforts, and we suggest that it is more comprehensive than the current dominant paradigm, which is largely ad hoc in nature. To date, while discussion ensues about assessment of the sustainability of biomass production and bioenergy systems, no clear consensus has been reached. The traditional economy–environment–social assessment approach has been put forth (Elghali et al. 2007) and several task forces and working groups explored these possibilities (e.g. Cramer et al. 2006; Moret et al. 2006; van Dam et al. 2006). In this chapter, we suggest the use of a systems approach to more comprehensively inform the development of sustainability criteria and indicators, and as an integrating tool to synthesize the many insights from wide-ranging research on biomass-to-energy as well as the associated ranges of social and economic values.

Building on current efforts The basis of forest sustainability assessments today (such as the Montreal Process, Forest Stewardship Council, Sustainable Forestry Initiative, etc.) is the ecology–economy–social framework sometimes referred to as the threelegged stool of sustainable development that grew out of the 1987 Brundtland Commission report’s initial definition of sustainability (Harris and Goodwin 2001). In this method the three main concepts of sustainability are populated by indicators through brainstorming, consensus building among stakeholders, categorizing, and then prioritizing measures. The criteria and indicator structure itself provides no particular means to assess the comprehensiveness of the sustainability measures, and as such it is especially subject to differences based on who participates. This essentially amounts to an ad hoc approach to determining how sustainability is measured. The framework based on these three broad categories leaves much open for both interpretation and omission and does not provide for an assessment of the interactions and effects of one indicator on another. In the nearly two decades since the framework for these tools was developed, the recognition of biological, sociological and technological complexity of forests has grown. The study of forest ecosystems continues to reveal that the mechanistic approach of summing the parts does not fully reflect the functioning of the system and has been surpassed by dynamic, evolutionary methods (e.g., Hjorth and Baheri 2006). This would also be true of measuring sustainability of efforts that include such systems. We are limited in our ability to assess sustainability by the validity and reliability of the indicators we select. Using a systems approach to assess the sustainability of bioenergy

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efforts allows us to build on the knowledge and experience gained from previous labors while providing a means to gauge how well the selected indicators reflect our understanding of these complex systems. Sustainability is an open, evolving concept that is based on human values and therefore is subject to many individual interpretations and values. Efforts to operationalize sustainability encounter several challenges: the evolving nature of sustainability makes it hard to predict, its integrated nature makes it difficult to understand the system and quantify its components, and the many human values associated with sustainability make it hard to decide (Buchholz et al. 2007). Joyce (2003: 339) puts it this way: ‘. . . the challenge is to do planning and decision making while balancing three tensions: (1) maintaining scientific credibility, (2) assuring practical saliency, and (3) legitimizing the process to multiple participants.’ The use of systems thinking has been suggested in several natural resource applications as a more comprehensive framework for decision-making (e.g., Abel 2004; Hjorth and Bagheri 2006; Ikerd 1993; Kelly 1998; Vatn and Bromley 1994). This approach may also be applied to bioenergy sustainability. Describing bioenergy in terms of its many linking elements helps to clarify what and where sustainability measures can be employed, and at what points in the system they may be implemented. Bioenergy can be described as a system with three main components: 1) the growing and harvesting of biomass; 2) marketing, transporting, and transforming it into a useful energy product; and 3) marketing and delivery of that energy to consumers (Buchholz et al. 2007). Each of these interacting components may be understood as a sub-system of the larger complex itself. And as such, they each have environmental, economic, and social factors of their own, including actors who influence it with their decisions. Viewed in this way, it is easy to see bioenergy as a complex, adaptive system, which is defined as involving agents who adapt and learn, thereby changing the systems from within (Dooley 1997).

Sustainability and participation Inherent in complex adaptive systems is the concept of co-evolution, initially described by Ehrlich and Raven (1964) as genetic change of one species in response to the evolution of a second species. This concept has been successfully adapted to social applications including agricultural and societal development (e.g. Norgaard 1994; Machlis et al. 1997) and it is exactly the co-evolution of human and environmental systems that concern us in bioenergy sustainability assessment. Of course uncertainty is inherent in such complex interactions and as such procedures that provide the strongest foundation for decision-making must be pursued. Kates et al. (2001) suggested that participatory approaches that include scientists, stakeholders, advocates, and active citizens are critical when the focus is a complex system fraught with uncertainty yet requiring decisions. This is precisely the case with bioenergy.


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General systems principles offer a high scientific credibility and strong analytic capability and a participatory systems approach enables decisionmakers to evaluate bioenergy systems for sustainability in a transparent, timely, and informed manner that incorporates information from experts and non-experts while keeping the decision process transparent to third parties. The involvement of a diverse array of stakeholders during the planning, assessment and development of a bioenergy system can help to avoid the collapse of projects later in the process, as has been the case in several situations (Upreti and van der Horst 2004). Mechanisms like multi-criteria analysis help guide society in making decisions and to build bridges between science and decision-making (e.g. Buchholz et al 2007). Structuring in this participatory way presents the opportunity for social learning as the paradigm from which we engage the assessment of the sustainability of bioenergy endeavors, providing a more comprehensive and solid foundation for decision-making.

A five-step participatory systems approach to assessing sustainability We propose a five-step participatory approach that uses systems concepts both to specify the system and to assess the results for comprehensive coverage and interactions among indicators (summarized in Table 9.1). The first two steps are to identify the participants for the sustainability assessment process and to describe the system to be assessed. Step three is the development of a diagram that reflects the full set of interactions (material and energy flows) that are conceived in the system. This also provides a means to identify leverage points for change within the system. Step four requires using a comprehensive human development framework to serve as a guide to fully capturing concerns related to the deployment of bioenergy systems. Step five is the identification of specific indicators to monitor and assess the sustainability of the system. Step 1: Establish a group Identifying the group of scientists, stakeholders, advocates, active citizens and users of knowledge that will participate in the sustainability assessment process is the initial step. This may include regulators, biomass energy Table 9.1 The five-step participatory systems approach to assessing sustainability Step One Step Two Step Three Step Four Step Five

Identify participants and secure commitments to engage Write general description of the system to be assessed Diagram material and energy flows through the system Consider human and environmental systems together Use the diagram to specify indicators of sustainability of the system

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producers, biomass growers, energy marketers, among others, and of course the participants will vary from project to project. The process will benefit tremendously by framing it as a social learning effort and involving a broad and representative cross-section of those interested and impacted (Buchholz et al. 2007; Kates et al. 2001). The strong literature on participatory approaches suggests the importance of identifying the main reasons for participation, which may include information sharing, accountability and legitimization, education, community empowerment and actual powersharing (e.g. Graham and Phillips 1998; Fiorino 1989). Bioenergy efforts at different scales and led by different entities will differ somewhat in their primary purposes and the array of stakeholders they engage, but all bioenergy sustainability assessments will benefit from doing so. The selection of participants is equally important and should consider whether the effort is intensive or extensive, and if the people engaged will represent themselves as individuals or as representatives of groups or organizations (Graham and Phillips 1998). Attention to underrepresented communities is critical in the current political climate and may influence the perceived legitimacy of the effort. Step 2: Create a system description The group begins its work together by describing the system of interest in terms of the main interacting human and non-human components. This first description should be general in nature, as per our earlier description of bioenergy systems: a system encompassing the growing and harvesting of biomass, marketing, transporting, and transforming it into energy, and marketing and delivery of that energy to an end user. This description serves as a starting point for the more detailed representation of the system that will serve as both the foundation for identifying criteria and indicators and synthesizing knowledge and values associated with the complex system. Step 3: Diagram material and energy flows through the system Having bounded the effort by describing bioenergy system to be assessed, step three involves diagramming the flow of material and energy through the system, and noting decision points within it. Such diagramming is a useful means of visualizing interactions among coevolving sub-systems (for example Odum 1971). Identifying decision or leverage points provides a basis for adjusting behavior to impact the sustainability of the system (Meadows 1997). System dynamics serve as a ‘thinking model’ and visualization approach to identifying interactions in complex adaptive systems (Kelly 1998; Hjorth and Bagheri 2006; Abel 2004). One of the main activities of the participatory effort will be the step-by-step creation of this visualization of the system being assessed. The diagram can be created using standard systems


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diagramming techniques if members of the group are familiar and comfortable with this approach. Alternatively, it can be created by simply capturing the flow of material and energy from sunlight to waste products of the final consumption using symbols or caricatures acceptable to the group. It would also be possible to engage the assistance of experts familiar with systems diagramming to facilitate this portion of the process. The resulting diagram may look complicated, but gaining that understanding will be of critical value to the group. Also essential to the process is the identification of key points to indicate sustainability, as well as the identification of points where people make decisions that impact the system (also referred to as leverage or decision points), for use to develop more effective and efficient policies. This process will also help to identify parts of the system that may be poorly understood, indicating a need to pull it out for further analysis and specification, as misunderstood elements may lead to conflict within the process. A technique called ‘causal looping’ may be especially useful to further delineate the linkages among the components for this type of systems diagramming effort (see e.g. Abel 2004). We show this technique in the example presented below. This process is invaluable in illuminating linkages among components that might otherwise have been overlooked. Step 4: Consider human and environmental systems together Once the group establishes the boundaries of the base system, and diagrams it as outlined in step three, the fourth step involves identifying key elements associated with its impact on overall human development and the environment. Any comprehensive human development framework intended to link human and environmental systems could provide the list of elements to consider. We suggest two possibilities: Norgaard 1994 and Machlis et al. 1997, with a preference for the first due to its relative simplicity. Norgaard’s (1994) description of the co-evolutionary process of development provides us with a useful, comprehensive set of variables to consider: values, organization, technology, environment, and knowledge (VOTEK) (see Figure 9.1). Each of these can be understood as a different perspective on the outcomes and interactions of the full system. Using the VOTEK variables, the group assesses the bioenergy system to determine important outcomes and interactions that are likely to influence overall sustainability. This process ensures attention to components that specifically may impact this development – the ultimate societal goal, and hence the primary focal point for sustainability assessments. The Human Ecosystem Model (HEM) (Machlis et al. 1997) framework also provides a means to specify human-ecosystem interactions of any complex adaptive system although it is less well developed than the VOTEK interactions. The primary limitation of the HEM is its lack of specification of process (Luzadis et al. 2002), however the use of the HEM in conjunction

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Figure 9.1 The VOTEK framework of five co-evolving sub-systems of development. Source: adapted from Norgaard (1994).

with systems dynamics diagramming undertaken in the previous step may overcome this weakness. The main point of this step is to consider the impact of the system being assessed on human development goals. Under the largely ad hoc approach that currently dominates sustainability efforts, this consideration may or may not occur as criteria and indicators are developed to represent broad economic, social or environmental goals. By using a more comprehensive set of variables like VOTEK or HEM, which were designed to represent human and environmental system interactions, the group is less likely to leave out important elements of human systems that may be impacted by the bioenergy endeavour under consideration. While the specific outcomes and interactions identified are a product of the participants, the use of such a set of variables reduces the possibility of missing important elements and improves the likelihood of identifying interactions that may greatly impact sustainability. Step 5: Specify indicators Upon completion of the system diagram(s) and potential impacts on human development, the next step is to assess the system to determine priorities for monitoring to assess sustainability. This prioritization process for the group can be assisted by using analytical decision tools such as multi-criteria analysis, which allows for differing levels of knowledge, varying values, both qualitative and quantitative inputs, and accounts for uncertainty (Elghali et al. 2007). Using a human development framework such as VOTEK or HEM in conjunction with a system dynamics approach provides a more reliable and comprehensive, logical, and efficient assessment of the bioenergy system at any scale, than the largely ad hoc approach of listing indicators in the three broad groupings of economic, social and environmental.


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A bioenergy example Building on the general definition of bioenergy systems given earlier, we have created an example to illustrate the five-step process and its results. For publication purposes, we begin with step 2 and define the system as follows: Bioenergy can be described as a system with three main components: 1) the growing and harvesting of biomass; 2) marketing, transporting and transforming it into a useful energy product; and 3) marketing and delivery of that energy to consumers. For our example, we consider a community watershed within which a bioenergy system, in particular a biomass power plant, is embedded, and we then look further to identify how a privately owned facility would differ from one that is publicly owned. Step three produces a basic systems diagram of a biomass power plant within a community watershed (Figure 9.2). The general flows of material and energy are indicated using traditional systems dynamics symbols, as per Odum (1971, for example). The flows move from the left, beginning with solar energy through to the ‘outside community’ indicated on the right side of the diagram. The main biophysical processes involved are indicated below the box delineating the boundary of the community watershed itself. Points where critical decisions can influence the system (leverage points) are indicated with circles. Within the diagram, the human development components are indicated by the star overlay, meant to reflect the five VOTEK variables considered in step four. Figure 9.3 is an expansion of the VOTEK variables to represent the scenario of the privately owned biomass power plant. These relationships are visualized using causal looping diagrams. In Figure 9.3, the flows between the environment, the power plant, its owners and managers, labor/households, and government are illuminated as main interactions. Figure 9.4 shows interactions among sub-systems for a publicly owned biomass power plant and shows a different balance of interactions with government institutions much more involved. Step five involves specifying the criteria and indicators to best indicate sustainability. Since we didn’t engage a group to participate in our example exercise, we instead used these diagrams to evaluate the comprehensiveness of a set of biomass production criteria and indicators produced in the Netherlands reflecting the more ad hoc, dominant paradigm in use (Cramer et al. 2006). This provided us with the opportunity to test the contention we put forth throughout this chapter that a systems approach provides a verifiable, more comprehensive means of identifying an appropriate range of sustainability indicators than the approach that currently dominates such efforts. We began by itemizing the criteria from the Netherlands on a paper copy of the systems diagram, placing them at the point in the system where they would actually be measured. This enabled us to actually see overspecifications and omissions. We found that while the set of indicators covers

Figure 9.2 A systems diagram of a community watershed in which a bioenergy system is embedded.

Figure 9.3 A causal loop diagram of relationships associated with a privately-owned biomass power plant embedded in a community.

Figure 9.4 A causal loop diagram of relationships associated with a publicly-owned biomass power plant embedded in a community.


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many important components, they did not effectively account well for interactions. Additionally, the social focus was on the production of biomass from the edge of the field through conversion, without much attention to all that is involved with growing it. We also observed that certain indicators were measured at one point in the system, but they needed to be assessed at other places in the system as well. This suggested that the listing approach to identifying these indicators might obscure the need to measure it at different places within the system. It would be easy to see the indicator on a list and assume it is appropriately measured at all points or at all scales necessary, but our test case showed that is not always true. For example, some indicators required inputs or outputs with certain characteristics, but did not include indicators to reflect the parts of the system that produce them (c.f. chapter 8). One other observation was that while we appreciate the complexity that social aspects of bioenergy present, the set of social indicators suggested by the Netherlands group overlap one another when viewed using the systems and causal loop diagrams. This is what we refer to as over-specification. This simple analysis supports our assertion that using a systems approach to assess sustainability definitively improves the effort.

Conclusions Advances in thinking, understanding, and methods provide the opportunity for making the process of establishing standards for biomass sustainability assessment more comprehensive and more efficient. Using a participatory systems approach to assess sustainability improves the validity, reliability, and efficiency over current approaches to assessing sustainability. The participatory systems approach described in this chapter focuses the effort on identifying and agreeing more comprehensive sustainability measures for bioenergy systems more efficiently.

References Abel, T. (2004) ‘Systems diagrams for visualizing macroeconomics’, Ecological Modeling, 178: 189–194. Buchholz, T.S., Volk, T.A. and Luzadis, V.A. (2007) ‘A participatory systems approach to modeling social, economic, and ecological components of bioenergy’, Energy Policy, 35: 6,084–6,094. Clark, W.C. (2007) ‘Sustainability science: a room of its own’, Proceedings of the National Academy of Sciences of the United States of America, 104: 1,737–1,738. Cramer, J., Wissema, E., Lammers, E., Dijk, D., Jager, H., van Bennekom, S. et al. (2006) ‘Criteria for sustainable biomass production’, Final report of the project group, ‘Sustainable production of biomass’, Energy Transition Task Force, The Netherlands. Dooley, K.J. (1997) ‘A complex adaptive systems model of organization change’, Nonlinear Dynamics, Psychology, and Life Sciences, 1: 69–97.

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Ehrlich, P.R. and Raven, P.H. (1964) ‘Butterflies and plants: a study in coevolution’, Evolution, 18: 586–608. Elghali, L., Clift, R., Sinclair, P., Panoutsou, C. and Bauen, A. (2007) ‘Developing a sustainability framework for the assessment of bioenergy systems’, Energy Policy, 35: 6,075–6,083. Fiorino, D.J. (1989) ‘Environmental risk and democratic process: a critical review’, Columbia Journal of Environmental Law, 14, 501–547. Graham, K.A., and Phillips, S.D. (1998) Citizen Engagement: lessons in participation from local government, Monographies sur l’administration publique canadienne, 22, Toronto: Institute of Public Administration of Canada. Harris, J.M. and Goodwin, N.R. (2001) ‘Volume introduction’, in J.M. Harris, T.A. Wise, K.P. Gallagher, and N.R. Goodwin (eds) A Survey of Sustainable development: social and economic dimensions, Frontier Issues in Economic Thought Series, Washington, D.C.: Island Press, pp. xxvii–xxxvii. Hjorth, P. and Bagheri, A. (2006) ‘Navigating towards sustainable development: a system dynamics approach’, Futures, 38: 74–92. Ikerd, J.E. (1993) ‘The need for a systems approach to sustainable agriculture’, Agriculture, Ecosystems & Environment, 46: 147–160. Joyce, L.A. (2003) ‘Improving the flow of scientific information across the interface of forest science and policy’, Forest Policy and Economics, 5: 339–347. Kates, R.W., Clark, W.C., Corell, R., Hall, J.M., Jaeger, C.C., Lowe, I. et al. (2001) ‘Environment and development: sustainability science’, Science, 292: 641–642. Kelly, K.L. (1998) ‘A systems approach to identifying decisive information for sustainable development’, European Journal of Operational Research, 109: 452–464. Luzadis, V.A., Goslee, K.M., Greenfield, E.J. and Schaeffer, T.D. (2002) ‘Toward a more integrated ecosystem model’, Society and Natural Resources, 15: 89–94. Machlis, G.E., Force, J.E. and Burch, W.R. (1997) ‘The human ecosystem, part I: the human ecosystem as an organizing concept in ecosystem management’, Society and Natural Resources, 10: 347–367. Meadows, D. (1997) ‘Places to intervene in a system’, Whole Earth Review, Available: http://www.wholeearth.com/ArticleBin/109.html (accessed 17 March 2008). Moret, A., Rodrigues, D. and Ortiz, L. (2006) ‘Sustainability criteria and indicators for bioenergy’, translated by Mark Luttes, Report of the Energy Working Group of the Brazilian Forum of NGOs and Social Movements (FBOMS), Available: http://www.foei.org/en/publications/pdfs/bioenergy.pdf (accessed 17 March 2008). Norgaard, R.B. (1984) ‘Coevolutionary development potential’, Land Economics, 60: 160–173. —— (1989) ‘The case for methodological pluralism’, Ecological Economics, 1: 37–57. —— (1994) Development Betrayed: the end of progress and a coevolutionary revisioning of the future, London and New York: Routledge. Odum, H.T. (1971) Environment, Power, and Society, New York: Wiley-Interscience. UN-Energy (2007) Sustainable Bioenergy: a framework for decision makers, New York: United Nations. Available: ftp://ftp.fao.org/docrep/fao/010/a1094e/ a1094e00.pdf (accessed 17 March 2008). Upreti, B.R. and van der Horst, D. (2004) ‘National renewable energy policy and local opposition in the UK: the failed development of a biomass electricity plant’, Biomass & Bioenergy, 26: 61–69. van Dam, J., Junginger, M., Faaij, A., Jurgens, I., Best, G. and Fritsche, U. (2006)


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Part III


10 Cost and financial feasibility of two biomass power technologies Dana M. Johnson, James H. Whitmarsh and Jillian R. Waterstraut

Background As the demand for fossil fuels continues to grow and the pressure for reductions in CO2 emissions increases, the need to find cost-effective alternative energy technologies is of paramount importance. The fossil-fueled power generation industry contributes significantly to CO2 emissions, not to mention conventional air emissions. The co-firing of biomass with coal, and biomass gasification for electric power generation, are two important alternatives to coal and fossil fuel plants. A study by the U.S. Department of Energy (DOE) and Electric Power Research Institute (EPRI) served as a starting point for the case study research reported in this chapter (DeMeo et al. 1997). This report included an initial performance and cost analysis expressed in mid1990s dollars. This required extensive updating of cost information through the use of consumer price indexes and inflation (Table 10A.1), producer price indexes (numerous tables), interest rates, and tax rates for complete capital investment and electricity production cost calculations and analyses. As a result of this comprehensive and updated analysis, the biomass cofiring case is found to be the most economically viable, financially feasible, and cost competitive in relationship to existing, traditional and renewable sources for electricity generation. In the next section, the biomass co-firing case study will be discussed, followed by a presentation of the biomass gasification case. We will then compare the co-firing case to the gasification case study, and then to a reference case of electricity generation costs. The chapter will close with several conclusions and recommendations.

Capital investment analysis – biomass co-firing for electricity generation This is the first of two cases that will review the economic and financial feasibility of alternative biomass electric technologies. Biomass co-firing typically uses 15 to 20 per cent biomass, with the rest of the fuel being coal. This analysis was adapted from DeMeo et al. (1997). All costing figures were originally reported in 1997 USD and updated in this analysis to 2005 USD to

214


reflect inflation trends. This analysis was performed for five different states based on location criteria. Location criteria The selection criteria used for the biomass co-firing case study were feedstock volume, incentives offered, coal consumption for combined heat and power (CHP), NOx and SO2 emissions for CHP, CO2 emissions for CHP, CHP power generation, and state population. Based on the selection criteria, locations in five different geographic regions with significant amounts of biomass feedstock available were selected for further investigation for locating a biomass co-firing plant. These locations are by no means recommendations for locating biomass co-firing facilities; they were used to minimize the feedstock delivery distances and costs. The resulting locations are: (a) Stevenson, Alabama, (b) Holbrook, Arizona, (c) Joppa, Illinois, (d) Shawville, Pennsylvania, and (e) Wheatland, Wyoming (Johnson et al. 2008). County level feedstock data, statewide county maps, and locations of preexisting coal burning electric power only/combined heat and power (EPO/ CHP) plants were used to select a reasonable location within each state that would minimize the average delivery distance from the feedstock resource to the processing facility. Assumptions This analysis is based on plant capacity of 150 megawatts (MW) electricity with a co-firing rate of 15 per cent biomass. The size of the plant is based on the proximity of the biomass to the plant within a 120 km (75 mile) radius. Beyond a 120 km radius, the feedstock delivery cost increases and may become prohibitively expensive and uneconomical, even with increased plant capacity. The following assumptions were used in this analysis: 1) the project will be completed in one year; 2) a separate feeder system is to be purchased for the biomass input since the contribution from biomass is greater than 5 per cent; 3) drying equipment has not been included in the costs, because according to DeMeo et al. (1997) the benefit-to-cost ratio is usually low, the fuel sources are assumed to be moderately dry, and the biomass rate is low enough that a dryer is unnecessary; 4) availability of existing front end loaders and truck scales for unloading and pile management of coal will be used for the biomass as well; 5) biomass will be delivered by live-bottom trucks, avoiding the purchase of a truck tipper; 6) existing plant facilities will be used to accommodate a five-day supply of biomass; 7) initial capital investment was assumed to be the same for all locations; 8) variable costs such as chemicals, water and waste disposal were assumed to be the same for all locations; and 9) SO2 allowances were assumed to stay constant throughout the entire analysis at the current price of $700.

Two biomass power technologies

215

Initial capital investment The initial capital investment from DeMeo et al. (1997) was $5.4 million, which includes the equipment cost plus installation and other costs, which are shown below. The updated capital equipment cost was calculated using the producer price index (PPI) for capital equipment shown in Table 10A.2. A summary of the total capital investment is shown in Table 10.1. The total capital requirement was calculated at $5.7 million. The 1997 DeMeo report noted that initial investment costs are highly site specific depending on several factors, and the initial capital investment has an uncertainty of ±25 per cent. The useful equipment life is 30 years, typical for this type of equipment (DeMeo et al. 1997). Tax depreciation For the capital investment analysis, the tax depreciation was applied according to the seven-year double-declining balance method. However, for the production cost analysis, the capital recovery factor was applied based on the duration of the analysis, which was ten years. This method was used so that Table 10.1 Breakdown of initial capital investment Equipment Description

Cost in 2005 USD

Biomass Handling System Conveyor Separation Equipment, Conveyor Hogging Tower and Equipment Pneumatic Conveying System (Vacuum) Wood Silo with Live Bottom Collecting Conveyors Rotary Airlock Feeders Pneumatic Conveying System (Pressure) Controls Total Equipment Cost

$268,299 $72,959 $444,812 $94,140 $115,322 $136,503 $11,768 $355,379 $218,876 $1,718,055

Biomass Handling System Installation Total Installed Cost

$1,066,136 $2,784,191

Other Costs: Civil Structural Work Modifications at Burners Electrical Total Installation + Other Costs

Cost in 2005 USD $769,595 $63,545 $341,258 $3,958,587

Contigency Funds (30% of total install + other) Total Direct Costs

$1,187,576 $5,146,163

Engineering Costs (10% of total direct costs) Total Capital Requirement

$514,616 $5,660,779

Source: Johnson et al. (2008).

216 Regional case studies the production costs are not overestimated in the early years and underestimated in the later years. Details of variable operating costs Variable operating costs include biomass feedstock, non-feedstock fuel inputs, electric utility requirements, chemicals, boiler feed water, waste disposal, and SO2 allowance credits. Delivered feedstock costs The delivered feedstock costs were determined using the method described in Johnson et al. (2008). The total delivered feedstock costs by location are shown in Table 10.2. Non-feedstock fuel inputs Non-feedstock fuel input requirements include residual fuel oil and natural gas. The consumption of fuel oil and natural gas for a coal-fired power plant was determined from a report by Mann and Spath (2001) and was adjusted to include only the fuel requirements attributable to the biomass system. The adjusted requirements for fuel oil and natural gas were 1.5 and 0.19 liters equivalent per kWh (0.4 and 0.05 gallons equivalent kWh), respectively. To estimate the cost, these requirements were converted to a cost per unit of generated electricity. This was accomplished using the combustion energy of each fuel (11.25 kg kWh−1) for fuel oil, 13.38 kg kWh−1 for natural gas) to obtain the ratio of energy input to electricity output in kWht kWhe. Once the energy ratio was determined, the cost of fuel oil and natural gas on a dollar per kWh of heat energy was used to calculate the cost of electricity generated by biomass. Since the amount of residual fuel oil and natural gas is small relative to the other variable costs, the projected average price to electric power producers was used for the different locations. The price forecasts originated from the Annual Energy Outlook 2005 (AEO) (EIA 2006) and were adjusted 3 per cent per year as opposed to adjustments based on the PPI for residual fuel oil and natural gas (BLS 2005a). The forecasted prices used for this analysis are shown in Table 10.3. Electricity utility requirements The electricity requirements for the biomass feeder system were estimated based on the addition of a pulverizer, which would operate at 0.9 MW under normal loading conditions. The electricity prices used in the analysis are shown in Table 10.4. The prices for the locations were taken from the AEO 2005 (EIA 2006) and adjusted 2.4 per cent per year based on the PPI for electricity (BLS 2005a).

50.1 68.6 42.4 28.6 74.7

Alabama Arizona Illinois Pennsylvania Wyoming

$27.68 $40.38 $27.32 $25.43 $32.42

2008

$27.89 $40.67 $27.50 $25.55 $32.74

2009

$0.024

U.S.

$0.024

2008 $0.024

2009 $0.023

2010

$0.024

U.S.

$0.024

2008 $0.023

2009 $0.022

2010 $0.022

2011

$0.024

2011

$28.10 $40.96 $27.68 $25.67 $33.05

2010

2007

$0.052 $0.076 $0.060 $0.052 $0.049

Location


$0.052 $0.075 $0.059 $0.052 $0.050

2008 $0.053 $0.073 $0.059 $0.053 $0.051

2009 $0.054 $0.072 $0.058 $0.054 $0.051

2010

$0.055 $0.071 $0.057 $0.055 $0.051

2011

Table 10.4 Forecasted industrial electricity prices, 2007–2016 ($ per kWh)

2007

Location

Industrial Natural Gas Prices ($ per kWh)

2007

Location

Residual Fuel Oil Prices ($ per kWh)

Table 10.3 Forecasted prices for non-feedstock fuel inputs

$27.48 $40.10 $27.15 $25.31 $32.12

Avg. Delivery 2007 Distance (km)

Location

Table 10.2 Delivered feedstock cost by location ($ per dry ton)

$0.055 $0.072 $0.058 $0.055 $0.053

2012

$0.023

2012

$0.025

2012

$28.32 $41.26 $27.87 $25.79 $33.38

2011

$28.77 $41.88 $28.25 $26.05 $34.05

2013

$0.057 $0.075 $0.060 $0.057 $0.054

2013

$0.024

2013

$0.025

2013

$28.54 $41.57 $28.05 $25.92 $33.71

2012

$0.058 $0.076 $0.062 $0.058 $0.056

2014

$0.024

2014

$0.026

2014

$29.00 $42.20 $28.44 $26.19 $34.40

2014

$0.059 $0.077 $0.063 $0.059 $0.056

2015

$0.024

2015

$0.027

2015

$29.24 $42.52 $28.65 $26.32 $34.76

2015

$0.061 $0.075 $0.065 $0.061 $0.058

2016

$0.025

2016

$0.028

2016

$29.49 $42.86 $28.85 $26.46 $35.12

2016

218


Chemicals The chemicals required include ammonia and other unspecified chemicals, which were adjusted based on the inorganic chemicals index (BLS 2005a). The cost of ammonia was obtained from Froese et al. (2008), and the cost of the other chemicals was given in DeMeo et al. (1997). Feed water Water is a requirement for steam generation and a specific index could not be found. Therefore, the original cost was adjusted based on the industrial commodities less fuels index (BLS 2005a). Waste disposal Waste disposal includes bottom ash and fly ash from the boiler and a specific index could not be found. Therefore, the original cost was adjusted based on the industrial commodities less fuels index shown in Table 10A.2. It should also be noted that the market for fly ash as a concrete admixture was ignored due to concerns that fly ash from biomass may not meet existing American Society for Testing and Materials (ASTM) fly ash specifications. SO2 emission reduction credits DeMeo et al. (1997) estimated that co-firing biomass at a 15 per cent rate for a 150 MW power plant would reduce SO2 emissions by 2,100 tonnes per year. Co-firing biomass with coal allows power producers to earn SO2 emission allowances under the Clean Air Act Amendments of 1990 (CAAA). These allowances can be sold or traded to other electric power plants, which may need them for compliance. Recent price information indicates that SO2 credits are being sold between $700 and $750 (EIA 2006). This analysis assumed SO2 prices would remain constant at $700. Details of fixed operating and maintenance costs Fixed operating and maintenance costs are briefly described below: Labor The labor costs attributed to the biomass system include two power plant operators responsible for managing biomass deliveries as well as the handling and processing equipment. The labor costs by location are shown in Table 10.5. Labor cost rates were adjusted 3 per cent per year based on the labor rate index (BLS 2005b).


219

Table 10.5 Labor wages by location, 2005 Location

Operator Wages

Number of Operators

Total Wages


$47,790 $49,860 $56,060 $51,050 $53,180

2 2 2 2 2

$95,580 $99,720 $112,120 $102,100 $106,360

Source: BLS (2005a).

Maintenance Maintenance costs were estimated in DeMeo et al. (1997) as 2 per cent of the total investment and adjusted 2.75 per cent per year based on the PPI for maintenance repair index (BLS 2005b). Electricity pricing assumptions The projected wholesale electricity prices are shown in Table 10.6. The prices were taken from the AEO 2005 (EIA 2006) and adjusted 2.4 per cent per year based on the PPI for electric power (BLS 2005a). Volume assumptions and operating capacity DeMeo et al. (1997) assumed the power plant capacity factor to be 85 per cent. Therefore, the total annual energy delivery amounts to 1.68 × 108 kWh. This is calculated output at the generator terminals, and does not take into consideration losses as electricity is transmitted. The electricity generation from biomass in this case has a net operating capacity of 22.5 MW. Discount rate assumptions The investment costs for biomass co-firing are small compared to the other technologies, and the technology itself is already mature and has been proven on a commercial scale. The net present value (NPV) in this analysis was calculated using discount rates of 15 per cent and 20 per cent to test the sensitivity of the discount rate on the NPV. The 20 per cent discount rate is more reflective of the risk associated with the uncertainty surrounding the amount and availability of feedstock. Another risk factor is the use of biomass as a feedstock in existing technology that may require some modifications to handle multiple feedstocks simultaneously.

2007

$0.052 $0.066 $0.051 $0.061 $0.033

Location


$0.051 $0.065 $0.050 $0.058 $0.034

2008 $0.052 $0.063 $0.050 $0.056 $0.034

2009 $0.053 $0.061 $0.050 $0.055 $0.034

2010

Table 10.6 Projected electricity revenues, 2007–2016 ($ per kWh)

$0.053 $0.061 $0.050 $0.055 $0.034

2011 $0.053 $0.062 $0.052 $0.060 $0.035

2012 $0.055 $0.065 $0.054 $0.064 $0.036

2013

$0.056 $0.066 $0.055 $0.064 $0.038

2014

$0.057 $0.067 $0.057 $0.065 $0.037

2015

$0.059 $0.066 $0.059 $0.067 $0.039

2016


221

Summary of operating and maintenance costs & NPV for co-firing A summary of the total operating and maintenance costs by location is shown in Table 10.7, while a summary of the NPV is shown in Table 10.8. Under the biomass co-firing case, the NPV is positive at both discount rates. More detailed comparisons are at the end of the biomass gasification section, and the conclusions and recommendations. A summary of the payback periods for the biomass co-firing cases is shown in Table 10.9. The next section discusses the biomass gasification for electricity generation case and provides a comparison with biomass co-firing. Table 10.7 Operating & maintenance costs by location, 2007 AL

AZ

IL

PA

WY

$4,289,785 $26,524 $73,523 $110,943 $56,395 $596,644 $28,365

$2,904,321 $26,524 $73,523 $110,943 $56,395 $471,819 $28,365

$2,707,436 $26,524 $73,523 $110,943 $56,395 $412,017 $28,365

$3,435,713 $26,524 $73,523 $110,943 $56,395 $390,129 $28,365

$2,741 $5,184,921

$2,741 $3,674,631

$2,741 $3,417,944

$2,741 $4,124,333

$101,401 $113,216 $214,616

$105,793 $113,216 $219,009

$118,948 $113,216 $232,164

$108,318 $113,216 $221,533

$112,837 $113,216 $226,053

$3,864,175

$5,403,929

$3,906,795

$3,639,478

$4,350,386

Variable Costs Feedstock $2,939,050 Ammonia $26,524 Chemicals $73,523 Water $110,943 Waste Disposal $56,395 Electricity $412,017 Residual $28,365 Fuel Oil Natural Gas $2,741 Total Variable $3,649,559 Costs Fixed Costs Labor Maintenance Total Fixed Costs Total O & M Costs


Table 10.8 NPV summary for biomass co-firing Location

2007 Electricity Wholesale Price ($ per kWh)

NPV @ 15% Discount Rate



$0.052 $0.066 $0.051 $0.061 $0.033

$17,011,755 $17,960,717 $16,251,940 $21,254,462 $5,183,682

$13,281,997 $14,166,514 $12,625,680 $16,807,079 $3,423,099


222


Table 10.9 Biomass co-firing payback summary Location

Payback (years)

15% Discounted Payback (years)

20% Discounted Payback (years)


1.23 1.09 1.26 0.97 2.53

1.48 1.30 1.53 1.15 3.48

1.57 1.38 1.63 1.22 3.97


Capital investment analysis – biomass gasification for electricity generation The process for biomass gasification is the other alternative energy technology evaluated. This analysis was also adapted from DeMeo et al. (1997). All cost figures were originally reported in 1997 USD and updated in this analysis to 2005 US$ to reflect inflation trends. This analysis was performed for five different states based on location criteria. Location criteria The selection criteria used for the gasification cases were feedstock volume, incentives offered, current operating gasification plants, and state population. The states chosen were different than those used in the other case study. Based on the selection criteria, one state from each of region was selected for further investigation for locating a biomass gasification plant. These selected locations are not recommendations for locating biomass gasification facilities. They were used to minimize the feedstock delivery distances and costs. The resulting locations are: 1) Wallace, Idaho, 2) Coffeyville, Kansas, 3) Tallulah, Louisiana, 4) Rocky Mount, North Carolina, and 5) Centralia, Washington (Johnson et. al. 2008). In the selected states where coal burning gasification plants are operating, locations near those facilities were chosen if feedstock was available, thus eliminating the need to build a new facility. Otherwise, a location was chosen that had infrastructure suitable for the scale of facility being investigated. Assumptions This analysis is based on plant capacity of 100 MWe. The following assumptions were outlined in the 1997 DeMeo report and used in this analysis include: 1) gasification technology is a biomass gasification combined cycle (BGCC), 2) initial capital investment was assumed to be the same for all locations, and 3) variable costs such as chemicals, water and waste disposal


223

were assumed to be the same for all locations, and 4) labor costs were assumed to be the same for all locations since the 1997 DeMeo report did not give detailed information regarding labor. Initial capital investment The initial capital investment for this analysis includes only those costs attributed to the equipment and not the plant facility. After adjusting to account for equipment costs only, the cost estimate from the original report was $155 million. The original capital equipment cost was updated using the PPI for capital equipment shown in Table 10A.2. A summary of the total capital investment is shown in Table 10.10. The total capital requirement was calculated at $165 million. The 1997 DeMeo report estimates that the initial capital investment has an uncertainty of ±20 per cent. The project life is 30 years, typical for this type of equipment (DeMeo et al. 1997; Johnson et. al. 2008). Tax depreciation For the capital investment analysis, the tax depreciation was applied according to the seven-year double declining balance method. However, for the production cost analysis, the capital recovery factor was applied based on the Table 10.10 Breakdown of initial capital investment Equipment Description

Cost in 2005 US$

Fuel Preparation Gasifier Gas Turbine Steam Turbine Balance of Plant Control System Hot Gas Cleanup Total Equipment Cost Installation Total Installed Cost

$10,564,600 $39,434,200 $22,593,600 $5,020,800 $20,606,200 $941,400 $3,556,400 $102,717,200 $16,422,200 $119,139,400

Other Costs: Turbine Building Waste Pond General Plant Facilities Contigency Funds Engineering Fees Start-up Costs Total Other Costs Total Capital Requirement

$730,200 $243,400 $9,984,070 $16,408,602 $13,260,744 $4,861,808 $45,488,824 $164,628,224


224


duration of the analysis, which is ten years. This method was used so that the production costs are not over estimated in the early years and underestimated in the later years. Details of variable operating costs Variable operating costs included in the 1997 DeMeo et al. report were feedstock, labor, maintenance, chemicals, water and waste disposal. The report estimates the variable operating costs not including the feedstock have an uncertainty of ±20 per cent. Delivered feedstock costs The delivered feedstock costs were determined using the same method described in the biomass co-firing section. The total delivered feedstock costs by location are shown in Table 10.11. Labor Variable labor costs were updated to 2005 US$ and adjusted 3 per cent per year based on the labor index (BLS 2005b). Maintenance labor and material Maintenance costs were updated to 2005 US$ and adjusted 2.75 per cent per year based on the PPI for maintenance repairs (BLS 2005a; 2005b). Chemicals The specifics of the chemicals were not defined in the 1997 DeMeo report, but the cost was updated to 2005 US$ and adjusted 1.25 per cent based on the inorganic chemicals index (BLS 2005a). Water Water is required for steam generation and was updated to 2005 US$ and adjusted 1.5 per cent based on the index for industrial commodities less fuels in Table 10A.3. Waste disposal Waste includes both ash and solids, and was updated to 2005 US$ using the industrial commodities less fuels index in Table 10A.3.

Avg. Delivery Distance (km)

50.4 59.9 42 56.2 55.7

Location

Idaho Kansas Louisiana North Carolina Washington

$32.54 $39.88 $49.80 $33.51 $36.08

2007

$32.74 $40.12 $49.97 $33.74 $36.33

2008

$32.95 $40.37 $50.15 $33.98 $36.59

2009

Table 10.11 Delivered feedstock cost by location ($ per dry ton)

$33.17 $40.63 $50.33 $34.21 $36.85

2010

$33.39 $40.89 $50.51 $34.46 $37.12

2011

$33.61 $41.16 $50.70 $34.71 $37.40

2012

$33.84 $41.43 $50.89 $34.97 $37.68

2013

$34.07 $41.71 $51.08 $35.23 $37.97

2014

$34.31 $41.99 $51.28 $35.49 $38.26

2015

$34.56 $42.28 $51.49 $35.77 $38.56

2016

226


Details of fixed operating and maintenance costs Fixed costs include operating, supervision and clerical, and maintenance costs and are described briefly below: Operating Detailed operating costs were not specified in the 1997 DeMeo report but were updated to 2005 US$ and adjusted 2.7 per cent per year based on the PPI for all commodities in Table 10A.4. Supervision and clerical Supervision and clerical costs were updated using the labor index (BLS 2005b). Maintenance labor and material Maintenance labor and material costs were updated using the PPI for maintenance repairs (BLS 2005a and BLS 2005b). Electricity pricing assumptions The electricity revenue prices are shown in Table 10.12. Volume assumptions and operating capacity The 1997 DeMeo et al. report assumed the capacity factor to be 80 per cent. Therefore, the total annual energy delivery amounts to 7 × 108 kWh (Johnson et. al. 2008). The assumed operating capacity is 100 MWe (DeMeo et al. 1997). Discount rate assumptions Gasification is a mature technology, but requires a high initial capital investment which would most likely have to be financed through alternative lending options such as venture capital firms. For this reason, the discount rate will be higher than a co-firing startup. The major risk factors are feedstock supply and availability, technology risk, and financial risk. Even if the risks factors were reduced and a more traditional discount rate of 15 per cent was used, it is unlikely to change the financial outcome associated with gasification. For gasification, 20 per cent and 30 per cent were used for the capital investment analysis.

2007

$0.033 $0.052 $0.052 $0.052 $0.033

Location


$0.034 $0.051 $0.051 $0.051 $0.034

2008 $0.034 $0.052 $0.052 $0.052 $0.034

2009 $0.034 $0.054 $0.053 $0.053 $0.034

2010

Table 10.12 Projected electricity revenues by location, 2007–2016

$0.034 $0.053 $0.053 $0.053 $0.034

2011 $0.035 $0.053 $0.053 $0.053 $0.035

2012 $0.036 $0.055 $0.055 $0.055 $0.036

2013

$0.038 $0.057 $0.056 $0.056 $0.038

2014

$0.037 $0.058 $0.057 $0.057 $0.037

2015

$0.039 $0.061 $0.059 $0.059 $0.039

2016

228


Summary of operating and maintenance costs A summary of the total operating and maintenance costs by location is shown in Table 10.13. Summary of NPV for gasification A summary of the NPV is shown in Table 10.14. The initial investment cost associated with the biomass gasification for generating electricity is significantly higher than the co-firing case. All other costs associated with the Table 10.13 Operations & maintenance costs by location, 2007 ID

KS

Variable Costs Feedstock $12,382,913 Chemicals $317,062 Water $511,458 Waste $255,729 Disposal Labor $3,203,672 Maintenance $564,389 Total Variable $17,235,223 Costs Fixed Costs Operating $927,995 Supervision & $779,613 Clerical Maintenance $3,995,377 Total Fixed $5,702,984 Costs Total O & M $22,938,207 Costs

LA

NC

WA

$15,177,830 $18,954,267 $12,754,380 $13,731,043 $317,062 $317,062 $317,062 $317,062 $511,458 $511,458 $511,458 $511,458 $255,729 $255,729 $255,729 $255,729 $3,203,672 $3,203,672 $3,203,672 $3,203,672 $564,389 $564,389 $564,389 $564,389 $20,030,139 $23,806,577 $17,606,689 $18,583,352

$927,995 $779,613

$927,995 $779,613

$927,995 $779,613

$927,995 $779,613

$3,995,377 $5,702,984

$3,995,377 $5,702,984

$3,995,377 $5,702,984

$3,995,377 $5,702,984

$25,733,124 $29,509,561 $23,309,674 $24,286,337


Table 10.14 NPV summary for gasification Location

2007 Electricity Wholesale Price ($ kWh−1)




$0.033 $0.052 $0.052 $0.052 $0.033

($132,474,502) ($104,402,437) ($115,082,958) ($98,377,809) ($136,301,096)

($138,790,539) ($118,276,229) ($126,035,863) ($113,691,507) ($141,591,148)



229

process are also higher than the co-firing case. At both discount rates the NPV is negative (Johnson et. al. 2008).

Comparison of biomass co-firing versus biomass gasification for electricity generation The wholesale price forecasts for electricity remained the same for both cases because it is unlikely that consumers would be willing to pay higher prices for electricity generation. The co-firing case, on a capital investment analysis perspective only, provides a more feasible opportunity than the gasification case. There is still risk and uncertainty under both cases that will be discussed in more detail in the conclusions and recommendations section. Caveats to the analysis When new technology is in developmental stages, the desired target costs and learning curves are more aggressive than what materializes in practice. This is an important consideration when developing costs over multiple periods of time for technology that has not yet been commercialized. Companies must be willing to invest in new technology even if the existing cost may be lower; otherwise, new process technology will be stifled (IEA 2003). Economic analysis is useful in identifying the fundamental aspects of market structure that need attention but provides only part of the framework and analysis needed for designing and implementing policy. Economic variables assist in identifying market growth potential based on population and estimated consumption of non-renewable and renewable fuels. The indexes allow for adjusting cost in the appropriate years and for forecasting future periods to adequately allow for cost and price adjustments to be factored into the analysis. Risk and uncertainty Risk is simply the known probability of expected cash flows; unquantifiable uncertainty occurs when this probability is unknown (Accola 1994). In any business decision there is a certain amount of risk and uncertainty associated with proceeding further. Obviously a new technology investment such as this entails a large amount of uncertainty. This adds even more complication to an attempt to calculate the discount rate and makes the option of selecting a range the most practical alternative. To factor in the risk and uncertainty that cannot always be quantified, assumptions are made regarding the significance that the risk and uncertainty can play on the ultimate outcome. The risk and uncertainty is factored into the discount rates used for the capital investment analysis.

230


Capital investment analysis considerations Although an alternative may appear to be financially viable based on the capital investment analysis, because of the number of assumptions and estimates required to develop the analysis, caution should be exercised in making a decision based on the projected financial outcomes. Typical capital investment analyses are conducted for a five- to seven-year timeframe. The longer the time frame, the more risk and uncertainty play a role in the outcome. In the case of the capital investment analysis for this study, ten years represents a longer timeframe than typically conducted. Because of the longer timeframe, there is inherent risk in the forecasting of all costs and a higher level of uncertainty associated with assumptions.

Production cost Electricity generation The production costs for electricity generation in the U.S. for 2000–2006 are shown in Tables 10.15 and 10.16. The 2005 and 2006 costs were forecast using the PPI for electricity, which is 2.4 per cent per year. These data will serve as the basis for forecasting electricity generation costs for comparison with the biomass co-firing and gasification options. Electricity generation from biomass co-firing The projected production cost for electricity from biomass co-firing is shown in Table 10.17. The projected electricity production costs from biomass cofiring on an energy basis are shown in Table 10.18.

Table 10.15 Electricity production cost, 2000–2006 ($ per kWh) Year

2000

2001

2002

2003

2004

2005

2006

Production Cost ($ Kwh−1) – Nuclear Production Cost ($ Kwh−1) – Fossil Steam Production Cost ($ Kwh−1) – Hydroelectric and Pumped Storage Production Cost ($ Kwh−1) – Gas Turbine and Small Scale

0.089

0.090

0.090

0.094

0.088

0.091

0.093

0.026

0.027

0.028

0.028

0.030

0.030

0.031

0.050

0.052

0.054

0.048

0.054

0.055

0.057

0.049

0.039

0.030

0.030

0.029

0.030

0.031

Source: EIA (2006).


231

Table 10.16 Electricity production costs, 2000–2006 ($ per MMBtu) Year

2000

Production Cost ($ MMBtu−1) – Nuclear Production Cost ($ MMBtu−1) – Fossil Steam Production Cost ($ MMBtu−1) – Hydroelectric and Pumped Storage Production Cost ($ MMBtu−1) – Gas Turbine and Small Scale

26.09 26.29 26.50 27.49 25.90 26.52 27.15 7.47

2001

7.85

2002

8.23

2003

8.12

2004

8.72

2005

8.93

2006

9.14

14.76 15.33 15.90 14.07 15.86 16.24 16.63 14.42 11.54

8.67

8.75

8.63

8.84

9.05

Source: EIA (2006).

Electricity generation from biomass gasification The projected production cost for electricity generation from biomass gasification is shown in Table 10.19. The projected electricity production cost from biomass gasification on an energy basis is shown in Table 10.20. Cost comparison In comparison to existing technologies for electricity generation, the production costs for the co-firing case are on average either less than or equal to the production costs for electricity generation using fossil steam. Electricity generation from fossil fueled steam plants is the lowest cost existing technology. The co-firing case represents 15 per cent biomass feedstock and the remaining fuel is coal or other fossil based feedstock. Gasification production costs are comparable to existing renewable energy technologies, such as hydroelectric power. Even though the production costs are not any higher during the forecast period, the initial capital investment costs preclude this technology from further consideration.

Conclusions and recommendations Biomass co-firing for electricity generation as an option The co-firing case is the most financially viable alternative. This is in part because the initial capital investment is lower than other alternatives. Also, CO2 emissions reduction from substitution of woody biomass for coal is very desirable, because coal is the highest emitting fossil for electricity generation. The percentage of co-firing with biomass feedstock could also impact the decision. It was assumed in the capital investment analysis that the co-firing with biomass represents 15 per cent of the electricity generation. Varying the percentage of biomass used for co-firing could impact or change the decision.

$0.026 $0.036 $0.027 $0.025 $0.026


$0.027 $0.036 $0.027 $0.025 $0.026

2008 $0.027 $0.036 $0.027 $0.025 $0.026

2009 $0.027 $0.036 $0.027 $0.026 $0.027

2010 $0.027 $0.036 $0.027 $0.026 $0.027

2011 $0.028 $0.037 $0.027 $0.026 $0.027

2012 $0.028 $0.037 $0.028 $0.026 $0.028

2013

2007

$7.73 $10.41 $7.80 $7.34 $7.59

Location


$7.78 $10.48 $7.85 $7.37 $7.68

2008 $7.85 $10.52 $7.90 $7.43 $7.76

2009 $7.93 $10.58 $7.93 $7.49 $7.84

2010 $7.99 $10.64 $7.98 $7.53 $7.93

2011

$8.07 $10.74 $8.05 $7.59 $8.03

2012

$8.15 $10.85 $8.14 $7.66 $8.14

2013

Table 10.18 Biomass co-firing electricity production cost by location, 2007–2016 ($ per MMBtu)


2007

Location

Table 10.17 Biomass co-firing electricity production cost by location, 2007–2016 ($ per kWh)

$8.23 $10.95 $8.21 $7.72 $8.25

2014

$0.028 $0.037 $0.028 $0.026 $0.028

2014

$8.31 $11.05 $8.30 $7.78 $8.34

2015

$0.028 $0.038 $0.028 $0.027 $0.028

2015

$8.41 $11.11 $8.39 $7.86 $8.46

2016

$0.029 $0.038 $0.029 $0.027 $0.029

2016

$0.056 $0.060 $0.066 $0.057 $0.058


$0.057 $0.061 $0.066 $0.057 $0.059

2008 $0.057 $0.061 $0.067 $0.058 $0.059

2009 $0.058 $0.062 $0.067 $0.058 $0.060

2010 $0.058 $0.063 $0.068 $0.059 $0.061

2011 $0.059 $0.063 $0.068 $0.060 $0.061

2012 $0.060 $0.064 $0.069 $0.060 $0.062

2013

2007

$16.47 $17.64 $19.22 $16.63 $17.04

Location


$16.63 $17.81 $19.37 $16.79 $17.20

2008 $16.80 $17.98 $19.53 $16.96 $17.37

2009 $16.96 $18.15 $19.70 $17.13 $17.55

2010 $17.14 $18.33 $19.86 $17.31 $17.73

2011

$17.32 $18.52 $20.04 $17.49 $17.92

2012

$17.50 $18.71 $20.21 $17.68 $18.11

2013

Table 10.20 Biomass gasification electricity production cost by location, 2007–2016 ($ per MMBtu)


2007

Location

Table 10.19 Biomass gasification electricity production cost by location, 2007–2016 ($ per kWh)

$17.69 $18.90 $20.40 $17.87 $18.31

2014

$0.060 $0.065 $0.070 $0.061 $0.062

2014

$17.88 $19.11 $20.58 $18.07 $18.51

2015

$0.061 $0.065 $0.070 $0.062 $0.063

2015

$18.08 $19.31 $20.78 $18.28 $18.72

2016

$0.062 $0.066 $0.071 $0.062 $0.064

2016

234


However, even if the biomass percentage is increased, the marginal costs associated with using this feedstock are lower and would not likely change the financial outcome (Johnson et. al. 2008). The risks are much lower for co-firing as this requires a modification of existing technology as opposed to the introduction of a new technology or major process innovation. Most of the risk factors indicated in this study do not apply to this alternative, with the exception of feedstock availability. Biomass gasification for electricity generation as a substitute The capital investment required for the equipment and installation associated with biomass gasification is large and the initial investment cannot be recouped from the revenue charged on a per kWh basis in any of the regions under study. This alternative, even without the inherent risks of feedstock availability, technology risk, financial and business risk, should not be considered further at this time (Johnson et. al. 2008). Overall feasibility In evaluating renewable energy systems, they must be economically feasible, environmentally acceptable, and technically viable. These key factors must mesh in the decision making process in order to proceed. Each of the factors is discussed below (Wimberly 2005: 53):

•

•

•

Economic feasibility: in order to be deployed, commercial-scale bioenergy enterprises have to compete economically with large-scale systems using fossil fuel (e.g. on a $ kWh−1 or $ gal−1). It’s not good enough to just break even . . . the economic performance and profit margins of a bioenergy enterprise must be sufficiently high to attract the necessary capital investment. Environmental benefits: dedicated energy crops and associated bioenergy facilities are closed-loop (i.e. they are carbon-neutral). Carbon emitted to the atmosphere during processing will be recaptured by energy crops during plant growth, thereby entailing zero net atmosphere carbon contribution. Technical viability: first, all system components must actually work, i.e. technological risk (at both farm and processing levels) should be minimal. Second, the net energy balance of the enterprise must be positive and should be as high as possible.

Overall enterprise performance should be optimal, and can be measured in megawatt-hours per acre per year (MWh ac−1 yr−1) or gallons per acre per year (gal ac−1 yr−1) for electricity and biofuels production enterprises, respectively. These units essentially reflect the net yield of an enterprise and the efficiency of use of production and processing assets.


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References Accola, W.L. (1994) ‘Assessing risk and uncertainty in new technology investments’, Accounting Horizons, 8?3): 19–35. Bureau of Labor Statistics, U.S. Department of Labor (BLS) (2005a) Producer Price Indexes, Available: http://www.bls.gov/ppi/home.htm (accessed 30 July 2006). —— (2005b) Compensation Cost Trends. Available: http://www.bls.gov/ncs/ect/ home.htm (accessed 30 July 2006). DeMeo, E., Schweizer, T., Bain, R., Craig, K., Comer, K., Entingh, D. et al. (1997) Renewable Energy Technology Characterizations, Office of Utility Technologies, Energy Efficiency and Renewable Energy, U.S. Department of Energy and Electric Power Research Institute, TR-109496, Washington, D.C. Energy Information Administration (EIA) (2006) Annual Energy Outlook 2005, DOEEIA-0383, U.S. Department of Energy, Washington, D.C. Available: http:// www.eia.doe.gov/oiaf/aeo/index.html (accessed 15 March 2006). Froese, R., Waterstraut, J.R., Johnson, D.M., Shonnard, D.R., Whitmarsh, J.H. and Miller, C.A. (2008) ‘Economic and financial viability of lignocellulosic ethanol’, Environmental Quality Management, forthcoming. Internal Revenue Service (IRS), U.S. Department of Treasury (2006) ‘Definition of excise tax’. Available: http://www.irs.gov/businesses/small/article/0,,id= 99517,00.html (accessed 19 March 2008). International Energy Agency (IEA) (2003) Creating Markets for Energy Technologies, Paris: OECD Publication Services. Johnson, D.M., Froese, R.E., Waterstraut, J.R., Whitmarsh, J.H., Martin, A. and Miller, C.A. (2008) ‘Business viability of biomass co-firing and gasification for electricity generation’, International Journal of Environment and Waste Management, forthcoming. Mann M.K. and Spath, P.L. (2001) ‘A life cycle assessment of biomass cofiring in a coal-fired power plant’, Clean Technologies and Environmental Policy, 3: 81–91. Schilit, K. (1994) ‘Evaluating the performance of venture capital investments’, Business Horizons, Available: http://www.findarticles.com/p/articles/mi_m1038/ is_n5_v37/ai_15859254 (accessed 10 July 2006). Wimberly, J. (2005) ‘Pursuing realistic opportunities in home-grown energy’, BioCycle, 46(6): 53–57.

Appendix: supporting economic indices Overview There are several economic performance indices that are used in the development of cost and financial forecasts. There are also country indicators/ measurements that evaluate the economic impact from decisions associated with the renewable energy markets. Several indices are used to adjust the monetary units to the appropriate year, translate exchange rates, and to forecast future costs.

236


Consumer price index and inflation The harmonized consumer price indices are shown in Table 10A.1. These values represent the average purchase price of all consumer commodities and can be used to measure yearly inflation. PPI for capital equipment The producer price index (PPI) for capital equipment in Table 10A.2 was used to adjust the equipment costs for the biomass co-firing and gasification cases. PPI for industrial commodities less fuels The PPI for industrial commodities less fuels in Table 10A.3 was used to adjust several costs in which a specific index could not be found. These items included urban wood waste, water and waste disposal. PPI for industrial commodities The PPI for all industrial commodities in Table 10A.4 was used to adjust the operating costs in the biomass gasification case since the details were not sufficient enough to use multiple indices. Table 10A.1 Consumer price indices, 1996–2005 1996 U.S.

1997

1998

1999

2000

2001

2002

2004

2005

100.0 102.3

103.9

106.2

109.8

112.9

114.7 117.3 120.4

124.5

1.56 per cent

2.21 per cent

3.36 per cent

2.85 per cent

1.58 per cent

3.39 per cent

Percent Change

2.29 per cent

2003

2.28 per cent

2.66 per cent

Source: BLS (2005b).

Table 10A.2 PPI for capital equipment, 1997–2005 Year

1997

1998

1999

2000

2001

2002

2003

2004

2005

Index

100.0

99.6

99.6

100.4

101.1

100.7

100.9

102.3

104.6


Table 10A.3 PPI for industrial commodities less fuels, 1997–2005 Year

1997

1998

1999

2000

2001

2002

2003

2004

2005

Index

100.0

99.9

100.2

102.4

102.8

102.7

104.2

108.8

113.7



237

Table 10A.4 PPI for all industrial commodities, 1997–2005 Year

1997

1998

1999

2000

2001

2002

2003

2004

2005

Index

100.0

97.5

98.4

104.0

105.2

102.7

108.2

115.0

123.4


Interest rates The interest rates for financing capital for a start-up plant vary depending upon several factors, including the maturity of the technology. For start-ups that have already been commercialized and proven, the interest rates for financing are typically between 10 and 20 per cent. However, financing a start-up that has not been commercialized or proven is a riskier venture, and may require monetary resources from venture capital firms (Schilit 1994). According to a study conducted by the Joint Economic Committee (JEC) of the U.S. Congress, venture capital firms expect a minimum rate of return of 30 per cent on an investment (Schilit 1994). Tax rates For simplicity, a flat rate of 35 per cent was used for all cases. Excise taxes – energy products Excise taxes are paid on the manufacture or sale of a specific good or activity such as gasoline, cigarettes, liquor, firearms, road use, or gambling, either per unit or ad valorem (a percentage of the item’s value). One of the major components of the government excise program is motor fuel (IRS 2006).

11 Willow biomass production for bioenergy, biofuels, and bioproducts in New York Timothy A. Volk and Valerie A. Luzadis

Introduction The sustainable production of biomass as feedstock for biofuels, bioproducts, and bioenergy is a critical national priority due to concerns about energy security, environment and human health, rural economic development, and the need to diversify products and markets for the forestry and agriculture sectors. Biomass can come from a variety of sources including forests, agricultural crops, residues from the agriculture and forestry sectors, and dedicated woody or herbaceous crops. A recent study indicates that under a high yield scenario 1.2 billion oven dry tonnes (odt) of biomass could be produced in the U.S. by 2030 in addition to the production of food and fibre to meet traditional needs (Perlack et al. 2005). Under the conditions in this scenario, forestry resources will supply almost 27 per cent of this biomass. The agriculture sector will supply the rest. Perennial woody and herbaceous energy crops are included in the biomass from agriculture and would provide 3.41 million dry tonnes of biomass by 2030, which is about 35 per cent of the annual production from agricultural sources. Their deployment will put over 24 million hectares (ha) of land into production, create thousands of rural jobs, and produce several environmental benefits. There are over 40 million ha of idle or surplus agricultural land in the U.S. that could be used for the production of short-rotation woody crops (SRWC) (Graham 1994). Future energy crop production is most likely to occur on marginal agricultural land. Recent estimates indicate that 800,000 ha of such land, suitable for energy crop production, exist in New York alone (NASS 2005). The types of perennial energy crops that will be developed and deployed will vary by eco-regions that have different climate and soil quality characteristics. Shrub willow biomass crops are one of several perennial crops that have been identified as having high potential to contribute to the U.S. supply of biomass (Johnson et al. 2007). The shrub willow varieties that are currently being developed will be successful in north east and north central regions of the U.S., parts of the south east and south central U.S., and across much of Canada. This chapter will provide a case study of potential development of willow biomass in New York, its benefits, and economics.

Willow biomass production

239

Biomass produced from a woody perennial energy crop such as shrub willow can be easily combined with woody biomass from forests and manufacturing residues to provide a consistent and reliable year-round feedstock stream for the production of power, heat, fuels and other bioproducts. Because the chemical and thermal characteristics of willow are similar to other hardwoods, the ratio of willow to hardwoods in the supply can vary depending on the availability of material. In much of the area where willow is expected to be grown, there are also large quantities of woody biomass from forests that can be harvested sustainably. The net annual incremental growth on over 200 million ha of timberland across the U.S. exceeds removals by almost 50 per cent. The figures for North east and North central U.S. are 125 per cent and 95 per cent respectively (Smith et al. 2001). Sixty-three per cent of the land cover in New York is forest, which equates to 7.5 million ha of forestland. Of this forestland, 6.2 million ha are classified as timberland. The net annual incremental growth rate of the growing stock on this timberland is 317 per cent greater than the current harvest rate (Smith et al. 2001). After accounting for current removals for solid wood products, an estimated 5.6 million odt of woody biomass is technically available annually in New York, assuming that only 75 per cent of the net annual growth of growing stock is harvested along with 1 per cent of the standing biomass of the nongrowing stock. Much of this is low value material that has been previously used in the production of pulp and paper. However, the number of pulp and paper operations in New York has been declining for decades – in 1939 there were 42, but they had declined to 18 by 1963 and to five by 1999 (Canham and King 1999). Today there are only three active integrated pulp and paper operations in New York.

Willow biomass crops Interest in short-rotation woody crops (SRWCs) for the production of biomass has developed in Europe and North America over the past few decades because of the multiple environmental and rural development benefits associated with their production and use (Börjesson 1999; Volk et al. 2004; Rowe et al. 2008). SRWC development in the U.S. has concentrated on willow shrubs (Salix spp.) and hybrid poplar (Populus spp.), but other species have been explored and may be developed (Johnson et al. 2007). Willow shrubs, the focus of this chapter, have several characteristics that make them an ideal feedstock for biofuels, bioproducts and bioenergy: high yields that can be obtained in three to four years, ease of propagation from dormant hardwood cuttings, a broad underutilized genetic base, ease of breeding for several characteristics, ability to resprout after multiple harvests, and chemical composition and energy content similar to other northern hardwood species. A common misconception about willow is that it makes a poor choice for biomass production because it has a lower energy content. On a volume basis, the energy content of willow is lower than other hardwoods due to willow’s

240


lower specific gravity. However, the energy content of willow on a weight basis is similar to other hardwoods. The specific gravity of three-year-old shrub willow stems grown as a short rotation woody crop is 0.41 + 0.04 g cm−3. Similar aged hybrid poplar stems have a specific gravity of 0.35 + 0.02 g cm−3 (Tharakan et al. 2003). In contrast, the specific gravity of northern hardwoods is typically in the 0.5 to 0.6 g cm−3 range (Woodcock and Shier 2003). While the specific gravity of willow is lower, the energy content of willow on a weight basis is similar to other hardwoods. On a weight basis, three-year-old willow stems averaged 19.4 MJ kg−1 (Miles et al. 1996) compared to energy contents of 19.4 to 19.8 MJ kg−1 for northern hardwoods (White 1987). Since willow produces ten times or more biomass per ha than natural forests, the energy yield per ha is much greater. The development of shrub willow production systems for bioenergy and bioproducts is relatively new, but it builds on an extensive knowledge base and long history of use of these plants. The Romans systematically managed plantations of willow using coppicing for the production of vine trellis, rope, agricultural tools, baskets and furniture (Dickmann 2006). For centuries Native Americans in North America understood and made use of the beneficial attributes of shrub willows for medicinal purposes, as construction material for a wide array of items including sweat lodges, furniture, baskets, rope, whistles, arrows and nets (Moerman 1998), and for stream bank stabilization (Shipek 1993). In the mid-1840s, European immigrants transferred plant material, and knowledge of cultivation and basket production skills that formed the basis of a willow basket industry. By the late 1800s, the cultivation of willow for the production of baskets and furniture had spread from the shores of Maryland to the western borders of Wisconsin and Illinois. New York dominated willow cultivation and basket making at the time with 60 per cent of the total reported area and about 45 per cent of the income generated from willow products (Hubbard 1904). However, by the end of the 1800s demand for willow baskets and other products was declining due to competition from cheaper materials and competition from basket production overseas. By the 1930s only isolated pockets of willow cultivation for basket production remained. Interest in the cultivation of shrub willow was revitalized in the mid-1980s in upstate New York when the first research plots were planted to explore the potential of developing a biomass production system (White et al. 1992). Researchers in Sweden had been conducting research trials with shrub willow since the mid 1970s and had shown that when planted and managed as a perennial crop it had the potential to produce large amounts of biomass (Christersson et al. 1993). Initial trials in upstate New York focused on woodgrass systems with a density of almost 108,000 plants ha−1 and an annual harvest. While yields from this system with unimproved varieties of shrub willow exceeded 16 odt ha−1 yr−1 under irrigated and fertilized conditions (Kopp et al. 1993), it became apparent that establishment costs and the management of this system prohibited it from becoming commercialized. At the


241

same time studies in both Sweden (Willebrand et al. 1993) and North America (Kopp et al. 1997) on planting density and rotation length indicated that a three year rotation with a plant density between 15,000 and 37,000 plants ha−1 produced the highest mean annual yield with three- to four-year rotations. In irrigated and fertilized trials in New York, yields from three-year rotations were about 23 odt ha−1 yr−1 compared to 11–13 odt ha−1 yr−1 for annual harvests and 16–17 odt ha−1 yr−1 for biannual harvests with the same plant density (Kopp et al. 1997). Since that time the production system that has been studied and developed for commercial deployment builds on a model first developed in Sweden that uses a double row planting design and a three-year harvest cycle with an initial plant density of 15,400 plants ha−1. Alternative designs with a similar planting density based on a single row system have also been studied and deployed since the early 1990s in Quebec (Labrecque et al. 1994). The encouraging production results from the early trials with shrub willow in upstate New York and the growing concerns about the environmental impacts associated with fossil fuel consumption created enthusiasm among many groups in the region and an optimism that the system could be commercialized. As a result, over 20 academic, industrial, government, and environmental organizations joined together in the mid-1990s to form an entity called the Salix Consortium (Volk et al. 2006). Despite having different interests and perspectives, these organizations agreed on a common goal of facilitating the commercialization of willow biomass crops in the Northeastern and Midwest regions of the U.S. The Salix Consortium was awarded one of three national demonstration projects under the U.S. Department of Energy’s Biomass Power for Rural Development program in 1996. Consequently, the willow production system was developed and refined, and over 280 ha of shrub willow biomass crops were planted for R&D in New York. In addition to crop production research, willow biomass crops were tested at several conversion facilities as part of this project including being co-fired with coal at two pulverized coal plants in New York, gasified at the pilot facility at the McNeil power station in Burlington, Vermont, and tested in a fluidized bed boiler used to heat Colgate University in central New York. The crossdisciplinary nature of this project and the information it generated laid the foundation for developing a commercial production system for willow biomass crops. Since the early trials in central New York, yield trials have been planted in seven other states and at least four provinces in Canada (Kiernan et al. 2003; Smart et al. 2008). In addition to studies on potential yields of different varieties of willow across a range of sites, research has also been done on various aspects of the production cycle, including nutrient amendments and cycling, alternative tillage practices, incorporating cover crops into these systems, growth characteristics important for biomass production, use of willow plantations by birds, changes in soil microarthropod communities under willow, changes in soil carbon, economics of the production system, and life

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cycle assessments of willow bioenergy systems. In addition, a breeding and selection program for shrub willows has been developed and is producing improved varieties of willow for both the biomass production and agroforestry markets (Smart et al. 2008). Results from this and other initiatives in North America and Europe have provided a base from which to begin to expand and deploy willow biomass crops. Production system The shrub willow cropping system consists of planting genetically improved varieties in fully prepared open land where weeds have been controlled. Weed control usually involves a combination of chemical and mechanical techniques and should begin in the fall before planting if the field is in permanent cover, which is often the case with marginal land. It is essential to eliminate this permanent cover, and prepare the soil before willows are planted in the spring. Trials incorporating cover crops such as winter rye (Secale cereale L.) to reduce erosion during establishment have been successful, but approaches using no tillage or conservation tillage techniques still need to be developed to be effective (Volk 2002). Willows are planted as unrooted, dormant hardwood cuttings in the spring as early as the site is accessible at about 15,000 plants ha−1 using mechanized planters that operate at about 0.8 ha hr−1 (Figure 11.1). To facilitate the management of the site with agricultural machinery, willows are planted in a double-row system with 1.5 m between double-rows, 0.76 m between rows and 0.61 m between plants within the rows. Following the first year of growth, the willows are cut back close to the soil surface during the dormant season to force coppice regrowth, which increases the number of stems per stool from 1–2 to 8–13 depending on the variety (Volk 2002). After an additional three to four years of growth the stems are mechanically harvested and chipped using forage harvesters with specially designed heads (Figure 11.2) (Abrahamson et al. 2002; Volk et al. 2006). The chipped material is then delivered to end users for conversion to various bioenergy or bioproducts. The plants will sprout again the following spring when they are typically fertilized with about 100 kg N ha−1 (Abrahamson et al. 2002; Adegbidi et al. 2003). The willows are allowed to grow for another three to four year rotation before they are harvested again. Projections indicate that the crop can be maintained for seven rotations (Figure 11.2) before it is no longer accessible with harvesting equipment due to the expansion of the willows’ stools. A rapid growth rate is one of the attributes that make shrub willows an appealing biomass crop. Yields of fertilized and irrigated unimproved varieties of willow grown for three years have exceeded 27 odt ha−1 yr−1 (Adegbidi et al. 2001; Labrecque and Teodorescu 2003). Due to the costs associated with irrigation and the relatively low value for biomass, irrigation will not be used for most large-scale production operations, with the exception of situations where willow crops could be irrigated with wastewater as part of a


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Figure 11.1 The Step planter takes 1.5–2.5 m long dormant whips of one-year-old willow, cuts them into 15–20 cm long cuttings and inserts them into the ground. The planter can cover about 0.8 ha hr−1 at a planting density of 15,000 plants ha−1.

nutrient management plan. However, this work sets a benchmark for the potential of willow shrubs grown in this type of system, and higher yields may be possible with improved genetic material from breeding and selection programs. First-rotation, non-irrigated research-scale trials in central New York have produced yields of 8.4 to 11.6 odt ha−1 yr −1 (Adegbidi et al. 2001; Adegbidi et al. 2003). Second rotation yields of the five best producing varieties in these trials increased by 18–62 per cent compared to first-rotations (Volk et al. 2001). The large genetic diversity across the genus Salix and the limited domestication efforts to date provide tremendous potential to improve yield and other characteristics, such as insect and disease resistance, and growth form of willow biomass crops. Worldwide there are 350 to 500 species of willow (Kuzovkina et al. 2008), with growth forms ranging from prostrate, dwarf species to trees with heights of greater than 40 m. The species used in woody crop systems are primarily from the subgenus Caprisalix (Vetrix), which has over 125 species worldwide (Kuzovkina et al. 2008). While these species have many characteristics in common, their growth habits, life history, and resistance to pests and diseases vary, which is important in the successful development of woody crops. Willow’s ability for vegetative propagation

Figure 11.2 The willow biomass production system includes an establishment year followed by harvests on a three to four year rotation (indicated by the cycle of arrows). The willows re-grow rapidly after each harvest for seven or more harvest cycles (from Volk et al. 2004).


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means that once superior individuals with genetic gains are identified, they can be maintained and those individuals can be multiplied rapidly for deployment. To improve the economics by increasing yields and other characteristics associated with willow biomass crops, a breeding and selection program was initiated in the mid-1990s in New York (Smart et al. 2008). Breeding programs in Sweden and the UK (Kuzovkina et al. 2008) have produced large yield increases, but most of the improved varieties from these programs that have been tested in the State have been damaged by potato leaf hopper (Empoasca fabae), making them unsuitable for large scale deployment. Increasing the yield of willow biomass crops from 10 to 12.5 odt ha−1 yr−1 more than doubles the internal rate of return (IRR) of the crop from 4.1 to 9.0 per cent with all other factors held constant. A 50 per cent increase in yield from 10 to 15 odt ha−1 yr−1 more than triples the IRR (4.1 to 12.8 per cent). Since the late 1990s over 700 accessions have been collected from around the world and over 575 controlled pollinations have been attempted producing about 200 families (Smart et al. 2008). Selection trials of new varieties from the breeding and selection programs have produced yields that are up to 40 per cent greater in the first rotation than the standard varieties used in early yield trials. Second rotation results from these same trials indicate that the yield of some of the new willow varieties is more that 70 per cent greater than the standard varieties. These results indicate that there is a large potential to make use of the wide genetic diversity of shrub willows to improve yields with traditional breeding and selection. Commercial development The recognition of the essential role of biomass feedstocks to meet the nation’s demand for bioenergy, biofuels and bioproducts has resulted in a recent surge of interest in the potential of shrub willows to be part of a long term, sustainable supply of woody biomass in the north east and north central U.S. As a result, several businesses have engaged in addressing key bottlenecks in the production system that limit commercial development of the crop. One of these barriers has been the availability of large quantities of shrub willow planting stock. Over the past two years a commercial nursery in western New York, Double A Willow, planted over 30 ha of willow nursery beds to meet the projected annual demand for millions of cuttings for planting stock. Commercial sales of willow planting stock began in the winter of 2007, and are projected to reach about 5 million cuttings in 2008 and increase to 15 million cuttings in the winter of 2008 (Rak 2008). In 2006, the first commercial willow biomass crops in North America were established by Catalyst Renewables in upstate New York, although the area was limited by the amount of available planting stock at that time. Catalyst Renewables’ willow expansion goals are to establish about 8,000 ha in the next four years to support the production of bioenergy and biofuels (Benson 2008).

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Another significant bottleneck in the willow biomass production system has been how to efficiently and economically harvest the crop and produce a consistent quality product that is acceptable to end-users. Since 2004, Case New Holland (CNH) has been working with SUNY-ESF to develop a harvesting system for willow biomass crops based on the New Holland forage harvester fitted with a specially designed willow cutting head (Figure 11.3). Trials with this systems indicate that for three-year-old willow biomass crops with the majority of stems < 75mm in diameter, consistent high quality chips (> 95 per cent of the chips being smaller than 37.5 mm) can be produced with the harvester at a rate of about 0.8 to 1.6 ha hr−1. Unpublished economic analysis at SUNY-ESF indicates that harvesting comprises one third of the final delivered cost of willow biomass, so efforts are ongoing to improve the current harvesting system. The near-term end uses for willow biomass are for heat and/or electric power production, which will require the establishment of thousands of hectares of willow in the next five years. One coal-fired power plant (AES Greenidge) is retrofitting for co-firing with a capacity of 15 MW from woody biomass. The potential for co-firing using woody biomass in New York alone is about 300 MW (State of NY PSC 2005). The 19 MW CHP Lyonsdale Biomass plant has tested over 100 tonnes of willow biomass in its system and is actively developing additional acreage of willow biomass crops. In addition, there are several woody biomass projects for the production of heat,

Figure 11.3 A specially designed cutting head mounted to a New Holland forage harvester is being developed to cut and chip willow biomass crops in a single pass.


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power and/or biofuels being developed in central New York, ranging in size from less than 1 MW to over 20 MW (Figure 11.4). Other facilities are being discussed, but firm commitments to develop the projects have not occurred. The development of these facilities, which would have the capacity of producing over 100 MW, would be fuelled with a mixture of willow and other sources of woody biomass. Since the production of 1 MW of power requires about 325 ha of willow biomass crops, the development of several of these facilities would create a demand for the establishment of thousands of hectares of willow biomass in central New York in the next few years. In the near future, biofuels, bioproducts and bioenergy will be produced from willow biomass and other hardwoods in a wood-based biorefinery. A version of this biorefinery that is currently being tested at the pilot scale is based on the autohydrolysis of wood to produce hemicellulose sugars and acetic acid prior to using the wood chips for bioenergy or pulp production. The sugars will be used in ethanol production facilities and/or to produce other bioproducts. Research has shown that both willow and mixed northern hardwoods provide good yields of sugars and acetic acid (Amidon 2006; Blowers 2003; Liu et al. 2006). Estimates indicate that the sugars from one tonne of wood should yield over 45 liters of ethanol. The remaining 85 per cent of the wood chip mass is used for renewable power, pulp or other products. The acetic acid produced during this process is valued at about $1.00 kg−1, so although it is only a small portion of the total mass, it can add significant value to a tonne of woody biomass. In December 2006 the New York Department of Agriculture and Markets awarded a $10.3 million grant to Catalyst Renewables with partners O’Brien and Gere and SUNY-ESF to construct and operate a pilot plant at the 19 MW Lyonsdale biomass facility (Figure 11.4). Construction of the facility began in 2008, and it should be operating in early 2009 with an annual capacity of 5 × 105 liters of ethanol. A reliable, consistent and affordable long-term supply of biomass is essential to the success of any bioenergy or biofuels project. The figure above indicates that over 100 MW of bioenergy capacity could be installed in New York in the next few years if all of the highlighted projects are brought online. This raises concerns about the availability of woody biomass in this region, and whether it can be supplied to these projects sustainably over a long time period. An assessment of the woody biomass resources that are technically available from natural forests and willow biomass crops in a 40 km radius around Syracuse was recently completed (Castellano et al. 2008). Forests cover 46.5 per cent (236,555 ha) of this supply shed, but just over 70,000 ha were determined to be inaccessible because they were in protected areas, in parcels too small to manage, too steep for harvesting, or in areas designated as wetlands. There are 223,000 odt of woody biomass available from the accessible portions of these forests after assuming that only 76 per cent of the net growth of growing stock including current removals is available, 65 per cent of the harvesting residues are recovered and 1 per cent of the

Figure 11.4 Woody biomass projects that are active or being developed in central New York that may include willow as part of their feedstock supply.


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noncommercial species are used. Depending on the conversion efficiency of the systems used, this woody biomass could provide between 34 and 60 MW of power. About 35.3 per cent (179,430 ha) of the area in this supply shed is classified as agricultural land cover. After removing areas where parcels are too small to manage, the slope is too steep for harvesting equipment or access would be limited because it is classified as a wetland, about 67,880 ha would be suitable for willow biomass crops. Assuming that 10 per cent of this land was used for willow production (3.5 per cent of the total land area in the supply shed), and yields were 11.25 odt ha−1 yr−1, there is the potential to produce another 76,363 odt of woody biomass each year, enough for an additional 12–20 MW of power. These figures represent an assessment of technically available biomass and do not account for factors such as landowners’ attitudes and opinions about harvesting forested areas or producing willow, collection and transportation limitations, costs associated with production and harvesting biomass, or alternative uses for the land or biomass. Even if only half of the technically viable biomass could be used for bioenergy facilities, there is the potential to support 23–40 MW of power production from a 40 km supply shed around Syracuse with woody biomass from forests and willow biomass crops. Additional supplies of woody residues are also available in the region that could increase the amount of available biomass and bioenergy that could be produced. An assessment of the available biomass supply is needed for the other proposed projects in the area since their supply sheds overlap and there is the potential for competition for supplies. Ecological and environmental benefits Willow biomass crops are being developed as sustainable systems that simultaneously produce a suite of ecological and environmental benefits in addition to a renewable feedstock for bioproducts and bioenergy (Volk et al. 2004; Rowe et al. 2008). The perennial nature and extensive fine-root system of willow crops reduces soil erosion and non-point source pollution relative to annual crops, promotes stable nutrient cycling and enhances soil carbon storage in roots and the soil (Ranney and Mann 1994; Aronsson et al. 2000; Tolbert et al. 2002; Ulzen-Appiah 2002). In addition, the crop is constantly in its rapid juvenile growth stage, so the demand for nutrients is high, resulting in very low leaching rates of nitrogen even when rates of applications exceed what is needed for plant growth (Adegbidi 1999; Mortensen et al. 1998; Aronsson et al. 2000). The period with the greatest potential for soil erosion and nonpoint source pollution is during the first 1.5 years of establishment of the crop when cover is often limited because weeds need to be controlled and the willow canopy has not closed. The use of a winter rye cover crop has proven to be effective at providing cover for the soil without impeding the establishment of the willow crop (Volk 2002). Since herbicides are only used to control weed competition during the establishment phase of willow biomass

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crops, the amount of herbicides applied per hectare is about 10 per cent of that used in a typical corn–alfalfa rotation in upstate New York (Figure 11.5). Nutrient removal from willow biomass crops is limited because only the aboveground woody portion of the crop is harvested after the leaves have dropped and nutrients have been translocated to the root system. Nutrients that are not translocated from the foliage are returned to the system in litter, which when mineralized can supply between one-third and two-thirds of the annual nutrient demand for established willow biomass crops (Ericsson et al. 1992). For willow biomass crops with typical production rates of 10–12 odt ha−1 yr−1, nutrient removal is in the range of 60–70 kg N ha−1 yr−1, 8–10 kg P ha−1 yr−1, 20–24 kg K ha−1 yr−1, 53–55 kg Ca ha−1 yr−1, and 4 kg Mg ha−1 yr−1 (Adegbidi et al. 2001). For most soils in the region where willow is being deployed, the only nutrient addition that is recommended is N, which is typically added at the rate of about 100 kg N ha−1 once every three to four years in the spring after the crop is harvested. Since the current recommendations are general and do not always produce the desired effect, soil testing should be conducted at each site to ensure that nutrient levels are being maintained. The recommended planting scheme for willow biomass crops is designed to maintain both genetic and structural diversity across a field and the landscape. Blocks of four or more willow varieties from different diversity groups should be planted in each field. Varieties from the SUNY-ESF selection and breeding program with similar genetic makeup have been grouped together, so that people who are establishing the crop can easily determine which varieties should be mixed across a field. This practice is recommended so that the

Figure 11.5 Amount of herbicide applied to willow biomass crops is about 10 per cent of that used on a three-year corn – three-year alfalfa (CCCAAA) rotation in upstate New York.


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structural and functional diversity of the system across the field is improved and any potential impact associated with pests and diseases in the future is reduced. In the UK the recommended planting practice is to randomly mix different varieties of willow within each row in order to reduce the spread and impact of leaf rust diseases (McCracken and Dawson 2001). At the landscape level, willow biomass crops will be in different stages of growth each year because they are managed on a three-year coppice cycle, which will further increase the structural diversity of the system. A study of bird diversity in willow biomass crops over several years found that these systems provide good foraging and nesting habitat for a diverse array of bird species (Dhondt et al. 2007). Thirty-nine different species made regular use of the willow crops and 21 of these species nested in them. The study found that diversity increased as the age of the willows and the size of the plantings increased (Figure 11.6), and that birds have preferences for some varieties of willow over others (Dhondt et al. 2004). Willow biomass crops supported a similar number of bird species as other natural ecosystems, such as early successional habitats and intact eastern deciduous forest natural ecosystems (Figure 11.7). So, rather than creating monocultures with a limited diversity across the landscape, willow biomass crops should increase diversity, especially in contrast to the open agricultural land that it will replace. Soil microarthropods are essential in the decomposition of organic matter and nutrient cycling in soils, and have been used as an indicator of

Figure 11.6 Diversity of bird species in willow biomass crops as a function of growth year and size from a multi-year study (from Dhondt et al. 2007). Filled points are fields > 3 ha and open circles are plots < 3ha. The slope of the regression lines is significantly different (p < 0.001).

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Figure 11.7 Comparison of species diversity in willow biomass plots that were greater than 3 ha in size and 4–5 years old compared to data for other habitats from the breeding bird census (from Dhondt et al. 2007). Numbers above the symbols represent the sample size. Symbols represent the interquartile range with the median number of species and whiskers indicate the range.

below ground biodiversity and soil health. Studies have shown that following a combination of mechanical and chemical weed control and planting, the diversity and density of soil microarthropods is similar to what is found in agricultural fields of annual agricultural crops, but lower than in undisturbed fallow fields. Within four years, both the diversity and density of soil microarthropods in willow biomass crops had risen to levels similar to nearby undisturbed fallow fields (Minor et al. 2004). These levels should be maintained or enhanced over time since the willow crop is perennial, and herbicides are not used to maintain them once they are established. Life cycle analysis of willow biomass crops has shown that they are CO2 neutral, i.e. the amount of CO2 taken up and fixed by the crop during photosynthesis is equal to the amount of CO2 that is released during the production, harvest, transportation and conversion of the biomass crop to renenwable energy (Heller et al. 2003). The cycle is balanced for all the CO2 inputs into the atmosphere from the system, because only the aboveground portion of the willow biomass crop is harvested and used in the conversion process. When willow biomass is used to offset fossil fuels, it can help reduce the amount of CO2 emitted to the atmosphere. If the 40 million ha of available land in the U.S. were planted and harvested with SRWCs to offset coal use for power production, up to 76 per cent (0.30 Pg of C yr−1) of the carbon


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offset targets for the U.S. under the Kyoto Protocol could be met (Tuskan and Walsh 2001). The low input intensity of willow biomass crops relative to agricultural crops and their perennial nature result in a large, positive net energy ratio for the biomass that is produced. Accounting for all the energy inputs into the production system, starting with the nursery where the planting stock is grown through to the harvesting of biomass, converting it to chips and delivering it to the side of the field, results in a net energy ratio of 1:55 (Heller et al. 2003). This means that for every unit of nonrenewable fossil fuel energy used to grow and harvest willow, 55 units of energy stored in stored biomass are produced. Replacing commercial N fertilizers, which are produced with large inputs of fossil fuels, with organic amendments, such as biosolids, can increase the net energy ratio to 73–80 (Heller et al. 2003). Transporting the woody biomass 40 km from the edge of the field to a coal plant where it is co-fired with coal to generate electricity results in a net energy ratio of 1:10.9. If a gasification conversion system is used, the net energy ratio is slightly higher (Keoleian and Volk 2005). In contrast, the net energy ratio for ethanol produced from corn is 1:1.67 (Shapouri et al. 2002), and 1:0.4 for a natural gas system (Mann and Spath 1999). Rural development benefits Willow crops provide rural development benefits by diversifying farm crops, creating an alternative source of income for landowners, and circulating energy dollars through the local economy. Currently more than $2,900 per capita is exported from New York each year to purchase energy (NYSERDA 2007). Circulating some of those expenditures through the local economy to buy and add value to feedstocks through value added conversion processes would have a significant impact. Farm and farm-related jobs have steadily decreased in New York for the past two decades (USDA ERS 2002). This decline leaves untapped a source of knowledgeable workers who could likely make an easy shift to production of willow crops for biomass. About 75 direct and indirect jobs and over $520,000 in state and local government revenue would be generated annually for every 4,000 ha of willow crops (Volk et al. 2004). A recent assessment in the UK indicated that growing and producing willow biomass crops for renewable electricity would create between two and four full time jobs for every MWe of power (Thornley 2007). The link between local government empowerment and the location of an energy source has been shown to impact a rural community’s sense of self-determination, self-sufficiency and sustainability (Greenfield 2005). Historically, as the sources of energy moved outside the boundaries of local communities, both their social power and sense of self-determination suffered (Greenfield 2005). In our globalized economy, rural communities often feel powerless to direct their future well being, since local government authority is

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legally restricted to geopolitical boundaries. Willow biomass crops have the potential to help improve these rural communities by bringing specific economic development opportunities and by strengthening local community ties to energy production.

Economics of willow biomass crops Despite the numerous environmental and rural development benefits associated with willow and other perennial energy crops, their use as a feedstock for bioproducts and bioenergy has not yet been widely adopted due to several barriers, one of which is the current high cost of producing and delivering perennial energy crops to end users. Under current management practices and yields, one hectare of willow produces about 10.9 dry tonnes of biomass (210 GJ of energy) each year at a cost of about $63.00 per dry tonne ($3.00 GJ−1) (Tharakan et al. 2005). This cost is only slightly higher than current coal prices, but lower than prices for natural gas. Demand for wood manufacturing residues is increasing, especially as the demand for wood pellets increases, which is pushing up the price of wood manufacturing residues in the region. Recent unpublished assessments by SUNY-ESF of wood residue availability in upstate New York indicate that it is becoming less available, and that prices have risen to the $27.5–55 tonne−1 range. As prices of these alternative sources of woody biomass increase and the demand for biomass expands, willow biomass crops will become more cost competitive, since the biomass from this crop can be secured over the long term and is currently not in demand in other traditional wood markets. Willow biomass crops and other SRWC are essential components of biomass feedstocks to meet the nation’s demand, but feedstock production, markets and supply systems are in their infancy. Significant improvements can be made in the production systems such as increases in yield, as noted earlier, and reductions in harvesting costs, which will reduce the cost of production while maintaining the environmental attributes of the system. Improving the planting designs in order to increase the efficiency of harvesting operations can reduce production costs over the seven rotations of the willow crop. Increasing the length of the rows of the willow crop reduces the amount of time that is spent turning the harvester around at the end of the field, which reduces harvesting costs. For example, according to unpublished assessments at SUNY-ESF, increasing the length of the row from 100 to 300 m can reduce the harvest cost from $25.85 to $18.70 tonne−1 over 40 ha of willow biomass crops. Alternatively the implementation of federal or state policies that create a monetary value for the environmental attributes of willow biomass crops would help to overcome economic barriers associated with their deployment. Some potential avenues of support are through green power pricing premiums, support for growers through the Conservation Reserve Program (CRP), and/or renewable energy tax credits (Tharakan et al. 2005). Willow


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biomass crops have been approved as a permanent cover on CRP land in New York, which allows the producer to recover 50 per cent of the establishment costs and receive an annual rental payment of about $95 ha−1. Growing willow under the CRP program would reduce the delivered cost of production by about a third from about $63 to $40 tonne−1 (Tharakan et al. 2005). While these incentives are essential to jump-start the development and deployment of willow biomass crops in the near future, their long-term viability cannot be assured. To make willow biomass economically sustainable, it is essential both to reduce production costs and to increase the value of a tonne of willow biomass by developing new uses and markets. Several other non-technical barriers that limit the deployment and use of perennial energy crops have been identified in the U.S. and Europe. A lack of awareness of production and management approaches for perennial energy crops among potential producers, Policy Makers and end users has been identified as a barrier in several biomass projects (Helby et al. 2006; Hoffman and Weih 2005; McCormick and Kaberger 2007). It is essential to overcome this barrier early in the process of developing bioenergy systems, so that projects can be initiated and the supply of biomass from perennial energy crops is sustained for the life of the project. Another barrier that has been identified is the lack of a functioning and organized biomass supply chain that addresses the concerns and meets the needs of all the stakeholders in the bioenergy system (Hoffman and Weih 2005; McCormick and Kaberger 2007). The two key players in bioenergy systems are the end users and the biomass suppliers. However, there are often dozens or even hundreds of potential biomass suppliers, depending on the size of the project, while many end users only want to interact with a limited set of reliable suppliers. The development, coordination and consistent operation of a biomass supply chain are paramount to the success and long-term sustainability of any bioenergy project.

Conclusions In order to meet the projected demand for biomass for the production bioenergy, biofuels, and bioproducts in the U.S., perennial energy crops will need to be developed and deployed across millions of hectares over the next 25–30 years. Shrub willows have a long history of cultivation and use in the U.S. by Native Americans for a wide range of applications and later by Europeans for the basket willow industry. Over the past few decades, research in Sweden, the UK and U.S. has resulted in the development of a shrub willow production system for current use. Thousands of hectares of shrub willow crops have been deployed in Europe, and the system is beginning to be commercialized in the U.S., but the future of it as a sustainable system will depend on continued research on biological, ecological and socioeconomic factors, the development of a feedstock production and supply infastructure, and supportive renewable energy policies. Many of the characteristics of shrub willows, and the production system

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that has been developed, contribute to the sustainability of the system. The perennial nature of the plants, their extensive diffuse root system and the coppice management approach that has been developed, result in a crop that can be maintained and productive for more than two decades after it is planted. These characteristics create tight nutrient cycles and a permanent crop in the landscape that will improve soil and water quality and biological and landscape diversity relative to traditional annual agricultural crops. The CO2 neutral nature of the system and its high-energy return on investment are other key features of the system that contributes to its sustainability. Under existing policy structures the economics of willow biomass crops are marginal because of the relatively high cost of establishment, the low prices for woody biomass, and the limited experience with the crop. In addition to optimizing the production system and improving crop yields, changes in policies to support the commercial deployment of willow and other perennial energy crops in the near term are necessary to transition these crops to commercially viable systems. Once this occurs, the potential socioeconomic benefits associated with producing a marketable product from marginal agricultural land should begin to accrue to rural areas, especially if the crop and other sources of woody biomass are converted to higher value products such as bioenergy, biofuels and bioproducts. Since the biomass from shrub willow crops will be integrated with the supply of woody biomass from other sources, such as low-grade material from forests and residues from forest harvesting operations, there is potential for benefits to accrue to local communities from the revitalization of these sectors as well. The challenge is to simultaneously optimize the production system, to engage further interest from potential producers, and to develop long-term markets for willow biomass crops and other sources of woody biomass. To accomplish this and to develop a sustainable system, strong links between researchers, potential producers, and end users are required. With these links in place, the development of a vibrant willow biomass enterprise will play an important role in bolstering the farm and forestry sectors, while increasing energy independence, providing environmental benefits, and mitigating pollution problems.

References Abrahamson, L.P., Volk, T.A., Kopp, R.F., White, E.H. and Ballard, J.L. (2002) Willow Bioenergy Producer’s Handbook (rev), State University of New York College of Environmental Science and Forestry (SUNY-ESF), Syracuse, 31 pp. Adegbidi, H.G. (1999) Organic residual as soil amendments for willow crops: impact on biomass production and soil nitrogen dynamics. Ph.D. dissertation, SUNY-ESF, Syracuse. —— , Briggs, R.D., Volk, T.A., White, E.H. and Abrahamson, L.P. (2003) ‘Effect of organic amendments and slow-release nitrogen fertilizer on willow biomass production and soil chemical characteristics’, Biomass & Bioenergy, 25: 389–398.


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National Agricultural Statistics Service (NASS) (2005) ‘Rural landowner survey 2005, NASS and New York State Department of Agriculture and Markets, Albany, NY, 15 pp. NYSERDA (2007) Patterns and Trends: New York state energy profiles 1991–2005, Albany: New York State Energy Research and Development Authority, 82 pp. Perlack, R.D., Wright, L.L., Turhollow, A.F., Graham, R.L., Stokes, B.J. and Erbach, D.C. (2005) Biomass as Feedstock for a Bioenergy and Bioproducts Industry: the technical feasibility of a billion-ton annual supply, DOE/GO-102005–2135. Prepared by Oak Ridge National Laboratory for the U.S. Department of Energy and U.S. Department of Agriculture, Washington, D.C. Rak, D. (2008), Double A Willow, personal communication, February 2008. Ranney, J.W. and L.K. Mann. (1994) ‘Environmental considerations in energy crop production’, Biomass & Bioenergy, 6: 211–228. Rowe, R.L., Street, N.R. and Taylor, G. (2008) ‘Identifying potential environmental impacts of large-scale deployment of dedicated bioenergy crops in the UK’, Renewable & Sustainable Energy Reviews, in press. Shapouri, S.H., Duffield, J.A. and Wang, M. (2002) ‘The energy balance of corn ethanol: an update’, U.S. Department of Agriculture, Office of Energy Policy and New Uses, Agricultural Economics, Washington, D.C., Report No. 813, 14 pp. Shipek, F. (1993) ‘Kumeyaay plant husbandry: fire, water, and erosion management systems’, pp. 379–88, in T.C. Blackburn and K. Anderson (eds) Before the Wilderness: Environmental Management by Native Californians, Menlo Park, CA: Ballena Press. Smart, L.B., Cameron, K.D., Volk, T.A. and Abrahamson, L.P. (2008) ‘Breeding, selection, and testing of shrub willow as a dedicated energy crop’, NABC Report 19 Agricultural Biofuels, National Agricultural Biotechnology Council, Ithaca (in press). Smith, W.B., Vissage, J.S., Darr, D.R. and Sheffield, R.M. (2001) Forest Resources of the United States, 1997, USDA Forest Service, North Central Research Station, Gen. Tech. Rep. NC-219. State of New York Public Service Commission. (2005) ‘Case 03-E-0188 – Proceeding on motion of the commission regarding a retail renewable portfolio standard’, Order approving implementation plan, adopting clarifications, and modifying environmental disclosure program, Albany, 14 April. Tharakan, P.J., Volk, T.A., Abrahamson, L.P. and White, E.H. (2003) ‘Energy feedstock characteristics of willow and hybrid poplar clones at harvest age’, Biomass & Bioenergy, 25: 571–580. Tharakan, P.J., Volk, T.A., Lindsey, C.A., Abrahamson, L.P. and White, E.H. (2005) ‘Evaluating the impact of three incentive programs on cofiring willow biomass with coal in New York State’, Energy Policy, 33: 337–347. Thornley, P. (2007) Life Cycle Assessment of Bioenergy Systems. Available: http:// www.supergenbioenergy.net/Resources/user/Research%20Output/LCA%20Report%20(P%20Thornley)%20–%20SG%20Research%20Output.pdf (accessed 25 January 2008). Tolbert, V.R., Todd Jr., V., Mann, L.K., Jawdy, C.M., Mays, D.A., Malik, R. et al. (2002) ‘Changes in soil quality and below-ground carbon storage with conversion of traditional agricultural crop lands to bioenergy crop production’, Environmental Pollution, 16: S97–S106. Tuskan, G.A. and M.E. Walsh. (2001) ‘Short-rotation woody crops systems,

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atmospheric carbon dioxide and carbon management: a U.S. case study’, Forestry Chronicle, 77: 259–264. Ulzen-Appiah, F. (2002) Soil organic matter in experimental short-rotation intensive culture (SRIC) systems: Effects of cultural factors, season and age, Ph.D. dissertation, SUNY-ESF, Syracuse. U.S. Department of Agriculture Economic Research Service. (2002) ‘The economics of food, farming, natural resources, and rural America: New York farm and farm-related employment, 2002’. Available: http://www.ers.usda.gov (accessed 25 January 2008). Volk, T.A. (2002) Alternative methods of site preparation and coppice management during the establishment of short-rotation woody crops, Ph.D. dissertation, SUNYESF, Syracuse. Volk, T.A., Kiernan, B.D., Kopp, R. and Abrahamson, L.P. (2001) ‘First- and secondrotation yields of willow clones at two sites in New York State’, Fifth Biomass Conference of the Americas, 17–21 September, Orlando. Available: http:// www.brdisolutions.com/pdfs/bcota/abstracts/22/z146.pdf (accessed 22 January). Volk, T.A., Verwijst, T., Tharakan, P.J. and Abrahamson, L.P. (2004) ‘Growing energy: assessing the sustainability of willow short-rotation woody crops’, Frontiers in Ecology and the Environment, 2: 411–418. Volk, T.A., Abrahamson, L.P., Nowak, C.A., Smart, L.B., Tharakan, P.J. and White, E.H. (2006) ‘The development of short-rotation willow in the northeastern United States for bioenergy and bioproducts, agroforestry and phytoremediation’, Biomass & Bioenergy, 30: 715–727. White, E.H., Abrahamson, L.P., Kopp, R.F. and Nowak, C.A. (1992) ‘Wood biomassthe potential of willow’, Progress Report, November 1987–December 1990. New York State Energy and Research Development Authority, Report 92–2, Albany. White, R.H. (1987) ‘Effect of high lignin content and extractives on the higher heating value of wood’, Wood and Fiber Science, 19: 446–452. Willebrand, E., Ledin, S. and Verwijst, T. (1993) ‘Willow coppice systems in short rotation forestry: Effects of plant spacing, rotation length and clonal composition on biomass production’, Biomass & Bioenergy, 4: 323–331. Woodcock, D.A. and Shier, A.D. (2003) ‘Does canopy position affect wood specific gravity in temperate forest trees? Annals of Botany, 91: 529–537.

12 Woody biomass feedstock availability, production costs and implications for bioenergy conversion in Mississippi Donald L. Grebner, Gustavo Perez-Verdin, Changyou Sun, Ian A. Munn, Emily B. Schultz and Thomas G. Matney Introduction Mississippi’s forests cover approximately 8.1 × 106 ha and each year they generate over $1 billion worth of timber and related forest products (Munn and Tilley 2005). The forest products industries (logging, solid wood products, pulp and paper, and wood furniture manufacturing) on average process 3.6 × 107 m3 yr−1 (Howell et al. 2005; MAFES 2007; USDA 2007). The industry employed 54,853 people in 2001, roughly 3.7 per cent of the state’s total employment, and its impact on the state’s economy was about $13 billion (Munn and Tilley 2005). Wood furniture accounted for 44 per cent of these jobs, followed by the solid wood products industry (28 per cent), pulp and paper (13 per cent), and logging and miscellaneous forest products (15 per cent). Mississippi’s forests have experienced important changes in the last decade. On the one hand, more than 1.0 × 106 ha (8 per cent of total area) have been planted with trees as part of afforestation and reforestation activities (MFC 2007). A considerable portion of the planted areas included marginal agricultural land that, supported by various government programs, contributed to the increase of forestlands from 7.5 to 8 × 106 ha in the same period. On the other hand, roundwood production, especially pulpwood, decreased from 36 to 25 × 106 meters3 (Stratton et al. 1998; Howell et al. 2005; MAFES 2007). These changes are reflected in land use trends, stand composition, age, growth, and mortality. The increase in the area of new plantations, coupled with decreasing roundwood production, will result in an increasing amount of young forests that need thinning in the short term. One of the most promising uses of thinning and logging residues is bioenergy, specifically fuel ethanol production. Reasons favoring forest residues for ethanol production over corn starch or other agricultural feedstocks include a widespread resource base, low production costs, and high sugar yield per hectare (Hamelinck et al. 2005). Thinning and recovery of logging residues increase forest health by reducing competition for resources, generate

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additional revenue by utilizing forest by-products, and reduce the accumulation of material that feeds forest fires (Polagye et al. 2007; Solomon et al. 2007). In spite of these advantages, industries to process cellulosic ethanol from forest residues have been relatively slow to develop due to financing and resource uncertainty concerning biomass feedstock supply, production costs, and economic impacts as well as competition from starch-based ethanol production (Wyman 2003; Coleman and Stanturf 2006; Solomon et al. 2007). This chapter addresses some of the barriers that constrain cellulosic ethanol development from forest biomass. Specifically, feedstock availability, production costs, and economic impacts for the state of Mississippi are analyzed. The chapter begins with a description of the methods used to estimate availability of woody biomass feedstocks and production costs. It presents results that include feedstock estimates, production costs, and a sensitivity analysis. Then, a discussion on major implications for developing cellulosic ethanol in the state follows. The chapter closes with a brief summary and conclusions.

Woody biomass types and estimation Logging residues Logging residues are the branches, tops, bark, and other woody parts that are left on-site in typical harvesting operations. Recovery of these residues reduces fire risk and reforestation costs (Gan and Smith 2007). In this study, estimates of forest residues were based on Forest Inventory Analysis (FIA), a new statewide forest inventory, and timber production databases. The FIA Timber Products Output is a web-based interface that reports volume of logging residues for various years including 1995, 1999, and 2002 and for species group (softwoods and hardwood).1 The new satellite imagery-based forest inventory, developed by the Mississippi Institute for Forestry Inventory (MIFI), is being conducted to update changes in land use, stand structure, age, species composition, and type of forestlands by region and county (Figure 12.1). The information can be used to estimate the availability of forest resources, particularly small-diameter trees, and eventually associated production costs within user-defined regions. To compensate for unrecoverable residues, due to size, decay status, and dispersion, we applied a 65 per cent recovery factor and assumed that logging residues are removed during the harvest of conventional products (Grado and Chandra 1998; Perlack et al. 2005; Gan and Smith 2006). This assumption avoids additional harvesting and transportation costs incurred if residues were removed later. Availability of logging residues may also depend on ecological factors. Excessive extraction of logging residues produces loss of soil nutrients and in some cases promotes soil erosion (Sanchez et al. 2003). Sufficient residuals should be left on-site to minimize this potential effect. The amount of residues needed to compensate for the extraction of essential

Woody biomass feedstock in Mississippi

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Figure 12.1 Regions of the State of Mississippi: Northeast (NE), Delta (DE), Central (CE), Southwest (SW), and Southeast (SE). The source of the forest inventory database is indicated in parentheses: Forest Inventory Analysis (FIA) or Mississippi Institute for Forestry Inventory (MIFI).

nutrients (e.g. calcium, magnesium and potassium, and phosphorus) varies between 0.70 and 2 tonnes ha−1 per rotation period, depending on the region and local conditions (Börjesson 2000). Using MIFI information, we calculated the amount of standing stocks ha−1 for stem, branches, and leaves in each county. The proportion of leaves and the non-accessible share of logging residues (35 per cent) were added together to determine the total amount of biomass left on site. The resulting biomass left on site was greater than the amount recommended by Börjesson (2000) and therefore no additional adjustment to leave sufficient residues to compensate for extraction of nutrients was necessary.

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Small-diameter trees Removing excess small-diameter trees from overstocked stands has the potential to improve forest health, reduce fire hazard risk, and enhance other ecosystem services such as wildlife habitat improvement and recreation (Polagye et al. 2007). To estimate the amount of small-diameter trees that can be removed from overstocked stands for bioenergy purposes, stand density indices (Reineke 1933) were developed for each county and species group. A stand density index (SDI) measures the level of stocking a forest possesses and it is independent of the forest age and soil productivity (Davis et al. 2001). SDI is calculated as follows:

SDIij = Nij

qmdij c , 25

冢

冣

(12.1)

where SDI is stand density index, N is the average number of trees ha−1, qmd is the quadratic mean diameter (cm), and c is a constant (assumed to be 1.605). The subindices i and j stand for county and species group, respectively. The county-specific species group definition was based on the predominance of pine, hardwoods, or mixed forests. The SDI was compared to an empirically observed maximum SDI to determine a county-level thinning intensity rate. The maximum SDI is defined as the maximum density that a given site with a mean tree size of 25 cm can support (Woodall et al. 2005). Typically, this maximum SDI is determined through field observations of the most heavily stocked stand on the landscape (Woodall et al. 2005). The maximum, weighted SDI for pine forests are 1,242, hardwoods 1,028, and mixed forests 1,176 (Davis et al. 2001; Donnelly et al. 2001). Two key attributes are derived from the maximum SDI: 1) minimum site occupancy (crown closure), which occurs at 30 per cent of maximum SDI, and 2) mortality (self-thinning), which occurs at 50 per cent of maximum SDI (Doruska and Nolen 1999; Zeide and Zhang 2006). Thinning is scheduled in those counties above minimum site occupancy levels and below mortality levels. County-level thinning intensity rates were determined by calculating the proportion of number of trees ha−1 to be removed (i.e. to equal minimum site occupancy threshold) over total number of trees ha−1. Land covered by small-diameter biomass was estimated using archival satellite imagery classified into forest/non-forest map on an approximately five-year cycle dating from 1973 to 2002 (Collins et al. 2005). Assuming that thinning is applied at age 15, all juvenile stands (e.g., 15-year-old planted or naturally regenerated stands) were identified from this satellite imagery. The resulting thinning intensity rates, MIFI biomass estimates, and the area covered with juvenile stands were used to estimate the amount of smalldiameter trees (dry tonnes) for each county. In addition, an 80 per cent recovery rate and a 30-year planning cycle (Perlack et al. 2005) were applied


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considering local conditions such as road density, forest growth rates, and the dominance of the private landownership. Other types of woody biomass feedstocks included in this research are the portion of unused mill residues and urban waste. The information used to estimate unused mill residues availability was obtained from the FIA database (Timber Product Output). Urban waste data, which include construction and demolition waste, wooden pallets, packaging residues, tree and yard trimmings, was derived from annual reports of the Mississippi Department of Environmental Quality (MDEQ 2007). This agency records solid waste management at landfills, rubbish sites, and land application facilities. Each year, facility owners must submit a report to the agency on the solid waste management conducted at each disposal facility. This report provides information on the amounts of solid waste received at the waste management facilities for municipal solid waste, non-municipal solid waste, classes I and II rubbish sites, and land application facilities. From these reports we obtained the gross amount of waste and applied a recovery factor of 4 per cent for municipal and non-municipal solid waste (Wiltsee 1998) and 25 per cent for rubbish sites and land application facilities (Sandler 2003) to determine the urban waste potentially available for bioenergy conversion. Production costs The availability of woody biomass as a feedstock is restricted by delivered costs. These include the price paid to the landowner for the right to harvest (applicable to logging residue and small diameter trees), harvest and transportation costs, and profit to the logger. The latter is assumed to be 15 per cent of harvest and transportation costs (McCollum and Hughes 1983). Since there is currently no commercial production of ethanol in Mississippi, probability distribution functions were used to estimate a reasonable range of marginal costs given the cost variability of the various production components. Sources of cost information included Wyman 1999; McNeil Technologies 2003; Wyman 2003; Garrard and Leightley 2005; Perlack et al. 2005; Gallagher et al. 2006; Timber-Mart-South; and Petrolia 2006. Whenever woody biomass prices were unavailable, cost of pulpwood was substituted in calculations. Due to its low value, stumpage costs for woody biomass should be lower than conventional forest outputs such as sawtimber and pulpwood (Grebner et al. 2005). However, current low pulpwood prices and high biomass demand for energy are expected to steadily increase small-diameter tree utilization and thus affect stumpage price (Sun and Zhang 2006). We assumed that stumpage price ranges from $3.3 to $9 dry tonne−1 (Sun and Zhang 2006), harvesting costs range from $5.5 to $14.5 dry tonne−1 (Timber-Mart-South 2006–2007; Polagye et al. 2007), and transportation costs vary from $0.10 to $0.31 dry tonne−1 km−1 (McNeil Technologies 2003; Perlack et al. 2005; Timber-Mart-South 2006–2007). Cost differences are due to the

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characteristics of woody biomass (such as size, on-soil dispersion, and energy content), road conditions, equipment, and amount/type of supplies used. Thus, delivery price ranges from $16 to $38 dry tonne−1. To account for different procurement distances, we estimated the associated production costs for intervals from 40 to 240 km (25 to 150 miles), which includes 3.2 km of in-woods hauling. Centres of the supply areas were assumed to be the GISderived centroids of counties with woody biomass transported one-way from this centre to various destinations, including neighboring states. We assumed that all woody biomass types, except mill residues, are chipped on site and loaded into trucks for transportation to the manufacturing facility. Chipping and loading costs range from $4.4 to $6.6 dry tonne−1 (Gallagher et al. 2006; Petrolia 2006). All costs are shown in Table 12.1. Table 12.1 Production cost assumptions (most likely values) for woody biomass availability in Mississippi. Forecast values were generated for the parameter ‘sum of costs’ Costs

Logging residues

Harvest ($ dry tonne−1)a,*

Small-diameter Unused mill trees residues

Urban waste

6.41

13.95

0.00

0.00

7.67 0.12

7.67 0.12

7.67 0.12

7.67 0.12

13.67

13.67

13.67

13.67

Profit to logger ($ dry tonne−1)c

3.01

4.14

2.05

2.05

Residual stumpage value ($ dry tonne−1)a,b,*

5.18

6.60

0.00

0.00

28.27

38.36

15.72

15.72

5.58

5.58

0.00

5.58

4.63

6.07

20.35

27.37

Transportation Fixed ($ dry tonne−1)b,** Incremental ($ dry tonne−1km−1)a,b,* Cost (80-km radius) ($ dry tonne−1)

Delivery price ($ dry tonne−1) −1 d,

Chipping cost ($ dry tonne ) * Selling/separating ($ dry tonne−1)b,e,* Sum of costs ($ dry tonne−1)

33.85

43.94

Notes: To avoid negative values, we did not include a cost (residual value) for urban waste. We did include a separating fee (i.e., a cost for separating woody from non-woody or decomposed woody material) paid to the facility owner. Source: a Average for pine and hardwood pulpwood, and north and south zones Timber-Mart-South (2006–2007). b McNeil Technologies (2003). c Estimated at 15 per cent of harvest and transportation costs (McCollum and Hughes 1983). d Gallagher et al. (2006); Petrolia (2006). e Garrard and Leightley (2005). * Triangular probability distribution functions assumed. ** Uniform probability distribution function assumed.


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Due to the range of cost variation, Monte Carlo simulation was used to test several price scenarios of harvest, transportation, and stumpage costs for each woody biomass type. Monte Carlo simulation assigned different values to key cost parameters, such as stumpage value, fixed and variable transportation, and harvesting, according to a pre-established probability function. The process was repeated until precision control limits were reached and there were no significant variations in the iterations (confidence level at 95 per cent). Based on the cost bounds mentioned previously, triangular probability distributions were assumed for all key parameters, except fixed transportation costs. The triangular distribution is commonly used when the minimum, maximum, and most likely values are known. This distribution has three underlying conditions: 1) a minimum value that is fixed, 2) a maximum value that is also fixed, and 3) a most likely value which falls between the minimum and maximum values forming the triangular shaped distribution. Thus, this distribution shows that values near the minimum and maximum are less likely to occur than those near the most likely value. The values shown in Table 12.1 correspond to the most likely values. Uniform probability distribution function was assumed for fixed transportation costs. In this distribution, all values between the minimum and maximum occur with equal likelihood. Monte Carlo simulation was performed using a sequence of 20,000 trials for each woody biomass type. Minimum spatial distribution density and temporal analysis Due to size, bulk density (energy content), and dispersion not all woody biomass left on site may be recoverable for bioenergy use. A minimum spatial distribution density index (Gan and Smith 2006) was developed to estimate the amount of recoverable logging residues as a function of the distance required to transport raw material and the capacity of a manufacturing plant to utilize these products. If the spatial distribution density index for residues is low, then a large area is necessary to supply a processing facility. Longer distances and, therefore, higher transportation costs limit the amount of recoverable logging residues available to a bioenergy plant. The minimum spatial distribution index (Mmin) of woody biomass for a plant with a given capacity and distance can be written as (Overend 1982; Gan and Smith 2006): Mmin = 0.467

nτ2Pmin , φR¯ 2

(12.2)

where Pmin is the production plant capacity (dry tonne day−1); τ is the tortuousity factor that accounts for terrain effects and is calculated as the ratio of actual travel distance to line of sight distance; n is the number of supply slices to complete a circle, assuming a ‘pie slice’ shape for the harvest area with the processing plant at the apex; φ is the fraction of forestland in the area, and R¯

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is one-way distance expressed in km. The basic idea to assume a pie slice shape representing the harvest area was to facilitate the planning and collection process of woody biomass. For simplicity, the non-covered area resulting of packing the circles was excluded in estimations. Following Van Belle et al. (2003) and Gan and Smith (2006), R¯ was set to 80 km (50 miles), the number of slices n to 1, and τ = 1.35. We also set Pmin to 907 dry tonne day−1, although other values were considered in the sensitivity analysis. The minimum spatial distribution density index was compared to the actual index for each county. The actual index for county i (Mi) was estimated by dividing the annual availability (dry tonne year−1) by the area (km2). If for county i, Mmin > Mi, procurement of woody biomass for ethanol production is not feasible. All feasible counties were included in the analysis and their biomass production was equal to the annual availability. A temporal analysis of woody biomass availability was conducted to estimate future supplies by determining rates of change in all woody biomass types. Based on timber severance tax data (MAFES 2007), the ratio of logging residues to timber removals for past years was calculated. A rate of change was calculated and used to project constant trend based availability of logging residues for the years 2010, 2015, and 2020. Growth projection of small diameter biomass was based on MIFI/FIA estimated growth rates for each county and species group.

Availability of woody biomass After considering the spatial density restriction, annual woody biomass production in the state is about 3.6 × 106 dry tonnes. Of this amount, 69 per cent is logging residues, 21 per cent is small-diameter trees, 7 per cent is urban waste, and 3 per cent is from mill residues. Considering currently available ethanol production technology, (Hamelinck et al. 2005), enough woody biomass is available in Mississippi to produce 1.2 billion liters (318 million gallons) annually. Alternatively, it can be used to generate up to 7.4 × 106 MWh of electricity. Annual woody biomass projections by logging residues, small-diameter trees, mill residues, and urban waste through 2020 are given in Figure 12.2. Availability of logging residues decreased over time, driven by a reduction in pulpwood harvests, primarily hardwoods. In 1995, production of pulpwood from hardwoods was 14.3 × 106 m3 (506 × 106 ft3), decreased to 6.6 × 106 m3 (233 × 106 ft3) in 2002, and slightly decreased to 6.0 × 106 m3 (213 × 106 ft3) in 2006. In contrast, small-diameter biomass increased slightly due to a higher growth rate and reduced harvesting. Unused mill residues and urban waste from distances up to 240 km (150 miles) are less expensive than logging residues. However, mill residues and urban waste constitute only a small percentage of the total supply. Currently, many industries use mill residues for heat, electricity generation, or other outcomes. Garrard and Leightley (2005) found that about 45 per cent of the wood waste produced in north


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Figure 12.2 Projected annual woody biomass supplies available as a feedstock to produce ethanol in Mississippi from 2002 to 2020.

Mississippi was used for energy conversion and 30 per cent in the production of paper, particleboard, and other engineered wood products. For all woody biomass types, transportation costs accounted for 41 per cent of total production costs, followed by harvest costs (22 per cent), separation of urban waste costs (22 per cent), and stumpage price (15 per cent). Sensitivity analysis An initial Monte Carlo simulation, based on triangular probability distributions, indicated that most probability distributions for woody biomass production costs were positively skewed. Based on the Anderson–Darling goodness-of-fit test, we found that Gamma probability distribution functions were most appropriate for this simulation. The resulting statistics of Gamma probability distributions for each woody biomass type are shown in Table 12.2. The mean of marginal cost per dry tonne of logging residues, the largest feedstock, is $36 with a standard deviation of $3.01. Marginal costs for other woody biomass types are: small-diameter trees, $46 dry tonne−1; mill residues, $26 dry tonne−1; and urban waste $32 dry tonne−1. Rank correlation coefficients, which measure the strength of association between two components, indicate that the largest influence on total costs is incremental transportation costs (measured in $/dry tonne km−1). Incremental transportation cost had a rank correlation of 0.95 for all biomass types, which means that this cost component had the greatest impact on total production cost. Using weighted averages for all woody biomass types and various procurement distances, production costs most likely fall between $37 dry tonne−1 for

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Table 12.2 Forecast values of marginal production costs of woody biomass using Monte Carlo simulation for an 80-km procurement radius in Mississippi ($ dry tonne−1) Statistic

Logging residues

Small-diameter trees

Mill residues

Urban waste

Mean* Median Standard Deviation Minimum Maximum Trials Distribution fitted

35.99 35.63 3.01 27.56 45.72 20,000 Gamma

46.24 45.98 3.12 37.41 56.47 20,000 Gamma

25.87 25.56 3.13 18.02 36.92 20,000 Gamma

31.70 31.39 3.23 23.12 43.47 20,000 Gamma

* The weighted average for all woody biomass is $37 dry tonne−1 ($0.14 liter−1 of ethanol).

an 80-km (50-mile) procurement radius and $48 dry tonne−1 for a 160-km (100-mile) procurement radius. These numbers, fairly analogous with Wyman (1999), suggest that feedstock costs for ethanol production range from $0.14 to $0.18 liter−1 ($0.52 to $0.70 gal−1). However, other costs such as manufacturing, plant processing, engineering, and marketing are not considered. Thus, actual production costs for ethanol can be higher (cf. Chapter 3). Other parameters influencing cellulosic ethanol feedstock production are the current available conversion technology, stumpage price, size of the manufacturing plant, rate of residue recovery, and land converted to forestry uses (Table 12.3). Hamelinck et al. (2005) suggest that the current technological efficiency, based on dilute acid pretreatment and fermentation, can be increased up to 48 per cent if pre-treatment and other biotechnological processes are improved. This improvement would decrease production costs by 29 per cent. Other studies have demonstrated that conversion yields have greater impact than other parameters on production costs (Lynd 1990; Grado and Chandra 1998). Production costs also decrease as the size of the manufacturing plant increases. However, this decrease is because of the spatial distribution density restrictions that exclude counties with low spatial density. If the manufacturing facility increased from 900 to 4,500 dry tonnes day−1 more area would be required to supply the plant. Longer distances and low spatial densities would result in unpractical conditions to transport biomass. As a result, fewer counties would be incorporated in biomass supply estimation (ceteris paribus). Recovery rates of logging residues and small-diameter trees also have a great impact on the availability of woody biomass. Recovery rates depend on the size and distribution of wood residues and the type of equipment (Grado and Chandra 1998). High recovery rates translate into more biomass


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Table 12.3 Sensitivity analysis for ethanol production in Mississippi Parameter

Units

Land use changeb

Million of ha

Plant sizec Procurement radiusd

Dry tonnes day−1 km

Recovery rate

per cent

Stumpage price of logging residues Technological efficiency

$ dry tonne−1 per cent

Base estimation 7.7 907 80 65 5.3 35

Change A (% impact)a

Change B (% impact)a

20.4 (9%) 4,500 (−2.5%) 75 (14%) 75 (13%) 0 (−21%) 40 (−14%)

21.8 (19%) 9,000 (−4.7%) 100 (26%) 85 (23%) 14.5 (25%) 45 (−29%)

Notes: a Per cent changes based on estimated cost of production of $0.14 liter−1 ($0.52 gal−1) of ethanol. b Changes estimated over 10 (change A) and 20 years (change B). Woody biomass yield (ethanol) is assumed to be directly proportional to land use changes. c Plant size impact is based on equation (2). The per cent of impact decreased due to the restrictions of the spatial distribution density index. The index restricted recovery of forest residues in 26 (change A) and 40 counties (change B). d The spatial density distribution index allowed recovery of forest residues in 3 (change A) and 2 (change B) counties. In the base estimation, five counties were excluded.

recovered and ethanol produced. More biomass recovered implies utilization of more inputs, such as labor, consumables, and capital. Therefore, an increase on total delivery costs is expected. This increase, however, tends to be gradual as more quantities are supplied (Figure 12.3). Another parameter in the sensitivity analysis is land use changes. Over the last 20 years, forestland in Mississippi has increased from 6.9 to 8.0 × 106 ha, a rate of 0.8 per cent annually. If current land use change trends continue for the next 20 years, then Mississippi’s forestland should comprise about 8.9 × 106 ha. Assuming a direct relationship between land use change and woody biomass availability, the expected biofuel production at that change rate would be about 1.2 × 109 liters (329 × 106 gal) yr−1 from logging residues and small diameter trees alone. The sensitivity analysis provides a tool to identify factors with the largest impact on cellulosic ethanol development. For example, conversion of land to forestry uses coupled with increased recovery rate, result in higher feedstocks quantities. Medium-sized plants (1,000 to 2,000 dry tonnes day−1) are preferable than larger facilities due to concentration of supply areas, reduced transportation costs, and limited plant scale economies.

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Figure 12.3 Adjusted marginal cost curves of woody biomass feedstocks for procurement distances of 120, 80, and 40 km in Mississippi.

Implications for developing cellulosic ethanol industry in Mississippi The available woody biomass could easily sustain up to five manufacturing plants with an average feed rate of 1,800 dry tonnes day−1. The expected ethanol production from each plant would be about 197 × 106 liters (52 × 106 gal) yr−1. While the exact location of a plant requires detailed study, the five MIFI regions can serve as indicators for area location (see Figure 12.1). For instance, the Delta region, which had the lowest woody biomass production, can be joined with other regions to determine an optimal procurement area.2 Likewise, the Central and Southwestern regions had the highest biomass potential and combined can supply three 1.97 × 108 liter capacity manufacturing plants. Other scenarios are possible by varying plant capacity and procurement radius. Development of portable processing facilities to reduce transportation costs, as opposed to centralized manufacturing plants, is also possible. However, portable processing facilities may require sophisticated equipment and technology (e.g. fast pyrolosis) not currently available for commercial-scale ethanol production. We focus our analysis on centralized manufacturing plants and fermentation technology and discuss three types of potential impacts of developing cellulosic ethanol in Mississippi: 1) economic impacts, 2) resource utilization, and 3) land-use changes.


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Economic impacts One of the main advantages of developing a cellulosic ethanol industry is an expected increase in employment, wages, and expenditures in the region. Based on input–output models (see Schaffer 1999), estimated direct and indirect economic impacts will be high for the first 24 months due to plant design and construction that requires capital and labor3 (Burnes et al. 2005). For example, a plant processing 1.97 × 108 liters of cellulosic ethanol year−1 would need an estimated capital investment of $250 million (Solomon et al. 2007). Temporary jobs, wages, and expenditures for materials and capital equipment used will be generated during the construction phase (Burnes et al. 2005). The population in the region will increase during this period due to economic migration. While most of the effects occur during the construction phase, other mid- to long-term impacts are expected during operation. Once in operation, each manufacturing plant would spend $125 million yr−1 for feedstocks, enzymes, maintenance, labor, and administrative costs (Solomon et al. 2007). After 20 years, the local economy would have grown by $2.96 billion, population would increase by 670 people, and the residents would have received an additional $640 million of personal income (see Chapter 13). Studies have demonstrated specific impacts of procurement of logging residues and small diameter trees. Gan and Smith (2007) found that for every person employed in the procurement of logging residues, 1.15 more (indirect or induced) jobs are generated in the region. Likewise, for each dollar of output produced, 0.67 dollars worth of indirect and induced output is generated in other local industries. Removals from overstocked stands also generate economic benefits to the region. The employment multiplier is about 1.45 and the output multiplier ranges from 1.3 to 1.6 (Hjerpe 2006)4. These numbers, though explicitly region-specific, are fair indicators of the positive economic impacts of developing cellulosic ethanol. Advanced cellulosic ethanol production offers rural communities high-paying jobs in an industry that assists in reducing the dependency on foreign oil imports, utilizes forest by-products, and improves forest health. Resource utilization Despite the positive economic impacts to local economies, a question remains about the conflicts utilization of woody biomass for bioethanol production could generate between cellulosic ethanol and pulpwood or cellulosic ethanol and other forms of energy such as electricity or heat. The pulp and paper industry might be the one most affected by cellulosic ethanol due to the number and capacity of established manufacturing plants.5 As stated earlier, recent trends indicate that pulpwood production in Mississippi has been decreasing over the last ten years (Figure 12.4). This decrease is a result of external market forces such as low prices, overcapacity,

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Figure 12.4 Roundwood production and stumpage price of pulpwood in Mississippi. The line representing total production includes pulpwood, sawtimber, and other products. Source: Timber-Mart-South (2006–2007); MAFES (2007).

and industrial adjustments rather than cellulosic ethanol, bioenergy pressures (Gan and Smith 2007; MAFES 2007; USDA 2007). These trends are not unique to Mississippi. Since 1996, U.S. pulping capacity has declined slightly and southern capacity had dropped by about 16 per cent by 2003 to almost 1985 levels (USDA 2007). Inflation-adjusted prices for softwood pulpwood had also fallen to their lowest level since 1997 (Timber-Mart-South 2006– 2007; Guo et al. 2007). It is uncertain what will be the future of the pulpwood industry. If current conditions persist in the short term, the competition for raw material would be relatively minimal due to low pulp demand and extensive woody biomass supplies6. If, however, pulpwood production shifts to 1996 levels, other scenarios might be necessary. For example, instead of five big ethanol plants, Mississippi might only be able to accommodate two or three smaller capacity plants. These tradeoffs obviously require more detailed analysis. Another potential conflict involves the utilization of woody biomass for electricity generation. According to the National Renewable Energy Laboratory, in 2002 there were 13 wood-fired electricity plants in Mississippi (NREL 2007). These plants had a combined capacity of 280 megawatts (MW) or 1.7 per cent of the total electricity capacity in the state (DOE 2007). Coal is Mississippi’s leading energy source, accounting for more than one-third of electricity produced in the state, followed by natural gas and nuclear power. A single nuclear reactor, located in Claiborne County, provides 25 per cent of the total electricity consumed in the state (EIA 2007). The rest is imported from neighboring states to satisfy consumer demand. The 13 wood-fired


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plants operating in the state produced on average 180 × 103 MWh, which suggests small to medium-sized plants. Given the problems of fossil fuels supply, high input prices, and clean air regulations, it is expected that woody biomass use as a feedstock for power generation will increase.7 This increase is justified by the need of utilizing raw material with neutral energy balance capability and produced at relatively low cost. In addition, natural gas plants are generally the least expensive capacity to build but have relatively high fuel costs. In contrast, renewable plants are expensive to build but have relatively low operating costs and receive tax credits under public mandates (EIA 2006). Existing power plants, such as biomass co-firing systems, or other pulp mills would take advantage of equipment and technology to process raw material8. More production capacity, however, would be necessary to process available feedstocks. The 3.6 × 106 dry tonnes of woody biomass estimated in this study can generate up to 7.4 × 106 MWh of power (see Gan and Smith 2007, for details in energy conversion), roughly 16 per cent of the total electricity consumed in Mississippi annually (DOE 2007). The same available feedstock would displace about 1.9 × 106 tonnes of CO2 emitted from coal-fired power plants. Thus, if using woody biomass for power generation is competitive, a compromise solution will be necessary to balance commercial wood-fired electricity and fuel ethanol needs. Both forms of bioenergy are compatible with the objectives of reducing non-renewable energy dependency and offsetting greenhouse gas emissions. Land-use changes Over 95 per cent of the ethanol produced in 2006 nationwide was based on corn (Solomon et al. 2007). The rest came from wheat, barley, milo, and other materials. The production of corn ethanol has driven important changes in the agricultural sector. In the last five years, for instance, corn cultivation increased from 32 to 37 × 106 ha while the price per bushel increased by 32 per cent in the same period (ERS 2007). Similar effects were seen in Mississippi; corn area increased more than 44 per cent while price per bushel also increased by 22 per cent during the same period (NASS 2007). However, impacts on forestland (e.g. conversion of forest lands to agricultural crops) have been minimal. The expansion of corn area resulted from a land-use redistribution that left less area for other crops such as cotton, rice, and soybeans (Figure 12.5). In particular, while no sustained increase of prices was reported due to large carryover stocks, cotton registered the lowest planted area on record in 2007 (Petrolia 2007). Cropland redistribution may result from a rebound in demand for affected crops (Petrolia 2007). Even so, it may take more years to really appreciate the effects of the land use redistribution and price of agricultural crops. A different scenario is expected in forestland trends. They are expected to remain stable at least in the short term due to the high costs of clearing, particularly in young forests, and strong government involvement that supports forest conservation programs.

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Figure 12.5 Acreage of major crops planted in Mississippi in the last five years. Source: NASS (2007).

Summary and conclusions This study estimated the amount and production costs of woody biomass available for feedstock to produce cellulosic ethanol in Mississippi. It also analyzed the impacts of the variations in major production cost components and the implications of developing ethanol industry in the state. According to our analysis, up to 3.6 × 106 dry tonnes of woody biomass feedstock can be used to produce 1.2 × 109 liters (318 × 106 gal) of ethanol each year. Transportation accounted for 40 per cent of total production costs, followed by harvesting (22 per cent), stumpage (15 per cent), and other costs such urban waste management and separation (22 per cent). Based on Monte Carlo simulations, the mean marginal cost for each woody biomass type was: logging residues, $36 dry tonne−1; small-diameter trees, $46 dry tonne−1; mill residues, $26 dry tonne−1; and urban waste $32 dry tonne−1. Rank correlation coefficients indicated that the largest influence in production costs resulted from the component incremental transportation cost, expressed as the price paid per tonne of woody biomass per km transported. These simulations were based on a 1.97 × 108 liter manufacturing plant and an 80-km procurement radius. Development of cellulosic ethanol in Mississippi could bring several benefits to local economies. Additional jobs, wages, expenditures, and regional production would result from ethanol industry development. Feedstockrelated threats that could deter this development are increased demand for pulpwood and electricity from wood-fired power plants. No significant losses to the forest land base due to increased demand for corn are expected in the short term. Government programs, such as the Conservation Reserve


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Program, have stimulated the conversion of marginal agricultural lands to forestry uses. Further research needs to address social and ecological issues of biomass utilization to provide a more comprehensive analysis of biofuel generation. Studies need to explore consumer perceptions of alternative forms of bioenergy use (e.g. ethanol, bio oil, or electricity). More research is also necessary to evaluate the establishment of integrated biorefineries that could process all products for chemical feedstocks and energy as a byproduct of the pulping process (Coleman and Stanturf 2006).

Notes 1 It can be accessed at http://srsfia2.fs.fed.us/php/tpo2/tpo.php. 2 The study focused on naturally produced woody biomass resources. Other types of feedstocks such as corn, corn stover or dedicated feedstocks could increase the availability of total raw material in the area. 3 Input–output models provide multipliers that estimate the relationship between the initial effect of a change in final demand and the total effects of that change. The effects can be direct, indirect or induced and can be derived using mathematical procedures such as the Leontief inverse approach (Schaffer 1999). The multipliers describe the change of output for each and every local industry caused by a onedollar change in final demand and, usually, they include effects on employment, output, and value-added. 4 Estimates based on IMPLAN type SAM multipliers. 5 Currently, the pulpwood industry excludes smaller trees (i.e. minimum dbh of 3.6 inches), tops, and limbs, but in the near future, material of all sizes may be utilized through more efficient harvest techniques. 6 The methods used in this study to estimate available woody biomass suggest the utilization of product not incorporated in previous statistics. There are additional 3.6 × 106 dry tonnes yr−1 that can be added to the stocks for potential use in these types of industries. Note that the share of the non-used mill residues portion is low, which is explained by the fact that part of the material has been recycled in the manufacturing industry or used for energy conversion in another facility (Garrard and Leightley 2005). 7 The Energy Information Administration has estimated that electricity generation from biomass in the country is expected to increase from 1 per cent to 1.7 per cent in 2030 (EIA 2006). 8 An example of these plants is the Cypress Bend pulp and paperboard mill (Potlatch Corporation) located in southeastern Arkansas. In 2006, a project was announced to utilize forest and agricultural residues from the Delta region that comprises southeast of Arkansas and west of Mississippi. The project however was halted due to technical and financial complications.

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13 Regional economic impacts of cellulosic ethanol development in the North Central states Barry D. Solomon

Introduction Chapter 3 discussed the history and impressive growth of the U.S. ethanol industry, as well as its dependence on corn for feedstock and government subsidies. It was also noted that a shift away from corn-based ethanol is highly desirable for environmental and other reasons. A new concern arises from indications that the rapidly expanding use of corn for ethanol may be greatly increasing corn prices, thereby harming poor people in the U.S. and overseas (Runge and Seanuer 2007). A transition away from corn-based ethanol in the U.S. is thus desirable on economic and environmental grounds and is also in the offing, with the emergence of cellulosic (biomass) ethanol technology (Wyman 2003; Hamelinck et al. 2005; Gray 2007). While the resource and environmental implications of cellulosic ethanol are more attractive, a practical question for state and local governments is what are the regional socioeconomic effects of these biomass ethanol refineries? Answering this question is the purpose of this chapter, focusing on a case study region in three North Central states: Minnesota, Michigan and Wisconsin. The rest of this chapter is divided into four sections. In the next section the biomass ethanol resources of the three state study region are reviewed. After this, a discussion of the commercialization of cellulosic ethanol technology is presented. The main focus of the chapter is a detailed regional economic and demographic analysis of potential cellulosic ethanol plants in the study region. After a brief literature review the REMI Policy Insight Model is described, as well as three hypothetical development scenarios and major assumptions used in the application of this model. Following this the major economic and demographic results are provided. Finally, several analytical and policy-related conclusions close the chapter.

Biofuel resources of the North Central states The North Central states of Minnesota, Wisconsin and Michigan play a central role in the domestic ethanol industry, although production is largest

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in several of the major farm states father south (Iowa, Illinois and Nebraska). Twelve states in this region formed the North Central Bio-Economy Consortium in April 2007, with the purpose of coordinating policy and research for renewable energy and fuel development (Hunter 2007). These states are: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota and Wisconsin. Minnesota was the fifth largest ethanol producer in the nation as of July 2008, with 19 plants operating at a total 3.4 × 109 liters year−1 production capacity; Wisconsin was seventh, with nine plants and 1.5 × 109 liters yr−1; while Michigan ranked twelfth with four plants and 0.8 × 109 liters yr−1 (RFA 2008). Nine additional plants are under construction in these states. One of the major benefits of cellulosic ethanol is its large and diverse resource base, as noted earlier, as compared to grain ethanol (although a wide variety of feedstocks could be used to produce conventional ethanol as well it is not economical to do so). An additional advantage is that cellulosic ethanol feedstocks need not displace food production (Tilman et al. 2006). Consequently, the use of cellulosic materials will not affect consumer and agricultural prices. Previous research has modified the linear programming model POLYSYS to estimate the agricultural land allocation decisions to produce biomass energy crops that would maximize returns above costs (Walsh et al. 2003). This model includes a crop supply module that represents 305 Agricultural Statistical Districts in the U.S. While this study only considered three biomass energy crops (switchgrass, hybrid poplar and willow), the results are instructive. POLYSYS found that multiple feedstocks could have large crop yields in the three-state region of Minnesota, Michigan and Wisconsin. The authors estimated the costs of bioenergy crop production with two other models: the APAC Budgeting System and BIOCOST, using a net present value approach. It was found that the three feedstocks examined could be competitively produced in this region. A follow-up study provided a more detailed assessment of the technical potential for cellulosic ethanol feedstock availability in the three-state region (Halvorsen et al. 2008). Table 13.1 summarizes these findings. The largest available feedstock in the region is the corn stover residues, especially in Minnesota, with a much more modest quantity of wheat straw in the State (Ugarte et al. 2006). There is a limit to the amount of agricultural waste that could be taken off of the land, however, since some of these residues are needed to minimize soil erosion. The next largest quantity of available feedstock is switchgrass on Conservation Reserve Program (CRP) lands in Minnesota (McLaughlin et al. 2006). Switchgrass has the added advantage of reduced loss of soil, soil nutrients and organic matter from its cultivation (Sanderson et al. 2006). Next largest is the waste paper supply in Michigan, followed by the residues from forest harvesting and paper production, which is more evenly distributed between the three states.

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Table 13.1 Technical potential for feedstocks in the three state upper Midwestern region (in 1.0 × 106 kg) Feedstock

Forestry residues1

Agricultural Residues2

Switchgrass on CRP Lands

Waste paper

Total

Michigan Wisconsin Minnesota

2,356 2,411 2,596

3,253 4,008 12,908

1,316 2,835 7,121

4,536 805 747

11,461 10,059 23,372

Source: most calculations based on Milbrandt (2005); waste paper data from Halvorsen et al. (2008). Notes: 1 Includes residues from forestry operations in the field, paper mills, and urban wood residues. 2 Primarily corn stover and wheat straw.

Commercialization of cellulosic ethanol The idea of converting agricultural residues or other cellulose-rich materials into ethanol is not new, but technology to do so has never been cost-effective. Following extensive university and corporate laboratory research and analysis, Iogen Corporation opened the first pilot plant for making cellulosic ethanol in Ottawa, Canada in 1985, using wood chips as the feedstock (Chapter 3). The development of cellulosic ethanol has attracted growing international interest, with about half of the 17 subsequent pilot plants being commissioned outside of the U.S. Several additional pilot plants have been announced in the last few years. Among the existing plants, one of the most valuable is located at the headquarters of the U.S. National Renewable Energy Laboratory. This bioethanol test facility has a capacity of 1 kg of dry feedstock day−1, and has been available for private companies and academic researchers to use since 2001. Most of the cellulosic ethanol pilot projects have been considered successful and most of the projects have used wood chips or residues for feedstock. The next step, the opening of commercial demonstration plants, began in 2004 with another Iogen project in Ottawa that coverts wheat straw, followed by plants in Japan and China (Chapter 3). Two commercial plants are slated to open in California by 2009, with three plants following in later years in our study region. A variety of existing and new ethanol and food companies have entered this emerging market, which is helpful since the capital investment cost of these projects is much greater than for grain ethanol (see Chapter 3). Many of the new ethanol projects involve co-location of the refinery at an existing grain ethanol plant, which requires the addition of technology for pre-treatment of the cellulose, purchase of a turbo-generator, and in many cases expansion of the storage facilities. As noted by Wyman (1999) among others, a critical barrier impeding the commercialization of cellulosic ethanol facilities is financing, especially given

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the large capital requirements for these facilities (costing $100–300 million) and the riskiness of unproven technology. As a result, the DOE announced a total of $385 million in cellulosic ethanol grants in February 2007 (Zibel 2007), to be followed up by major loan guarantees that were authorized under Energy Policy Act of 2005, section 1510 (EPAct). The loan guarantees are available for only four commercial projects, at up to $250 million each or 80 per cent of the cost of the project (RFA 2008). Nonetheless, such governmental actions help to ensure a mix of debt–equity financing that these projects require. Additionally, other federal and state policies to promote ethanol development, such as renewable fuel mandates and excise tax credits or exemptions, will be available to projects using cellulosic materials, Finally, in December 2007 the U.S. Congress passed additional legislation that will dramatically expand cellulosic ethanol production in the U.S. beyond the 9.45 × 108 liters yr−1 that was to be required under EPAct in 2013 and beyond (see Chapter 3). Given the uncertainties of outside financing and concerns about untested technology numerous commercial cellulosic ethanol project proposals have been announced in recent years, only to be delayed or reconfigured, often several times. Thus the opening of the first commercial plant has been pushed back several times, but finally occurred in Japan in 2008. A few proposals are on track in the U.S. but it is also possible that Brazil could dramatically increase its production of this fuel (Chapter 3).

Regional economic and demographic impact analysis Previous research The main attraction of cellulosic ethanol facilities in areas where they have been proposed is their potential positive regional economic effects. The direct and indirect economic effects will be especially high for the one- to two-year plant construction period, and include the creation of temporary jobs, wages, and expenditures for materials and capital equipment used during this phase (Burnes et al. 2005: 1,161). A short-term surge in population typically will be experienced during this period. In addition, induced effects in the regional economy will be felt from the new spending by households and firms based on expansion in disposable income and other payments. Finally, following the construction phase an ethanol facility will generate more modest, longerterm economic and demographic effects during its operation and maintenance period. Three main economic models can be used to trace inter-industry transactions to assess the economic and demographic effects of development projects and policies on a regional economy: RIMS II, IMPLAN and REMI’s Policy Insight (Rickman and Schwer 1995). These models are based on input–output (IO) analysis and can be built for analysis at the state, county or regional level. In addition, a hybrid Canadian model similar to REMI has


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been developed (Thomassin and Baker 2000). Such model can assess the direct and indirect effects on the economy of an exogenous or endogenous stimulus (Type I multipliers), as well as the induced effects from changes in local spending that result from income changes in the directly and indirectly affected industry sectors (Type II multipliers). While these models have been used to assess a wide range of economic stimuli they will be discussed herein in turn solely in the context of ethanol facilities. RIMS II (Regional Input–Output Modeling System) provides the most recent final demand multipliers based on a national model developed by the Bureau of Economic Analysis (BEA) of the U.S. Department of Commerce in the early 1970s. The model has been regionalized based on six digit industrial code location quotients (Drake 1976). RIMS II is low-cost, accessible, easy to use, and usually produces acceptable results. While RIMS II can generate economic impact estimates that are similar to more sophisticated models such as REMI, it is usually considered the weakest of the three. In particular, its use of location quotients in regionalizing the BEA national input–output model has received strong criticism. In essence, application of RIMS II requires the modeler to categorize the various commodity and services input requirements of the construction and operation of a facility as final demand vectors. The next step is to multiply the various physical factor inputs by their prices, which results in a change in output value. The sectoral outputs of RIMS II are multiplied by regional industrial sector multipliers in order to estimate the overall macroeconomic effects. These multipliers are available for two aggregation levels (60 or 473 industrial sectors). Since RIMS II multipliers for output, income, and employment are readily available and have been widely applied they have also been used in several studies of ethanol development. These include corn ethanol development in the North Central states (Evans 1997; Wisconsin Energy Bureau 1994), California (Burnes et al. 2005) and the U.S. as a whole (Urbanchuk 2006). A recent regional analysis, the Burnes study of a 1.5 × 108 liter conventional ethanol plant in California’s San Joaquin Valley, estimated an $80 million increase in annual state output but only 287 total jobs. Since cellulosic ethanol plants are even more capital intensive, their economic effects may be even larger though with modest employment effects. Another widely used regional economic model is IMPLAN (Impact Analysis for Planning), which was originally developed by the U.S. Department of Agriculture Forest Service as a national IO model in the late 1970s. The model was regionalized and extended by the Minnesota IMPLAN Group in the 1990s. While generally similar to RIMS, IMPLAN is a package of flexible software and county-level databases that can be used to provide a complete set of regional social accounts. It can generate Type III multipliers, which calculate the induced effects based on information in the social accounting matrix (SAM)1 for 21 economic and demographic variables and up to 528 industrial sectors. Type III multipliers in IMPLAN also differ from standard Type II multipliers in that the consumption function is non-linear,

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i.e. the marginal propensity to consume decreases as the regional income increases. The technique for calculating the regional purchase coefficients (RPC), which is the regional demand satisfied by regional supplies, can lead to overestimation as can its restriction of impacts to a single forecast year. Nevertheless, IMPLAN has been applied to cellulosic ethanol development for California (California Energy Commission 2001); grain ethanol in Iowa (Otto and Gallagher 1997); and grain ethanol in Ohio (Sporleder et al. 2001). While IMPLAN is a useful model, it shares the shortcoming of RIMS of being a static tool most useful for short-term analyses that do not require forecasts, and is less useful for applications beyond ten years when structural economic change may be more pronounced (as would be the case with cellulosic ethanol). On the other hand, the regional input data for IMPLAN are updated more frequently than for RIMS. The other major regional economic model is Policy Insight of REMI (Regional Economic Models, Inc.), which is the only dynamic regional economic modeling system that can account for price changes, structural economic changes, and technological developments. This model has also evolved and been refined since 1980 and is currently in its ninth version. Policy Insight was originally called the REMI Economic-Demographic Forecasting and Simulation model (Treyz et al. 1992). The model has several strengths: a strong theoretical foundation, it combines tools of IO, economic base and econometric models, it is calibrated to local conditions with extensive local data, allows for manipulation of a large number of input variables, and it can forecast over any combination of future years and for a wide variety of state, regional and county geographic scales. Importantly, the technique in REMI for calculating the RPC is to regionalize the national technical coefficients based on County Business Patterns, and is considered the most accurate (Stevens et al. 1983). Otto et al. (1991) used an earlier version of REMI to estimate the effects of corn ethanol development in Iowa. Policy Insight was recently used to estimate the economic effects of a renewable fuel standard in Michigan and a production tax credit for ethanol (Edison et al. 2007). The model has also been used to estimate the macroeconomic effects of co-firing a coal-fired power plant with switchgrass (Weisbrod and Lin 1996) and a biodiesel industry in New York (REMI 2004). Finally, a hybrid Canadian model has combined a national, survey-based IO model with econometric estimates of ethanol feedstock crops, first for Jerusalem artichoke (Thomassin et al. 1992) and then for corn (Thomassin and Baker 2000). Applying REMI Policy Insight For the current study REMI’s Policy Insight model was customized to create a three-state regional model of Minnesota, Michigan and Wisconsin with 70 sectors. Before describing the application to cellulosic ethanol development and the major results, a general overview of the model will be useful.


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The model was created from the REMI model building system, which has hundreds of programs and thousands of equations developed over more than 20 years. REMI uses data from several federal agencies: Bureau of Economic Analysis, the Bureau of Labor Statistics, Department of Energy, Bureau of the Census, and other sources. Policy Insight is a highly simultaneous structural model, based on neoclassical assumptions such as producers maximize profits, households maximize utility, and regional markets reach economic equilibrium. More specific assumptions are that businesses produce goods to sell to other firms, consumers, investors, governments, and purchasers both within and outside their region. Economic output is produced using labor, capital, fuel, and intermediate factor inputs. The demands for labor, capital, and fuel per unit of output in turn depend on their relative costs, since an increase in the relative cost of one factor leads to substitution away from that input to increased use of other factors. Labor supply depends on the number of residents in the region and the labor force participation rate, as well as labor force migration. Many workers will migrate into a region if the real after-tax wage rate or the likelihood of being employed increases in that region. Policy Insight has several feedback relationships (Treyz et al. 1992). For example, the supply and demand for labor determines the sectoral wage rate. These wage rates, along with other prices and productivity, determine the cost, competitiveness, and opportunity of doing business for every industrial sector in the model. An increase in costs would decrease the markets supplied by firms. The regional market share (or RPC) combined with the demand described earlier determines the amount of regional output. The model has many other feedbacks. For instance, changes in wages and employment affect income and consumption, while economic growth changes investment and population growth, affecting government spending. There are five blocks to the model structure (Figure 13.1). The Output block contains the IO components of the model, and links businesses in specific sectors to other sectors based on final demands as well as to industries that provide inputs. The Labor and Capital Demand block shows how labor and capital requirements depend on both output and their relative costs. Factor input use is controlled by Cobb-Douglas production functions that assume constant-returns-to-scale in this block. Consequently, unlike pure IO models such as IMPLAN, the relative factor intensities respond to changes in factor costs. A Demographic block includes population and labor supply, contributing to demand and wage determination. Labor force (economic) migrants in turn respond to wages and other labor market conditions. Supply and demand interact in the Wage, Prices and Profit block. Finally, relative production costs determine results in the Market Shares block, while output depends on market shares and the components of demand. Policy Insight integrates the relationships in the five blocks to determine the value of each of the variables for each year in a baseline forecast, as well

Figure 13.1 Structure of the Policy Insight model.


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as for policy simulations for alternative scenarios that can be compared to the baseline. The model includes all of the inter-industry interactions in the Output block that are included in other IO models, but goes well beyond this through other model components. The other components require econometric estimation of key relationships, which allows for theoretically sound long-term forecasts from the model. Scenarios and assumptions Three development scenarios were constructed. Since neither grain ethanol manufacturing nor cellulosic ethanol manufacturing had their own sector in the model, we entered the operations of the hypothetical ethanol plants through the Chemical Manufacturing sector since that is where it best fits (North American Industry Classification System code 325). Thus the average inter-industry transactions table for this aggregate sector was used in this analysis. REMI also provides an alternative to build a ‘custom industry’ for an economic sector, including a new one. In our application, we attempted to mimic this option by adding the specific feedstock input demands and transportation requirements for cellulosic ethanol, although other intermediate demand input requirements were the average for the chemicals sector. In Scenario A, a single cellulosic ethanol demonstration plant was assumed to be sited somewhere in the case study region, with a capacity of 1.0 × 106 liters yr−1. This scale is comparable to Iogen’s demonstration plant in Ottawa (that plant actually has a large capacity but normally produces a smaller output). Scenario B assumes a mid-sized commercial plant of 7.6 × 107 liters yr−1, again without a specific location in the region. However, a commercial facility that may grow to this size is planned by Flambeau River Biorefinery in Park Falls, Wisconsin, to be located adjacent to an existing paper mill and which will use spent pulping liquor as feedstock (Table 3.4). Another biofuels company, Mascoma Corporation, announced in 2008 that it would build a cellulosic ethanol plant in northern Michigan that will eventually produce 1.5 × 108 liters yr−1 (Bevill 2008). Yet another company, SunOpta, announced later that year that it would build a 3.8 × 107 liters yr−1 cellulosic ethanol plant in Little Falls, Minnesota in the center of the state, adjacent to an existing corn ethanol plant that is twice as large (Clean Edge News 2007). The last scenario (C) considers a larger commercial plant of 1.97 × 108 liters yr−1. Only one commercial project of this size or larger has thus far been announced (Table 3.4). Development of the three scenarios requires data input from cellulosic ethanol companies and specific projects. In particular, the level of capital investment (for equipment and construction), employment, and wage bills by sector is needed for both the construction and operation & maintenance phases of the projects. Employment in the construction period is the most labor-intensive, but it probably will last only one to two years. There are also more modest employment requirements for the forestry, agricultural, loading

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and trucking sectors, as well as for chemical (cellulosic ethanol) manufacturing during the operational phase of the facilities. Finally, since the ethanol plant size assumed was progressively larger, we modeled the project lives for 10, 15 and 20 years respectively (i.e. through 2016, 2021 and 2026 for Scenarios A–C). The feedstock assumed for these scenarios included a combination of trees (aspen, willow or hybrid poplar) and switchgrass. Facility equipment requirements are extensive, and included: mechanical and chemical pretreatment and neutralization technologies; hydrolysis equipment; fermentors; a distillation column; boilers and turbogenerators for energy production; and equipment for feedstock handling and storage, neutralization and conditioning, wastewater treatment and storage, and other utilities. The application of the Industry Employment variable allows for an increase in employment without displacing the current regional market activity in a particular industry. The decision to model without local competition for labor and market shares was made because the type of product to be produced is unusual, and demand for it is expected to be high, even national. Conversely, changes in the forestry and agricultural sectors are modeled as an offsetting adjustment to the Intermediate Demand variable, as noted below. During the construction period there is a large demand for the specialized equipment (as noted earlier) to meet an exogenous final demand. The model determines the proportion of the equipment that can be supplied within the region, based on a 3,000 by 3,000 county-level trade flow dataset and the regional purchase coefficient. In additional, a Non-Residential Capital Stock policy variable accounts for the total investment as a single year adjustment to this variable. Finally, other variables account for the atypical suppliers of the ethanol refining facilities by changes in Intermediate Demand, which is adjusted along with Value Added with no effect on Sales or Employment variable for the applicable industry; these two variables must be used together and are offsetting. Results Figures 13.2–13.7 show the growth in the regional economy in three distinct phases, with figures on annual changes and cumulative project life tallies. The construction phase from 2007 to 2008 creates the highest average number of jobs each year due to the increased demands within the construction industry and other manufacturing industries that supply the advanced ethanol plants with durable equipment. The construction phase is a temporary, yet very important, contribution to the regional economy that brings immediate benefits. Longevity of economic returns is another very important factor when evaluating development policies. In the years following the construction phase, positive economic growth both in the short-term and long-term phase illustrates the net gains that the North Central region will reap due to direct employment increases at the facilities, as well as increased employment in the


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Figure 13.2 Scenario A: employment and population effects.

Figure 13.3 Scenario A: output, gross regional product & real disposable personal income ($ 2006).

agricultural and ground transportation sectors. The results featured in these figures are the total net effects of the direct stimulus, plus indirect and induced economic effects. It should be noted that since these results are for individual cellulosic ethanol plants of different scales in each scenario, if multiple plants are built there would be a linear increase in the economic and demographic effects. All sectors of the regional economy will experience strong growth during the analysis time frame. The only exception occurs during the short-term period because of a decrease in demand and sales for the construction industry in the region, reflecting the cyclicality observed during and after

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Figure 13.4 Scenario B: employment and population effects.

Figure 13.5 Scenario B: output, gross regional product & real disposable personal income ($ 2006).

the construction phase. Overall, the strong growth in employment, largely in the chemical manufacturing and service sector, results from the direct employment of plant personnel and the increase in real disposable income (the increase in real disposable Income directly affects the increase in consumption). If Scenario A, a 1.0 × 106 liters yr−1 demonstration plant, is implemented, it would generate 104 net new jobs annually in the region, mostly in the manufacturing and services sectors (Figure 13.2). At the end of the analysis


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Figure 13.6 Scenario C: employment and population effects.

Figure 13.7 Scenario C: output, gross regional product & real disposable personal income ($ 2006).

period in 2016, the population would increase by 106 people, mostly due to economic migration. By 2016, total output in the region would grow by $250 million, total gross regional product (GRP) would grow by $128 million, and the residents will have received an additional $56.4 million of real disposable personal income (Figure 13.3). If Scenario B, a 7.6 × 107 liters yr−1 commercial plant, is implemented, it would generate 403 net new jobs annually in the region, again primarily in the

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manufacturing and services sectors (Figure 13.4). At the end of the analysis period in 2021, the population would increase by 451 people, mostly due to economic migration. By 2021, total output in the region would grow by $1.406 billion, total GRP would grow by $670.9 million, and the residents will have received an additional $304.9 million of real disposable personal income (Figure 13.5). Finally, if Scenario C, a 1.97 × 108 liters yr−1 commercial plant, is implemented, it would generate 631 net new jobs annually in the region. In this case the employment is more widespread: the services, manufacturing, transportation, and trade sectors all would experience major growth, as would the natural resources, mining, utility and construction sectors even beyond the plant construction phase (Figure 13.6). At the end of the analysis period, 2026, the population would increase by 670 people, mostly due to economic migration. By 2026, total output in the region would grow by $2.96 billion, total GRP would grow by $1.317 billion, and the residents will have received an additional $639.4 million of real disposable personal income (Figure 13.7). Another way to look at these economic effects is a function of cellulosic ethanol plant scale, which is illustrated in Figures 13.8 and 13.9.

Conclusions Rapid development of a cellulosic ethanol industry could lead to important benefits in terms of a reduction in CO2 emissions and increase in national

Figure 13.8 Ethanol employment effects as a function of plant size.


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Figure 13.9 Ethanol output and gross regional product as a function of plant size.

energy security. While the North Central region has a large potential for provision of feedstocks to this industry, it is only one of several regions that will play a role and it is unclear if there will be a significant regional comparative advantage. Thus, a useful way to envision this industry is to scrutinize data on the potential fuel sources, which will be much more diverse than the corn based grain ethanol industry. For example, our assessment has shown that Minnesota has a large supply of corn stover and wheat straw, a large existing market for ethanol, and very strong state policy support. Wisconsin has the largest supply of forest residues in the region, and would seem to have great potential to expand its switchgrass plantings in the southern part of the state. Finally, Michigan has the largest supply of waste paper in the region, which is available near its paper mills and in its urban areas. Given the larger population in the state and the more modest corn crop, the ultimate demand for cellulosic ethanol will probably be largest in Michigan. Our regional economic and demographic analysis of potential cellulosic ethanol plants in the North Central region has to be considered preliminary, given the lack of commercial experience. It is clear, however, that the effects of a commercial-scale plant will be much greater than for a demonstration or pilot plant. The current state of the industry suggests that the ramp up toward larger facilities will occur at a relatively slow pace. The largest employment growth will be experienced during the plant construction phase,

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which may only last two years. Any boost to the regional population will be minimal and may even be difficult to detect. Overall the manufacturing and services sectors will experience the largest gains, and would be amplified with multiple facilities. While the employment effects of cellulosic ethanol may be minimal, increases in output, GRP and disposable income could be significant. The increase in GRP is less than half of the change in total output due to leakage out of the region, but is still well over $1 billion over a 20-year period. Although the increase in disposable personal income is just half of the change in GRP, for a particular state or sub-region experiencing economic difficulties such as Michigan this could be very important. These results should be seen as illustrative of the potentially much larger impacts of a fully developed cellulosic ethanol industry in the U.S. The combination of economic, ecological and environmental benefits will undoubtedly lead some states as well as the federal government to enact additional policies to support this industry. Additional ecological and environmental benefits could be experienced by a transition away from corn-based ethanol that may occur as the cellulosic ethanol industry matures.

Note 1 A Social Accounting Matrix (SAM) is a means of presenting flows of all transactions in an economy in a matrix system that elaborates the linkages between a production and use table with all institutional sector (firms, households, governments, etc.) represented as both buyers and sellers. Typically it will include subdivision of the household sector and labor markets.

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Thomassin, P.J., Henning, J.C. and Baker, L. (1992) ‘Macroeconomic impacts of an agro-ethanol industry in Canada’, Canadian Journal of Agricultural Economics, 40: 295–310. Tilman, D., Hill, J. and Lehman, C. (2006) ‘Carbon-negative biofuels from low-input high-diversity grassland biomass’, Science, 314: 1,598–1,600. Treyz, G.I., Rickman, D.S. and Shao, G. (1992) ‘The REMI economic-demographic forecasting and simulation model’, International Regional Science Review, 14: 221–253. Ugarte, D.D., English, B.C., Menard, R.J. and Walsh, M.E. (2006) ‘Conditions that influence the economic viability of ethanol from corn stover in the Midwest U.S.A.’, International Sugar Journal, 108: 152–156. Urbanchuk, J.M. (2006) ‘Contribution of the ethanol industry to the economy of the United States’, prepared for the Renewable Fuels Association, Washington, D.C. Walsh, M.E., Ugarte, D.G.D.L., Shapouri, H. and Slinsky, S.P. (2003) ‘Bioenergy crop production in the United States’, Environmental and Resource Economics, 24: 313–33. Weisbrod, G. and Lin, X. (1996) ‘The economic impact of generating electricity from biomass in Iowa: A general equilibrium analysis’, unpublished manuscript, Economic Development Research Group, Boston, MA. Wisconsin Energy Bureau. (1994) ‘The economic impacts of renewable energy use in Wisconsin’, Department of Administration, Division of Energy and Intergovernmental Relations, April. Wyman, C.E. (1999) ‘Biomass ethanol: technical progress, opportunities, and commercial challenges’, Annual Review of Energy and the Environment, 24: 189–226. —— (2003) ‘Potential synergies and challenges in refining cellulosic ethanol to fuels, chemicals, and power’, Biotechnology Progress, 19: 254–262. Zibel, A. (2007) Government plans $385M in ethanol grants’, Business Week, 28 February 2007. Available: http://www.busienssweek.com/ap/financialnews/ D8NITMBG1.htm (accessed 2 March 2007).

14 Wood methanol as a renewable energy source in the western United States Daniel J. Vogt, Kristina A. Vogt, John C. Gordon, Michael L. Miller, Calvin Mukumoto, Ravi Upadhye and Michael H. Miller Linking energy, climate change and sustainable development There are several global issues that, at first glance, seem unrelated. These issues include: higher incidences of catastrophic forest fires, global climate change, the need for increased energy sources, the global peaking of oil and gas supplies, the need to develop substitutes for fossil fuel energies, developing sustainable rural economies, widespread poverty, and the loss of productive lands (Azar and Rodhe 1997; Demirbas 2001; IPCC 2003; Jagadish 2003; Sayer and Campbell 2003; de Oliveira et al. 2005; FAO 2006; California Energy Commission 2005; Stokstad 2005; Vogt et al. 2005; Alanne and Saari 2006; Farrell et al. 2006; Jefferson 2006; Richards et al. 2006; Ragauskas et al. 2006; Schindler et al. 2006; Varun and Singal 2007). In the past, each of these issues was treated as a separate problem in which solutions were derived by focusing on only one individual issue at a time. Today these global issues are being formally linked because the combustion of fossil fuels to produce energy, the main ingredient fueling industrialization, is now causally linked to climate change and the emission of greenhouse gases (GHGs). Fossil fuel combustion is a major contributor to CO2 emissions and these levels are increasing as more countries become industrialized. It is therefore logical to develop strategies that shift our reliance from fossil fuels to alternative energy resources that are carbon neutral and can help to reduce our total emissions of CO2 (Gustavsson and Svenningsson 1996; Vogt et al. 2005; Schindler et al. 2006). Mitigating climate change is driving the development of technologies to convert renewable resources into biofuels that can be substituted for fossil fuels. Even though renewable resources are used to produce biofuels, some of these biofuels may not be ‘climate friendly’ or ‘carbon neutral’ when fossil fuels are consumed in their production. For example, if fossil fuels are used to increase the growth rates of crops or are used to transport them to the markets, these biofuels mitigate less CO2 emissions and are not carbon neutral. This fact is clearly demonstrated by Schindler et al. (2006) where they compare the costs of various fuels using a ‘well to wheel’ approach that is based on the vehicle km traveled and the GHG emissions of different fuels. What

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distinguishes these fuels from one another is their CO2 equivalent emissions. In that report, methanol from residual wood had the lowest net CO2 equivalent emissions (5 g kWh−1) compared to most food crops used to produce biofuels (e.g. ethanol from wheat had CO2 equivalent emissions of 110 g kWh−1, while ethanol from sugar beets emitted 80 g kWh−1). In that study, gasoline/diesel was listed as having CO2 equivalent emissions of 160 g kWh−1. Therefore, the sustainability of biofuel production can be effectively evaluated by comparing its CO2 equivalent emissions. In fact, the controversy regarding using food crops to produce ethanol is driven in part by concerns that conventional ethanol has a large carbon footprint and uses as much energy as it generates (Nilsson and Schopfhauser 1995; Berndes et al. 2001; Shapouri et al. 2002; Shapouri et al. 2003; de Oliveira et al. 2005; Parrish and Fike 2005; Patzek 2004; Pimentel et al. 2005; Pimentel and Patzek 2005; Vogt et al. 2005; Hill et al. 2006; Lewandowski and Schmidt 2006; Sticklen 2006; Wu et al. 2006). The energy crisis is also raising concerns about the environmental and social impacts of our dependence on energy derived from fossil fuels. So even if new energy supplies are developed, they will have to be accepted by the stakeholder groups and satisfy their criteria for both sustainable management and environmental friendliness (Gordon 1994). The social, economic, or environmental impacts of producing the different biofuels will ultimately determine which biofuel will become a fossil fuel substitute. Biofuels that are produced from essential food crops, or when forests are cut to grow biofuel crops, are less acceptable alternatives to society. Recently, corn grown in Mexico was being sold to the U.S. instead of being consumed in Mexico because of the higher prices being paid by the U.S. corn ethanol-producing industries. However, corn is a staple component of the diets of people in Mexico. This of course has resulted in an immediate increase in the price of corn in Mexico and also a marked increase in food prices in general. Because of the resulting social strife related to rising food costs in Mexico, the Mexican government had to intervene by increasing the corn supply available for food products (Grillo 2007). In another example demonstrating the need for biofuels to be environmentally friendly, the European Union recently decided that it will not import palm oil from Malaysia and Indonesia for biodiesel production because of deforestation concerns (Rosenthal 2007). This loss of forests is detrimental to the survival of the local people that are dependent on those forests for their primary source of energy (i.e. fuelwood). This forest loss is also occurring at a time when fuelwood supplies are inadequate to meet the energy needs in many developing countries (FAO 1998). Even in the U.S., a recent survey showed that people supported the use of biofuels, but half of the survey respondents questioned the advisability of using food crops to produce ethanol and would not use biofuels if it caused food prices to rise (Hopkins 2007). The future acceptance of biofuel production from biomass hinges on

Wood methanol as a renewable energy source 301 whether it can provide significant environmental (e.g. that mitigate climate change, decrease deforestation rates, conserve species) and societal (e.g. that decrease poverty, develop sustainable rural livelihoods) benefits. Since systematic assessments of the environmental benefits of using biomass to produce biofuels are scarce, especially from forests (though see Chapter 8), the goal of this chapter is to assess the amounts of methanol production possible from agriculture and forest materials/products. In addition, it will assess the associated potential avoidance of carbon emissions when these biofuels are substituted for gasoline and for fossil fuels used to produce electricity in 11 western states in the U.S. Information provided by this assessment can help individual states to determine whether they should develop programs to produce biofuels and, if so, what biomass materials should be used to produce them.

Energy consumption, carbon emissions and biomass availabilities in 11 Western U.S. states State profiles Prior to determining whether biomass energy systems should be adopted to reduce carbon emissions, it is important to understand the current energy profile and annual carbon emissions for each state. This information can then be used to determine whether sufficient biomass exists for conversion to biofuels and whether it is a viable alternative to replace the fossil fuels currently used in each state. If the substitution only replaces a small fraction of the energy consumed or it mitigates only a small fraction of the carbon emitted in a state, a state-level policy for using biomass to produce biofuels is probably not worth implementing. For example, converting all of the corn and soybean crops annually grown in the U.S. to produce biofuels is probably not a realistic solution. Hill et al. (2006), for example, reported that only 12 per cent of U.S. gasoline demand and 6 per cent of its diesel demand could be substituted by the biofuels derived from all the corn and soybean crops. The 11 Western U.S. states examined in this chapter have highly variable energy profiles. When comparing these states, each emits different levels of CO2 annually. Several factors explain this variability: the type of energy source used to produce electricity, the population density, and the amount of gasoline consumption (Tables 14.1–14.2). Since 82 per cent of the variance in carbon emissions can be explained by the net amount of electricity generated in these states, it appears that targeting the replacement of energy supplies used to generate electricity would be ideal when the goal is to optimize the reduction in CO2 emissions. However, this scenario is more complex than what the first analysis would suggest and, accordingly, the solution is more complicated. For example, 58–73 per cent of the state-level carbon emissions in Arizona, Montana, Nevada, New Mexico, Utah, and Wyoming are from the combustion of coal or natural gas to produce electricity. Despite the high

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level of carbon emitted during coal combustion, the lower population densities in most of these states means that the total state-level emissions are at the lower end when compared to more populated Western states (Table 14.1 and 14.2). In these lower populated states, there is less demand for electricity and motor vehicle fuels so their total state-level carbon footprint is lower. In contrast, Arizona also generates most of its electricity using coal but has a significantly higher population density compared to the other coal-powered states. Arizona’s high population density means that it has a higher demand for electricity and it consumes more motor vehicle fuels. The situation in Colorado is similar to Arizona in that it also has a high population density and uses coal to generate 71.7 per cent of its electricity. However, Colorado differs from Arizona in that most of its state-level carbon emissions are as a result of motor gasoline consumption and not from electricity generation (37 per cent of total state emissions). Three of the Western states (Idaho, Oregon and Washington) primarily produce power using hydroelectric dams and therefore have the lowest emissions of carbon resulting from their electricity generation (22–29 per cent; see Tables 14.1 and 14.2). Yet the total carbon emissions were not consistently low in all three states and again varied based on the state’s population density. Unlike the other two states, Washington has one of the higher population densities in the West, high levels of total state carbon emissions, as well as Table 14.1 Electricity and carbon emissions profiles for 11 western U.S. states State

Arizona California Colorado Idaho Montana Nevada New Mexico Oregon Utah Washington Wyoming

State’s primary energy source for electricity generation in 2005

Coal Gas Coal Hydroelectric Coal Gas [coal] Coal Hydroelectric Coal Hydroelectric Coal

Source: EIA (2007).

Electricity generated using primary energy source / total electricity consumed

Total carbon emitted from electricity generation

(%)

(Mg C yr−1)

39.6 46.7 71.7 78.9 63.8 46.6 [45.7] 85.2 62.7 94.2 70.7 95.1

14,016,648 14,923,581 11,146,561 364,083 5,335,426 7,083,515 8,935,590 2,447,052 9,801,037 4,068,504 12,399,290

Total carbon emitted from electricity generation / total state carbon emissions (%) 59 15 37 9 62 59 57 22 58 18 73

Wood methanol as a renewable energy source 303 Table 14.2 The amount of annual electricity generated and gasoline consumed, and the amount of carbon emitted annually for 11 western U.S. states, 2001–2005 State


Net electricity Annual motor Total state generated in gasoline carbon state consumption emissions (MWh yr−1)

(103 liters yr−1)

(Tg C yr−1)

101,478,654 200,292,817 49,616,695 10,842,984 27,938,778 40,213,752 35,135,642 49,325,002 38,165,131 101,965,850 45,567,307

10,263,775 60,910,465 7,834,099 2,598,517 2,484,805 3,832,215 3,644,169 5,961,100 4,162,850 10,860,106 1,278,464

23.9 100.5 29.9 4.2 8.7 12.0 15.8 11.2 16.8 22.1 17.0

Total resident population (including Armed Forces) in 2001

5,297,684 34,533,050 4,428,786 1,321,309 905,954 2,094,633 1,829,110 3,472,629 2,279,590 5,992,760 493,720

Source: DOE (2001); EPA (2004); and EIA (2006, 2007).

high rates of motor vehicle fuel consumption and net electricity generation. Washington is more similar to Arizona, with both states having comparable population densities and energy consumption rates (Table 14.2). Recently the State of Washington decided not to accept the avoided CO2 emissions from hydroelectric power generation because of the adverse environmental impacts created by the placement of dams. This means that Washington will have to find alternative strategies to meet its mandated reduction in carbon emissions. In contrast to Washington and the other Western states, Idaho and Oregon have lower population densities and comparatively lower CO2 emission rates. However, all three of these Western states, characterized by generating electricity mainly from hydroelectric dams, also have large and vibrant agricultural and forestry industries. Thus these states have the potential to reduce a significant proportion of their own CO2 emissions by converting biomass to biofuels. Idaho and Oregon also have a large capacity to market biofuels to other states. The highest level of total state CO2 emissions occurs in California, the most populated U.S. state. Even though only 15 per cent of California’s statelevel CO2 emissions occurs during electricity generation (half of California’s electricity production is from natural gas, the lowest carbon emitting fossil fuel), the total state-level CO2 emissions are four times higher than those in Arizona and Washington (Tables 14.1 and 14.2). When comparing the motor

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vehicle fuel consumption rates found in California to those reported in Arizona and Washington, California consumes five times more automotive fuels and generates twice the amount of electricity. For the State of California to significantly reduce its CO2 emissions, it should focus on substituting motor vehicle fuels derived from non-renewable fossil fuels with biofuels that are renewable and emit less CO2. In summary, state energy profiles for the 11 Western states suggest that those states using coal as their primary energy source to generate electricity should target the adoption of alternative energy systems to lower their CO2 emissions. These data show that the states that used coal to generate electricity emitted more than half of their CO2 emissions during the production of electricity. These data also show that those states with higher population densities (such as Arizona, California, Colorado, Washington) also consume more motor vehicle fuels and should therefore explore the substitution of fuels produced from nonrenewable fossil fuels with those produced from renewable materials. Even in states where electricity production is largely based on hydroelectric power (e.g. Idaho, Oregon and Washington), biofuels have a role in lowering CO2 emissions since the environmental impacts of generating electricity with hydropower makes it a less acceptable solution for some citizens. Biomass availabilities for biofuel production An analysis of the power generation and consumption profiles and the amount of CO2 emitted by each of the Western states suggests that all of them should supplement or substitute their energy production with biofuels. The next question that should be asked is whether sufficient supplies of biomass exist in each state to make the building of biofuel production facilities worth pursuing. In addition, it is important to characterize the different types of biomass that are available: 1) waste biomasses from agriculture, forests and municipal wastes, i.e. ‘wastes’; 2) sustainably collected biomass from unmanaged forests, i.e. ‘sustainable forest biomass’, which was assumed to be 2 per cent of the total live aboveground biomass; and 3) the collection of the small diameter (5–23 cm) forest materials considered to increase the fire risk of forests, i.e. ‘high fire risk biomass’. Unlike all the other biomass materials that can be annually and sustainably collected, the high fire risk biomass cannot be collected annually but will continue to be produced through time. In this assessment, the three types of biomass materials were included in the data set for each of the 11 Western states. Total annual biomass availability for the 11 Western states examined was high in all states except Arizona, Nevada, New Mexico, Utah, and Wyoming (Figure 14.1). The available biomass also varies considerably by biomass type (Table 14.3). For example, ‘wastes’ supplies were high in the five states where the agriculture and forestry industries are strong (Figure 14.1). The remaining six states would need to use ‘sustainable forest biomass’ or the ‘high fire risk biomass’ to produce biofuels (Figure 14.1; Table 14.3).

Wood methanol as a renewable energy source 305

Figure 14.1 Annual ‘wastes’ (municipal landfills, forests, agriculture) and ‘sustainable forest biomass’. See Table 14.3 for biomass data by type and state, and from forest ‘high fire risk biomass’ (5–23 cm diameter material). Table 14.3 Amount of dry biomass annually available from municipal landfills, agriculture, and forests Total annual sustainable collection of biomass

State

(103 Mg yr−1)

One time biomass collection (103 Mg)

Wastes in Wastes in Wastes from Forest municipal agriculture forests aboveground landfills biomass (2 % collected)

Aboveground biomass with high fire-risk in forestlands

Arizona 526 California 38,000 Colorado 451 Idaho 427 Montana 500 Nevada 232 New Mexico 191 Oregon 1,653 Utah 228 Washington 3,472 Wyoming 59

351 4,200 1,550 1,788 1,560 4 168 1,500 88 2,412 106

209 26,830 292 5,873 2,641 22 245 12,700 150 8,104 317

2,509 24,100 7,912 14,012 13,312 216 2,809 29,561 2,827 21,032 3,591

35,830 113,490 43,820 72,940 70,220 450 29,210 82,550 1,450 57,150 6,800

Source: Milbrandt (2005); WSU (2005); Western Governors’ Association (2006); and Oregon (2007).

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If only considering the collection of ‘wastes’ (e.g. from municipal, agriculture or forests) within each state, forest ‘wastes’ would provide the greatest amount of biomass materials in those states where viable forestry industries exist (such as California, Idaho, Montana, Oregon and Washington) (Figure 14.1). In most cases, even when viable agricultural industries were present in a state, the amount of agricultural ‘wastes’ was not as high as that created by forestry operations. This analysis only calculated the collection of 25 per cent of the agricultural ‘wastes’, since soil productivity is better maintained if some biomass wastes are left in the field. In California, the amount of available ‘wastes’ being transported to landfills is so high that this could become an excellent source of material for energy production. The forest ‘wastes’ in California are also a good source of biomass materials (Figure 14.1). States lacking strong forestry industries (such as Arizona, Colorado, Nevada, New Mexico, Utah and Wyoming) need to use all of their ‘waste’ supplies (e.g. from agriculture, forests and municipal residues) for energy production since no one type of waste is available in sufficient quantities by itself to produce biofuels economically. Alternatively, if one compares the ‘sustainable forest biomass’ supplies available from collecting a sustainable amount of forest biomass from all of the forests in the 11 Western states, even states without a viable forestry industry could annually collect enough biomass materials to convert to biofuels (Figure 14.1; Table 14.3). Furthermore, those states with strong forestry industries and where a large amount of wastes are already generated (e.g. Idaho, Montana, Oregon and Washington) could increase their supply of biomass resources by two to six times if ‘sustainable forest biomass’ materials was included with their ‘wastes’ mix. If the small diameter aboveground materials with high fire risk were collected from forests in these 11 Western states, an overabundance of readily available woody biomass exists in most of the Western states to produce biofuels economically; the only exception to this pattern occurs in Nevada (Table 14.3). This means that even those states without a viable forestry industry have sufficient supplies of ‘high fire risk biomass’ materials that could be converted to biofuels. Most of these Western states have the potential to acquire significant environmental benefits from managing forests for biofuel production. By thinning forests to reduce their fire risk (USDA Forest Service 2003), the western states can contribute towards reducing carbon emissions twice – first by reducing carbon emissions that occur during catastrophic fires and second when biofuels are substituted for fossil fuels.

Bio-methanol link to gasoline consumption and electricity generation in 11 Western U.S. states The production of methanol, instead of ethanol, from biomass wastes was examined here. There are several reasons that make methanol production from biomass worth pursuing, and why it is the focus of this assessment.

Wood methanol as a renewable energy source 307 First, because of existing technologies wood biomass can be more efficiently and completely converted to methanol than to ethanol. This is primarily because methanol is a one-carbon alcohol and ethanol is a two-carbon alcohol (Upadhye 2006). Secondly, alcohol produced from wood biomass generates almost twice the amount of liquid fuels than when converting the same amount of agricultural crop biomass (DOE 1990; NREL 1995; Oasmaa et al. 2003; Upadhye 2006; Nakagawa et al. 2007). Mobile conversion technology is also used in these assessments (Vogt et al. 2005) since it becomes a more viable option to produce biofuels at each site where the biomass is actually located rather than transporting the biomass to a central location much farther away. Transporting woody biomass can be cost-prohibitive when distances greater than 100 km are needed to supply centralized facilities. Since biomass availability is sufficiently high in most of the Western states, it is worth determining the quantity of biofuel production that is possible from the three categories of biomasses mentioned earlier. Since the technology for converting biomass to biofuels (e.g. methanol) is approaching efficiencies of about 50 per cent today (Vogt et al. 2005), producing biofuels from biomass will provide more energy than historically possible. This means that the high biomass supplies available in the Western states can be converted to biofuels at a level where it can potentially substitute or supplement a significant portion of the energy consumed in each of the Western states analyzed. The amount of bio-methanol produced from the different biomass types was calculated for each of the 11 states. For this discussion, we quantified the amount of methanol that could be produced from either 1 Mg of dry biomass or wastes collected from municipal landfills, agricultural fields, or forests with a small scale, mobile conversion system using methodology described in Vogt et al. (2008). For these calculations, a 50 per cent conversion efficiency (630 L of methanol produced per 1 dry Mg of biomass) was used to convert biomass to bio-methanol (Vogt et al. 2008). These data were then used to calculate the annual sustainable amount of methanol that could be produced from these ‘wastes’, from ‘sustainable forest biomass’, and from the ‘high fire risk biomass’ for the 11 Western states. How much of each state’s gasoline consumption could be substituted annually by bio-methanol produced from these three types of biomass/wastes was calculated, as well as the energy-equivalent amount of natural gas-methanol that could be substituted by bio-methanol to produce electricity with fuel cells. Vehicle fuel Since motor vehicle fuel is almost entirely petroleum based in all the states studied, and it is a relatively high-value fossil energy product, substituting or supplementing gasoline with an alcohol produced from biomass is a logical comparison to make. Methanol is already used in fleet vehicles as M85 fuel (85 per cent methanol with 15 per cent gasoline), but today most of the

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methanol supplies are derived from natural gas (Reed and Lerner 1973; Ohlström et al. 2001; Ekbom et al. 2003). Based on the biomass values provided in Table 14.3, the proportion of each state’s gasoline consumption that can be substituted by equal volumes of bio-methanol produced from the three types of biomass is summarized in Table 14.4. Out of the 11 states, those with the poorest developed agricultural or industrial forest bases had the least amount of ‘wastes’ available from biomass to produce biofuels as fossil fuel substitutes. In states with poorly developed agricultural or forestry industries, if all ‘wastes’ were converted to biofuels only 5.2 per cent of the gasoline could be substituted by biofuels in Arizona, 9.3 per cent in Colorado, 4.3 per cent in Nevada, 8.3 per cent in New Mexico and 6.1 per cent in Utah. These numbers suggest that biomass wastes are not ideal material to convert to biofuels in states with poorly developed agricultural or forest industries because their quantities are too low. An exception is Wyoming, which does not have a large-scale agricultural or forestry industry, where ‘wastes’ would be able to substitute for 20.2 per cent of the state’s gasoline use because its lower population density means that gasoline consumption rates are also low. As would be expected, states with strong agricultural and forestry industries can use ‘wastes’ to substitute for a significant proportion or all of their annual gasoline consumption (Figure 14.2; Table 14.4). For example, California could convert its agricultural, forestry and municipal ‘wastes’ to biofuels and substitute for about 72.4 per cent of its annual gasoline consumption. Similarly, Washington could use bio-methanol to substitute for 79.8 per cent of its annual gasoline consumption, Montana 104.1 per cent, Oregon 153.9 per cent, and Idaho twice the annual gasoline consumed (197.2 per cent of total gasoline used). In these states where the available supplies of ‘wastes’ that can be used to produce methanol are high, it is a practical solution to substitute bio-methanol for gasoline. Yet despite the high potential shown for methanol to substitute for the gasoline consumed in the western U.S., its production from biomass to date has not been included in the mix of biofuels being produced. Those states with poorly developed agricultural and forestry industries that lack sufficient supplies of ‘wastes’ (Table 14.4) may have a sufficient supply of the other two biomass types that can be converted to biofuels. The quantity of bio-methanol produced from ‘sustainable forest biomass’ is capable of substituting for 15.6 per cent of the gasoline consumed in Arizona, 64.6 per cent of the gasoline consumed in Colorado, 49.3 per cent in New Mexico, and 43.4 per cent of the gasoline consumed in Utah. Furthermore, states that appear to be poor candidates for converting biomass to biofuels are clearly strong candidates for biofuels production when the ‘high fire risk biomass’ materials are converted. The only state where biomass conversion to biofuels does not appear to be worth pursuing because of low biomass availability is Nevada; even the availability of ‘high fire risk biomass’ is low in this state.

Wood methanol as a renewable energy source 309

Figure 14.2 Comparison of the annual gasoline consumed (in billion liters yr−1) in each state and the amount of bio-methanol (in billion liters yr−1) that could be used as a gasoline substitute in each state annually. Biomethanol is produced from ‘wastes’ (municipal, agriculture, forests), ‘sustainable forest biomass’ and from ‘high fire risk biomass’ from forests.

The potential benefits of bio-methanol production on the local economy can be demonstrated using Missoula, Montana as a case study. Missoula County has a population of around 100,000 people. The per capita consumption of gasoline in Montana is about 7.57 liters per day. Therefore, the population of Missoula County would consume approximately 757,000 liters of gasoline each day. If Missoula County replaced half of its gasoline with M85 fuel, this would eliminate the need to purchase 295,262 liters per day of gasoline from outside Missoula’s economic zone. Production of the necessary M85 fuel for this scenario would require 495,889 liters of methanol. A plant located near the city could, using ‘sustainable forest biomass’ from within the local area, process 780 dry Mg of wood per day to produce this methanol. Assuming a wholesale value of $2.10 [February 2008 price] per 3.79 liters of methanol, this would yield revenues of $274,767 per day, or $100 million per year. This $100 million will remain in the Missoula economic zone rather than being disbursed into the global petroleum supply system. Such an

Bio-methanol from municipal wastes

3.3 39.9 3.7 10.5 12.9 3.9 3.3 17.7 3.5 20.5 3.0

State


0.6 4.3 3.2 42.1 23.2 < 1.0 0.7 9.9 0.3 11.6 1.3

Bio-methanol from agricultural wastes 1.3 28.2 2.4 144.6 68.0 0.4 4.3 136.3 2.3 47.7 15.9

Bio-methanol from forest wastes 15.6 25.3 64.6 345.0 342.7 3.6 49.3 317.3 43.4 123.9 179.7

Bio-methanol from 2% of forest biomass

233.3 119.2 357.8 1795.7 1807.9 7.5 512.8 885.9 22.3 336.7 340.3

Bio-methanol from forests with high fire risk (5–23 cm diameter)

Proportion of total gasoline consumption annually substituted with bio-methanol by state Proportion of total gasoline use annually substituted with biomethanol from a one time collection by state

Table 14.4 Potential annual gasoline consumption (%) that could be replaced by bio-methanol produced from a diversity of biomass sources in 11 western states

Wood methanol as a renewable energy source 311 operation would need to employ 100 or more people, from the forest to the fuel pump, requiring loggers, truck drivers, technicians, administrators, clerks, etc. Electric power production Each of the Western states’ electric power is already produced to a small extent from forest by-products, i.e., co-generation. Most of the wood biomass used to produce electricity in co-generation facilities has already been transported to the facility, such as a pulp mill, so the transportation costs are not prohibitive. However, a barrier to the broader use of forest biomass for power production in centralized facilities is its high bulk density and relatively low energy density, which makes it expensive to transport. This, along with the amount of wood required by the typical steam generation power plant, creates a challenge to produce energy at a competitive cost. These constraints can be overcome if mobile biomass conversion systems that convert woody biomass at the site are adopted. Producing the biofuels at the site where the biomass already exists would only require the transportation of a high energy-density material to the markets (Vogt et al. 2005). Methanol is much more energy-dense than wood, and can be readily transported to the site where it is needed. Fuel cell technology has also matured to the extent that fuel cells can be used to produce electricity at not only higher efficiencies but in more environmentally friendly ways than in the past, if methanol is used as the preferred fuel source (Vogt et al. 2005, Idatech 2003). The Idatech fuel cells use methanol at the rate of 960 ml kWh−1 or 3.94 kWh gallon−1. Another option is to generate electricity on site in small Integrated Gasification Combined Cycle (IGCC) plants to satisfy local need, and put the excess energy on the electricity grid. Similar to the patterns reported when substituting bio-methanol for gasoline, using ‘wastes’ to produce electricity generated by fuel cells is best pursued in those states where the agriculture and forestry industries are strong and are a vital part of the state economies (Figure 14.3; Table 14.5). In these states, potentially 10.5–23.9 per cent of the total electricity generation could be met by fuel cells using bio-methanol derived from ‘wastes’. These values represent the potential that exists for using bio-methanol and fuel cells to produce electricity, even though today in these states fuel cells are not a major source of electricity (Vogt et al. 2008). Fortunately, even states with negligible quantities of ‘wastes’ do have sufficient quantities of ‘sustainable forest biomass’ that could be converted to bio-methanol and used in fuel cells to make electricity (Figure 14.3; Table 14.5). For example, Colorado could potentially generate 10.6 per cent of its electricity annually using fuel cells driven by bio-methanol, while New Mexico, Utah and Wyoming could generate 8.9 per cent, 7.4 per cent and 16.5 per cent of their annual electricity requirements, respectively. The potential for Idaho, Montana, and Oregon to generate electricity using

312


Figure 14.3 Annual electric power generation for each of the 11 western states and the amount of electricity that potentially could be generated from the conversion of the different biomass types into bio-methanol for electricity generation using fuel cells.

biofuels and fuel cells increases dramatically when bio-methanol is also produced from ‘sustainable forest biomass’ materials (Table 14.5). For example, bio-methanol derived only from ‘wastes’ allows, at most, a 20 per cent substitution of these states’ electricity needs. In contrast, 41.4–64.2 per cent of the electricity demands in these three states could be satisfied when methanol derived from ‘sustainable forest biomass’ is fed into fuel cells. Similarly, Washington and Wyoming could potentially generate about 16 per cent of their annual electrical needs when converting ‘sustainable forest biomass’ materials into bio-methanol to use in fuel cells for electricity generation. In Arizona and Nevada, however, if the goal was to produce electricity only, the supplies of ‘wastes’ or even ‘sustainable forest biomass’ are too low to make bio-methanol production economical for either state. However, Arizona does have a large quantity of ‘high fire risk biomass’ in forests that

Bio-methanol from municipal wastes

0.5 9.7 0.6 1.3 2.4 0.5 0.6 2.3 0.6 2.7 0.3

State


< 0.1 1.0 0.5 5.1 4.4 < 0.1 0.1 1.3 < 0.1 1.5 0.1

Bio-methanol from agricultural wastes 0.2 6.9 0.4 17.5 12.7 < 0.1 0.8 17.8 0.4 6.3 1.5

Bio-methanol from forest wastes

Proportion of total electricity generated annually from a one time collection by state

2.4 6.2 10.6 41.7 64.2 0.4 8.9 41.4 7.4 16.4 16.5

33.6 29.0 58.9 217.0 338.6 0.9 92.0 115.6 3.8 44.5 31.3

Bio-methanol from 2% Bio-methanol from of forest biomass forests with high fire risk (5–23 cm diameter)

Proportion of total electricity generated annually using bio-methanol-fuel cells by state

Table 14.5 Potential annual electricity consumed (%) that could be generated from fuel cells using bio-methanol produced from a diversity of sustainably collected biomass sources in 11 western states

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should be removed to prevent catastrophic forest fires. If this material were available for conversion to bio-methanol, Arizona could potentially meet 33.6 per cent of its annual electricity needs from these materials alone. In addition, its conversion to bio-methanol would eliminate the potential CO2 emissions from related forest fires. Nevada lacks a sufficient supply of all types of biomass, even ‘high fire risk biomass’, so it appears that mandating biofuel production in the State may be inappropriate (other alternative energy sources, such as solar and geothermal energy, are more abundant in Nevada). Three of the 11 western states could satisfy their entire annual electricity demand just from a one-time conversion of their ‘high fire risk biomass’ to bio-methanol, using it in fuel cells to generate electricity, and still have a surplus to sell to other states (Table 14.5). For example, if Idaho were to convert its ‘high fire risk biomass’ materials to biofuels, it could replace 217 per cent of the electricity it generates in a year; Montana could replace 338.6 per cent; and Oregon could replace 115.6 per cent of its electricity generated. Again, these numbers illustrate the potential if biomass is converted to methanol for use in powering fuel cells. Today, fuel cells supply only a very small fraction of the electricity consumed globally, but they have the potential to substitute for the electric generation required when older technology needs to be replaced. This potential is especially relevant for rural communities where there are no transmission lines because it is too expensive to install them (such as in Indonesia, Namibia, etc.) or where the power provided is not reliable. Using these biomass materials would allow several states, with low supplies of ‘wastes’ and where the agricultural and forestry industries are not as prevalent, to generate a large portion of their electricity from ‘high fire risk biomass’ from their forests (e.g., Arizona 33.6 per cent, Colorado 58.9 per cent, New Mexico 92.0 per cent, and Wyoming 31.3 per cent of the amount annually produced).

Potential reductions in carbon emissions from using bio-methanol to produce energy in 11 states in western U.S. When biomass is converted into biofuels, the amount of carbon emissions avoided is considerable because of the high efficiency of converting biomass to methanol and because bio-methanol is an environmentally friendly substitute for fossil fuels used to produce energy. For example, 420 kg of carbon emissions are avoided when replacing or supplementing gasoline with an energy equivalent amount of bio-methanol produced from 1 dry Mg of biomass (Vogt et al. 2008). Substituting bio-methanol produced from 1 Mg dry biomass for natural gas-derived methanol and generating electricity could potentially avoid emissions equivalent to 462 kg carbon. Although the electricity portion that could be generated from bio-methanol is less than the gasoline portion that could be replaced by an energy equivalent of bio-methanol, the total carbon emissions that could be avoided are

Wood methanol as a renewable energy source 315 greater when using bio-methanol to generate electricity than when using bio-methanol to replace gasoline (Figure 14.4). Therefore, if the goal is to optimize the reductions in CO2 emissions, the best strategy is to use biomass to generate electricity because that provides the highest possible avoidance of emissions. When comparing the three types of biomass available for conversion to bio-methanol in the 11 western states, ‘wastes’ would be available in sufficient amounts for energy conversions on an annual sustainable basis only in California, Oregon, Washington, Idaho and Montana. These are the states where large-scale agricultural and forestry industries are located. Substituting fossil fuels with liquid fuels produced from ‘wastes’ could result in potentially significant reductions in CO2 emissions in these states. California has the greatest potential to significantly cut its emissions by converting its ‘wastes’ into an energy product. The high population densities in California and Washington suggest that biofuels should be generated from ‘wastes’ to

Figure 14.4 The potential portion of total state carbon emissions that could be avoided by substituting bio-methanol for motor vehicle fuels (MF) or by substituting electricity produced from fuel cells for electricity derived from fossil fuels (E) in each of the 11 western states. Biomass wastes = waste biomasses from agriculture, forests and municipal wastes; sustainable forest biomass = 2 % of the total live aboveground forest biomass; high fire risk = small diameter (5–23 cm) forest materials considered to increase the fire risk of forests. [NOTE: Idaho E-High Fire Risk = 800.6 % and MF-High Fire Risk = 727.8 %; Montana E-High Fire Risk = 374 % and MF-High Fire Risk = 340 %; Oregon E-High Fire Risk = 340.5 % and MF-High Fire Risk = 309.5 %].

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replace or supplement motor vehicle fuels. However, since Montana generates 63.8 per cent of its electricity using coal, which contributes 62 per cent of the State’s annual CO2 emissions, it should consider converting its waste materials into electricity (Table 14.1). This contrasts with both Idaho and Oregon where hydroelectric power production is the primary source of energy, and only 9 and 22 per cent, respectively, of their CO2 emissions result from electric power generation. In these states, greater CO2 emissions can be avoided by using their ‘wastes’ materials for producing transportation fuels. Several western states (i.e. Arizona, Colorado, New Mexico, Utah, and Wyoming) use coal as their primary energy source (Table 14.1) to produce electricity and do not have large-scale agricultural or forestry operations. These states could still collect enough ‘sustainable forest biomass’ materials to produce electricity and at the same time reduce from 12.2–4.9 per cent of their states CO2 emissions (Figure 14.4). If these states (except Utah) converted their ‘high fire risk biomass’ materials to bio-methanol, their ability to reduce CO2 emissions during electricity generation would be considerably higher. This would be a onetime reduction in emissions if all this ‘high fire risk biomass’ were converted. Under this onetime scenario, the possible CO2 emissions avoided varies from 18.5 per cent in Wyoming, to 69.5 per cent in Arizona, 67.6 per cent in Colorado, and 85.6 per cent in New Mexico. These states would benefit from substituting bioenergy for their electricity production whenever possible so the potential to reduce their CO2 emissions during energy production could be realized. Four states are worth mentioning because of the tremendous potential to reduce their CO2 emissions by converting their ‘sustainable forest biomass’ or their ‘high fire risk biomass’ into a substitute transportation fuel or by generating electricity – using bio-methanol as the hydrogen source for fuel cells. Three of the states (Idaho, Oregon and Washington) use hydropower as their primary source for producing electricity while the fourth state (Montana) primarily uses coal to generate electricity. All four of these states have well developed agricultural and forestry industries and have a significant portion of state lands in forests. These four states are ideal candidates for producing biofuels and have the potential to significantly reduce CO2 emissions within their own state boundaries as well as to market their biofuels to other states.

Conclusions Most of the funding and incentives for producing biofuels in the U.S. is going towards the development of ethanol and biodiesel from agricultural crops, residues and grasses. This technology is also focusing on building larger-scale commercial facilities capable of producing 380 million liters per year from one plant (e.g., Coskata 2008). There is only minimal discussion of the use of methanol to produce bioenergy, and the inclusion of forest materials in the mix of biomasses to produce these biofuels (Vogt et al. 2008). This contrasts with China, which is leading in the use of methanol as an

Wood methanol as a renewable energy source 317 alternative transportation fuel (though made from coal) and was already blending about 3.0 × 109 liters of methanol with gasoline in 2007. Bus fleets and taxis in China are already running on blends ranging from 85 to 100 per cent methanol (BBJ 2008). Our assessment suggests that alternative approaches should be pursued to produce energy. Biomass sources of all types are ideally suited to provide the fuel to power both motor vehicles, and the newly emerging small appliances that are powered by methanol-fuel cells. Electricity in remote areas or where the supply of electricity is not consistent could be provided by the conversion of locally available biomass to methanol. Using biomass materials to produce methanol is an economically feasible and realistic option to consider adopting when mobile integrated technology converts biomass supplies at its source. This idea is similar to the vision recently articulated by the Boeing Company for the need to move away from a single, large repository of biofuel feedstock for supplying airlines to using a distributed network of multiple feedstocks that are appropriate for each region’s geography and climate instead (Trimble 2007). This is an option that needs to be pursued in those locations where biomass supplies are high and can be sustainably collected. An added benefit of producing methanol from wood biomass and using it as a transportation fuel is its ability to lower CO2 emissions at a higher rate when compared to some of the other biofuels produced today (Schindler et al. 2006). Sourcing biofuels from forest materials is especially relevant for Western states to pursue because of the greater conversion efficiency compared to fermentation-based cellulosic biomass conversions (Upadhye 2006). Because of the high supply of the three types of biomasses available in the Western states, these states can use these resources to produce substitute motor vehicle fuels or to produce bio-methanol to generate electricity from fuel cells. An ideal attribute of bio-methanol is its potential for substituting for motor vehicle fuels or for fossil fuels used in fuel cells to generate electricity. In addition, these states can dramatically reduce the CO2 emissions that would have resulted from the combustion of fossil fuels within their boundaries. In addition, they have the ability to market their surplus biofuels to states with lower quantities of biomass, but who need to reduce their CO2 emissions. If well-designed and environmentally friendly technologies are used to convert biomass, biofuel production could link solutions for environmental problems such as global climate change with energy production, while potentially promoting rural economic development. The analyses presented in this chapter were conducted at the state level but it is at the local level where the potential for biofuels will be reached. Many small and medium-sized communities in these western states could benefit from the jobs and revenue generated by a local bio-methanolprocessing facility. Another added benefit from producing biofuels would be the creation of jobs in rural areas that are generally higher paying than many service jobs found in urban areas. Many of these rural areas in the Western

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states need sustainable development activities to revitalize them. Biofuel production, therefore, provides an opportunity to revitalize rural areas in these states. As an example of the potential impact, consider that much of the money that changes hands in the sale of gasoline today is directed to the gasoline supplier to defray crude oil prices, and for refining and delivery costs, so this money will generally not even remain in the community. If, however, the gasoline is replaced with locally produced bio-methanol fuel, the associated costs for transportation and distribution are greatly reduced. Additionally, not only would more biofuel generated money stay in the community, but the fuel price would be much more stable and not be subject to the vagaries of geopolitics or supply issues halfway around the world.

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Afterword Valerie A. Luzadis

As the reality of steadily increasing fossil fuel costs has brought home the challenges we face with global climate change and increasing oil scarcity, politicians naturally turn to technologies already at hand to answer the need. Corn-based ethanol production was one of the first alternative energy technologies to take off in the U.S., bringing with it hopes for salvation from technology in the face of the looming energy crisis. It has also brought increased land use for input-intensive corn crops that has had a striking impact on numerous other corn and corn-related markets beyond ethanol and raised questions about sustainability. In this context, research on woodbased bioenergy alternatives has accelerated and begun to gain more notice. Researchers in the U.S. and beyond are now being called upon to respond to growing public concern about affordable, sustainable energy supplies. This attention has redoubled efforts to develop economically feasible, sustainable energy from renewable forests in the U.S. The snapshot shared in this volume of current research and practice of using renewable forest resources in the U.S. toward that end provides the opportunity to speculate about the future. The background and case studies of the technical, economic, and ecological abilities of forests to meet future U.S. energy needs suggests that indeed, U.S. forests have the potential to contribute positively to the carbon mitigation efforts and to significantly offset petroleum consumption. And with some adjustment to current policies, these contributions are likely to grow. The general consensus is that future energy supply will be met by employing multiple strategies for sustainable energy development, considering both local conditions and global needs. Pacala and Socolow (2004) include biomass fuel substituting for fossil fuel as one of their seven ‘wedges’ of technologies that have the potential to mitigate the carbon and climate problems in the next 50 years, if implemented with six other successful strategies. Another of their possible wedges is to decrease deforestation, increase afforestation, and create new plantations. Current efforts to use short-rotation woody crops present an interesting scenario for further analysis of their contribution to not only reduction of fossil fuel use, but to increased carbon storage. Koonin (2006) continued this dialogue by suggesting the strong potential for bioenergy, and

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Righelato and Spracklen (2007) broadened the debate to consider issues at a scale appropriate for the decisions at hand, questioning the balance of land use for production of biofuels versus carbon stores. Issues of land use for bioenergy crops and the potential impact on food security, especially in developing countries, is of global concern (UN-Energy 2007). With it, this concern brings a responsibility on the part of all nations to contribute to sustainable strategies to mitigate carbon emissions and increase carbon storage while adequately addressing food security for all people, including the poorest among us. The chapters in this volume demonstrate the potential contribution of U.S. forest resources toward these ends, including natural forests and dedicated woody energy crops. Societal values will direct the specific future of these developments through expression of social and economic concerns and options for policy change at every scale. As with all sustainability questions, the engagement of uncertain futures must involve as many stakeholder groups as possible in the development of responses (Kates et al. 2001). While this may be new to some scientists, engagement with stakeholders as a function of research and development continues to expand as we embrace the social and ethical challenges of managing our complex human ecosystem. As we move forward, the balance of local to global scale concerns and short- and long-term needs will present continual challenges. In short, we need to find the most effective ways forward in the face of uncertainty. Uncertainty about the future is nothing new; adjusting our means of organizing ourselves to sustain life by incorporating scientific understanding, uncertainty, and social values in ways that balance power and respect for one another and life-supporting ecosystems is the ultimate challenge.

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Glossary

Afforestation The process of adding a forest in an area that has lacked forest cover for a very long time or has never been forested. Bagasse Biomass residues that remain after sugarcane stalks are crushed for their juice. Best management practices Practical and economically achievable practices for reducing or preventing non-point sources of water pollution. Bioenergy system The life cycle of biomass energy and fuels from production of biomass to its refining, transportation, and end use by consumers. Black liquor A byproduct of the Kraft pulping process comprised of an aqueous solution of lignin, hemicellulose, and inorganic chemicals used in the process. Buck To cut a felled tree into logs. Cellulose The chief component of the cell walls of green plants. Cetane rating The measure of how easily a fuel will burn, where a lower number indicates a lower temperature needed to achieve combustion. It is commonly used to indicate the quality of diesel fuel. See: Octane rating. Chip van A tractor/trailer equipped with a large-capacity, typically toploaded enclosed trailer that is designed to haul wood chips. Co-firing The practice of combusting biomass along with conventional fuels such as coal in an electric power plant. Cogeneration The combined production of heat and electric power. Coking coal A solid carbonaceous material usually used in steelmaking, derived from destructive distillation of low-ash, low-sulfur bituminous coal by baking it in an airless oven at high temperatures. Comminution To reduce particle size by grinding, milling, cutting, or a similar process. Composite residue log (CRL) Logging residues compacted into a package of similar size and shape to a log (typically about 3 m long and 1 m in diameter). Compressed natural gas The compression of natural gas to around 3,500 psi to increase its practicality for use as motor vehicle fuel. Conservation Reserve Program A U.S. Department of Agriculture Program administered by the Natural Resources Conservation Service, which offers

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Glossary

technical assistance and annual payments for 10–15-year contracts to eligible farmers who establish grass, shrub and tree cover on environmentally sensitive lands. Co-products Non-energy outputs from the conversion of biomass to energy, including animal feed and chemicals. Cord A quantity of fuel wood equal to 3.62 m3 (128 ft3). Corn stover The stalks and leaves of corn crops, normally left in the field after harvest. Corporate Average Fuel Economy (CAFE) Fuel economy standards for automobiles and light trucks required by the National Highway Traffic Safety Administration. Ethyl tertiary butyl ether (ETBE) A fuel blending component and octane enhancer made from a reaction between ethanol and isobutylene that is used in oxygenated gasoline. ETBE use is common in North Central states as an alternative to MTBE in order to lower the production of tropospheric ozone or carbon monoxide. See: Methyl tertiary butyl ether (MTBE) and Octane rating. Eutrophication An increase in chemical nutrients in an ecosystem that leads to excessive plant growth and decay, usually resulting in a decline in oxygen, water quality, and animal species. Excise tax A tax on the manufacture or sale of a specific good or activity such as gasoline, cigarettes, liquor, firearms, road use, or gambling, either per unit or ad valorem (a percentage of the item’s value). Feedstock Raw material input used for industrial processes. Feller buncher A timber-harvesting machine that cuts a tree at the base, holds it upright with other fallen trees in an accumulator pocket, and then lays them down in a pile for transport. Fermentation The process of producing energy in a cell without oxygen. Fischer–Tropsch reaction A catalyzed chemical process in which hydrogen and carbon monoxide are converted into liquid hydrocarbons. Forwarder A vehicle that carries cut logs from a forest to a roadside landing. Fuel treatment thinning The process of removing excess forest biomass (live, diseased, or dead) to reduce the risk of wildfires. Gasification A process that converts hydrocarbons into carbon monoxide and hydrogen by reacting the raw material at high temperatures with a controlled amount of oxygen. Gross regional product The market value of all final goods and services produced within a region in a given time period. Hammer mill A high-speed machine that uses rotating hammers to reduce material particle size. Harvester A machine that fells trees, de-limbs them, and crosscuts them into logs (for cut-to-length logging). Hydrolysis A reaction in which a chemical compound is broken down by water. Input–output (IO) analysis An accounting framework of an economy containing a matrix representation of inter-industry transactions. It can be

Glossary 327 used to predict the effects of changes in one sector on all others by tracing through the direct, indirect and induced linkages between sectors for businesses, government and consumers. Knife mill A machine similar to a hammer mill, except that it uses rotating knives to reduce material particle size. Landing A central location where logs and woody biomass are gathered for transport to the end user. Preprocessing such as de-limbing and grinding sometimes occurs at the landing. Life cycle assessment A systematic framework optimized for the analysis or comparison of the environmental impact of products, processes, and systems throughout their life cycle. Lignin An amorphous polymer that provides rigidity and together with cellulose forms the woody cell walls of green plants and the cementing material between them. See: Cellulose. Lignocellulosic ethanol Ethanol manufactured from the woody part of trees, plants, grasses or residues. Also called cellulosic ethanol or biomass ethanol. Liquefaction Converting biomass into liquid form. Logging residues The unused portions of harvested trees that are left in the woods. Merchantable timber Wood that meets the minimum dimensions established by a mill and thus can be sold. Methyl tertiary butyl ether (MTBE) A fuel blending component and octane enhancer made from a reaction between methanol and isobutylene that has been used since 1979, which became widely adopted in the 1990s in oxygenated gasoline in order to lower the production of tropospheric ozone or carbon monoxide. It is currently being phased out because of its link with groundwater toxicity. See: Ethyl tertiary butyl ether (ETBE) and Octane rating. Monoculture The cultivation of a single crop or tree species over a wide geographic area. Monte Carlo simulation A computer method for iteratively evaluating a deterministic model using sets of random numbers as inputs. National Fire Plan A U.S. plan developed in August 2000, following a landmark wildfire season, to provide guidance for responding to severe wildfires and their impacts to communities. Naval stores The resin based components that were used in building wooden sailing ships, such as mask, turpentine, rosin, resin and tar, usually derived from pine sap. Non-attainment areas Geographic areas not meeting one or more of the air quality standards of the Clean Air Act. Octane rating The measure of a fuel’s resistance to auto-ignition, primarily used for gasoline. If a blend of gasoline used in an internal combustion engine has too low an octane rating then the gasoline will ignite from pressure before the spark from the spark plug ignites it. This is commonly called ‘knocking’. See: Cetane rating.

328

Glossary

One-pass (or single-pass) harvest system For bioenergy feedstock, an integrated harvest of logging residues or small-diameter trees simultaneously with roundwood. Organization of Petroleum Exporting Countries A 13-nation cartel of oil producers in the Middle East and other regions that sets prices and allocates oil production among its members. Paperboard Thick paper-like material such as cardboard and pasteboard. Pulping The process of converting wood to separated pulp fibers for papermaking. Pyrolysis The chemical decomposition of organic materials by heating, typically without oxygen. Queue Temporary storage of material, perhaps wood chips in the case of a biorefinery awaiting processing. Rack price A refiner’s wholesale price for gasoline or ethanol. Recoverability Woody biomass that can be collected from the forest subject to machine limitations, terrain and weather. Renewable Fuel Standard A program implemented by the U.S. Environmental Protection Agency that requires fuel refiners, blenders and importers to produce a minimum volume of their fuel based on specified renewable feedstocks. A similar program exists in several states. Renewable Portfolio Standard A state program that requires electric utilities to generate a minimum portion or quantity of electricity from specified renewable energy sources. The program is usually implemented by public utilities commissions. A similar program has been proposed at the federal level but has yet to be approved. Roundwood Logs and other round timber generated from harvesting trees for industrial or consumer use. Saccharification The process of breaking a complex carbohydrate, either as starch or cellulose, into simple soluble fermentable sugars. Short rotation woody crops Rapidly-growing, resilient tree species with wide side adaptability such as hybrid poplar, willow, sweet gum, sycamore, maple, eucalyptus, and loblolly pine. Skidder A wheeled or tracked vehicle used for sliding and dragging logs from stump to landing. Slash Unutilized, and generally non-merchantable, accumulation of woody biomass (i.e. limbs, tops and stumps) that remain as logging residue after timber harvesting. Stumpage The market value of standing timber. Switchgrass A hardy, warm season perennial grass. It can grow to over 2 m tall and is one of the dominant species of the central North American tallgrass prairie. Two-pass harvest system For bioenergy feedstock, harvest of logging residues independently of associated roundwood. Whole-tree harvest A method of logging that removes the entire tree (roundwood, limbs and top) typically by skidding.

Index

agricultural systems 136, 139 alternative energy sources 5, 7, 314 biodiesel 19–22, 42, 49, 50, 62, 116, 119, 122, 124, 130, 154, 167, 300, 316 bioenergy systems 23, 137, 142, 153, 168, 180, 196–97, 199, 200, 203, 207, 242, 255 biofuels policy 30 biomass energy 1, 4, 11–13, 22, 23, 28, 32, 41, 133, 164, 165, 168, 199, 282, 301 biomass fuels 15, 19, 22, 49, 116, 323 biomass storage 81–82, 84–86, 89, 92–96, 98, 105–112, 283 biorefinery 58, 60, 61, 81, 247, 289 butanol 19, 21, 22, 116, 117, 120, 130 CAFÉ (Corporate Average Fuel Economy) 53, 116, 120, 121 cellulosic ethanol 19, 22, 23, 50, 55–58, 61–64, 262, 270–274, 276, 281–296 cogeneration 9, 15, 18 combined heat and power 15, 28, 182, 214 combustion 15–16, 18–19, 22, 43, 98, 116–17, 119, 120, 123–130, 164–65, 182–83, 216 comminution 81–2, 89, 93–97, 106, 110 Conservation Reserve Program 11, 134, 254, 282 decision-making 35, 133, 198–99 economic development 6, 30, 35, 63, 238, 254, 317 economic feasibility 189, 234 economic models 284–286 efficiency 16–19, 22–23, 35–36, 39, 49, 56, 80–83, 87, 119–131, 188, 191, 207, 234, 249, 254, 270, 307, 314, 317

electricity generation 3, 9, 15, 17, 31, 182, 186, 213, 219, 221, 229–234, 268, 274, 277, 302–03, 306, 311–12, 316 employment 171, 261, 273, 277, 285, 287, 289–296 energy balance 50, 175, 180, 183, 192, 234, 275 energy consumption 4–9, 121, 163, 173, 192, 303 Energy Policy Act 52, 61, 284 Energy Tax Act 52, 60 environmental costs 28, 133 equipment: biomass 81–89, 94–98, 102, 111–12; costs 223, 234, 266, 270, 284, 290; drying 214–15, processing 41, 218, 273, 275; harvesting 139, 242, 249; transportation 106, 109 fertilizer 134, 136, 138–142, 145, 154, 171, 182, 185, 188, 191–92, 253 fire risk 262, 304–316 food security 196, 324 forest biomass resource 4, 275–76 forest land area 4, 275–76 forest products industry (sector) 7–9, 12 forest residues 11, 12, 17, 69–77, 86–89, 92–94, 101–111, 165, 171, 261–262, 295 gasification 15–18, 44, 55, 165, 170, 182–83, 185, 213, 221–31, 234, 253, 311 growth rate 265, 268, 299; hybrid poplar 138; willow 239, 242 human development 199, 201–3 hybrid poplar 134, 138–39, 145, 149, 155, 176, 239–40, 282, 290

330

Index

hydrogen (H2) 17, 19–22, 49, 116–124, 130, 165, 167, 170, 180, 316 incentive 28, 31–36, 39, 44, 60–61, 63, 130, 214, 222, 255, 316 input-output analysis 273, 284–85 land conversion 70, 183 landscape 133, 146, 148–49, 151, 155, 250–51, 256, 264 life cycle assessment (analysis) 29, 92, 119, 122, 163–192, 252 lignocellulosic 18–20, 50, 69, 85, 145, 163–68, 180, 183, 189 logging residues 69–70, 74, 77, 87, 96–97, 102, 140, 261–62, 267–273, 276 managed ecosystems 136, 139–141 mandates 9, 10, 20, 28, 123, 189, 275, 284 market barrier 38–39, 45, 73 market growth 31, 34, 229 methanol 8, 16–7, 19, 20–24, 49–50, 116–19, 124, 165, 180, 183, 299–318 mill residues 11–14, 134, 165, 177, 186, 265–66, 268–70, 276 multi-criteria analysis 199, 202 oil displacement 13, 29, 30 participatory approaches 23, 198–200, 207 perennial crops 11, 13, 134, 136, 139, 142, 145, 147, 153–54, 175, 238–240, 249, 251–56 policies (biofuels, power generation) 22, 28–35, 37, 39, 51–2, 60–4, 88, 133, 135, 156, 167, 179, 189, 191, 201, 229, 254–55, 281, 284, 288, 290, 295–6, 301, 323–24 pulp and paper industry 6–7, 13 pulping liquors 13, 58, 289 pyrolysis 15–18, 55, 165, 182

regional economic analysis 35, 164, 281–296 Renewable Fuel Standard (RFS) 10, 28, 51, 54, 61, 286 residue (collection, harvesting 247; packaging 265, forest, logging, mill) residue collection 77, 79, 88–9, 110 short rotation woody crops 136, 154, 165, 172, 238–39, 323 stumpage costs265, 267 subsidies 20, 28, 35–37, 52–3, 55, 60–4, 281 supply system 92–113, 254, 309 sustainability 23, 35–6, 88, 92, 141–42, 153, 173, 189, 196–207, 253, 255–6, 300, 323–4; criteria and indicators 196–97, 200, 202–03 sustainable development 35, 196–97, 299, 318 sustainable forest biomass 304–312, 316 switchgrass 51, 57–9, 136, 142, 145, 147, 151, 153, 175, 188, 282, 286, 290, 295 tax 28, 30–34, 36, 51–2, 54–55, 60–2, 136, 213, 215, 223, 237, 254, 268, 275, 284, 286–7 transportation (biomass) 82–89, 93, 96–105, 108–112, 152, 165, 249, 252, 262, 265–69, 276, 289, 311, 318 wastes 141, 165, 186, 304, 306–316 water pollution 50, 133, 145, 164 whole-tree harvesting 77–8, 83, 141–2 wildlife habitat 88, 133, 147, 149, 153, 156, 264 willow 31, 134, 136–38, 149, 155, 168, 185, 238–256, 282, 290 wood energy 4–9 yields (biomass) 37, 60, 136–141, 188, 239–245, 249, 254, 256, 270, 282

Renewable Energy from Forest Resources in the United States (Routledge Explorations in Environmental Economics)

Waste and Environmental Policy (Routledge Explorations in Environmental Economics)

Alternatives for Environmental Evaluation (Routledge Explorations in Environmental Economics)

Renewable Energy Resources

Surfactants from Renewable Resources (Wiley Series in Renewable Resource)

Energy, Natural Resources and Environmental Economics (Energy Systems)

Explorations In Environmental History

Explorations in Pragmatic Economics

Renewable Energy in South East Europe (Renewable Energy Report)

Renewable Energy in Power Systems

Energy Supply and Renewable Resources (Global Issues)

The Economics of Climate Change (Routledge Explorations in Environmental Economics, 3)

Renewable Energy in Power Systems

Renewable Energy in Power Systems

Renewable energy in power systems

Renewable Energy in Power Systems

Environmental and Natural Resources Economics

Law in united states

Renewable Resources and Renewable Energy: A Global Challenge

The United States in Asia

Semiotics in the United States

The United States in Asia

Civilisation in the United States

Energy Resources and Systems, Volume 2: Renewable Resources