The Global Forest Products Model: Structure, Estimation, and Applications

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Author: Joseph Buongiorno | Shushuai Zhu | Dali Zhang | James Turner | David Tomberlin

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The Global Forest Products Model

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The Global Forest Products Model Structure, Estimation, and Applications Joseph Buongiorno Shushuai Zhu Dali Zhang James Turner David Tomberlin

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This book is printed on acid-free paper. ∞ Copyright 2003, Elsevier Science (USA) All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford. UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, email: [email protected]. You may also complete your request on-line via the Elsevier Science homepage (http://elsevier.com), by selecting “Customer Support” and then “Obtaining Permissions.” Academic Press An imprint of Elsevier Science 525 B Street, Suite 1900, San Diego, California 92101-4495, USA http://www.academicpress.com Academic Press An imprint of Elsevier Science 84 Theobald’s Road, London WC1X 8RR, UK http://www.academicpress.com Academic Press An imprint of Elsevier Science 200 Wheeler Road, Burlington, Massachusetts 01803, USA www.academicpressbooks.com Library of Congress Catalog Card Number: 2002096660 International Standard Book Number: 0-12-141362-4 PRINTED IN THE UNITED STATES OF AMERICA 03 04 05 06 9 8 7 6 5 4 3 2 1

For our teachers


ABOUT THE AUTHORS

Joseph Buongiorno is the John N. McGovern Professor in the Department of Forest Ecology and Management at the University of Wisconsin, Madison. His specialty is forest economics and management. Buongiorno holds an Ingénieur degree from the École Supérieure du Bois, Paris, an M.S. from the College of Environmental Science and Forestry, Syracuse, and a Ph.D. from the University of California, Berkeley. Before joining the University of Wisconsin in 1975 he worked for the Food and Agriculture Organization of the United Nations. Buongiorno has received the Forest Science Award and the Alwin Schenk Teaching Award of the Society of American Foresters, the Hardwood Research Council Award, and the Carl J. Jung Teaching Award and the Hilldale Award in Social Studies of the University of Wisconsin, Madison. Shushuai Zhu is a Senior Software Engineer at Oracle Corporation. He works on Oracle Enterprise Manager, database administration software used worldwide. He is also a visiting scientist in the Department of Forest Ecology and Management at the University of Wisconsin, Madison. His research has dealt especially with the modeling of the international forest product markets. Zhu holds a B.S. degree in management and information sciences from the University of Science and Technology of China, an M.S. in computer sciences, and a Ph.D. in forestry economics from the University of Wisconsin, Madison. Zhu received first prize in the National Contest on International Economics and Trade in China in 1992. Dali Zhang is an Associate Principal at Jaakko Pöyry Consulting (AsiaPacific) Pte Ltd. Zhang joined Jaakko Pöyry Consulting in Singapore in 1996 as Vice President to lead the Business and Marketing Strategies team in AsiaPacific. In September 2000 he was appointed the Chief Representative of the newly established Shanghai Representative Office, with responsibility for developing consulting businesses in China. Before joining Jaakko Pöyry Consulting,

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About theContents Authors

Zhang was for four years, a Research Associate at the University of Wisconsin, Madison, working on forest sector modeling. Zhang holds a B.S. degree in electrical engineering from Harbin Institute of Technology in China, an M.S. in forest operations from the University of Alberta in Canada, and a Ph.D. in forest economics from the University of Wisconsin, Madison. James Turner is a Research Assistant studying toward a Ph.D in forest economics in the Department of Forest Ecology and Management at the University of Wisconsin, Madison. His research has dealt with forest product trade and even-aged forest management. Turner received a Bachelor’s degree in forestry science from the University of Canterbury in New Zealand and was awarded the Schlich Memorial Prize. Prior to studying at the University of Wisconsin he worked for five years for the New Zealand Forest Research Institute in the areas of plantation management and strategic market intelligence. David Tomberlin is a research economist with the National Marine Fisheries Service. He works primarily on models of commercial fishing fleet dynamics and of optimal strategies for endangered species protection and restoration. While completing his doctorate in the Department of Forest Ecology and Management at the University of Wisconsin, Madison, Tomberlin focused on timber supply and links between international trade and the environment. Tomberlin also holds an M.S. in resource economics from North Carolina State University and a B.A. in literature from Princeton University.

CONTENTS

xiii

Preface and Acknowledgments

PART

I

Introduction 1 Background and Objectives 1.1 1.2 1.3 1.4

Background and Justification Previous and Related Models A Brief History of the GFPM Objectives and Outline

3 6 11 13

2 Overview of the Global Forest Products Sector 2.1 2.2 2.3 2.4 2.5 2.6

Introduction Production International Trade Forest Product Price Trends Economic and Demographic Prospects Summary and Conclusion

15 16 22 31 33 35

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Contents

PART

II

Model Construction 3 GFPM Structure and Formulation 3.1 3.2 3.3 3.4 3.5 3.6

Overview of the GFPM Spatial Global Equilibrium Market Dynamics Linear Approximation of Demand and Supply Implementation of Trade Inertia Constraints Welfare Analysis

39 41 44 47 51 52

4 Model Calibration and Validation 4.1 4.2 4.3 4.4 4.5

Introduction Countries and Products Base-year Data Parameter Estimation Model Validation

57 57 58 61 75

PART

III

GFPM Applications 5 The Base Scenario 5.1 5.2 5.3 5.4

Introduction Model Data, Parameters, and Assumptions Base Scenario Projections from 1997 to 2010 Summary and Discussion

91 91 98 124

6 Effects of the Asian Economic Crisis 6.1 Introduction 6.2 GFPM Application 6.3 Summary and Conclusion

127 129 142

7 Effects of Tariff Liberalization 7.1 Introduction 7.2 The Uruguay Round of GATT and the ATL Proposal

145 145

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Contents

7.3 Previous Studies 7.4 GFPM Simulations 7.5 Summary and Conclusion

147 148 164

8 Impact of U.S. Paper-Recycling Policies 8.1 8.2 8.3 8.4 8.5 8.6

Introduction Paper Recycling in the United States Previous Studies GFPM Analysis International Effects Summary and Conclusion

167 168 170 171 174 181

9 Effects of Regional Trade Agreements on New Zealand 9.1 9.2 9.3 9.4 9.5

Introduction Trade Agreements GFPM Analysis GFPM Results Summary and Conclusion

183 184 186 188 193

10 Impacts of U.S. Timber Harvest Restrictions 10.1 10.2 10.3 10.4 10.5 10.6

Introduction Previous Studies Objectives Forest Policies Affecting Timber Harvest in the United States Modified Global Forest Products Model Effects of Further U.S. Harvest Restrictions on the Pacific Rim Forest Sector 10.7 Sensitivity to Trade Regime 10.8 Summary and Conclusion

195 196 197 197 199

PART

IV

205 212 213

GFPM Software 11 Using the GFPM Software 11.1 Introduction 11.2 Installation and Configuration 11.3 GFPM Base-Year Input

219 220 220

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Contents

11.4 11.5 11.6 11.7 11.8

Exogenous Change Data Running the GFPM Examples of GFPM Output Welfare Analysis with the GFPM Troubleshooting GFPM Errors

229 232 235 239 240

12 GFPM Software Design and Implementation 12.1 12.2 12.3 12.4 12.5 12.6

Introduction Overview of GFPM Software Structure Control Programs and Program Execution Data Preparation Data Processing and Problem Solving Output Retrieval

243 244 245 249 251 258

PART

V

Conclusion 13 Summary, Evaluation, and Prospects 13.1 Summary of Results 13.2 Evaluation and Prospects 13.3 Conclusion

265 268 276

References

279

Index

291

PREFACE AND ACKNOWLEDGMENTS

Many years ago, I dreamed of working for the Forestry Department of the Food and Agriculture Organization (FAO) of the United Nations. This was a natural inclination for the son of a farmer growing up in southwestern France and the grandson of a forest guard from the Italian Veneto. In 1971 my dream came true when Jack Westoby, then director of FAO’s Forest Operations Division, hired me as a Junior Forestry Officer in Rome. Mr. Westoby made it clear to me that what he wanted more than a forester was someone who could work with computers. I had then just gotten my Ph.D. at Berkeley, and was fully trained in biking across campus, from Mulford Hall to the computer center, with large boxes of computer cards. During my stay at FAO, from 1971 to 1975, I perfected my training by climbing and descending the stairs from the fourth floor to the basement computer room of the Terme di Caracalla building, with boxes of cards or stacks of computer output. Remote, let alone personal, computing was still far in the future. At FAO, I helped publish the Yearbook of Forest Products and establish the first computerized database of international forestry statistics. It was not always easy. I recall being in the computer room even on the eve of my marriage, to Angela, who never protested. I am very grateful to her for the freedom she has always given me since, to work as I please and to do what has to be done or what I simply enjoy. In those days I also began working intensively on methods to predict the world demand and supply of forest products. This interest, which led ultimately to this book, was stimulated by my wonderful FAO supervisors and colleagues: Dr. Stan Pringle, a pioneer in the application of econometrics to forestry; Alf Leslie, a true Renaissance man, who ordered me to think on my own; and Phil Wardle, who challenged my strongest tenets.

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Preface and Acknowledgments Contents

I owe my first experience in forest sector modeling to Nils Svanqvist, who took me away from FAO headquarters in 1975 to work on the FAO-UNDP Malaysia Project. A key feature of the project was a computer model to plan the conversion of natural forests to oil palm and rubber plantations. Dr. Svanqvist was also instrumental in bringing me to Indonesia in 1978 and 1979, after I had already joined the University of Wisconsin, to develop a model of the Indonesian forest sector. This was my first programming model integrating the many activities ranging from logging, manufacturing, and transportation to consumption. To some extent it was the precursor of the spatial equilibrium model structure that led to the Global Forest Products Model described in this book. This was also one of the last times I worked largely on my own. Since then, many students and researchers have contributed directly or indirectly to the model that is the subject of this book. They include, in chronological order: J. Keith Gilless, who worked on the initial Price Endogenous Linear Programming System (PELPS) in 1985 and tested it with a model of the North American paper industry; Patrice Calmels, who wrote the PELPS II software in 1990, also applying it to the paper industry; Peter Ince, who collaborated on PELPS III and applied it extensively to the North American pulp and paper sector. More recently, Yibing Zhang and Bintang Simangunsong improved the estimation of the international demand equations used in the GFPM, and Chia-Sheng Liu helped develop the method to calibrate the inputoutput coefficients, described in Chapter 4. Among our staunchest external supporters and collaborators I wish to thank Mafa Chipeta, Michael Martin, and Adrian Whiteman at FAO. Mafa Chipeta was especially instrumental in taking the responsibility that the 1999 FAO Forest Outlook Study would be based on projections developed by my research team at Madison in collaboration with Antti Rytkönen. The first version of the GFPM, the FAO Provisional Outlook of 1997, resulted from this decision. David Brooks at the USDA Forest Service, Pacific Northwest Station, took early notice of the potential of the GFPM for policy analysis within the United States, secured further research support, and collaborated in the study that led to Chapter 7. G. P. Horgan, of APR Consultants, and F.M. Maplesden, of the New Zealand Forest Research Institute, collaborated in the application in Chapter 9, first published in the New Zealand Journal of Forestry Science. The material in Chapter 8 originally appeared in the International Forestry Review. At Academic Press, I thank Charles Crumly and Angela Dooley for managing this project with care and cordial efficiency. The research presented in this book was supported in part by the USDA Forest Service, Pacific Northwest Forest Experiment Station, by several McIntireStennis grants and USDA-CREES grants, and by the Food and Agriculture Organization of the United Nations. The USDA Forest Products Laboratory in Madison for many years also supported the research on the PELPS system.

Contents Preface and Acknowledgments

xv

I thank especially the Department of Forest Ecology and Management and the School of Natural Resources at the University of Wisconsin, Madison, for the exceptional environment they have given me for more than 25 years of research and teaching. Most of all, I am grateful to my coauthors, Shushuai Zhu, Dali Zhang, James Turner, and David Tomberlin, for their hard work and dedication firstly in the development of the GFPM itself and secondly in the writing of this book. Without them it would not have been possible. SUPPLEMENTARY MATERIALS: The GFPM software and the data used in various chapters of this book are available at: www.forest.wisc.edu/facstaff/ buongiorno/book/index.htm. We welcome comments and suggestions. Please, send them to: [email protected]. Thank you. J.B. Madison, Wisconsin


P AR T

Introduction

I


CHAPTER

1

Background and Objectives

1.1 BACKGROUND AND JUSTIFICATION Although separated by great geographic distances, forest industries in different countries are increasingly linked through international trade and global environmental policies. To a large extent, the future of national or regional forest industries and even of individual firms depend on world markets. It is therefore critical to understand how these markets work and how they can be affected by uncontrollable economic events and by deliberate policies. The object of this book is to describe a method, the Global Forest Products Model (GFPM), to simulate how the forest sector operates in different countries and how the countries interact through international trade. The forest sector includes timber production and harvesting, manufacturing in various industries, and transportation of products from forest to industries and to markets. The purpose of the GFPM is to predict how, by how much, and when production, consumption, imports, exports, and prices of forest products may change, depending on external or internal forces and policies such as economic growth, global trade liberalization, and new environmental policies governing either the use of forest products or the management of forests.

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ECONOMIC GROWTH AND TRADE For the past 40 years, the demand for forest products has grown steadily, and the related international trade has expanded even faster, attracting increased public attention and becoming more important in debates on forest policy (Esty 1994). As shown in Chapter 2, historical trends reveal a shift in global exports from raw materials, such as logs, toward higher-value products (panels and papers). Now paper and paperboard products alone account for approximately half of world forest product exports (FAO 1999a). Global economic trends and policies affect these exports. As a case in point, Asia has long been, for many producers, especially in Northern Europe and America, a main target market justifying massive new investments. But the Asian economic crisis that began in 1997 has seriously disrupted forest products trade worldwide. Macroeconomic shocks, such as the recent Asian crisis, have effects on many other countries, altering trade levels and market shares significantly. For example, the softening Asian demand may have caused a 39% drop in Canadian newsprint shipments to Asia for the first half of 1999, while the surplus Asian newsprint has been diverted into the U.S. market (FAO 1999b). Due to the weakness in global paper markets brought on by the Asian economic crisis, U.S. paper and paperboard production fell in 1998, the first year since 1985 that production failed to expand, and exports declined 7.2% compared with 1997, while imports increased 6.1% (Advanstar 1999). Part of this book is aimed at clarifying the causal mechanism and magnitude of the relation between general economic growth, the forest sector of individual countries, and the corresponding exchanges between countries. Changes in the forest sector have been profound (Routson et al. 1998, Shield 1998, Lee 1999), and rigorous quantitative analysis is needed to better understand the linkage between macroeconomic shocks and the global forest sector response.

TRADE POLICIES In terms of trade policies, the most recent important treaty is the 1994 Uruguay Round of the General Agreement on Tariffs and Trade (GATT). It has been estimated that as a result of the Uruguay Round, the value of world trade would increase by 9–24% in a decade (GATT 1994). Barbier (1996) suggested that the tariff liberalization resulting from the Uruguay Round would affect global forest markets to a limited extent. Because the U.S. tariffs on most forest products were already quite low before the agreement, the substantial reduction in tariffs by other countries would benefit the U.S. forest industry (Wisdom 1995). Indeed, the U.S. forest products industry predicted about $10 billion in direct additional exports of forest products attributable to the Uruguay Round agreements


5

(AF&PA 1994). However, these numbers were quite speculative, being based often on rather scant evidence. Despite the Uruguay Round agreement, it appears that tariffs remain a significant barrier to trade for forest products (Bourke and Leitch 1998). In 1997, to liberate global trade, ministers of the Asia-Pacific Economic Cooperation (APEC) economies called for the nomination of sectors for early voluntary sectoral liberalization (EVSL) among their nations (FAO 1999b). The United States, Canada, Indonesia, and New Zealand proposed the forest sector. In 1998, APEC leaders agreed to move the tariff portion of the EVSL initiative to the World Trade Organization, to conclude an agreement by the end of 1999. To help the U.S. trade representative office evaluate this initiative (Council on Environment Quality 1999), we analyzed the possible effects of accelerating tariff liberalization (Zhu et al. 2001). That exercise revealed shortcomings in the GFPM methodology, such as the treatment of transportation costs and lack of welfare analysis, that were addressed in the model version presented in the following chapters.

ENVIRONMENTAL POLICIES With regard to environmental policies, it has been argued that they can also act as nontariff trade barriers. This book will examine the case of paper recycling. Several states in the United States have implemented regulations on minimum recycled fiber content for newsprint (Elliot 1994, Bourke 1995, Bourke and Leitch 1998, Barbier 1999). Together with the promotion of recycling by government agencies and environmental groups (Tappi 1998), these measures may act as subsidies for countries with large supplies of wastepaper and thus influence international competitiveness. It is likely that the paper-recycling policies of the United States and other countries have significant impacts on global competitiveness and, therefore, on the trade of pulp and paper products. Ince (1994) projects that the wastepaper recovery rate in the United States will increase to 52% by 2010, while the wastepaper utilization rate could reach 43%. Mabee and Pande (1997), Mabee (1998) project that the wastepaper recovery rate of the United States could exceed 60% in 2010. The trade patterns and production locations of fiber and paper products will be changed significantly due to this trend and adopted recycling policies. One aim of this study is to explore this international alteration of competitive advantage induced by recycling policies, through import and export connections. In a global economic context, few major issues pertaining to the forest sector can be analyzed in isolation. Exogenous economic shocks like the Asian economic crisis may have a major impact on the well-being of industries in countries outside of Asia. International agreements abolishing tariffs could change

6


the relative competitiveness of countries in ways that are not yet known. The push for increased paper recycling in large regions, such as the United States and the European Union, may lead to unforeseen advantages for each such region’s pulp and paper industry, to the detriment of foreign paper producers, causing foreign retaliation. The applications of the GFPM reported in this book are meant to show how national objectives can be placed in a global context to facilitate better-informed policies.

1.2 PREVIOUS AND RELATED MODELS There are few truly global forest sector models. But several national or regional models have theoretical structures or empirical implementations that are similar to those adopted for the GFPM. The specific models reviewed here are chosen to underline some of the differences from and similarities with the GFPM. The models are grouped according to their economic scope. General equilibrium models tend to cover all economic activities, spatial partial equilibrium models concentrate on the forestry sector, while timber supply models deal more with the supply side of the forestry sector.

COMPUTABLE GENERAL EQUILIBRIUM MODELS Computable general equilibrium (CGE) models emphasize the links between the forest sector and the macroeconomy. The central component of a CGE model is the social accounting matrix, a matrix of production coefficients accounting for all economic flows in an economy. Empirical data in the social accounting matrix are used to calibrate a nonlinear set of production, consumption, and transfer equations with endogenous prices (Taylor and Howitt 1993). The effect, in terms of prices, employment, gross domestic product, etc., of policy changes is estimated by recalibrating the model for changes in the empirical data in the social accounting matrix. The use of CGE models in the analysis of the forest sector has been motivated by the importance of links between the forest sector and the rest of the macroeconomy (Haynes 1993). In regions where the forest sector is an important contributor to employment and gross domestic product, the effect of changes in the forest sector on the macroeconomy may be of interest (Binkley et al. 1994). On the other hand, the effect of changes in nonforestry sectors on the forest sector may be the most important issue, in which case partial equilibrium analysis may still be used. For example, Prestemon and Buongiorno (1996) showed that the main effect of the North American Free Trade Agreement (NAFTA) on


7

forest products trade would not come from elimination of tariffs on forest products, but from the increase in forest products demand due to the stimulation of the general economy induced by NAFTA.

Global Trade Assessment Project The Global Trade Assessment Project (GTAP) model was recently used as part of an Asia-Pacific Economic Cooperation (APEC) study to assess the effects of the removal of specific nontariff barriers to forest product trade on country gross domestic product, welfare, and trade. In particular, the effect of environmental certification of tropical wood products, the Indonesian log export ban and prohibitive export taxes on sawnwood, the Canada–United States softwood lumber agreement, and trade liberalization were analyzed (New Zealand Forest Research Institute 1999). The GTAP database combines detailed bilateral trade, transport, and tariff data and input–output tables, which describe intersectoral linkages within regions. The APEC study used 24 regions, with most detail for the APEC economies, 11 sectors, three of which are forest sector related, three primary input factors (labor, land, and capital), and a single government entity. A detailed description of the GTAP database is available in McDougall et al. (1998).

Other CGE Models Other CGE models have a much smaller geographic scope than the global GTAP, yet they well illustrate cases where general equilibrium analysis may be relevant. For example, Binkley et al. (1994) used a CGE model to analyze the economic impact of reductions in the annual allowable cut in the Canadian province of British Columbia. A CGE model was used because the forest industry is a major economic activity in British Columbia. All sectors and regions in the model interact with one another, including factor markets, though factor mobility between Canadian provinces was set exogenously. Other applications of general equilibrium models to British Columbia include Alavalapati et al. (1999) and Alavalapati et al. (1997). Lin (1996) built a CGE model of the Indonesian economy, because the forest sector constitutes an important part of the Indonesian foreign trade, so that “policies that deal with a particular export will impact not only the wood industry, but also other parts of the economy.” In contrast, Vincent (1992), and Manurung and Buongiorno (1997) elected to use a partial equilibrium approach to study the effects of the ban on tropical log exports on the forestry sector of Indonesia.

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PARTIAL EQUILIBRIUM MODELS Partial equilibrium models are the most common approach to forest sector analysis. In particular, spatial models of this kind have been applied widely. This type of model, based on the work of Samuelson (1952) and Takayama and Judge (1971), is characterized by the endogenous determination of demand, supply, bilateral trade flows, and prices, conditional on exogenous economic activity outside the sector. There are several models of this kind, but only the CINTRAFOR Global Trade Model (CGTM) and the GFPM are truly global in geographic scope.

CINTRAFOR Global Trade Model The CINTRAFOR Global Trade Model (CGTM) was originally the Global Trade Model (GTM), developed at the International Institute of Applied Systems Analysis (Kallio et al. 1987). It is now maintained at the University of Washington. The GTM was the first forest sector model to provide a broad coverage of forest products and markets and their many interlinkages (Cardellichio and Adams 1990). The CGTM has been applied to many forest sector issues, including the likely effects of reduced timber harvests in the Pacific Northwest on forestland use in other countries (Perez-Garcia 1995), the impacts on the global forest sector of proposed U.S. climate-warming mitigation policies (PerezGarcia 1994), the economic impacts of climate change on the global forest sector (Perez-Garcia 1994), and an assessment of the competitiveness of the Japanese forest sector (Eastin et al. 2002). The CGTM projects production, consumption, prices, and trade for 10 forest products in 43 log-producing regions and 33 product-consuming regions. Over 400 trade flows link the supply and demand regions. Products considered in the model range from hardwood and softwood sawlogs to wood pulp. The CGTM describes all aspects of forest products production: forest growth, wood supply, processing capacity, and final demand. One feature in the descriptive detail of the CGTM is the use of four different wood supply specifications representing regional differences in forest policy regarding wood supply. For example, for China, eastern Europe, and United States federal lands, wood supply is exogenously set each period, to reflect the fact that planning, rather than prices, determine harvest levels (Perez-Garcia et al. 1997). Dynamic elements in the CGTM are interperiod changes in forest inventory, which shift wood supply relations using a growth-drain identity, and changes in production capacity. Detailed descriptions of the CGTM are presented in Kallio et al. (1987) and Cardellichio et al. (1989).


9

Other Partial Equilibrium Models The Timber Assessment Market Model (TAMM) (Adams and Haynes 1980) focuses mostly on North America. Nevertheless, the TAMM has also been used to analyze international issues, such as the effects of harvests from provincial forestlands in Canada (Adams and Haynes 1996). It is also worth noting here because of its strong similarities with the CGTM and the GFPM and because of the potential improvements to both suggested by the TAMM experience (Alig et al. 2001). The two main components of the TAMM are a market model and an inventory projection module. The market model covers the hardwood and softwood supply and solidwood sectors for 12 supply regions and six demand regions. Exogenous inputs to the model are pulp fiber requirements provided by the North American Pulp and Paper model (Ince 1994, Zhang et al. 1996) and projections of forest inventory and growth from the Aggregate TimberLand Assessment System (Mills and Kincaid 1992). Most descriptive detail in the TAMM model is in the wood supply and solidwood sectors. Hardwood and softwood supply is disaggregated by region and private and public land, with supply from public land further disaggregated into price-responsive and price-inelastic supply. TAMM distinguishes between industrial and nonindustrial forest owners, with supply from the latter being responsive to nonforest income as well as inventory, stumpage prices, and interest rates (Adams and Haynes 1996). Solidwood demand is for 10 end-use categories for hardwoods and over 20 for softwoods, with substitution possibilities linked to prices. A difference between the TAMM and the CGTM and GFPM is the method to solve for spatial equilibrium. The CGTM and GFPM use the “programming approach,” which determines equilibrium by solving an optimization problem (Samuelson 1952). In TAMM the spatial equilibrium is found by “reactive programming.” A limitation of reactive programming is that it is difficult to represent policy scenarios involving constraints on endogenous variables (Adams and Haynes 1996). Other spatial partial equilibrium models include Adams’ (1985) model of the African–European trade. It covers three African regions plus Europe and logs and sawnwood; the methods are similar to those of TAMM, including reactive programming to calculate equilibria. Schirmer and Buongiorno (1985) used a similar model structure for the trade of tropical logs between Africa and Europe and between Asia-Pacific and Europe. They computed the equilibrium via quadratic programming and the equilibrium prices from the dual solution. Buongiorno and Manurung (1992) used spatial partial equilibrium models of the trade of tropical logs, sawnwood, and plywood, with 12 importing countries and eight exporting countries. The equilibrium was found by maximizing

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producer and consumer surplus, and the model was used to predict the effects of a European tax on tropical timber imports. In contrast, nonspatial partial equilibrium models with a strong international orientation include Vincent’s (1992) method of trade policy analysis. It relies on predermined elasticities of demand and supply, producing projections of trade with a minimum of effort. Vincent’s (1992) method was used by Manurung and Buongiorno (1997) to predict the effects of the ban on tropical log exports on the forestry sector of Indonesia.

TIMBER SUPPLY MODELS The models just discussed consider in about the same detail the supply and the demand sides of the forestry sector. Some models are notable, however, for their concentration on the issue of the global timber supply. The Timber Supply Model (TSM) The models just reviewed are static, such as GTAP, or have dynamic elements that link each period’s solution, such as CGTM and TAMM, but are not optimal in an intertemporal sense. In contrast, the Timber Supply Model (TSM; Sedjo and Lyon 1990, 1996) maximizes the sum of consumer and producer surpluses over the entire planning horizon. This approach is based on rational expectation theory, which assumes that economic agents on average correctly anticipate future conditions. The TSM was developed to study the transition of the world’s forests from old growth to second growth and to plantation-grown wood. The modeling approach uses control theory to determine the economically optimal transition. To this end, most of the descriptive detail in the model is meant to accurately describe the wood supply sector. The TSM models industrial wood supply for eight regions, one of which is “unresponsive” to price (the level of wood supply is independent of market conditions). The seven “responsive” regions are subdivided into 22 timberland classes. Each land class is described in terms of its quality, location, accessibility, growth, and yield functions; existing inventories and their age distribution; silvicultural response to investment inputs; and harvest, transport, growing, and establishment costs. The use of control theory in the TSM means that wood supply is described in terms of a set of “initial conditions,” “laws of motion,” and “control variables,” which together determine the optimal transition of the world’s forests. The “initial conditions” in the TSM are forest inventory by region, age group, and land class. The “laws of motion” describe the growth of the forest in each land class, and the “control variables” are the areas harvested, by age group and land class,


11

and the investment in regeneration and magnitude of regeneration input, by land class. The “control variables” are endogenously determined as part of the solution of the TSM. As well as projecting the transition from old-growth to plantation-grown industrial wood in terms of the optimal harvest and regeneration effort, the TSM also projects timber prices (from the intersection of wood supply with exogenously determined demand) and trade flows. The Global Fiber Supply Model The Global Fiber Supply Model (GFSM) was developed for the FAO to forecast potential industrial fiber supply and to aid forest policy making through the analysis of industrial fiber sources (Bull 1998, Bull et al. 1998). The GFSM forecasts fiber supply, by forest type and country, for natural forests, industrial plantations, nonwood fibers (e.g., bagasse, bamboo, and hemp) and recovered fibers (e.g., wastepaper) using several yield equations. In contrast with the models described earlier, the GFSM is not an economic model of supply, in that prices and costs are not recognized explicitly in the model. Instead, it can be viewed as a description of the production functions that underlie timber supply in different countries. Forecasts of fiber supply levels from natural forests and other wooded lands are based on the forest area available for wood supply, logging intensity, and growth statistics. Fiber supply levels from industrial plantations are based on mean annual increment and plantation area statistics. Nonwood fiber supply forecasts are based on estimates of the percentage nonwood pulping capacity and country total pulp production. Recovered fiber supply is estimated from percentage wastepaper recovery and total paper production statistics. The GFSM allows the analysis of alternative fiber supply futures by varying exogenously specified policy variables such as natural forest deforestation rate, sustainable management policies as expressed by the cutting cycle, forest area legally protected, plantation afforestation rate, improvements in industrial plantation yield arising from research and development, changes in nonwood fiberpulping capacity, and wastepaper recovery rates.

1.3 A BRIEF HISTORY OF THE GFPM In contrast with CGE models, the GFPM does not attempt to predict the feedback of changes in the forest sector on the rest of the economy. Instead, it assumes that in all countries the forest sector is too small to affect substantially macrovariables such as gross domestic product. Nor does the GFPM attempt to optimize the forest sector over the planning horizon, like the TSM. It presumes

12


instead that markets operate optimally only in the short run, while long-term decisions such as investments are imperfect and based only on current information. In terms of the description of timber supply, the GFPM is much less detailed than the GFSM. On the other hand, the GFPM is fully integrated with the processing and consuming parts of the sector and reflects fully the role of prices. In terms of structure and assumptions, the GFPM is most directly related to the spatial partial equilibrium forest sector models that have been most actively developed during the past 20 years. The model structure used in the GFPM can be traced back to the development of the PAPYRUS model of the U.S. pulp and paper industry (Buongiorno and Gilless 1983a,b, Gilless and Buongiorno 1985a,b, 1987). The PAPYRUS model was developed for the U.S. Department of Agriculture (USDA) Forest Service to assist in the Timber Assessments required by the Resources Planning Act, in conjunction with the Timber Assessment Market Model (TAMM) of the solid wood sector (Adams and Haynes 1980). The PAPYRUS was built with a general structure and software to model any economic sector with spatial and dynamic elements, the Price Endogenous Linear Programming System (PELPS), described in Gilless and Buongiorno (1985a). The first international application of PELPS was a prototype model (GTM-1) of forest products trade developed at the International Institute of Applied Systems Analysis (IIASA) in the summer of 1983 (Buongiorno and Gilless 1983c, 1984) and later extended to include domestic activities (Buongiorno 1986). This prototype was much expanded and improved upon by IIASA researchers into the Global Trade Model (GTM), described in Kallio et al. (1987). A modified version of the GTM model (CGTM) is being maintained at the Center for International Trade in Forest Products (CINTRAFOR), University of Washington, and has been used in many international studies (Perez-Garcia and Abt 1997, Perez-Garcia et al. 1997, 1999). Meanwhile, the PAPYRUS model became the North American Pulp and Paper (NAPAP) model, developed and maintained by the USDA Forest Products Laboratory in collaboration with the University of Wisconsin and with the Canadian Forest Service (Ince 1994, Zhang et al. 1996). A similar model for the North American solidwood sector (NASAW) was also developed at the USDA Forest Products Laboratory (Phelps 1999). The PELPS software became PELPS II, which ran on a microcomputer with spreadsheet input and output (Calmels et al. 1989). This was improved into PELPS II PLUS (Zhang et al. 1990) and PELPS III (Zhang et al. 1993). The latest version, PELPS IV, which forms the structure of the GFPM, is described in this book. PELPS IV is a general microcomputer system for modeling economic sectors. It is based on price endogenous linear programming, a method of combining regional information on supply and costs into spatial sector models. The objective function of the spatial equilibrium problem is called the “net social payoff,”


13

that is, the value of the end products to consumers minus the cost of producing and transporting them. PELPS IV has static and dynamic phases. In the static phase, it computes the quantities and prices that match demand and supply for all commodities in all regions in a given year. In the dynamic phase, it predicts the evolution of this spatial equilibrium from year to year. A major international application of PELPS was to project trends in demand and supply of tropical timbers in the Asia-Pacific region (ITTO 1995, Zhang et al. 1997, later revised by Tomberlin 1999). This was followed by a global application of the system, leading to the original version of the GFPM to produce the 1997 FAO provisional outlook for global forest products consumption, production, and trade to 2010 (FAO 1997a). That report contained the first fully equilibrated (in an economic sense) long-term projections, by country and product, of production, consumption, imports, exports, and prices. The following year, the definitive projections to 2010 of global forest products consumption, production, trade, and prices were projected, with revised exogenous assumptions and an improved GFPM structure, for the FAO 1999 Global Forest Products Outlook Study (Zhu et al. 1998, Tomberlin et al. 1998a). Since then, the GFPM has been improved and simplified continually while being applied to several other studies, some of which are described in subsequent chapters.

1.4 OBJECTIVES AND OUTLINE In view of the issues pertaining to the global forest sector, and building on the knowledge accumulated by previous and related studies, the objectives of the studies presented in this book were to develop a rigorous and comprehensive economic model of the global forest sector and to apply it to measure the effects on the global forest sector of (i) unexpected economic shocks, such as the Asian economic crisis, (ii) trade policies, such as changes in tariff rates in various countries, and (iii) environmental policies, such as recycling and timber harvest limits. The following chapters detail each aspect of model building and some of its applications. Chapter 2 completes the introductory part of the book, with a summary of the main trends in production, consumption, trade, and prices of forest products in major world regions during the past 40 years. It is these variables and their trends that the GFPM is meant to extrapolate for one or two decades. In Part 2, Chapter 3 presents the structure of the model, expressed by equations that link the variables of the forest sector. The model structure is based on the neoclassical theory of competitive markets. Chapter 4 deals with the calibration and validation of the model. Calibration involves establishing the data for the base year of the simulation and estimating all the parameters, based on past data, other studies, and expert opinion. The validation involves comparing

14


the model prediction with actual outcomes in the past and judging the suitability of the model for different purposes. Part 3 deals with the model applications. In particular, Chapter 5 applies the model in a base scenario that is meant to predict the most likely changes in the world forest sector and to serve as a base for comparison with the scenarios studied in the following chapters. Chapter 6 shows an application of the GFPM to predict the effects of economic shocks on the forest sector over time, by product and by region. It uses the slowdown in economic growth dubbed the “Asian crisis” as a case study. Chapters 7 and 9 show applications of the model to study trade policies. Chapter 7 deals with the effects a worldwide elimination of tariffs proposed by the Asia-Pacific Economic Cooperation economies and others. Chapter 9 treats the impact on New Zealand of regional trade agreements under consideration in that country. In Chapters 8 and 10 the GFPM is applied to study the worldwide impact of environmental policies in the United States. Chapter 8 deals with the increase in paper recycling, and Chapter 10 with increased harvest restrictions in the Pacific Northwest. Part 4 documents the GFPM computer programs. Chapter 11 gives the information necessary to use the GFPM software and to modify the database and the parameters. Chapter 12 contains information regarding the design of the GFPM software and its implementation as a system of computer programs. The concluding Part V summarizes the results, discusses the strengths and limitations of the current version of the GFPM, and the prospects for improving the design and applications of the GFPM and of similar models.

CHAPTER

2

Overview of the Global Forest Products Sector

2.1 INTRODUCTION The world’s forest products sector is shaped as much by external economic, political, and demographic trends as it is by forces working within the sector itself. During the past decade, factors that have had a major effect upon forests include population growth, overall economic growth accompanied by regional disruptions, trade liberalization, and increasing environmental concerns. Trends in forest products consumption clearly illustrate the effects of population and economic growth on demand for forest products. Between 1960 and 1995, world population almost doubled and the world economy (as measured by real gross domestic product (GDP)) increased three and a half times. Over the same period, world production of roundwood and sawnwood increased more than 50%, production of wood-based panels increased five times, production of pulps increased four times, and productions of papers more than tripled (FAO 1999a). Today’s world population of about 6 billion is expected to grow to 7 billion by the year 2010 (UN Population 2000), and nearly all of this increase will occur in the developing world, where constraints 15

16


on agriculture and forestry will affect the ability of countries to attain longterm food security and to protect forests. Increased demand for forest products is likely to reinforce current efforts in forest plantation development, and in some countries may lead to increased production of industrial wood from farm woodlots and agroforestry systems (FAO 1997b). This trend is likely to be encouraged by decreased levels of timber harvesting in national forests due to environmental concerns. Environmental awareness and public pressure have continued to have an impact upon all aspects of the forestry sector: on forest management, harvesting and postharvesting activities, markets, and trade in forest products. Global forestry faces the increasingly difficult challenge of how to meet the growing demand for forest products while at the same time safeguarding the ability of forests to provide a range of environmental services (FAO 1997b). According to FAO (1995a), at the global level forestry is estimated to contribute about 2% of world GDP and 3% of international merchandise trade. In terms of share of regional GDP, forests appear to be most important in Africa1 (6%), followed by South America (3%). The share of forest products within merchandise trade is highest in North and Central America (5%), followed by the former USSR (4%). In South America, Europe, and Oceania it is about 3%. In Africa and Asia, the forestry proportion of trade is 2%. However, these numbers are dwarfed by the value of forests for biodiversity, protection, esthetics, wildlife habitat, recreation, and other amenities.

2.2 PRODUCTION In this study, forest products are categorized into five major groups (Table 2.1): roundwood (fuelwood and charcoal, industrial roundwood, including other industrial roundwood, as defined by FAO), sawnwood and sleepers, woodbased panels (plywood and veneer sheets, particleboard, fiberboard), total fiber furnish (mechanical pulp, chemical pulp and semichemical pulp, other fiber pulp, wastepaper), and paper and paperboard (newsprint, printing and writing paper, other paper and paperboard). The relationships among these products will be discussed in Chapters 3 and 4. Consumption and production of the major products are driven by many factors, of which population and income are the most important. Except for fuelwood, consumption usually rises with both population and income. In the case of fuelwood, however, an increase in income per capita tends to lower consumption (see Chapter 4).

1

The region names used in this and other chapters are defined in Table 4.1.

17

Overview of the Global Forest Products Sector TABLE 2.1

World Production of Forest Products Annual growth rate

Product

1961

1980

1997

1961–1980

1980–1997

103 m3 Fuelwood and charcoal Industrial roundwood Other industrial roundwood Sawnwood and sleepers Plywood and veneer sheets Particleboard Fiberboard

978,302 1,018,314 167,894

1,365,277 1,385,322 193,774

1,851,273 1,522,924 160,299

1.8% 1.6% 0.8%

1.8% 0.6% −1.1%

345,843 17,670

429,097 42,175

438,586 60,607

1.1% 4.7%

0.1% 2.2%

3,942 4,675

31,848 16,070

71,923 23,104

11.6% 6.7%

4.9% 2.2%

103 t Mechanical pulp Chemical pulp and semichemical pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard

17,341 40,578

24,976 92,232

35,174 124,081

1.9% 4.4%

2.0% 1.8%

3,944 15,383 14,362 15,596

7,199 45,891 24,586 37,696

19,285 110,519 35,830 89,965

3.2% 5.9% 2.9% 4.8%

6.0% 5.3% 2.2% 5.3%

47,535

96,878

172,105

3.8%

3.4%

Sources: Online FAO Yearbook of Forest Products, FAOSTAT statistics database, FAO, Rome.

TRENDS IN WORLD PRODUCTION Table 2.1 shows the world production of forest products in 1961, 1980, and 1997.2 Global production generally increased during the past 40 years. The production of paper products and wood-based panels increased much faster than that of other forest products, while the production of roundwood and sawnwood increased least. Generally, the annual growth rate of world forest products production was slightly lower during 1980–1997 than that during 1961–1980.

REGIONAL TRENDS, BY PRODUCT Tables 2.2 to 2.7 show regional production of major forest products in 1961, 1980, and 1997. Fuelwood production increased by 1.8% per year worldwide 2

All numbers cited in this chapter are from FAO (1999a), except where noted otherwise.

18 TABLE 2.2

The Global Forest Products Model Regional Production of Fuelwood and Charcoal (103 m3) Annual growth rate

Region Africa North/Central America South America Asia Oceania Europe Former USSR Developed, all Developing, all World

TABLE 2.3

1961

1980

167,196 70,991 104,109 445,761 7,488 85,057 97,700 251,479 726,823 978,302

289,096 133,541 158,554 658,527 6,906 39,753 78,900 219,839 1,145,438 1,365,277

1997 523,252 138,128 194,918 906,875 8,756 50,408 28,936 170,358 1,680,915 1,851,273

1961–1980 1980–1997 2.9% 3.4% 2.2% 2.1% −0.4% −3.9% −1.1% −0.7% 2.4% 1.8%

3.6% 0.2% 1.2% 1.9% 1.4% 1.4% −5.7% −1.5% 2.3% 1.8%

Regional Production of Industrial Roundwood (103 m3) Annual growth rate


TABLE 2.4

1961

1980

1997

25,170 341,124 27,833 131,923 15,848 223,116 253,300 881,012 137,302 1,018,314

50,801 492,415 85,487 233,821 27,930 217,168 277,700 1,047,723 337,599 1,385,322

66,366 613,534 130,085 277,153 41,285 291,482 103,019 1,072,919 450,005 1,522,924

1961–1980 1980–1997 3.8% 2.0% 6.1% 3.1% 3.0% −0.1% 0.5% 0.9% 4.8% 1.6%

1.6% 1.3% 2.5% 1.0% 2.3% 1.7% −5.7% 0.1% 1.7% 0.6%

Regional Production of Sawnwood (103 m3) Annual growth rate


1961

1980

1997

2,914 97,180 10,054 49,723 5,118 71,964 108,890 309,337 36,506 345,843

7,650 132,302 21,993 94,718 5,801 68,433 98,200 339,068 90,029 429,097

8,350 181,091 30,285 96,531 6,981 89,529 25,819 321,426 117,160 438,586

1961–1980 5.2% 1.6% 4.2% 3.4% 0.7% −0.3% −0.5% 0.5% 4.9% 1.1%

1980–1997 0.5% 1.9% 1.9% 0.1% 1.1% 1.6% −7.6% −0.3% 1.6% 0.1%

19


Regional Production of Wood-based Panels (103 m3) Annual growth rate


TABLE 2.6

1961

1980

1997

252 12,421 473 2,516 330 8,279 1,987 24,825 1,433 26,258

1,361 31,756 3,574 19,005 1,197 22,582 10,618 76,190 13,903 90,093

1,805 50,756 6,306 45,837 2,607 43,629 4,783 108,160 47,563 155,723

1961–1980 9.3% 5.1% 11.2% 11.2% 7.0% 5.4% 9.2% 6.1% 12.7% 6.7%

1980–1997 1.7% 2.8% 3.4% 5.3% 4.7% 3.9% −4.6% 2.1% 7.5% 3.3%

Regional Production of Fiber Furnish (103 t) Annual growth rate

Region

1961

1980

1997

1961–1980

1980–1997

Africa North/Central America South America Asia Oceania Europe Former USSR Developed, all Developing, all World

273 40,979 631 8,336 653 23,104 3,272 73,483 3,765 77,248

1,722 84,025 6,465 27,970 2,445 37,166 10,505 151,182 19,116 170,298

3,408 122,155 12,613 71,226 4,216 70,601 4,938 229,043 60,114 289,157

10.2% 3.9% 13.0% 6.6% 7.2% 2.5% 6.3% 3.9% 8.9% 4.2%

4.1% 2.2% 4.0% 5.7% 3.3% 3.8% −4.3% 2.5% 7.0% 3.2%

TABLE 2.7

Regional Production of Paper and Paperboard (103 t) Annual growth rate


1961

1980

1997

1961–1980

1980–1997

348 39,219 1,284 8,812 718 23,674 3,445 72,200 5,300 77,500

1,713 72,379 5,579 29,651 2,104 39,001 8,733 139,461 19,699 159,160

3,162 109,037 9,842 85,235 3,291 83,729 3,823 229,718 68,401 298,119

8.8% 3.3% 8.0% 6.6% 5.8% 2.7% 5.0% 3.5% 7.2% 3.9%

3.7% 2.4% 3.4% 6.4% 2.7% 4.6% −4.7% 3.0% 7.6% 3.8%

20


from 1961 to 1997. While developing countries’ fuelwood production increased about 2.3% per year after 1980, developed countries’ production decreased 1.5% per year (Table 2.2). Industrial roundwood production increased much more slowly after 1980 than from 1961 to 1980. The largest producer of industrial roundwood was North/Central America in 1997, which accounted for about one-quarter of world production. In countries of the former USSR, industrial roundwood production dropped 5.7% from 1980 to 1997, most of the decrease having occurred after 1991 (Table 2.3). World sawnwood production was almost flat from 1980 to 1997, due to a big drop of 7.6% per year in the former USSR’s production. North/Central America was the largest producer and contributed about 40% of world production in 1997 (Table 2.4). Although North/Central America is still the largest producer of wood-based panels, Asia and Europe follow very closely. Asia’s production grew almost twice as fast at that of North/Central America during the period from 1980 to 1997. For fiber furnish, North/Central America is a dominant producer that accounts for 40% of world production. But Asia’s production growth rate was also more than double that of North/Central America from 1980 to 1997, and its production accounted for one-quarter of world production in 1997. The situation of paper and paperboard is similar to that of fiber furnish, but there is less difference among the top three producers.

TRENDS IN FIBER FURNISH In industrialized countries, environmental concerns and economic pressures have led to efforts to use wood more efficiently. From 1980 to 1997, more products were being made with less wood from the forest. Although processed products grew very rapidly (by 44% for pulp, 70% for total fiber furnish, and about 90% for paper and paperboard), consumption of industrial roundwood increased by only 10% (Table 2.1). Table 2.8 shows the fiber furnish composition in 1980 and 1997. At the world level, the most significant change was the replacement of wood pulp with wastepaper. In 1980, about 68% of fiber furnish was wood pulp and 28% was wastepaper. In 1997, 55% of fiber furnish was wood pulp and 38% was wastepaper. This switch occurred faster in Oceania, Europe, and Asia. Besides using more wastepaper, Asia is also using more other fiber, such as straw. In 1997, only 32% of Asia’s total fiber furnish was wood pulp, while this ratio was 73% in the former USSR. In summary, these trends in wood and fiber production suggest that fuelwood remains important. Consumption of fuelwood currently exceeds that of

21


Change in Composition of Total Fiber Furnish (1980–1997) 1980

Region Africa North/ Central America South America Asia Oceania Europe Former USSR Developed, all Developing, all World

1997

Wood pulp

Other fiber pulp

Wastepaper

Wood pulp

Other fiber pulp

55.2% 77.1%

14.9% 1.2%

29.9% 21.7%

59.4% 69.7%

7.1% 0.4%

33.5% 30.0%

58.6% 46.5% 72.0% 66.1% 76.5% 72.6% 39.1% 67.6%

8.1% 13.9% 0.3% 1.5% 3.6% 1.3% 27.8% 4.1%

33.3% 39.6% 27.7% 32.4% 19.8% 26.2% 33.1% 28.4%

63.4% 32.3% 52.1% 57.2% 73.3% 61.6% 30.4% 54.8%

5.0% 20.6% 6.9% 0.5% 0.0% 0.4% 30.6% 6.7%

31.6% 47.1% 41.0% 42.4% 26.7% 38.1% 39.0% 38.5%

Wastepaper

industrial roundwood, and it will continue to grow because of population growth. Provision of fuelwood must remain a central part of the forest products and resource management agendas. While the production of some processed products expanded rapidly, the use of industrial roundwood grew by only 10% in more than two decades. This development shows that industry has succeeded in increasing output with less roundwood raw material input, in recycling more materials, and in using more residues. But the emergence of recycling paper fiber on a large scale may lower the price of pulpwood, and this may create problems for the profitable management of many forests.

MAJOR PRODUCERS Tables 2.9 to 2.14 show the top 10 consumers and producers of six major forest products. The top 10 countries account for more than 70% of the world consumption and production, except for fuelwood. For fiber furnish and paper products, that ratio is close to 80%. The United States is the largest consumer and producer of all products except fuelwood. India and China are first for fuelwood; Canada is the second largest producer of industrial roundwood and sawnwood. China is the second largest consumer and producer of wood-based panels, fiber furnish, and paper and paperboard. In 1997, as Tables 2.9–2.14 show, the five largest producing countries accounted for: 48% of fuelwood production, 56% of industrial roundwood, 56% of sawnwood, 55% of wood-based panels, 65% of total fiber furnish, and 62% of paper and paperboard.

22


TABLE 2.9 in 1997

The World’s Top 10 Consumers and Producers of Fuelwood and Charcoal

Consumer 1 2 3 4 5 6 7 8 9 10

India China Indonesia Brazil Nigeria United States Ethiopia Congo Kenya Philippines Total

TABLE 2.10 in 1997

Quantity (103 m3)

Share of world total

Producer

Quantity (103 m3)


281,417 204,182 154,661 135,635 108,807 74,632 49,827 47,715 39,984 38,007

15.2% 11.0% 8.4% 7.3% 5.9% 4.0% 2.7% 2.6% 2.2% 2.1%

India China Indonesia Brazil Nigeria United States Ethiopia Congo Kenya Philippines

281,425 204,299 155,700 135,652 108,908 74,600 49,827 47,715 39,981 38,218

15.2% 11.0% 8.4% 7.3% 5.9% 4.0% 2.7% 2.6% 2.2% 2.1%

1,134,867

61.3%

1,136,325

61.4%

The World’s Top 10 Consumers and Producers of Industrial Roundwood

Consumer

Quantity (103 m3)


Producer

1 2 3 4 5

United States Canada China Brazil Japan

399,868 191,699 113,549 83,296 68,860

26.0% 12.5% 7.4% 5.4% 4.5%

6 7 8 9

Sweden Finland Indonesia Russian Federation Germany

63,302 54,068 46,784 44,884

4.1% 3.5% 3.0% 2.9%

United States Canada China Brazil Russian Federation Sweden Indonesia Finland Malaysia

31,956

2.1%

Germany

1,098,266

71.5%

10

Total

Quantity (103 m3)


416,092 185,859 108,718 84,661 63,190

27.3% 12.2% 7.1% 5.6% 4.1%

56,400 47,289 47,187 35,788

3.7% 3.1% 3.1% 2.3%

35,488

2.3%

1,080,672

71.0%

2.3 INTERNATIONAL TRADE

TRENDS IN INTERNATIONAL TRADE There has been a continual upward trend in both the volume and value of forest products exports during the past 40 years (Figures 2.1 and 2.2). Since 1961, export volume of industrial roundwood has trebled to 134.6 million m3; that of

23


1 2 3 4 5 6 7 8 9 10

The World’s Top 10 Consumers and Producers of Sawnwood in 1997

Consumer

Quantity (103 m3)


United States Japan China Brazil Germany Canada India Russian Federation France United Kingdom

148,259 34,275 29,592 18,037 18,030 17,902 17,452 14,736 11,242 9,365

33.6% 7.8% 6.7% 4.1% 4.1% 4.1% 4.0% 3.3% 2.5% 2.1%

318,890

72.2%

Total

TABLE 2.12

1 2 3 4 5 6 7 8 9 10

Producer United States Canada China Japan Russian Federation Brazil India Sweden Germany Finland

Quantity Share of (103 m3) world total 111,425 64,764 26,969 21,698 19,500 19,091 17,460 15,619 14,849 10,670

25.4% 14.8% 6.1% 4.9% 4.4% 4.4% 4.0% 3.6% 3.4% 2.4%

322,045

73.4%

The World’s Top 10 Consumers and Producers of Wood-Based Panels in 1997

Consumer

Quantity (103 m3)


United States China Japan Germany United Kingdom Italy Canada Korea, REP France Spain Total

44,121 18,707 13,762 11,742 5,191 4,893 4,432 4,088 3,807 3,306 114,049

28.7% 12.2% 9.0% 7.6% 3.4% 3.2% 2.9% 2.7% 2.5% 2.2% 74.2%

Producer United States China Canada Germany Indonesia Malaysia Japan France Italy Brazil

Quantity (103 m3) 38,502 14,499 11,339 10,874 10,163 7,000 6,435 4,588 4,364 3,558 111,322

Share of world total 24.7% 9.3% 7.3% 7.0% 6.5% 4.5% 4.1% 2.9% 2.8% 2.3% 71.5%

sawnwood has almost trebled to 116 million m3; that of wood pulp has increased almost fourfold to 32.5 million metric tons; that of wood-based panels has increased more than 15 times to 47.7 million m3; and that of paper and paperboard has increased more than six times to 83.1 million metric tons. The relative importance in trade of the major forest products has been changing (Figure 2.3). While expanding in trade volume, the industrial roundwood share of the value of total exports dropped from 13% in 1961 to 8% in 1997, largely since the early 1980s. The wood pulp share of exports declined steadily as importers moved to importing final products. The share of sawnwood remained

24


TABLE 2.13

The World’s Top 10 Consumers and Producers of Fiber Furnish in 1997

Consumer 1 2 3 4 5 6 7 8 9 10

United States China Japan Canada Germany Sweden Finland Korea, REP France Italy Total

TABLE 2.14 in 1997


86,564 34,256 29,465 16,797 14,665 10,039 9,975 8,572 8,330 6,823

30.0% 11.9% 10.2% 5.8% 5.1% 3.5% 3.5% 3.0% 2.9% 2.4%

225,486

78.1%

Producer

Quantity (103 t)


United States China Canada Japan Germany Sweden Finland Brazil France Korea, REP

92,967 29,034 27,568 26,206 13,237 12,146 11,697 7,629 6,374 5,122

32.2% 10.0% 9.5% 9.1% 4.6% 4.2% 4.0% 2.6% 2.2% 1.8%

231,980

80.2%

The World’s Top 10 Consumers and Producers of Paper and Paperboard

Consumer 1 2 3 4 5 6 7 8 9 10

Quantity (103 t)

United States China Japan Germany United Kingdom France Italy Korea, REP Canada Brazil Total

Quantity (103 t)


89,502 37,096 31,481 15,756 11,880 9,285 9,091 6,834 6,575 6,317

30.5% 12.6% 10.7% 5.4% 4.0% 3.2% 3.1% 2.3% 2.2% 2.2%

223,817

76.2%

Producer

Quantity (103 t)


United States China Japan Canada Germany Finland Sweden France Korea, REP Italy

86,274 31,583 31,016 18,969 15,953 12,148 9,779 8,556 8,363 7,533

28.9% 10.6% 10.4% 6.4% 5.4% 4.1% 3.3% 2.9% 2.8% 2.5%

230,174

77.2%

relatively stable. In contrast, the share of both wood-based panels and paper and paperboard increased, reflecting the growing emphasis on higher-value forest products in world trade. In 1997, paper and paperboard products accounted for about 46% of the value of world forest product exports, followed by sawnwood’s 20% and wood-based panel’s 14%. The ratio of world export to world production has increased for all products (Figure 2.4). The ratio of world industrial roundwood export to production has risen from 4% in 1961 to 8% in 1997; for sawnwood it has risen from 12% to 26%; and for wood-based panel from 12% to 32%. The ratio of wood pulp

25


Thousand m3 or t

480000 360000 240000 120000 0 1961

1966

1971

1976

1981

1986

1991

1996

Year Ind. Roundwood

Sawnwood

Wood-based panels

Wood pulp

Paper and paperboard

FIGURE 2.1 World export volumes of major forest products from 1961 to 1997. 150

US$ billions

120 90 60 30 0 1961

1966

1971

1976

1981

1986

1991

1996

Wood pulp

Paper & paperboard

Year Industrial Roundwood

Sawnwood

Wood-based panels

FIGURE 2.2 World export value of major forest products from 1961 to 1997.

export to production has risen from 14% to 21%; and that of paper and paperboard has risen from 16% to 29%.

DOMINANCE OF THE DEVELOPED COUNTRIES One major feature of world trade in forest products is the dominance of the developed countries, both in exports and imports. In value terms, developed countries accounted for 88% of world total exports in 1997, down slightly from 1970 levels. On the import side, they accounted for 79%. Figures 2.5 and 2.6 show the breakdown of exports and imports by region in 1997. They clearly indicate the dominance of Europe and North America as both importers and exporters and of Asia as an importer.

26


60%

40%

20%

0%

1961

1966

1971

1976

1981

1986

1991

1996


Sawnwood

Wood-based panels

Wood pulp

Paper & paperboard

FIGURE 2.3 Share of total forest products export value for major forest products from 1961 to 1997.

40%

30%

20%

10%

0%

1961

1966

1971

1976

1981

1986

1991

1996

Year Ind. Roundwood

Sawnwood

Wood-based panels

Wood pulp


FIGURE 2.4 Ratio of world export to world production for major forest products from 1961 to 1997.

CONCENTRATION OF IMPORTS AND EXPORTS Although many countries are involved in trade, only a few account for the bulk of exports and imports (Figures 2.7 and 2.8). In 1997, the top five countries exported 55% of world exports of forest products, and the top 10 countries

27


FORMER USSR 5%

AFRICA 1%

NORTH/CENTRAL AMERICA 34% EUROPE 42%

SOUTH AMERICA 4% OCEANIA 2%

ASIA 12%

FIGURE 2.5 Regional shares of total forest products export value in 1997.

FORMER USSR 1%

AFRICA 2% NORTH/CENTRAL AMERICA 24%

EUROPE 42% SOUTH AMERICA 2%

OCEANIA 1%

ASIA 28%

FIGURE 2.6 Regional shares of total forest products import value in 1997.

exported 70%. Canada and the United States alone accounted for almost onethird of world exports. On the import side, the top five countries accounted for 49% of world imports and the top 10 accounted for 67%. The United States and Japan alone imported 30% of the world exports. In recent years, as their economies have grown, developing countries, particularly in Asia, have increased their share of global imports, mainly of logs and

28


30

25

US$ Billions

20

15

10

5

0 Canada

United States

Finland

Sweden Germany

Austria

Russian Indonesia Fed

France

Malaysia

FIGURE 2.7 Top 10 exporters of total forest products by value in 1997.

30

25

US$ Billions

20

15

10

5

0 United States

Japan

China Germany

United Kingdom

Italy

France

Netherlands

Hong Kong

Spain

FIGURE 2.8 Top 10 importers of total forest products by value in 1997.

semifinished wood products. China alone accounts for more than 20% of all developing country imports. Only plywood exports are dominated by developing countries (Indonesia alone accounts for 46% and Malaysia for a further 17%). Most of the exporting developing countries are in Asia, for wood-based panels, logs, sawnwood, and paper products. South American countries, however, are the major developing countries’ exporters of wood pulp. In the past decade,


29

exports by the developing countries have gradually moved toward value-added products.

GLOBAL TRADE LIBERALIZATION AND THE URUGUAY ROUND OF GATT During the past 50 years, there have been several regional trade pacts, such as the European Economic Community (EEC), Canada–U.S. Free Trade Agreement (FTA), European Free Trade Association (EFTA), and several negotiations, or “rounds,” under the GATT. While the regional trade pacts reduced trade barriers within the member countries, GATT rounds reduced them globally. The Kennedy Round in 1967 reduced average tariffs by about 35% (Krugman and Obstfeld 1994). After the Tokyo Round in 1979, the average tariffs in the most industrialized markets declined from 7% to 5%, and $300 billion worth of global trade was liberalized among many countries or regions (Esty 1994). Nevertheless, tariff escalation remained; that is, tariffs rose with increased processing. The Uruguay Round of GATT, ended in 1994, was meant to reduce import tariffs on industrial products by one-third from 1994 to 1999. By 2005, tariffs on some products, including pulp and paper, should be eliminated completely in major developed countries. According to WTO (2001), for industrial products, tariff cuts had been implemented before 1999 as agreed in the Uruguay Round, with a few exceptions for some countries and products, while escalation had been considerably reduced. Developed countries set bound rates (the maximum tariff rates) for all their tariffs, while developing countries set bound rates on more than half of the tariffs (Barbier 1996). Progress has also been made on reducing nontariff barriers in the form of subsidies, countervailing duties, and technical standards (Esty 1994). Barbier (1996) summarizes the Uruguay Round results for global forest products. In early 2001, the implementation of the provisions of the Uruguay Round agreement is fairly advanced (WTO 2001). Austria, Canada, the European Union, Finland, Japan, New Zealand, the United States, and several other major importers agreed to the elimination of tariffs on pulp and paper. Under the agreement, these countries would halve their 1986 level of tariffs by the year 2000 and eliminate the tariffs by 2004. The major developed countries were also committed to reducing tariffs by 50% on solidwood products over five years starting in 1995. In the United States, average tariffs would be cut from about 3.1% to 1.8%. Tariff escalation on forest products in developed countries would be reduced significantly. For paper and paperboard, tariff escalation would be eliminated completely. The Uruguay Round agreement committed all major developed countries and a high proportion of developing countries to set maximum rates for all

30


forest products. The Agreement on the Application of Sanitary and Phytosanitary Measures and the Agreement on Technical Barriers to Trade will decrease nontariff barriers for forest products. According to Barbier (1996), the expansion of international trade in forest products in the main trading countries when all tariff reductions have been implemented by 2004 may be between US$340 and US$472 million, but this is less than 0.5% of the forest product imports of the countries analyzed. A more significant indirect benefit of the Uruguay Round of GATT may be from the impetus that the Uruguay Round process has given to continuous reductions and improvement, such as Accelerated Tariff Liberalization (ATL). The ATL agreement will be studied further in Chapter 7.

EFFECTS OF ENVIRONMENTAL AWARENESS ON INTERNATIONAL TRADE The mutual effects of trade and environment have been closely examined in recent years. Environmentalists suggest that potential for high profits in exporting logs and other forest products is a major cause of damage to forests, especially in the tropics. But many economists argue that international trade has little effect on the environment. Dean (1992) surveys papers on trade and the environment. A more specific review dealing with forestry and trade is Tomberlin et al. (1998b). The effects on trade flow caused by environmental regulations range from small but significant (Robinson 1988) to no clear impacts (Leonard 1988). Esty (1994) summarizes many researchers’ results and points out that, while having an impact, trade is not a major direct cause of environmental problems and that trade actions alone cannot ensure sustainable management of forests. A variety of initiatives have been proposed and implemented as a response to environmental concerns, such as wastepaper recycling, pollution controls, processing methods, certification of forest products, Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), and packaging regulations. The increased recovery and recycling of waste disposal in many countries is also caused by economic incentives in some countries, especially in Asia. Trade in wastepaper worldwide has grown over the last 20 years. Exports have increased by almost 9% per year since 1980, while during the same period world consumption of wastepaper have grown by about 5% per year. In some cases, the policies promoting recycling of wastepaper have had significant effects on trade. For example, in 1989, about 56% of all newsprint consumed in the United States was supplied by Canada, but by 1992 the figure had declined to 50%. This reduction has been influenced by regulations on the mandatory minimum recycled fiber content for newsprint in some U.S. states (Raymond Com. 1999). Some programs to stimulate market demand for recycled paper also accompanied this regulation.

31


The mandatory regulation may provide an unfair cost advantage to U.S. producers. Because Canada has a relatively small and dispersed population, consumption of newsprint is small relative to production. To meet the U.S. regulations, Canadian newsprint producers have had to import wastepaper from the United States. This hypothesis, that recycling policies in the U.S. alter its competitive advantage relative to other countries, will be investigated further in Chapter 8.

2.4 FOREST PRODUCT PRICE TRENDS Figure 2.9 presents historical trends for industrial roundwood and sawnwood prices in 1997 U.S. dollars at the world level (here, the price is defined as the weighted average unit value of world imports and exports, net of inflation). Historically, the price of sawnwood paralleled the price of industrial roundwood, mainly because roundwood is a major part of the total cost of saw milling. The price of industrial roundwood remained stable, in real terms, until the early 1970s. Between the mid-1970s and the mid-1980s, the world price of industrial roundwood fluctuated, reaching a second peak in 1979/80, at almost the same level as in 1974. Since then the price has declined, reaching the lowest level in 1986. The price level of industrial roundwood around 1997 was slightly lower than that of the early 1960s. The price trends for sawnwood were very similar to those for industrial roundwood. The price peaked in 1974, followed by a drop. After 1974, prices stayed flat and began to fall in 1980. Not until the late 1980s did the price of sawnwood begin to recover, and by 1997 the price level reached $237/m3, slightly higher than that of the early 1960s.

1997 US$ per cubic meter

400

300

200

100

0 1961

1966

1971

1976

1981

1986


Sawnwood & sleepers

FIGURE 2.9 World prices of solid wood from 1961 to 1997.

1991

1996

32



1000 800 600 400 200 0 1961

1966

1971

1976

1981

1986

1991

1996

Year Veneer & Plywood

Particle board

Fibreboard

FIGURE 2.10 Real-world prices of wood-based panels from 1961 to 1997.

1997 US$ per metric ton

1500 1200 900 600 300 0 1961

1966

1971

1976

1981

1986

1991

1996

Year Newsprint

Printing & writing paper

Other paper & paperboard

FIGURE 2.11 World prices of paper and paperboard from 1961 to 1997.

Between 1961 and 1997, the price for wood-based panels fell, as shown in Figure 2.10. This steady fall in price was made possible mostly by reductions in costs of production brought about by new processes, higher yields from the same raw materials, economies of scale, and cheaper raw material from sawmill residues to make particleboard. The real price of paper and paperboard declined during the 1960s and early 1970s (Figure 2.11). Thereafter, there was a short sharp rise in real price in 1973/74, at the time of the energy crisis. After that, the prices never came up to the 1974 level, although they reached another peak around 1988. After 1990, the paper prices declined for four straight years, and only in recent years did

33


they begin to recover. The 1997 price of paper and paperboard was around 80% of its 1990 price.

2.5 ECONOMIC AND DEMOGRAPHIC PROSPECTS A meeting of the Expert Group on World Economic Situation and Prospects was held at the United Nations in April 2000 (UN Meeting 2000), with the object to review the world economic outlook and current policy issues. The economic outlook noted that following a two-year setback, a world economic expansion had begun since 1999 (Table 2.15). The fastest-growing economies were in China, East Asia, South Asia, and Latin America. But the Japanese economy was still sluggish. This strength of the world economy was attributed to the fast recovery of many emerging market economies from the recent Asian financial crisis and to the continued growth of the United States. In the late 1990s, there had been much increase in investment in information and communication technology (ICT) throughout the world. It was expected that this would provide opportunities for sustaining strong growth in the world economy during the first years of the 21st century. Other positive indicators were that world trade started to grow faster in 1999, financial conditions for emerging economies had improved, worldwide inflation remained low, and the employment situation in many economies was improving. Table 2.15 World Economic Growth as Assessed by International Agencies, 1999–2001 (percent per year) 1999

2000

2001

United Nations World Developed economies Developing economies Economics in transition

2.7 2.6 3.4 2.1

3.5 3 5.25 3.75

3.5 3 5 3.5

International Monetary fund World Advanced economies Developing countries Countries in transition

3.3 3.1 3.8 2.4

4.2 3.6 5.4 2.6

3.9 3 5.3 3

World Bank World High-income countries Low- and middle-income countries

2.6 2.6 2.7

3.1 2.6 4.5

3.1 2.6 4.5

Source: Report on the meeting of the expert group on the world economic situation and prospects (project LINK). United Nations, New York, April 2000.

34


Some attendees believed that a new economic era had started, driven by global economic integration and ICT. Klein (2000) felt that the new-economy effects of ICT would last for several decades. Hong (2000) pointed out that the spreading of the ICT revolution to the world economy coincident with global integration reinforced one another. Other studies agreed that the improvement in productivity in the United States during the 1990s was due partially to the widespread use of ICT and business-to-business commerce. Based on estimates of cost savings from cheaper inputs in 17 industries in the United States, Germany, Japan, and France, there would be an average decline in costs for the economy as a whole. In addition, business-to-business commerce would stimulate demand. The result would be an increase in output without any price increase in the long run. But the meeting report (UN Meeting 2000) noted uncertainties and risks that could markedly change this positive outlook. Among the concerns were the large and growing trade deficit, the unusually high value of equities, and the low saving rate in the United States. A slowdown in world economic growth could result from a major drop in equity values in the United States and other major developed countries.3 According to a publication by the United Nations (UN Population 2000), the world population was 6.06 billion in 2000, up from 5.98 billion in 1999 and 5.66 billion in 1995. The long-range projection prepared by the United Nations Population Division includes several scenarios for population growth for the world and its major areas over the period 1995–2150. The medium scenario assumes that fertility in all major areas stabilizes at replacement level (2.1 children per woman) around 2050. According to the projection of the medium scenario, world population rises from 5.7 billion persons in 1995 to 9.7 billion persons in 2150. There are significant shifts in the geographical distribution of the world population: The share of Africa doubles (passing from 12% of the world population in 1995 to 24% in 2150), whereas that of China is reduced by a third (from 22% in 1995 to 14% in 2150) and that of Europe by more than half (from 13% to 5%). There are also significant shifts in the age distribution toward older ages: The share of persons aged 0–14 declines from 30% in 1995 to 18% in 2150, whereas the share of persons aged 60 or over rises from 10% to 30% over the same period. These global economic and demographic considerations represent some of the background for the future scenarios set in GFPM applications. The World Bank and FAO projections used in Chapter 5 and other chapters incorporate these economic and demographic trends in the GDP growth rates, which are among the most important exogenous parameters of GFPM. 3 The U.S. NASDAQ composite stock index on April 4, 2001, had dropped about 34% compared with the index on December 29, 2000, and U.S. economic growth decreased substantially during the first half of 2001.


35

2.6 SUMMARY AND CONCLUSION The world demand for forest products, from fuelwood to paper, has been rising steadily in the past 40 years. Yet there is much variation in these trends by product and by region. Economic and demographic growth in individual countries are critical determinants of those trends. The implication for modeling the world forest sector is that to be accurate and most useful in policy analysis, the model should be as disaggregated as possible by products and should deal with small regions, possibly individual countries. International trade has been growing faster than production. Trade agreements are primary instruments in international and national forest policy issues. A useful model of the world forest sector should then have an adequate representation of trade flows, dealing with imports and exports of individual countries by product categories adequate to represent existing tariff schedules. In some cases, such as regional agreements, representation of bilateral trade flows may also be needed. Environmental issues, directly or indirectly tied to trade issues, form a central part of contemporary forest policy issues. A model of the global forest sector should represent technologies of production in sufficient detail that the impacts of changes in policies, such as increased recycling and decrease in timber harvest, can be fully traced from raw material availability, through manufacturing, and ultimately to trade. These are some of the features that the GFPM model described in the next chapter was meant to incorporate to help meet some of the challenges facing international forestry during the next two decades.


P AR T

Model Construction

II


CHAPTER

3

GFPM Structure and Formulation

3.1 OVERVIEW OF THE GFPM The GFPM integrates the four classical major components of forest sector models (Kallio et al. 1987): timber supply, processing industries, product demand, and trade. Each year equilibrium is computed by maximizing the global “net social payoff” (Samuelson 1952), while year-by-year changes are simulated by recursive programming (Day 1973). The GFPM is designed mainly as a policy analysis tool, to project the general future trends in quantities and prices at different stages of transformation, under different scenarios. The model shows how production, consumption, imports, exports, prices, and welfare are likely to change in response to changing economic environments (such as changes in economic growth, tariffs, or technology). Because it can predict prices, a critical input in making investment decisions, the model also gives relevant information for private concerns. In each projection year, for each country and commodity, supply (domestic production plus imports) is equal to demand (final consumption plus input in other processes plus exports). Final demand is price responsive; demand for wood or intermediate products derives from the demand for end products 39

40


through input–output coefficients that describe technologies in each country. The supply of raw wood and nonwood fibers in each country is price responsive. The supply of recycled paper is constrained by the wastepaper supply, which itself depends on the paper consumption and recycling rate. Each country exports to and imports from specific countries or the world market. Projected prices are such that they clear markets: At those prices demands are equal to supplies in each country, thus prices are endogenous (the equilibrium between demand and supply leads to the price). From one year to the next, demand changes in each country due to changes in income. The wood supply shifts exogenously according to the chosen scenario. The amount of recycled fiber used in making paper and paperboard changes with technology and recycling policy. Capacity increases or decreases according to new investments, which depend on past production and the profitability of production in different countries, as revealed by the shadow price of capacity. Tariff changes affect the cost of imports, advalorem. Then a new equilibrium is computed subject to the new demand and supply conditions, new technology, new capacity, and new tariffs. Trade changes with inertia tied to past trade and GDP growth. The general principle of the GFPM is, then, that global markets optimize the allocation of resources in the short run (within one year). Long-run resource allocation is governed partly by market forces, as in capacity expansion and trade, and also by political forces, such as the wood supply shifts determined by forest policy, the wastepaper recovery rates influenced by environmental policy, the trade tariffs that change the cost of imports, and the techniques of production determined by exogenous technological progress. Figure 3.1 symbolizes how the GFPM simulates dynamic market equilibria for the global forest sector. Every year, T, demand, supply, trade, and prices are computed that clear markets for all products in all countries. This is the spatial global equilibrium computed via the static phase of the model. In Figure 3.1, the quantity-price (QT, PT) symbolizes the global spatial equilibrium, obtained by maximizing consumer and producer surplus at T (symbolically the area between the supply and demand curves in Figure 3.1). Then the model parameters are updated to reflect exogenous and endogenous changes from one year to the next. This is the dynamic phase of the model. Exogenous changes are assumptions regarding economic growth, technical change, potential timber supply, and trade inertia. Endogenous changes, determined by the model, include capacity growth and availability of recycled fibers. This results in a new demand–supply system sketched in Figure 3.1 by the demand and supply curves at T + 1. The model then computes the quantityprice equilibrium next year (QT+1 and PT+1 in Figure 3.1) as shaped by the intervening changes. It reiterates the static and dynamic phases for every year until the end of the projection.

41


PT +1

PT

T +1 QT +1

T

QT

FIGURE 3.1 The GFPM simulates world forest product markets as a sequence of spatial equilibria that maximize consumer and producer surplus (shaded area).

3.2 SPATIAL GLOBAL EQUILIBRIUM The equilibrium for each year of the projection is obtained with an optimization model that simulates world markets. It finds the production, consumption, and trade that maximize the total value of consumption minus the total cost of production and transportation, for all products in all countries in a given year. All the variables refer to that year.

DEFINITION OF TERMS The products in the GFPM may be primary products (such as industrial roundwood), end products (such as sawnwood, panels, and paper), intermediate products (pulp), or recycled products (in the GFPM, only paper is recycled). Manufacturing refers to the transformation of a primary, recycled, or intermediate product into an intermediate or final product. Econometric equations represent

42


the supply of primary and recycled products and the demand for end products. Input–output activities represent the demand for intermediate products and the supply of intermediate and final products. The manufacturing cost of a product excludes the cost of the inputs recognized explicitly by the model. For example, the manufacturing cost for newsprint includes the cost of labor, energy, and capital but excludes the cost of pulp. The base year is the first year for which GFPM computes the equilibrium. This computation is meant to replicate the price and quantity conditions in that first year.

OBJECTIVE FUNCTION max Z =

∑∑∫ i

−

Dik

0

k

Pik (Dik )dDik −

i

∑∑Y

ik

i

∑∑∫

mik −

k

k

∑∑∑c

Sik

0

ijk Tijk

i

j

Pik (Sik )dSik [3.1]

k

where i, j = country, k = product, P = price in U.S. dollars of constant value, D = final product demand, S = raw material supply, Y = quantity manufactured, m = cost of manufacture, T = quantity transported, and c = cost of transportation, including tariff. In Equation [3.1], the first term is the value to consumers of the end products in the current year, defined by the sum of the areas under the demand curves for end products. The second term is the cost of production of primary and recycled products in the current year, defined by the sum of the areas under the supply curves. The third term is the cost of manufacturing intermediate or final products in the current year, which is also the area under their supply curves. The last term is the transport cost in the current year, including tariffs and other taxes, which is also the area under the supply curve of shippers. The objective function, therefore, measures the difference between the value of end products to consumers and the total cost of bringing those products to consumers. Identically, the objective function measures the sum of the producer and consumer surplus, that is, the total welfare in the forest sector.

END-PRODUCT DEMAND The demand for each end product in each country in a given year has a constant elasticity with respect to the price: Dik =

 P  ik   Pik,−1 

Dik∗ 

σ ik

[3.2]

43


where D∗ = current demand at last year’s price, P−1 = last year’s price, and σ = price elasticity of demand. As shown in Section 3.3, on market dynamics, D∗ depends on last year’s demand and the growth of GDP in the country. In the base year, D∗ is equal to the observed base-year consumption and P−1 is equal to the observed base-year price.

PRIMARY-PRODUCT SUPPLY The supply of primary and recycled products in a given year has a constant elasticity with respect to price, up to an upper bound: Sik =

 Pik    Pik,−1 

λ ik

Sik∗ 

[3.3]

Sik ≤ SikU where S∗ = current supply at last year’s price, Su = upper bound on supply, and λ = price elasticity of supply. As shown in Section 3.3, on market dynamics, S∗ and Su depend on last year’s levels and on exogenous supply shifters. For recycled paper, S∗ and Su depend on domestic paper consumption in the previous year (see Section 3.3). In the base year, S∗ is equal to the base-year production and P−1 is equal to the observed base-year price to obtain a predicted supply as near as possible to the observed supply.

MATERIAL BALANCE In each country, for each commodity, the amount of a commodity imported, supplied, and manufactured must be equal to the sum of domestic demand, quantity used in the manufacturing of other commodities in the same country, and exports:

∑T

jik

j

+ Sik + Yik − Dik −

∑a n

iknYin

−

∑T

ijk

=0

∀i, k

[3.4]

j

where aikn = input of product k per unit of product n.

TRADE INERTIA The imports and exports of each country are bounded to simulate inertia in trade patterns: u TijkL ≤ Tjik ≤ Tijk

∀i, j, k

[3.5]

44


where the superscripts L and U refer to a lower and upper bounds, respectively. The upper and lower bounds on imports and exports are functions of the previous year’s trade level and GDP growth rate (see Section 3.3). This is an extension of earlier trade inertia constraints (Buongiorno and Gilless 1984, Kallio et al. 1987), meant to recognize the effect of the size of the domestic market on trade (Devarajan et al. 1997).

PRICE The shadow prices of the material balance constraints of Equation [3.4] give the market-clearing prices for each commodity and country (Hazell and Norton 1986).

MANUFACTURING CAPACITY Manufacturing is represented by activity analysis, with input–output coefficients and a manufacturing cost. Thus, for each manufactured good, the supply curve is horizontal, at cost mik up to a capacity constraint: Yik ≤ Kik

∀i, k

[3.6]

where K = current capacity of production. Capacity changes from year to year according to the equations specified in the next section.

3.3 MARKET DYNAMICS From one year to the next, demand changes in each country due to changes in the gross domestic product (GDP). The wood supply shifts exogenously according to a chosen scenario. The amount of recycled fiber changes with technology and recycling policies. Capacity increases or decreases according to new investments that depend on past global production and the profitability of production in different countries, as revealed by the shadow price of capacity. Tariff changes affect the price of imports, ad valorem. Then a new equilibrium is computed, subject to the new demand and supply conditions, new technology, new capacity, and new tariff. Trade changes with inertia tied to past trade and GDP growth. Yearly changes in the market equilibrium conditions are described by the equations in the following sections.

45


SHIFTS OF DEMAND The demand curves for end products shift over time due to economic growth, according to this equation: Dik* = Dik,−1 (1 + α k gi )

[3.7]

where α is the elasticity of demand with respect to GDP and gi is the GDP annual growth rate.

SHIFTS OF SUPPLY The supply curves of primary products, such as wood and recycled paper, shift over time, according to these equations: Sik* = Sik,−1 (1 + βik ) SikU

=

SikU,−1 (1 +

[3.8]

βik )

where β is t he exogenous rate of shift of supply. For recycled paper, the rate of shift of the upper bound on supply is endogenous (see the upcoming section on “Changes in Wastepaper Recovery”). Chapter 10 reports an application in which shifts in timber supply also depended on endogenous changes in forest inventory (see Equation [10.4]).

CHANGES OF MANUFACTURING COEFFICIENTS The input–output coefficients, aikn, and the manufacturing cost, mik, may be changed exogenously over time to reflect technical change, particularly the increasing use of paper recycling.

CAPACITY CHANGES For each manufactured product, the change in the world capacity of production is a function of the past three annual changes in world production. This follows the accelerator theory, according to which output is the primary determinant of investment (Clark 1979): ∆Kk = b1k ∆Yk,−1 + b2k ∆Yk,−2 + b3k ∆Yk,−3

[3.9]

where ∆K is the annual change in world capacity, ∆Y is the annual change in world production, and the b’s are parameters.

46


The GFPM distributes the global capacity change to each country in proportion to its production level and the marginal value of capacity revealed by the shadow price of capacity in the static phase, according to the following equation (Gilless and Buongiorno 1987, Zhang et al. 1993): ∆Kik =

Yik π ik ∆K Σ jYjk π jk k

[3.10]

where ∆ = yearly change and π = shadow price of capacity (endogenous, from Constraint [3.6]).

CHANGES IN WASTEPAPER RECOVERY The upper bound on supply for recycled paper, in Equation [3.3], depends on last year’s consumption of recyclable paper, according to this equation: SirU =

∑w

ikr Dik, −1

[3.11]

k∈R

where the subscript r refers to recycled paper, R is the set of end-paper grades considered in the model, and wikr is the maximum possible recovery rate for paper of grade k.

CHANGES IN FREIGHT COST AND TARIFF The transport cost in any given year includes the cost of freight, the import tariff (applied to the c.i.f. price), and the export tariff. Thus, it is defined by: cijk = fijk + tIjk Pik,−1 + tikX Pik,−1

[3.12]

where c = transport cost per unit of volume, f = freight cost per unit of volume, tI = import ad valorem tariff, tX = export ad valorem tariff, and P−1 = last year’s equilibrium price predicted endogenously by the model. The freight cost and the import and export tariffs may change exogenously over time.

CHANGES IN TRADE INERTIA BOUNDS Market forces simulated by the equilibrium calculation determine the direction of changes of trade flows. However, institutional and other constraints limit the adjustment that can take place in any given year. Furthermore, trade is also determined by the size of the domestic market and is in turn stimulated by general

47


economic growth, independent of prices. This is especially the case of intraindustry trade (imports and exports of the same product by the same country), which makes up about one-fourth of world trade within standard industrial classifications (Krugman and Obstfeld 1994). Intraindustry trade cannot be explained by competitive advantage. It is product differentiation, imperfect substitution, and transportation peculiarities, such as long borders, that lead to intraindustry trade.1 Considering both interindustry and intraindustry trade, in the GFPM the general trade trends are set by GDP growth, because of the strong empirical relation between imports and GDP (see Chapter 4). Meanwhile variations around the trends are allowed for and, due to relative competitive advantage, reflected by the objective function. Specifically, the bounds on trade depend on past trade and economic growth according to the following equations: U Tijk = Tijk,−1(1 + α ijk gi + ε ijk )

[3.13]

TijkL = Tijk,−1(1 + α ijk gi − ε ijk ) where α = elasticity of imports, or exports, with respect to GDP, g = GDP growth rate, and ε = upper or lower bound on relative changes in trade flow around the trend (trade inertia parameter).

3.4 LINEAR APPROXIMATION OF DEMAND AND SUPPLY

DEMAND The area under the demand curves for end products and the area under the supply curves for raw materials are each a nonlinear function of the quantity demanded or supplied. This function is made linear by a stepwise local approximation, to allow for an efficient solution by linear programming. For example, consider a demand equation such as Equation [3.2]. Omitting the subscripts for region and product, the inverse demand equation in any given year is:  D P(D) = P0    D0 

1/ σ

[3.14]

1 The theory of intraindustry trade most amenable to empirical application recognizes that domestic and traded goods are imperfect substitutes (Francois and Reinert 1997). For example, products can be distinguished by country of origin (Armington 1969). This idea has been used in some partial and general equilibrium models (Rousslang and Suomela 1985, de Melo and Robinson 1989), and it has been extended to product differentiation at the firm level (Krugman 1979, 1980, Ethier 1979, 1982).

48


where P0 = P−1 (last year’s price) and D0 = D∗ (demand at last year’s price) define a known point on the demand curve. The approximation is done for a range of quantity demanded (Dmin, Dmax) defined by: Dmin = D0 (1 − r )

and

Dmax = D0 (1 + r )

[3.15]

where r is a fixed parameter. Then the width of a step is: Dwid =

Dmax − Dmin h

[3.16]

where h is the number of steps used to approximate the area under the demand curve. For example, Figure 3.2 shows the approximation for h = 3. Then the demand D can be replaced by the sum of h variables: h

D=

∑D

[3.16]

n

n=1

P1

P0 P3

C1 Dmin

D0

C3 Dmax

Dwid FIGURE 3.2

Approximation of the area under a demand curve.

49


each variable being constrained by the width of each step: D1 ≤ Dmin + Dwid Dn ≤ Dwid

n = 2,K, h

[3.17]

Let Cn be the middle of the nth demand step; then: C n = D min +

D wid (2n − 1) 2

[3.18]

and the approximated area under the demand curve is: h

∑ D P (C ) n

n

[3.19]

n=1

SUPPLY The supply curves for primary products corresponding to Equation [3.3] are treated in similar fashion. The inverse supply equation in any given year is:  S P(S) = P0    S0 

1/ λ

[3.20]

where P0 = P−1 (last year’s price) and S0 = S∗ (demand at last year’s price) define a known point on the supply curve. The approximation is done for a range of quantity demanded (Smin, Smax) defined by: Smin = S0 (1 − r )

Smax = S0 (1 + r )

and

[3.21]

where r is a fixed parameter. Then the width of a step is: S wid =

S max − S min h

[3.22]

where h is the number of steps used to approximate the area under the supply curve. For example, Figure 3.3 shows the approximation for h = 3. Then the supply S can be replaced by the sum of h variables: h

S=

∑S

n

[3.23]

n=1

each variable being constrained by the width of each step. In contrast with the demand approximation, the variable corresponding to the last step, Sh, is

50


P3

P0 P1

C1

S0

C3

Smin

Smax

Swid FIGURE 3.3

Approximation of the area under a supply curve.

unconstrained. If there is an upper bound on supply, it is defined by the variable Su in Equation [3.3]: S1 ≤ Smin + Swid Sn ≤ Swid

n = 2,K, h − 1

[3.24]

Let Cn be the middle of the nth supply step; then: C n = S min +

S wid (2n − 1) 2

[3.25]

and the approximated area under the supply curve is: h

∑ S P (C ) n

n=1

n

[3.26]

51


With this transformation of the demand and supply functions, the first two terms of Objective Function [3.1] become linear, and the equilibrium can be computed by linear programming. Because the demand curves are downward sloping and the supply curves are upward sloping, the demand variables D1, D2,…, Dh and the supply variables S1, S2,…, Sh enter the solution in that order. Therefore, only upper bounds on the variables are needed, and the linear program can be solved very efficiently.

3.5 IMPLEMENTATION OF TRADE INERTIA CONSTRAINTS The trade inertia constraints (Equation [3.5]) have two limitations when it comes to numerical implementation. First, because the linear program tends to have multiple optimal solutions (due to the large number of variables relative to the number of constraints), the solution can switch arbitrarily between the upper and lower bound. This is unrealistic. Unless there are strong economic reasons, the trade flows should not change much from year to year. Second, and more important, the bounds on trade may cause infeasible solutions that are hard to correct. Even if a feasible solution exists, if the bounds are binding, the resulting local prices may be many times larger than the world price. This is also unrealistic. Prices can be somewhat different between countries, but not that much. To avoid this, Trade Inertia Constraints [3.5] are made more flexible by modifying the constraints and the objective function. Each trade flow is kept near last year’s trade flow, unless substantial economic change occurs, by the following constraints: Tijk + ∆Tijk− ≥ Tijk,−1

[3.27]

Tijk − ∆Tijk+ ≤ Tijk,−1

+ − where ∆Tijk is the increase in trade between year t − 1 and t and ∆Tijk is the decrease in trade. Both variables are non negative and appear in the objective function with an arbitrarily small weight, w:

max Z = K −

∑ ∑ ∑ w(∆T

− ijk

i

j

+ + ∆Tijk )K

[3.28]

k

This ensures that trade flows stay equal to their previous year’s value, unless a substantial change in economic competitiveness occurs.

52


Each trade flow is also allowed to exceed or fall short of the bounds set by Equation [3.5], with the following constraints: Tijk + ∆TijkL ≥ TijkL

[3.29]

U U ≤ Tijk Tijk − ∆Tijk U where ∆TijkL is the amount by which trade falls short of the lower bound and ∆Tijk is the amount by which trade exceeds the upper bound. Both variables are non negative and appear in the objective function, with a weight equal to the previous year’s world price, Pk,−1:

max Z = K −

∑∑∑P

L k, −1 ( ∆Tijk

i

j

U )K + ∆Tijk

[3.30]

k

With this formulation, some trade flows may fall outside the bounds, if they must, without distorting local prices.

3.6 WELFARE ANALYSIS The goal of welfare analysis is to estimate the change in consumer surplus and producer surplus2 induced by a change in policy, for example, the elimination of import tariffs. Computing this total change in welfare is generally not feasible, because the GFPM uses only a segment of the demand and supply curves around the equilibrium point (Equations [3.19] and [3.26]). Instead, the GFPM produces estimates of the welfare changes when they can be derived subsequently to the calculations of production, consumption, imports, exports, and prices, for two different scenarios.

CONSUMERS’ WELFARE The calculation applies to consumers of end products, such as sawnwood, for which the demand is represented by demand curves of the form of Equation [3.2]. The calculation is restricted to cases where the GDP growth rate, and therefore the shift of the demand curves, are the same in two scenarios. In Figure 3.4, D is the quantity of a product consumed in a particular year and country, and P is the corresponding equilibrium price. dD is the change in quantity consumed between two scenarios, and dP is the corresponding price change. 2 Consumer surplus is the consumer’s gross benefit of consuming a good (the area under the demand curve) less the expenditure on the purchase of the good. Producer surplus is the value of the good produced minus the cost of producing it (the area under the supply curve).

53


A

P dP

B

P′

D FIGURE 3.4

dD

Change in welfare of consumers of end products.

Curve AB is the demand curve. Then the change in consumer surplus between scenarios for that product k, country j, and year t is approximately the area PABP′: c dWikt = −dPikt × Dikt −

1 (dDikt × dPikt ) 2

[3.31]

where dW c = change in consumer welfare between two scenarios, D = quantity of a product consumed in base scenario, dD = change in quantity consumed between two scenarios, and dP = change in price between scenarios.

SUPPLIERS’ WELFARE This calculation refers to the changes in the surplus of producers of primary and recycled products (e.g., industrial roundwood, wastepaper) with supply functions of the form of Equation [3.3]. It is restricted to cases where the supply curves are shifted at the same rates in different scenarios. Then the change in supplier welfare between scenarios is approximately the area of the polygon PABP′ (Figure 3.5): 1 s dWikt = dPikt × Sikt + (dSikt × dPikt ) 2

[3.32]

where dW s = change in supplier welfare between two scenarios, S = quantity of a product supplied in base scenario, and dS = change in quantity supplied between two scenarios. This equation applies even if the quantity supplied is at the upper bound. In that case, dS = 0 and dSW = dP × SU.

54


B

P′

dP

P

A

S

dS SU

FIGURE 3.5

Estimated change in welfare of suppliers of primary and recycled products.

B P

π

m

A

Y FIGURE 3.6

K

Definition of manufacturer welfare.

MANUFACTURERS’ WELFARE This computes the change in producer surplus for producers of manufactured products, of which the supply is represented by linear programming activities. In Figure 3.6, Y is the amount manufactured for a particular year, country, and product. The supply function is horizontal at manufacturing cost m, up to capacity K. P is the equilibrium price, and π is the shadow price of the capacity constraint.


55

The manufacturer welfare is the area of rectangle mABP, equal to pY. Therefore, the change in manufacturer welfare between two scenarios is approximately: dWikty = dπ iktY jkt + dYiktπ jkt

[3.33]

where dW y = change in manufacturers’ welfare between scenarios, dY = change in quantity manufactured between two scenarios, and dπ = change in the shadow price of capacity between scenarios.

SCALING WELFARE CHANGES To express welfare changes in relative terms (%), the welfare level cannot be used for scaling, because the level of consumer or producer surplus is generally not known and is sometimes zero. Instead, each type of welfare change is scaled with the corresponding product value. Thus, the change in welfare of consumers is scaled by the value of consumed products. The change in welfare of suppliers is scaled by the value of the supplied primary products. The change in welfare of manufacturers is scaled by the value of manufactured products. The change in total welfare is scaled by the value of consumed, supplied, and manufactured products.


CHAPTER

4

Model Calibration and Validation

4.1 INTRODUCTION Model calibration here refers to the definition of all countries and products, the choice of a base year, the assembly and checking of data for the base year, and the estimation of the various parameters of the model. Validation refers to comparison of the model predictions with observations over a predetermined period and confirmation of the suitability of the model for its intended use.

4.2 COUNTRIES AND PRODUCTS The version of the Global Forest Products Model described here deals with 180 individual countries and 14 products. Each country may produce and trade one or all of the products. This level of resolution was chosen to facilitate data verification, since most international data are collected at the country level. Calculating projections by country also facilitates review and criticism of the projections, because expert knowledge is more available at the country level than at more aggregate regional or global levels.

57

58


Making projections for most of the countries in the world instead of for only a few aggregated regions or main countries was required by the FAO in some previous GFPM applications, such as FAO (1997a) and Zhu et al. (1998). Regardless, it is much more efficient to get any desired aggregation for various world regions from a model containing most of the countries, instead of recalibrating a new model every time a new aggregation is required, which is costly and error prone. The complete list of countries is shown in Table 4.1. It is the same as that used by FAO (1997a) and employs the same nomenclature. The forest products considered in the GFPM are shown in Table 4.2. They correspond to product groups used by the FAO (1999a) in its statistical work. In the GFPM, there are demand equations for fuelwood and charcoal, other industrial roundwood, sawnwood, veneer sheets and plywood, particleboard, fiberboard, newsprint, printing and writing paper, and other paper and paperboard. There are econometric supply equations for industrial roundwood (sawlogs and pulpwood), other fiber pulp, and wastepaper. The demand and supply of all other products are represented with manufacturing activities (input–output coefficients and corresponding manufacturing costs). Figure 4.1 shows the wood and nonwood material flows from primary to intermediate and end products and back to primary products through recycling activities.

4.3 BASE-YEAR DATA For the version of GFPM described here, the base year, the year when the projections started, was 1997. The primary source of the data to establish the base year and to estimate some of the parameters when needed was the Food and Agriculture Organization of the United Nations (FAO). The base-year and historical data on production, imports, and exports (quantities and values) were those published in the FAO Yearbook of Forest Products, accessed through the online FAOSTAT statistics database (FAO 1999a). Consumption by country and product was estimated as production plus imports minus exports. When needed, base-year and historical data on GDP came from the World Bank Development Indicators (World Bank 1997). Lacking complete data on capacity, the base-year capacity was assumed to be 110% of the base-year production for all products. Total world imports and exports of each commodity from FAO statistics are rarely equal, due to data errors and lags between shipments and receipts. In the GFPM, base-year equilibrium world imports should equal world exports, so world production (supply) would equal world consumption (demand). To ensure this equilibrium, GFPM uses a dummy region that supplies and exports or demands and imports the difference between world imports and world exports. For example, if world imports of sawnwood exceed world exports, the

59

Model Calibration and Validation TABLE 4.1

Countries in the Global Forest Products Model

AFRICA Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Congo Côte d’Ivoire Djibouti Egypt Equatorial Guinea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Libyan Arab Jamahiriya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Niger Nigeria Reunion Rwanda Sao Tome and Principe Senegal Sierra Leone Somalia South Africa Sudan Swaziland Tanzania, United Republic Togo

Tunisia Uganda Zaire Zambia Zimbabwe NORTH/CENTRAL AMERICA Bahamas Barbados Belize Canada Cayman Islands Costa Rica Cuba Dominica Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Martinique Mexico Netherlands Antilles Nicaragua Panama Saint Vincent Trinidad and Tobago United States SOUTH AMERICA Argentina Bolivia Brazil Chile Colombia Ecuador French Guiana Guyana Paraguay Peru Suriname Uruguay Venezuela ASIA Afghanistan Bahrain Bangladesh

Region and country names are for this study only.

Bhutan Brunei Darussalam Cambodia China Cyprus Hong Kong India Indonesia Iran, Islamic Republic Iraq Israel Japan Jordan Korea, DPR Korea, REP Kuwait Laos Lebanon Macau Malaysia Mongolia Myanmar Nepal Oman Pakistan Philippines Qatar Saudi Arabia Singapore Sri Lanka Syrian Arab Republic Thailand Turkey United Arab Emirates Vietnam Yemen OCEANIA Australia Cook Island Fiji French Polynesia New Caledonia New Zealand Papua New Guinea Samoa Soloman Islands Tonga Vanuatu

EUROPE Albania Austria Belgium-Luxembourg Bosnia-Herzegovina Bulgaria Croatia Czech Republic Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Macedonia Malta Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland United Kingdom Yugoslav Federal Republic FORMER USSR Armenia Azerbaijan Belarus Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania Moldova, Republic Russian Federation Tajikistan Turkmenistan Ukraine Uzbekistan

60 TABLE 4.2

The Global Forest Products Model Products in the Global Forest Products Model

SITC Code 245 246/247 248 634.1, 634.3/4 634.2 634.5 251.2 251.91, .61, .62, .3, .4, .5 251.92 251.1 641.1 641.2/3 EX641 1

Product

Unit 3

Demand1

Supply1

3

Fuelwood and charcoal Industrial roundwood Other industrial roundwood Sawnwood Veneer/plywood Particleboard Fiberboard Mechanical wood pulp

10 m 103 m3 103 m3 103 m3 103 m3 103 m3 103 m3 103 t

E I E E E E E I

E E E I I I I I

Chemical/Semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard

103 t 103 t 103 t 103 t 103 t 103 t

I I I E E E

I E E I I I

E indicates demand or supply is represented with an econometric equation; I indicates that it is represented with input–output coefficients.

Primary product

Intermediate product

End product Fuelwood Other Ind. rwd

Fuelwood Other Ind. rwd Industrial roundwd

Sawnwood Plywood Particleboard Fiberboard Mech. pulp Chem. pulp

Other fiber pulp

Newsprint Printing & wrtg Other paper

Wastepaper FIGURE 4.1 Product transformation in GFPM.

dummy region supplies and exports the difference. The dummy region supply, demand, exports, and imports are then fixed for the projection period. In the base year, countries are assumed to face the same world price, except for differences in transport cost. For countries that were net exporters, the

61


World Export Prices and Freight Cost of Forest Products in 1997 Commodity

World export price1 (US$/t or US$/m3)

Fuelwood and charcoal Industrial roundwood Other industrial roundwood Sawnwood Veneer sheet/plywood Particleboard Fiberboard Mechanical wood pulp Chemical/semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard 1 2

44.5 83.6 83.6 227.7 453.7 201.1 293.8 317.1 448.6 860.0 98.7 543.1 886.3 739.6

Freight cost2 (US$/t or US$/m3) 7.9 14.8 14.8 28.1 18.9 8.4 12.2 27.6 39.0 74.8 20.2 34.7 66.7 55.7

Average world export unit values. Freight cost is the product of world export price and freight factor.

world price was calculated as the export (f.o.b.) unit value (i.e., the total value of exports divided by the quantity exported, for each commodity). For countries that were net importers, the world price was the world export price plus the transport cost (which includes tariff and freight; see Equation [3.12]). Table 4.3 shows the base-year export prices and the freight cost derived from Turner and Buongiorno (2001). The 1997 tariff rates are detailed in Table 7.1, where different future tariff scenarios are examined.

4.4 PARAMETER ESTIMATION

DEMAND ELASTICITIES The demand equations for the end products are based on derived demand theory for raw material inputs. Dynamic demand models then lead to empirical elasticities of demand for each forest product with respect to national income (measured by real GDP) and real product price, in constant U.S. dollars (Buongiorno 1978, 1979, Buongiorno and Chang 1986, Baudin and Lundberg 1987). The theory is that deciders combine forest product inputs, y, with other inputs, z, to minimize the cost of producing the output, g. The relevant prices are: price of the forest product, py, and price of other inputs, pz. The resulting function,

62


y(py, pz, g), is the conditional demand for the product of interest. If one assumes a Cobb–Douglas production function, the cost minimization problem is: min( yp y + zpz )

[4.1]

y,z

subject to: aybz c = g where y is the amount of forest product demanded, z is the amount of other inputs, g is the industry production, and a, b, and c are positive parameters. The derived demand for the forest product is then (Varian 1992): p  y = y( p y , pz , g ) = β0 g  y   pz  β1

β2

[4.2]

where: β1 = 1/(b + c) is the positive elasticity of demand for the product of interest with respect to output and β2 = −c/(b + c) is the negative elasticity of demand with respect to the real price of the product, relative to the price of other inputs: p = py/pz. Equation [4.2] is a static derived demand function, in the sense that demand adjusts immediately to output and prices. One may instead recognize adjustment lags explicitly. For example, the partial adjustment model (Johnston 1984) assumes that Equation [4.2] is a long-term relation, where y = y∗ is the equilibrium demand, conditional on price p and output g. The adjustment toward equilibrium from one year to the next is represented by the first-order difference equation: y  y∗  = y−1  y−1 

δ

[4.3]

where y−1 is last year’s demand and 0 < δ ≤ 1 is the speed of adjustment of demand. The static model of Equation [4.2] is a special case, where δ = 1 and full adjustment occurs within a year. Substituting Equation [4.2] for y∗ in Equation [4.3] leads to the dynamic model: y = β0δ g

δβ1 δβ 2 1−δ p y−1

[4.4]

where the exponents of g and p are the short-term output and price elasticities, respectively.

63


Although the theory applies to a firm, we assume that aggregation across firms applies and that Equation [4.2] or [4.4] can be applied to the total demand of a country. Typically, few compatible data are available for many countries. Here, g was proxied by the gross domestic product (GDP) of a country, and the price of other inputs, pz was proxied by the GDP deflator. Thus, after a logarithmic transformation to achieve linearity, the static model of Equation [4.2] takes the following empirical form: ln( yit ) = β0′ + β1 ln( git ) + β2 ln( pit ) + eit

[4.5]

while the dynamic model of Equation [4.4] has this form: ln( yit ) = δ ln β0 + δβ1 ln( git ) + δβ2 ln( pit ) + (1 − δ )ln( yi,t −1 ) + eit′

[4.6]

where yit is the amount of forest product consumed by country i = 1,…,n during year t = 1,…, T, p is its real price, and g is the real gross domestic product. eit and e′it are random errors. The coefficients in Equation [4.6] are short-term elasticities, while those in Equation [4.5] are long-term elasticities. According to economic theory, the price elasticity should be negative and the GDP elasticity should be positive. The coefficient of lagged consumption in Equation [4.6] should be between zero and 1. Knowledge of the coefficients of Equation [4.6] allows recovery of the long-term elasticities, since:

β1 =

δβ1 1 − (1 − δ )

and

β2 =

δβ2 1 − (1 − δ )

[4.7]

which, according to theory, should match the elasticities in Equation [4.5]. In the notations of the previous chapter, β1 corresponds to σk in Demand Equation [3.2] and β2 corresponds to α k in Demand Shift Equation [3.7] of the GFPM. Table 4.4 summarizes some of the price and income elasticities of demand for forest products that have been obtained in various studies, based on pooled crosssectional and time-series data from different countries and years. Because of differences in the countries and years covered as well as differences in definitions and methods of estimation, there is considerable variation in the results. Consequently, some arbitrariness is unavoidable in choosing particular values. The values of the elasticities of demand used in GFPM are reported in Table 4.5. Different elasticities were used for countries with high or low GDP per capita, based on the results summarized in Table 4.4 and on estimates obtained specially as part of FAO timber outlook studies (FAO 1997a, Zhu et al. 1998). The elasticities for low-income countries were generally larger in absolute value than the elasticities for high-income countries.

64 TABLE 4.4

Published Long-term Elasticity Coefficients of Demand for Forest Products (1)

Product

(a)

(8) (b)

(2)

(3)

(4)

(5)

(6)

(7)

(a)

(b)

(9)

(10)

Price elasticity Fuelwood and charcoal Other industrial roundwood Sawnwood Sawnwood, coniferous Sawnwood, nonconiferous Panels Plywood Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard

−1.88 −2.56

−0.21 −0.16

−0.72 −0.90

−0.24 −0.16

−0.80 −1.13 −0.53

0.38 −1.00

−0.46 −0.07

−0.62

−0.72 −0.49

−0.18 −0.17 −1.11 −0.76 −0.70

−0.09 0.00 −0.26 −0.27 −0.26

−0.25 −0.69 −0.68 −0.13 −0.09

−0.48 −0.89

−1.33 −0.83 −0.83 −1.02 −1.06

−0.72

−0.45

0.00

0.00

−0.30

−0.48

−0.37 0.15 −0.14 −0.17 −0.75 −0.74

−0.59 −0.21

−1.15 −0.78

−0.30 0.00

−0.55 −0.09 0.08 −0.05 0.00

−0.83

−0.29

−0.88

−0.10

−0.29

GDP elasticity Fuelwood and charcoal Other industrial roundwood Sawnwood Sawnwood, coniferous

1.07 0.22

0.71

1.57

1.41

0.91 0.85

1.00 0.16

0.28

Sawnwood, nonconiferous Panels Plywood Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard

0.53

0.88

1.26

0.25

0.77

0.81

0.95 1.03

0.30 0.60 0.14 1.14 1.29

0.10 0.97 1.38 0.73 1.52

1.47 1.02 1.55 1.54 1.47

0.63 1.07

0.94 0.80 0.72 1.00 1.09

0.98

1.00

1.61

1.02

0.41

1.04

1.37 1.02 2.32 1.07 1.07 1.20

0.84 1.56

1.23 1.24

0.84 1.52

1.46 3.08 1.7 1.08 1.31

1.65

1.41

1.24

0.62

1.59

(1) Buongiorno (1978), 43 countries, 1963–1973, (a) high income, (b) low income. (2) Buongiorno (1979), 43 countries, 1963–1973. (3) Wibe (1984), 103 countries, 1970–1979. (4) Uutela (1987), 40 countries, 1965–1980. (5) Buongiorno and Chang (1986), 10 OECD countries, 1961–1981, within-country estimates. (6) Baudin and Lundberg (1987), major consuming countries, 1961–1981. (7) Prestemon and Buongiorno (1993), 24 countries, 1968–1988. (8) Brooks et al. (1995), 8 countries, 1964–1991, (a) high income, (b) low income. (9) Chas-Amil and Buongiorno (2000), 14 EU countries, 1969–1992. (10) Simangunsong and Buongiorno (2001), 62 countries, 1973–1997.

65

66


TABLE 4.5

Price and GDP Elasticities of Demand for End Products in the GFPM

Product

Country 1

Price

Income

Fuelwood and charcoal

High income Low income2

−0.62 −0.10

−2.26 0.40

Other industrial roundwood

High income Low income

−0.05 −0.37

−0.58 0.19

Sawnwood


−0.16 −0.21

0.32 0.46

Veneer and plywood


−0.13 −0.22

0.73 0.74

Particleboard


−0.24 −0.05

1.15 0.65

Fiberboard


−0.52 −0.52

0.82 0.82

Newsprint


−0.05 −0.18

1.14 1.05

Printing and writing paper


−0.15 −0.37

1.66 1.11

Other paper and paperboard


−0.06 −0.14

0.94 0.92

1

Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Israel, Italy, Japan, Kuwait, Luxembourg, Netherlands, Norway, New Zealand, South Africa, Spain, Sweden, Switzerland, United Kingdom, and the United States. 2 Rest of the world.

SUPPLY ELASTICITIES The theory of timber supply has a long history in forestry economics, and it has reached considerable sophistication (Johansson and Lofgren 1985, Wear and Parks 1994). Nevertheless, feasible empirical models, especially at the international level, generally have a simple form, such as: S = S(P, I, Z)

[4.8]

where S is the quantity of timber supply, by country, in a given year, P is the price of timber, I is the level of the timber inventory, and Z stands for other variables that may influence timber supply (Binkley and Dykstra 1987). For a given level of I and Z, Equation [4.8] describes the short-term timber supply. Using a Cobb–Douglas functional form as a local approximation of this function leads to an expression of the short-term timber supply analogous to Equation [3.3] in the GFPM. Thus, in any given year and country, the supply of industrial roundwood is tied to its price by a constant price elasticity, measuring the percentage change

67


Estimates of Elasticity of Timber Supply

Source Adams (1977) Adams and Haynes (1980) Adams and Haynes (1989) Binkley and Dykstra (1987) Brannlund et al. (1985) Daniels and Hyde (1986) Kuuluvainen and Salo (1991) Manurung and Buongiorno (1997) Newman (1987)

Region

Timber

U.S. West U.S. South

0.24 0.47

U.S. Pacific Northwest Various world regions Sweden U.S. South

0.36

Price elasticity Sawlogs Pulpwood

0.13

0.33–1.0 0.61

Finland

2.20

Indonesia

0.45

0.76

U.S. South

Robinson (1974) Vincent (1987)

Chile Japan New Zealand U.S. South Indonesia Malaysia Philippines

0.74

0.27

0.23 0.55

Perez-Garcia and Abt (1997)

Inventory elasticity

1.20 0.39

2.84 0.95 1.00 0.11 0.51 0.37 0.32

in timber supply that would result from a 1% change in real price, other things being equal. Table 4.6 summarizes the price elasticities of timber supply estimated in various studies. They vary from 0.11 to 2.84. In most applications of the GFPM in this book, the price elasticity of supply was set at 0.8, as in Zhu et al. (1998). Only in Chapter 10 does the price elasticity of timber supply vary by country, as in Tomberlin (1999). In addition, Equation [4.8] implies that the timber supply curves would shift over time, depending on changes in the level of inventory and other relevant variables. In the GFPM, the shifts in supply are exogenous, defined by the parameters β in Equation [3.8]. These rates of shift are estimates of how much timber production would change without a change in price. They vary by country and are based on various information regarding past production, forest area and stock, growth rates, extent of plantations, and policies of each country. The shift rates assumed in the GFPM are detailed in Chapter 5. In the application reported in Chapter 10 the timber supply shifts were endogenous and depended on timber inventory.

68


Supply curves for fuelwood and charcoal, other industrial roundwood, and other fiber pulp have the same elasticities and same rates of shifts as those for industrial roundwood. The price elasticity of these materials was also set at 0.8, as in Zhu et al. (1998). The supply of wastepaper was assumed to be horizontal, with an upper bound defined endogenously by the previous year’s consumption and the exogenous maximum recycling rate for the country. The maximum recycling rates applied in the GFPM are described in detail in Chapter 5.

MANUFACTURING PARAMETERS The GFPM simulates the transformation, in each country, of wood and other raw materials (other fiber pulp, wastepaper) into end products (sawnwood, panels) or intermediate products (pulps), which are in turn transformed into end products (papers). These successive transformations and the attendant supplies and demands are represented by activity analysis, consisting of the input–output (I–O) parameters aikn in the material balance Equations [3.4] and of the manufacturing cost parameters mik in Objective Function [3.1]. For most countries these data are not available. In addition, the production data are often inaccurate. The method to estimate the I–O parameters and improve the production data for the GFPM has two steps (Buongiorno et al. 2000). First, estimate consumption and production of final products where data are missing or have obvious errors. Second, estimate input–output coefficients while adjusting the production data to make them coherent with prior knowledge of the techniques of production. The method assumes throughout that the trade volumes (imports and exports) are correct. Though imperfect, the trade data tend to be much more accurate than the production data. In many countries, production data are just rough office estimates of statistical agencies or are based on casual company reports. Instead, imports and exports go through the customs agencies, which tend to keep better records. Improving Final Product Data The first step deals with missing or implausible data, in particular, negative consumption, for the final products: fuelwood, other industrial roundwood (such as poles, piling, and posts), sawnwood, wood-based panels, and paper and paperboard. As noted earlier, there are systematic relations between the wealth of a country and the consumption of wood products. Therefore, the GDP per capita can be used to estimate per capita consumption of wood products. This is done by regressing consumption per capita on GDP per capita for countries where data


69

are available in a particular year, preferably the base year of the GFPM. For countries with missing or implausible reported consumption, the expected consumption per capita is then predicted from the GDP per capita with this regression and multiplied by population to get total consumption. Production is then, derived from consumption, assuming that the trade data are correct and recognizing that production cannot be negative: Pij = max(0, Cij + eij − iij)

[4.9]

where Pij = estimated production for country i and product j, Cij = estimated consumption, and eij, iij = reported exports and imports, respectively. For fuelwood and “other industrial roundwood,” which are not processed, there is no further calculation. For other products, the second step determines how wood and other fiber are used in making final products, such as sawnwood, or in making first an intermediate product (e.g., chemical pulp) and then a final product, such as newsprint. Estimating Input–Output Coefficients This second step estimates the wood and fiber utilization accounts of each country and the corresponding input–output coefficients. The objective is to obtain input–output coefficients that require the least adjustment of the production data while staying near prior knowledge of the technology (e.g., as held by experts). The method uses production data, reported or estimated from Equation [4.9], reported import and export data, and bounds on I–O coefficients and other constraints based on prior knowledge. The equations follow the material flow in Figure 4.1. Industrial roundwood is the input for mechanical pulp, chemical pulp, sawnwood, plywood, particleboard, and fiberboard. Then mechanical pulp and chemical pulp, together with other fiber pulp, and waste paper are inputs in newsprint, printing and writing paper, and other paper and paperboard. The final products are sawnwood, plywood, particleboard, fiberboard, newsprint, printing and writing paper, and other paper and paperboard. Pulp and paper products could be partly recovered after consumption to be reused as fiber intput. The model equations (Table 4.7) and the variables and parameters (Table 4.8) refer to one country and one year. Lowercase letters are reported data, production data estimated in the first step, or bounds on I–O coefficients and other constants. Uppercase letters are choice variables, all nonnegative, or variables that depend on the choice variables. The first term of Objective Function [4.10] in Table 4.7 is the sum of the absolute difference between estimated and reported production, weighted by the price of the products. The second term is the sum of the absolute difference between estimated inputs and inputs implied by prior input–output coefficients,

70 TABLE 4.7

The Global Forest Products Model Goal-Programming Model to Estimate I–O Coefficients in a Year and Country

Objective function: min Z = β

∑ (P

+ x

∑ ∑(C

+ Px− )w x + (1 − β )

x∈I ∪O

+ mn

− + Cmn )wmn

[4.10]

m ∈I n ∈O

Estimated production of inputs: Pm =

∑C

mn

+ e m − im

m ∈I

[4.11]

n ∈O

Expected input m in product n: Cmn =

1 L (a + aUmn )Pn 2 mn

m ∈ I, n ∈O

[4.12]

x ∈I ∪ O

[4.13]

Difference between estimated and reported production: Px − p x + Px− − Px+ = 0 Deviation of estimated from expected input: − + Cmn − Cmn + Cmn − Cmn =0

m ∈ I, n ∈O

[4.14]

Feasible range of input m for output n: L Pn amn ≤ Cmn ≤ Pn aUmn

m ∈ I, n ∈O

[4.15]

Feasible range of total inputs for output n: Pn anL ≤

∑C

mn

≤ Pn aUn

n ∈O

[4.16]

m ∈I

Feasible postconsumer recovery: (CNews + CPrint + COther)r L ≤ PWaste ≤ (CNews + CPrint + COther)rU

[4.17]

Implicit input–output coefficients: amn =

Cmn Pn

m ∈ I, n ∈O

[4.18]

weighted by the geometric mean of the price of the input and output. The weights reflect the assumption that the data, be they official statistics or technical knowledge, tend to be more accurate for higher-priced products. The parameter 0 ≤ β ≤ 1 marks the relative importance of getting estimates near the reported production or near the amounts implied by the prior I–O


71

Variables and Parameters Used in the Goal-Programming Model

Input data I O β wx wmn px , ix , ex L amn , aUmn anL , aUn rL, rU

Set of inputs {Round, Mech, Chem, Ofib, Waste}1 Set of outputs {Mech, Chem, Sawn, Ply, Part, Fib, News, Print, Opap}1 Weight of official data vs. prior input–output data Price of product x Geometric mean of the price of input m and output n Reported data on production, imports, and exports Lower, upper bounds on prior input–output coefficient Lower, upper bounds on prior total input per unit of output n Lower, upper bounds on prior postconsumer recovery rate

Choice variables, all nonnegative Pn Estimated production of output n Estimated production above reported production of product x Px+ Estimated production below reported production of product x Px− Cmn Estimated input m in output n + Estimated input above input implied by prior input–output coefficients Cmn − Cmn Estimated input below input implied by prior input–output coefficients Other variables Pm Cmn 1

Estimated production of input m Expected input m in output n, with average prior input–output coefficients

Round = industrial roundwood, Sawn = sawnwood, Ply = plywood, Part = particleboard, Fib = fiberboard, Mech = mechanical pulp, Chem = chemical pulp, Ofib = other fiber pulp, Waste = wastepaper, News = newsprint, Print = printing and writing paper, Opap = other paper and paperboard.

coefficients. Setting β = 0 means that estimated production should be consistent with the expected prior I–O coefficients, regardless of the reported production. Setting β = 1 means that estimated production should be as close as possible to the reported production, within the bounds set by prior knowledge. Adjusting the choice variables minimizes this objective function, subject to the following constraints. Equation [4.11] in Table 4.7 estimates the production of each input as the sum of the estimates of that input in all outputs, plus net trade (assumed to be known exactly). Equation [4.12] computes the expected input for a given output. It assumes that the prior I–O coefficients are random variables distributed symmetrically between a lower and an upper bound so that the expected value of each prior I–O coefficient is equal to the average of its bounds. Equation [4.13] defines the deviation of estimated production from reported production px, which may itself have been estimated from Equation [4.9].

72


Equation [4.14] defines the deviation of estimated input from the expected input implied by the prior input–output coefficient. Constraint [4.15] ensures that the input going to a product, say mechanical pulp to newsprint, is within the range of the prior input–output coefficient defined by experts. Equation [4.16] is similar to Equation [4.15], but the bounds apply to the total amount of different inputs going into a product, such as the total pulp going to newsprint. Constraint [4.17] keeps the production of wastepaper within bounds based on prior knowledge of the postconsumer recovery rate in a country. The model in Table 4.7 is a goal-programming problem, with the objective of minimizing the sum of the weighted deviations from the goals. All equations are linear in the choice variables, and the best solution can be found via linear programming. The solution gives estimates of production, Px, for all inputs and outputs, and estimates of the amount of input going into each intermediate or final output, Cmn. Then each implicit input–output coefficient (amount of input per unit of output) is derived via Equation [4.18]. These are the input–output coefficients of the GFPM. The method was applied to estimate input–output coefficients and related data for the year 1997 for the countries in Table 4.1, with the base-year data described earlier. The prior upper and lower bounds of the input–output coefficients and of the recovery rates of paper were based on the coefficients estimated by a group of experts for a previous study (Zhu et al. 1998). For the I–O coefficients involving industrial roundwood, the bounds suggested that wood utilization was in general less efficient in low-income than in high-income countries (both the lower bound and the upper bound were higher in low-income countries). For the transformation of wood pulp and other fibers to paper and paperboard, the only strong difference between low-income and high-income countries was the much greater possible use of other fiber pulp (from straw, bagasse, etc.) in low-income countries (Buongiorno et al. 2000). Weights wx and wmn were the unit value of world exports, by product. Coefficient β was set at 0.9. This gave major importance to obtaining production estimates near the official data rather than keeping the input–output coefficients near their expected prior value. They could vary freely as long as they stayed within their prior upper and lower bounds. Table 4.9 shows the input–output coefficients of the GFPM for the United States and Ukraine in 1997. The estimated I–O coefficients for the transformation of industrial roundwood into sawnwood, panels, and pulp were systematically higher for Ukraine. The estimated I–O coefficients for the transformation of fiber to paper and paperboard implied that Ukraine used more mechanical pulp and waste paper and less chemical pulp than the United States, in all products.

73

Model Calibration and Validation TABLE 4.9 Estimated Input–Output Coefficients and Manufacturing Cost in the United States and Ukraine in 1997 I–O coefficient Input

Output

U.S.

Ukraine

m3/m3 Industrial roundwood

Sawnwood Plywood Particleboard Fiberboard

1.69 1.41 1.31 1.33

1.91 2.90

Newsprint Printing and writing paper Other paper and paperboard Newsprint Printing and writing paper Other paper and paperboard Newsprint Printing and writing paper Other paper and paperboard Newsprint Printing and writing paper Other paper and paperboard

0.05 0.01 0.09 0.62 0.69 0.55 0.00 0.00 0.00 0.33 0.20 0.36

Chemical pulp

Other fiber pulp

Wastepaper

Ukraine US$/m3

114.56 377.41 101.57 196.62

3.04 4.34

157.42 206.16

0.16 0.20 0.16 0.40 0.33 0.19 0.00 0.00 0.00 0.44 0.37 0.65

251.21 633.86 426.23

37.93 226.31 39.54 152.90

US$/t

t/t Mechanical pulp

U.S.

2.27 2.72 2.23 2.12 m3/t

Mechanical wood pulp Chemical wood pulp

Manufacturing cost

62.96 124.78

US$/t 304.10 748.49 616.75

Manufacturing Costs Each production activity represented with input–output coefficients corresponds to a manufacturing cost mik in objective function [3.1]. This is the cost of all inputs (capital, wages, energy, etc.), excluding the cost of raw materials explicit in the model. This cost was estimated for the base year as the unit value of the output minus the cost of all inputs. Similar to the demand and supply prices, the output and input prices applied to calculate the manufacturing cost are the corresponding net importer and net exporter prices. For example, if a particular country is a net exporter of the output commodity and a net importer of the raw materials, then the export price and import prices are applied to the output and inputs, respectively, to calculate the manufacturing cost for this country. So the manufacturing costs and material costs almost exactly offset output revenues, and net profits are zero, as they should be in a competitive equilibrium. As an example, the input–output coefficients and manufacturing cost for the United States and Ukraine are shown in Table 4.9. For sawnwood, panels, and

74


pulps, the manufacturing costs were lower in Ukraine than in the United States. They were higher for all three paper and paperboard groups.

OTHER PARAMETERS Accelerator Equations In the GFPM, capacity changes are endogenous. They depend on changes in past production and on relative profitability of additional capacity in different countries. From its estimated level in the base year, global capacity growth was predicted, with a distributed-lag function linking capacity change to changes in past production. Then global capacity growth was allocated to different countries according to the shadow price (the marginal value of one unit of additional capacity) revealed by the equilibrium computations, according to Equation [3.10]. The distributed lag parameters in the accelerator model of global capacity change (Equation [3.9]) were based on past studies (Gilless and Buongiorno 1987, Zhu et al. 1998). Trade Inertia Parameters Depending on the application, trade flows in the GFPM may be either between each country and the world market or between individual countries. Table 4.10 shows estimates of import elasticities with respect to GDP for low- and highincome countries, the parameters αijk in Equation [3.13]. For exports, αijk was TABLE 4.10

Estimates of Elasticities of Imports with Respect to GDP

Product Fuelwood Industrial roundwood Sawnwood Plywood/veneer Particleboard Fiberboard Chemical pulp Mechanical pulp Other fiber pulp Recovered paper Newsprint Printing and writing paper Other paper and paperboard 1

High income1

Low income2

−2.26 1.00 0.32 0.73 1.15 0.82 1.00 1.00 1.00 1.00 1.14 1.66 0.94

0.40 1.00 0.46 0.74 0.65 0.82 1.00 1.00 1.00 1.00 1.05 1.11 0.92

Australia, Austria, Belgium-Luxembourg, Canada, Denmark, Finland, France, Ireland, Israel, Italy, Japan, Netherlands, Norway, New Zealand, South Africa, Spain, Sweden, Switzerland, U.K., U.S.A. 2 All other countries.


75

set to zero. After experimenting with different values, the trade inertia parameter for a particular product was set at three times the standard error of the mean percentage change of world imports and exports of that product from 1970 to 1997. Paper Recovery Rates The wastepaper recovery rate is the ratio of wastepaper production in the current year to last year’s paper consumption. Values of the maximum recovery rate, the coefficient wikr in Equation [3.11], were obtained from various sources (FAO 1999d; also see Chapter 5). Table 4.11 shows the estimated recovery rates in 1997 as well as the predicted upper bound on the recovery rates in 2010, used in the base scenario discussed in Chapter 5. Freight Cost The freight rate was estimated from the difference between world import (c.i.f.) and export (f.o.b.) unit values (Turner and Buongiorno 2001). It was assumed that the difference between f.o.b. and c.i.f. prices approximates the cost (including insurance to ship goods in international trade) (Weeks and Wisdom 1987). Freight rates estimated from trade data were expressed as a percentage of the import (c.i.f.) unit value of a commodity. This percentage is also referred to as the “freight factor.” Aggregate world export and import unit values from 1961 to 1998 were derived from the FAO forest product import/export volume and value data. The following average freight factor estimates were obtained: 20% of the import value for industrial roundwood, 17% for wastepaper, 12% for sawnwood, 8% for wood pulp, 7% for other paper and paperboard, 4% for newsprint, and 4% for wood-based panels. These freight factors were applied to the world import prices for each commodity to get the freight costs in Table 4.3. The freight costs were assumed to remain constant in real terms throughout the projection period.

4.5 MODEL VALIDATION Model validation may mean different things to different people. Kleindorfer et al. (1998) discuss the philosophical aspects of model validation. An extreme objectivist believes that validation is an algorithmic process, while an extreme relativist believes that validation is a matter of opinion. They conclude that model builders should increase their model credibility through any reasonable means. For example, communication with the client, judging the results by practical wisdom, and referees of journal articles. Rykiel (1996) states, “Validation means that a model is acceptable for its intended use because it meets specified performance requirements.” Although

76


TABLE 4.11

Estimated Maximum Wastepaper Recovery Rates in 1997 and 2010 (%)

Country AFRICA Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Congo Côte d’Ivoire Djibouti Egypt Equatorial Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Libyan Arab Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Niger Nigeria Reunion Rwanda Sao Tome and Principe Senegal Sierra Leone Somalia South Africa Sudan Swaziland Tanzania, United Republic

1997

2010

17 0 0 0 0 0 9 0 0 0 34 9 0 29 0 15 0 0 0 0 0 34 0 0 26 10 0 0 0 25 15 0 24 0 0 0 0 0 0 36 50 0 8 0

43 26 26 26 26 26 35 26 26 26 47 35 26 55 26 41 26 26 26 26 26 47 26 26 52 36 26 26 26 51 41 26 50 26 26 26 26 26 26 49 63 26 34 26

Country

1997

2010

Togo Tunisia Uganda Zaire Zambia Zimbabwe

15 23 0 20 26 17

41 49 26 46 52 43

NORTH/CENTRAL AMERICA Bahamas Barbados Belize Canada Cayman Costa Rica Cuba Dominica Dominican El Salvador Guatemala Haiti Honduras Jamaica Martinique Mexico Netherlands Nicaragua Panama Saint Vincent Trinidad United States

0 0 0 44 0 4 50 0 11 20 13 0 29 21 40 23 0 0 12 0 8 44

26 26 26 64 26 30 63 26 37 46 39 26 55 47 53 49 26 26 38 26 34 52

SOUTH AMERICA Argentina Bolivia Brazil Chile Colombia Ecuador French Guiana Guyana Paraguay Peru Suriname Uruguay Venezuela

31 0 38 28 34 17 0 0 60 30 0 37 36

44 26 51 54 47 43 26 26 60 43 26 50 49 (continues)

77

Model Calibration and Validation TABLE 4.11 (continued) Country

1997

2010

ASIA Afghanistan Bahrain Bangladesh Bhutan Brunei Cambodia China Cyprus Hong Kong India Indonesia Iran Iraq Israel Japan Jordan Korea, DPR Korea, Rep. Kuwait Laos Lebanon Macau Malaysia Mongolia Myanmar Nepal Oman Pakistan Philippines Qatar Saudi Arabia Singapore Sri Lanka Syrian Arab Thailand Turkey United Arab Emirates Vietnam Yemen, Rep.

0 0 0 0 0 0 35 21 32 14 30 14 66 20 49 13 9 55 35 0 13 28 0 23 40 0 36 11 0 18 44 16 0 32 38 0 38 0 0

26 26 26 26 26 26 41.5 47 45 40 43 40 66 46 62 39 35 55 48 26 39 54 26 49 53 26 49 37 26 44 57 42 26 45 51 26 51 26 26

OCEANIA Australia Cook Island Fiji

48 0 0

54.5 26 26

Country

1997

2010

French Polynesia New Caledonia New Zealand Papua New Guinea Samoa Solomon Islands Tonga Vanuatu

0 50 22 0 0 0 0 0

26 63 48 26 26 26 26 26

EUROPE Albania Austria Belgium Bosnia-Herzegouina Bulgaria Croatia Czech Republic Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Macedonia Malta Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland United Kingd Yugoslav FR

48 65 51 0 57 54 47 38 51 43 68 36 44 0 41 40 31 0 61 70 31 34 41 26 60 40 65 65 40 21

61 65 64 26 57 54 60 58 64 63 75 49 57 26 54 60 44 26 61 70 44 47 54 52 60 53 72 72 53 47

FORMER USSR Armenia Azerbaijan Belarus

0 0 54

26 26 54 (continues)

78


TABLE 4.11 (continued) Country Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania

1997

2010

11 0 0 0 30 55

37 26 26 26 43 55

Country Moldova, Republic Russian Federation Tajikistan Turkmenistan Ukraine Uzbekistan

1997

2010

0 36 0 0 34 0

26 49 26 26 47 26

Rykiel is thinking specifically of ecological models, this general philosophy seems useful for economic models as well. So before undertaking a model validation, one must specify the purpose of the model, the performance criteria, and the model context. To validate the model, we need to test the model operation, theory, and data. Forrester (1980) argues against traditional objectivist validation for economic models. He remarks, “It even seems to be a struggle to achieve from an econometric model a forecast that is substantially better than the naive process of simply saying the future will be a continuation of the past.” In Forrester’s opinion, forecasting in not an appropriate or valid test of a model and we should examine whether the model behaves reasonably with different policies within the model context. As stated earlier, the PELPS framework, which forms the basis of the GFPM, has been applied in many studies and has been evolving for more than two decades. The demand and acceptance of studies based on the PELPS represents by itself a validation of the model. There have also been explicit validations of the PELPS framework in the objectivist style, such as Zhang (1992). The particular application and extension of the PELPS methodology, the GFPM described here, emerged in 1996 and has been applied in several studies and by different organizations (see Section 1.3). These applications of the GFPM are also part of the validation process. Tomberlin (1999) explicitly validated an earlier version of the GFPM for countries in the Asia-Pacific region. In the base year, imports, exports, and capacity are fixed at their observed levels. Furthermore, all the supply curves for primary products and the demand curves for end products go through the base-year price-quantity observation. Furthermore, the input–output coefficients and manufacturing costs obtained with the method described earlier are fully consistent with equilibrium in the base year. Thus, the solution of the model in the base year should be almost identical to the observed quantities (production, trade, and consumption) and corresponding prices, for all products and countries. Thus, comparison of the base-year solution with the actual data gives a partial validation of the model and of its parameters.

79


One of the main purposes of the GFPM is to compare results in different scenarios to assess the likely magnitude of changes in forest sector variables resulting from policy and management decisions, for the world or large regions. Therefore, the reasonable behavior of long-run trends for aggregated regions is more important than the yearly fluctuation for an individual country. The model’s reasonableness was tested in part by simulating the global forest sector from 1980 to 1994 with 14 commodities and 162 countries (fewer than the countries in Table 4.1 because of the political changes that took place since 1980, leading to more countries). The simulations were done conditional on known exogenous changes (GDP growth rates and roundwood supply shift rates) between 1980 and 1994. Then, the model projections for each year were compared with the actual consumption, production, imports, exports, net trade, and prices. The validation model was started with 1980 as the base year, while the parameters listed in this chapter, such as demand elasticities, supply elasticities, capacity expansion rates, and trade inertia, stayed the same. However, some of the input–output parameters were changed to reflect the heavy increase in the use of recycled paper during that validation period; wastepaper utilization rates and tariff rates were also changed to reflect the trends in that period. The rates of shift of the roundwood supply equations for each country from 1980 to 1994 were estimated as the yearly rates of change of industrial roundwood production. This corresponds well to the definition of the rates of shifts of supply in the GFPM since real industrial roundwood prices have changed little from 1983 to 1994 (Figure 2.9). For maximum wastepaper recovery rates, exogenous technological changes, and tariff rates changes, smooth estimated trends based on historical data and international agreements (Tokyo Round under GATT) were used because of a lack of yearly data. The model’s performance over the historical period was judged in two ways. One was a visual comparison of model prediction and historical results via graphs. Another was a quantitative comparison of forecasts and observations with the mean absolute relative error (MARE) for the aggregated regions: E=

1 n

∑ t

Pt − At At

[4.9]

where E is the mean absolute relative error, n is the number of observations, Pt is the prediction for year t, and At is the actual observation for year t. To simplify comparisons, products were aggregated into five groups, and countries were aggregated into seven regions. Figures 4.2–4.11 show the actual and predicted data for consumption and net trade for industrial roundwood, sawnwood, wood-based panels, wood pulp, and paper and paperboard from 1980 to 1994. Figures 4.12–4.14 show the actual and predicted prices for eight end products.

80

The Global Forest Products Model Actual (1980 - 1994)

Projections (1980 - 1994)

Thousand cubic meters

2000000

1600000

1200000

800000

400000

0 1980

1985

1990

1980

1985

1990

Year Africa

North/Central America

South America

Asia

Oceania

Europe

Former USSR

FIGURE 4.2 Observed and predicted industrial roundwood consumption.

Actual (1980 - 1994)


45000


30000 15000 0 1980

1985

1990

1980

1985

1990

−15000 −30000 −45000 −60000 −75000

Year Africa


South America

Asia

Oceania

Europe

FIGURE 4.3 Observed and predicted net trade of industrial roundwood.

Former USSR

81

Model Calibration and Validation Actual (1980 - 1994)


600000


500000

400000

300000

200000

100000

0 1980

1985

1990

1980

1985

1990

Year Africa


South America

Asia

Oceania

Europe

Former USSR

FIGURE 4.4 Observed and predicted sawnwood consumption.

Actual (1980 - 1994)


30000


20000

10000

0 1980

1985

1980

1990

1985

1990

−10000

−20000

−30000

Year Africa


South America

Asia

Oceania

Europe

FIGURE 4.5 Observed and predicted net trade of sawnwood.

Former USSR

82




150000

100000

50000

0 Year

1984

1989

1994

1984

1989

Year Africa


South America

Asia

Oceania

Europe

Former USSR

FIGURE 4.6 Observed and predicted consumption of wood-based panels.

Actual (1980 - 1994)


6000


4000

2000

0 Year

1984

1989

1994

1984

1989

−2000

−4000

−6000

Year Africa


South America

Asia

Oceania

Europe

FIGURE 4.7 Observed and predicted net trade of wood-based panels.

Former USSR

83

Model Calibration and Validation Actual (1980 - 1994)


200000

Thousand metric tons

150000

100000

50000

0 Year

1984

1989

1994

1984

1989

Year Africa


South America

Asia

Oceania

Europe

Former USSR

FIGURE 4.8 Observed and predicted consumption of wood pulp.

Actual (1980 - 1994)


15000


10000

5000

0 Year

1984

1989

1994

1984

1989

−5000

−10000

−15000

Year Africa


South America

Asia

Oceania

Europe

FIGURE 4.9 Observed and predicted net trade of wood pulp.

Former USSR

84



250000


200000

150000

100000

50000

0 1980

1985

1990

1980

1985

1990

Year Africa


South America

Asia

Oceania

Europe

Former USSR

FIGURE 4.10 Observed and predicted consumption of paper and paperboard.

Actual (1980 - 1994)


7500


5000 2500

0 1980

1985

1990

1980

1985

1990

−2500

−5000

−7500

−10000

Year Africa


South America

Asia

Oceania

Europe

Former USSR

FIGURE 4.11 Observed and predicted net trade of paper and paperboard.

85

Model Calibration and Validation Projections (1980 - 1994)

Actual (1980 - 1994)


500

400

300

200

100

0 1980

1985

1990

1980

1985

1990


Sawnwood & sleepers

FIGURE 4.12 Observed and predicted world prices of roundwood and sawnwood.

Actual (1980 - 1994)



1200

1000

800

600

400

200

0 1980

1985

1990

1980

1985

1990


Particle board

Fibreboard

FIGURE 4.13 Observed and predicted world prices of wood-based panels.

86




2000

1600

1200

800

400

0 1980

1985

1990

1980

1985

1990

Year Newsprint



FIGURE 4.14 Observed and predicted world prices of paper and paperboard.

According to the figures, the projected consumption followed the general trend of actual consumption at regional and world levels but not its cycles. The projected net trade also parallels the trend of actual net trade but with less accuracy in the details. For example, the predicted net trade of industrial roundwood is too flat for Asia, due to the large export reduction from Indonesia and Malaysia. These two countries implemented a roundwood export ban in the early 1990s, and no attempt was made to replicate this policy in the validation model. The trends of projected prices followed the actual prices for all commodities. For example, similar to the actual prices, the projected sawnwood price paralleled the projected price of industrial roundwood, the major input for sawnwood production. However, the projected prices were generally higher than actual prices. The reason appears to be that the projected industrial roundwood price did not drop as much as the actual price, and this affected the prices of all downstream products. The decrease in the industrial roundwood price in the 1980s might also have been caused by some factors not considered in the validation model, such as technological advance, higher yields from the same raw materials, economies of scale, and substitution between wood and other materials (Zhang et al. 1996). The validation model considered only roundwood supply shifts and substitution between wood pulp and wastepaper. Further, the actual roundwood supply shift rates may have been larger than the rates of change in actual production used to shift roundwood supply in the validation runs.

87

Model Calibration and Validation TABLE 4.12 Mean Absolute Relative Errors (%) for the Validation Model Projections of Major Forest Products (1980–1994) Commodity

Region

Consumption

Production

Industrial roundwood

Import

Export

Africa North/Central America South America Asia Oceania Europe World

15.1 16.1 16.1 4.7 14.1 6.6 5.9

12.6 14.8 17.6 5.6 10.5 6.3 6.0

44.8 44.4 147.2 27.2 38.1 23.6 26.4

21.4 26.4 50.9 97.1 9.9 12.1 26.7

Sawnwood


7.6 11.6 7.7 4.0 4.9 12.4 5.9

4.0 11.3 5.1 4.0 4.7 12.9 5.8

15.1 11.8 150.0 18.8 33.0 19.7 10.6

15.8 7.3 65.2 17.3 43.9 24.5 10.1

Wood-based panels


21.7 7.3 9.0 8.0 5.2 8.8 2.8

11.8 6.1 4.0 6.6 9.7 11.9 2.9

72.5 21.4 198.0 18.8 39.3 12.7 5.0

35.6 22.7 11.4 13.6 42.3 26.0 4.8

Wood pulp


24.2 7.7 10.7 13.9 6.0 5.1 5.7

23.7 6.9 10.9 17.0 17.8 6.9 5.5

54.1 7.0 8.9 9.9 79.0 3.5 3.2

29.2 3.9 14.2 49.8 17.5 7.8 4.2



11.9 3.4 6.3 4.2 9.7 2.3 1.7

19.0 6.4 11.1 7.1 5.6 2.5 1.7

8.7 16.1 13.0 14.4 34.0 9.4 5.0

105.1 30.1 58.4 23.4 18.2 7.3 4.2

The MARE data in Table 4.12 show the magnitude of the errors during the validation period. Generally, the projections of consumption and production were more accurate that those of trade, and the world projections were more accurate than regional projections. The world consumption and production had MAREs of less than 6%. The MARE of world imports and exports was less

88


than 10%, except for industrial roundwood, due to the log trade restrictions not considered in the validation model. At the regional level, the projections were most accurate for Europe and North/Central America. Across commodities, the projections tended to be more accurate for processed commodities than for raw materials. Some of the projection errors were quite large; for example, the MARE of projected import of wood-based panels in South America was 198%. However, this sort of quantitative validation is not necessarily the only one or the best (Rykiel 1996, Kleindorfer et al. 1998). Thus, the MARE numbers and the visual comparisons done here are only indicative of the model validity. This validation by replicating past observations suggests that the GFPM projections are at best reasonable in terms of general trends but by no means in terms of annual detail. The validity of the GFPM was also tested by using it in extreme conditions. These included applications of the model to a single country, to two countries, removing trade, production, or consumption activities, and applying extreme exogenous shocks (such as sharp supply drops) and judging the consistency of the results with theoretical knowledge of competitive markets. The ability of the GFPM to give acceptable predictions of the general trends in production, consumption, trade, and prices over long time periods, combined with the agreement of the model predictions with a priori expectations in various conditions, suggests that the model could be used to address the questions asked in the following chapters, but the results must be viewed with strong caveats regarding their precision.

P AR T

GFPM Applications

III


CHAPTER

5

The Base Scenario

5.1 INTRODUCTION One main purpose of the GFPM is to compare results from different scenarios to assess the likely magnitude of changes in forest sector variables resulting from policy, macroeconomic shocks, or management decisions. The next chapters present applications of the GFPM obtained by changing some of its exogenous assumptions and analyzing their implications. The base scenario presented in this chapter gives detailed projections for all forest products from 1997 to 2010, with the assumptions listed in the next section. The purpose of this base scenario is to provide a common base of comparison for the three scenarios to be studied in Chapters 6, 7, and 8. The other two applications used slightly modified versions of the GFPM to represent multilateral trade (Chapter 9) and to model timber supply in more detail (Chapter 10).

5.2 MODEL DATA, PARAMETERS, AND ASSUMPTIONS1 The base-year (1997) data on production, imports, and exports and prices for the 180 countries and 14 products were established as described in Chapter 4, based on FAO statistical database data (FAO 1999a). Most parameters were the 1

The data for the base scenario, and the GFPM software, are available at: www.forest.wisc.edu/ facstaff/buongiorno/book/index.htm

91

92


same as those discussed in Chapter 4, including the base-year quantities and prices, the price and GDP elasticities of demand for end products, price elasticities of supply for primary products, manufacturing parameters, and other parameters. The parameters that changed according to assumptions specific to this scenario are discussed in the following subsections.

GDP GROWTH RATES The assumptions on the GDP growth rate of each country are shown in Table 5.1. These projections were based on World Bank projections, supplemented by FAO Economic and Statistics Services projections made in the year 2000. Real GDP was assumed to grow from about 3.32% to 3.95% per year from 1997 to 2010 in Africa, to shrink from 3.58% to 2.81% per year in North/Central America, and to grow from 1.60% to 4.10% per year in South America, from 2.20% to 3.60% in Asia, from 3.60% to 3.45% in Oceania, from 2.45% to 2.85% in Europe, and from 0.01% to 3.31% per year in the former USSR. In chapter 6, this assumption will be changed to study how the effect of the Asian economic crisis on these rates of economic growth has affected forest sector production and trade.

SHIFT RATES OF ROUNDWOOD SUPPLY The rates of shift of the supply curves (changes at constant price) for industrial roundwood relied mostly on the data of the Global Fiber Supply Model (GFSM) (FAO 1998), supplemented by other studies (Whiteman 2000). The shift rate is the annual percentage change in the “commercially available wood supply” projected by the GFSM. The resulting aggregate average rates of shift of industrial roundwood supply, by region, were 0.77% in Africa, 0.69% in North/Central America, 1.16% in South America, 1.71% in Asia, 1.05% in Oceania, 0.63% in Europe, and 1.46% in the former USSR. The detailed rates of shift used in the base scenario are shown in Table 5.2.

INPUT–OUTPUT COEFFICIENTS AND MANUFACTURING COST The technology, defined by input–output coefficients and associated manufacturing costs, was held constant at the 1997 level, as detailed in Chapter 4, except for paper and paperboard. It was assumed that the amount of recycled paper used in manufacturing paper and paperboard would increase gradually

93

The Base Scenario TABLE 5.1

Growth Rate of Real GDP, % per Year

AFRICA Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Congo Côte d’Ivoire Djibouti Egypt Equatorial Guinea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Libyan Arab Jamahiriya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Niger Nigeria Reunion Rwanda Sao Tome and Principe Senegal Sierra Leone Somalia South Africa Sudan Swaziland Tanzania, UR Togo Tunisia Uganda

1998– 2000

2001– 2005

2006– 2010

3.32 3.58 2.45 5.21 6.00 5.43 1.75 4.46 3.72 4.21

3.83 3.58 3.30 5.00 5.61 5.32 4.36 5.26 3.50 4.84

3.95 3.70 3.30 5.00 5.76 5.31 4.75 5.30 3.50 4.84

3.77 2.92 5.44 1.21 5.09 2.15 3.37 2.50 4.46 4.24 3.88 −5.22 2.26 1.70 1.71 2.51

4.00 3.48 4.37 3.02 4.60 4.26 4.56 3.60 4.63 4.21 3.78 3.52 3.97 5.15 1.68 3.00

4.54 3.97 4.25 2.79 4.75 4.26 4.79 4.26 4.53 4.07 3.78 3.52 4.15 5.00 2.34 3.00

3.47 5.42 4.36 3.90 4.16 2.86 6.29 4.56 2.66 6.00 9.80 2.85 5.34 −1.29 1.89 1.31 3.43 2.59 4.08 3.93 5.44 5.63

3.14 5.30 4.23 3.59 4.41 5.00 5.50 4.23 3.31 5.00 5.09 4.28 4.81 3.81 1.11 2.60 2.22 3.04 4.38 5.16 5.27 5.37

3.14 5.06 4.22 3.41 4.51 4.41 5.50 4.19 3.56 5.00 4.84 4.30 4.80 3.54 1.33 3.00 2.23 4.18 4.39 5.19 4.95 5.26

1998– 2000

2001– 2005

2006– 2010

Zaire Zambia Zimbabwe

5.00 2.21 2.23

5.00 3.41 3.55

5.00 3.44 3.60

NORTH/CENTRAL AMERICA Bahamas Barbados Belize Canada Cayman Islands Costa Rica Cuba Dominica Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Martinique Mexico Netherlands Antilles Nicaragua Panama Saint Vincent Trinidad and Tobago United States

3.58

2.71

2.81

2.51 3.80 5.02 3.09 4.00 4.18 2.51 2.51 6.56 2.57 4.10 2.71 2.09 −0.87 2.00 4.45 1.25 3.73 3.84 3.76 3.75 3.57

3.00 3.50 4.50 2.77 4.50 3.63 3.50 3.00 5.03 4.67 3.67 3.07 4.14 1.97 2.00 3.98 1.00 3.59 4.89 3.50 3.64 2.63

3.00 3.50 4.50 2.98 4.50 3.67 3.50 3.00 4.99 5.11 3.70 3.06 4.32 1.99 2.00 4.19 1.00 3.72 5.01 3.50 3.52 2.72


1.60 2.25 4.17 1.50 3.60 0.47 −0.61 1.50 2.67 0.09 3.16 3.75 2.49 −0.99

4.04 4.03 4.64 3.96 5.75 4.65 3.38 1.50 3.22 3.09 4.86 2.00 3.00 2.60

4.10 4.09 4.64 4.02 5.75 4.71 3.31 1.50 3.41 3.08 4.98 2.00 3.02 2.61

ASIA Afghanistan Bahrain Bangladesh Bhutan Brunei Darussalam Cambodia China

2.21 2.51 1.52 4.85 5.00 2.51 7.55 7.53

3.41 2.50 2.50 5.73 5.00 2.00 6.00 7.03

3.60 2.50 2.50 5.89 5.00 2.00 6.00 7.01

(continues)

94 TABLE 5.1

The Global Forest Products Model (continued) 1998– 2000

2001– 2005

2006– 2010

3.58 0.63 5.69 −1.12 2.38 13.08 2.23 0.45 1.50 2.51 3.49 0.99 6.29 8.83 6.92 2.27 2.50 3.83 2.14 2.58 3.40 2.67 1.00 1.13 6.88 4.76 0.09 −0.63 3.05 0.77 7.56 3.24

3.00 5.00 5.19 4.65 3.52 4.60 4.90 1.70 3.35 2.00 5.30 2.04 5.99 8.00 6.00 5.96 3.00 4.58 4.02 3.41 4.36 4.82 3.00 2.42 6.44 5.91 3.77 5.35 5.02 2.70 6.50 3.21

3.00 5.50 5.00 4.90 3.59 4.50 5.00 1.99 3.20 2.00 5.50 2.00 6.00 8.00 6.00 6.25 3.00 4.84 3.97 3.41 4.65 5.28 3.00 2.70 6.44 6.23 3.97 5.00 5.25 3.00 6.50 3.18

OCEANIA Australia Cook Island Fiji French Polynesia New Caledonia New Zealand Papua New Guinea Samoa Solomon Islands Tonga Vanuatu

3.62 3.87 2.00 0.67 4.39 1.25 2.00 2.08 1.50 6.29 3.00 1.88

3.41 3.43 3.00 4.64 4.00 1.50 3.10 3.83 1.50 5.50 3.50 2.00

3.45 3.49 3.00 5.00 4.00 1.50 3.00 4.28 1.50 5.50 3.50 2.00

EUROPE Albania

2.45 1.25

2.82 1.50

2.85 1.50

Cyprus Hong Kong India Indonesia Iran, Islm Rep Iraq Israel Japan Jordan Korea, DPR Korea, REP. Kuwait Laos Lebanon Macau Malaysia Mongolia Myanmar Nepal Oman Pakistan Philippines Qatar Saudi Arabia Singapore Sri Lanka Syrian Arab Republic Thailand Turkey United Arab Emirates Viet Nam Yemen

1998– 2000

2001– 2005

2006– 2010

Austria Belgium Bosnia-Herzegovina Bulgaria Croatia Czech Republic Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Macedonia Malta Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland United Kingdom Yugoslav Federal Republic

2.60 2.41 3.53 −0.78 3.41 2.68 2.36 4.34 2.58 1.95 3.30 4.13 4.22 8.58 1.58 −0.15 2.45 3.22 2.51 5.07 3.44 −3.36 2.68 3.40 3.59 2.72 1.58 2.34 1.68

2.68 2.50 4.41 3.85 4.41 2.83 2.34 3.40 2.59 2.75 3.30 4.58 2.60 6.62 2.66 1.93 4.04 2.90 3.09 5.62 3.04 3.24 2.83 4.41 3.48 2.62 1.82 2.60 2.92

2.70 2.50 5.09 4.19 5.09 1.14 2.40 3.40 2.58 2.79 3.00 4.54 2.70 6.70 2.93 2.60 3.94 2.90 3.23 5.62 2.90 3.35 1.14 5.09 3.60 2.60 1.70 2.57 3.59

FORMER USSR Armenia Azerbaijan Belarus Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania Moldova Rep Russian Federation Tajikistan Turkmenistan Ukraine Uzbekistan

0.01 6.06 7.36 6.86 7.80 12.25 3.43 7.90 6.92 6.83 0.50 −1.12 2.07 1.04 −4.45 2.68

2.83 3.82 8.14 2.83 3.33 4.31 2.34 3.33 3.33 3.33 2.83 2.83 2.34 3.33 1.94 2.83

3.31 3.60 6.05 2.62 3.11 4.09 2.13 3.11 3.11 3.11 2.62 3.60 2.13 3.11 2.62 1.14

Source: Whiteman (2000a) (based on World Bank projections and complemented with FAO Economic and Statistics Service projections, for year 2000).

95

The Base Scenario TABLE 5.2

Roundwood Supply Shift Rates, 1997–2010, % per year

Country AFRICA Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Congo Côte d’Ivoire Djibouti Egypt Equatorial Guinea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Libyan Arab Jamahiriya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Niger Nigeria Reunion Rwanda Sao Tome and Principe Senegal Sierra Leone Somalia South Africa Sudan Swaziland Tanzania, UR Togo Tunisia Uganda

Country 0.77 1.83 0.46 0.00 −0.25 0.00 0.00 2.13 0.00 0.98 0.00 2.57 −0.37 0.00 0.00 −0.13 −0.12 0.08 0.00 −1.10 −1.02 0.23 0.17 0.00 −0.30 3.25 0.39 0.86 0.00 0.00 0.00 1.92 −0.69 0.00 −0.35 0.00 3.01 0.00 0.33 −1.88 0.00 0.09 0.05 2.93 0.52 0.00 3.57 0.42

Zaire Zambia Zimbabwe

1.35 0.61 1.14

NORTH/CENTRAL AMERICA Bahamas Barbados Belize Canada Cayman Islands Costa Rica Cuba Dominica Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Martinique Mexico Netherlands Antilies Nicaragua Panama Saint Vincent Trinidad and Tobago United States

0.69 0.00 0.00 −0.34 1.08 0.00 1.98 3.15 0.00 −1.62 0.00 −2.06 0.00 −2.36 0.00 0.00 −0.81 0.00 −2.32 −2.17 0.00 0.20 0.69


1.16 1.21 1.11 1.23 2.43 0.55 −0.08 2.08 0.64 −0.65 0.52 1.75 0.65 0.76

ASIA Afghanistan Bahrain Bangladesh Bhutan

1.71 −0.48 0.00 1.35 0.71 (continues)

96

The Global Forest Products Model TABLE 5.2

(continued)

Country Brunei Cambodia Cyprus Hong Kong India Indonesia Iran Iraq Israel Japan Jordan Korea, DPR Korea, REP Kuwait Laos Lebanon Macau Malaysia Mongolia Myanmar Nepal Oman Pakistan Philippines Qatar Saudi Arabia Singapore Sri Lanka Syrian Arab Thailand Turkey United Arab Emirates Vietnam Yemen, Republic OCEANIA Australia Cook Island Fiji French Polynesia New Caledonia New Zealand Papua New Guinea Samoa Solomon Islands Tonga Vanuatu Source: Whiteman (2000b).

Country 0.32 −1.44 0.00 0.00 2.35 0.89 2.40 0.00 0.00 1.79 0.00 2.60 5.06 0.00 −0.86 0.00 0.00 −0.98 0.00 −1.13 −0.86 0.00 −2.67 −2.71 0.00 0.00 0.00 2.23 8.95 1.20 2.05 0.00 1.86 0.00 1.05 0.34 0.00 4.15 0.00 0.00 2.64 0.85 0.00 0.87 0.00 0.73

EUROPE Albania Austria Belgium Bosnia-Herzegovina Bulgaria Croatia Czech Republic Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Macedonia Malta Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland United Kingdom Yugoslav Federal Republic FORMER USSR Armenia Azerbaijan Belarus Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania Moldova Republic Russian Federation Tajikistan Turkmenistan Ukraine Uzbekistan

0.63 −1.44 0.52 0.00 0.00 1.23 3.17 0.17 0.26 0.87 0.81 0.18 0.00 0.74 0.00 3.34 0.05 0.00 0.00 0.84 1.20 0.11 0.61 0.00 0.10 0.77 1.43 0.78 0.38 2.30 0.00 1.46 0.20 0.20 0.23 3.50 0.20 0.20 0.20 1.61 2.39 2.10 1.45 0.20 0.20 2.10 0.20

The Base Scenario

97

between 1997 and 2010 by extending the historical trends in pulp and recycled paper input to paper and paperboard manufacture and adjusting these trends according to expert opinion, as in FAO (1997a) and Zhu et al. (1998). The assumed increase in the wastepaper utilization rate was high (about 0.70% per year) for countries in the Asia-Pacific; medium (0.35–0.50%) in Europe, South America, the former USSR, and North America; and low (0.20%) in Africa.

TARIFF RATES According to Barbier (1996) and WTO (2001), as a result of the Uruguay Round of the General Agreement on Tariffs and Trade (GATT), major developed countries were committed to reducing tariffs by 50% on solid wood products over five years, starting in 1995, and completely phasing out tariffs on pulp and paper by 2004. For solidwood products (fuelwood, industrial roundwood, sawnwoods, veneer and plywood, particleboard, and fiberboard), the base scenario assumed that tariffs had been reduced by one-sixth from 1995 to 1997 and that they would be reduced further by one-sixth each year in 1998 and 1999. For pulp (mechanical pulp, chemical pulp, other fiber pulp, wastepaper) and paper (newsprint, printing and writing paper, other paper and paperboard), tariffs in year 1997 were reduced by one-seventh each year from 1998 onward, leading to total tariff elimination by 2004. Chapter 7 will present a scenario with different tariff reduction rates to reflect the Accelerated Tariff Liberalization (ATL) initiative.

WASTEPAPER RECOVERY The base scenario deals only with ongoing important technological change: substitution of wastepaper for wood pulp. The medium scenario of Mabee and Pande (1997) and Mabee (1998) projects the world wastepaper recovery rate to be around 50% in 2010, which is similar to the projection by Cesar (1995). For the six regions in the world, the projected results from Mabee (1998) are: Europe increases its recovery rate from 46% in 1995 to 56% in 2010, Asia-Pacific from 40% to 41%, North America from 45% to 62%, South America from 31% to 32%, Africa from 23% to 41%, and the former USSR from 1.6% to 3.2%. In the past, wastepaper has been a much larger share of fiber furnish in developing countries, but administrative requirements and consumer preferences have resulted in increasing wastepaper use in developed countries. In the base scenario, the maximum wastepaper recovery rate is assumed to increase progressively for all of the countries between 1997 and 2010 (Table 4.11). These maximum recovery rates specify the potential wastepaper recovery for a

98


particular country. The recovery rates actually projected by the GFPM may be less than these rates. They depend on the domestic and international demand of wastepaper and each country’s paper production. The maximum recovery rates correspond generally to the medium scenario of Mabee and Pande (1997) and Mabee (1998), with some adjustment based on other studies and the assumption that all countries would converge slowly to the same wastepaper recycling rate because of environmental considerations and economic incentives (Tomberlin 1999). Compared with Mabee and Pande (1997) and Mabee (1998), the assumed maximum recovery rates are generally lower for African countries but higher for the former USSR and South America. For the United States in the base scenario, the maximum wastepaper recovery rate was set around 52% in 2010, up from 44% in 1997. There has been a decrease in recycling legislation in recent years, and the high price of wastepaper in 1994–1995 has diminished the push for more recycling laws. However, legislation favoring recycling is still being proposed and will continue to be proposed (Alig 1993). Still, the U.S. recovery rate (more than 60%2 in 2010) projected in Mabee and Pande (1997) and Mabee (1998) seems unlikely to happen. In Chapter 8, the wastepaper recovery rate and the utilization rate of the United States are assumed to increase toward the level projected in Mabee and Pande (1997) to simulate strong paper recycling policies.

5.3 BASE SCENARIO PROJECTIONS FROM 1997 TO 2010

ROUNDWOOD Total consumption of roundwood (fuelwood and industrial roundwood) in the world has increased almost 50% between 1961 and 1997, from 2.2 billion m3 to nearly 3.4 billion m3. The projections show that the world demand for wood would continue to rise, especially in Asia, where rapidly growing economies are concentrated (Figure 5.1). According to this base projection, the world total roundwood consumption would reach 3.6 billion m3 by the year 2000 and 4.2 billion m3 by 2010, an increase of 8% and 25%, respectively, from the 1997 level. In 1997, Asia and Europe were net importers of roundwood. By the year 2010, North/Central America would still be a net exporter, but barely, while countries of the former USSR would almost double their net exports compared 2

The wastepaper recovery rate used in the GFPM does not include preconsumer scrap. According to the American Forest and Paper Association (AF&PA), which includes preconsumer scrap, the recovery rate was around 45% for the United States in 1997 (Raymond Com. 1999). In contrast, the recovery rate was 38% in 1997, based on FAO statistics ignoring the preconsumer scrap.

99

The Base Scenario


4800000

3600000

2400000

1200000

0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


South America

FIGURE 5.1

Asia

Oceania

Europe

Former USSR

Consumption of roundwood.


50000 25000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

−25000 −50000 −75000

Year Africa


FIGURE 5.2

South America

Asia

Oceania

Europe

Former USSR

Net trade (Exports minus Imports) of roundwood.

to 1997 (Figure 5.2). World imports and exports of roundwood would continue to increase, and the imports of North/Central America would increase 70%, almost as much as the export increase of the former USSR (81%) (Figures 5.3 and 5.4). The world total consumption of fuelwood and charcoal increased 61%, from 1 billion m3 in 1965 to about 1.8 billion m3 in 1997. The projections show that the world demand for fuelwood would continue to rise, especially in Asia and Africa (Figure 5.5). However, in North/Central America, Oceania, and Europe, fuelwood consumption was projected to decrease slightly by 2010. This result is tied mostly to the positive income elasticity of demand for fuelwood in

100



200000 160000 120000 80000 40000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


South America

FIGURE 5.3

Asia

Oceania

Europe

Former USSR

Imports of roundwood.


200000 160000 120000 80000 40000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


South America

FIGURE 5.4

Asia

Oceania

Europe

Former USSR

Exports of roundwood.

low-income countries and to the negative elasticity in high-income countries (see Chapter 4). The world consumption of other industrial roundwood (such as poles, piling, and posts) decreased 25%, from 212 million m3 in 1965 to 160 million m3 in 1997. The projection shows that world consumption would be flat in the next decade, with slight increases in Africa and Asia but slight decreases in other regions (Figure 5.6).

101

The Base Scenario


2500000 2000000 1500000 1000000 500000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


FIGURE 5.5

South America

Asia

Oceania

Europe

Former USSR

Consumption of fuelwood.


250000 200000 150000 100000 50000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


FIGURE 5.6

South America

Asia

Oceania

Europe

Former USSR

Consumption of other industrial roundwood.

Between 1965 and 1997, world consumption of industrial roundwood showed sustained increases. Total consumption was about 1,523 million m3 in 1997, about one-third above the 1965 level. According to the projections, world industrial roundwood consumption would reach 2,007 million m3 by the year 2010, an increase of 31% from the 1997 level (Figure 5.7). World trade would also increase. The trade deficit of Asia would increase from 56 million m3 in 1997 to 70 million m3 in 2010, and the trade deficit of Europe would increase from 26 million m3 to 40 million m3 from 1997 to 2010 (Figure 5.8). Exports of the former USSR and South America were projected to almost

102



2400000 2000000 1600000 1200000 800000 400000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


FIGURE 5.7

South America

Asia

Oceania

Europe

Former USSR

Consumption of industrial roundwood.


50000 25000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

−25000 −50000 −75000

Year Africa


FIGURE 5.8

South America

Asia

Oceania

Europe

Former USSR

Net trade of industrial roundwood.

double, while countries of North/Central America would barely be net exporters in 2010.

SAWNWOOD Sawnwood and sleepers (sawnwood in short) includes coniferous and nonconiferous species. World sawnwood consumption rose slightly, from 383 million m3 in 1965 to 441 million m3 in 1997. In 1990 it reached its peak, 508 million m3, and then dropped sharply to reach a low in 1994 (Figure 5.9).

103

The Base Scenario


600000 500000 400000 300000 200000 100000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


South America

FIGURE 5.9

Asia

Oceania

Europe

Former USSR

Consumption of sawnwood.


40000 30000 20000 10000 0 1965 −10000

1970

1975

1980

1985

1990

1995

2000

2005

2010

−20000 −30000 −40000

Year Africa


South America

FIGURE 5.10

Asia

Oceania

Europe

Former USSR

Net trade of sawnwood.

This was due in large part to the decrease in the consumption of the former USSR, which in 1994 consumed only about one-fourth of its 1990 consumption. This base scenario projected that from 1997 to 2010 global sawnwood consumption would grow from 441 million m3 to 523 million m3. In 1997, the four net exporting regions were the former USSR, North/Central America, South America, and Oceania, in that order. They were projected to continue to be net exporters by 2010 (Figure 5.10). By 2010, North/Central America would increase its surplus with 14 million m3 of net exports. Net exports of Oceania would almost double, while the former USSR would increase

104


its exports slightly and South America would stay at about the 1997 level. Symmetrically, the largest net importer was projected to continue to be Asia, whose net imports would increase slightly to 20 million m3 by 2010, followed by Africa at 5.8 million m3.

WOOD-BASED PANELS From 1965 to 1997, the demand for wood-based panels grew faster than that for any other forest product. World consumption went from 42 million m3 to 154 million m3, with an average growth of 4.1% per year. The 1997 distribution of the world’s consumption, by panel type, was: plywood and veneer 40%, particleboard 45%, and fiberboard 15%. Wood-based panel consumption is expected to grow to 225 million m3 in 2010 (Figure 5.11). From 1965 to 1993, Asia, the former USSR, and South America were net exporters, but Asia became a net importer after 1993. The projections are that by the year 2010, the former USSR and North/Central America would become the largest and the second largest net exporters, respectively, while South America and the Oceania would still be net exporters. Asia would become by far the largest net importer, reaching 4.6 million m3 by 2010, while Europe would nearly balance its trade (Figure 5.12). World import and export of wood-based panels would continue to increase but at slower rates. Asian wood-based panel imports would increase about 29%, while European exports would increase approximately 26% (Figures 5.13 and 5.14).


250000 200000 150000 100000 50000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


FIGURE 5.11

South America

Asia

Oceania

Europe

Consumption of wood-based panels.

Former USSR

105

The Base Scenario


5000 2500 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

−2500 −5000 −7500

Year Africa


South America

FIGURE 5.12

Asia

Oceania

Europe

Former USSR

Net trade of wood-based panels.


80000

60000

40000

20000

0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


FIGURE 5.13

South America

Asia

Oceania

Europe

Former USSR

Imports of wood-based panels.

Veneer and Plywood World consumption of veneer sheets and plywood increased from 27 million m3 in 1965 to 60 million m3 in 1997. The GFPM projections suggested that world consumption would increase from 60 million m3 in 1997 to 84 million m3 in 2010, for an average annual growth rate of 2.6% (Figure 5.15). In 1997, Asia was the largest net exporter and Europe the largest net importer, but by the year 2010, the former USSR and South America were projected to be the largest net exporters of plywood and veneer, and Europe would continue to be the largest net importer (Figure 5.16).

106



80000

60000

40000

20000

0 1965

1970

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Year Africa


South America

Asia

Oceania

Europe

Former USSR

Exports of wood-based panels.

FIGURE 5.14


100000 80000 60000 40000 20000 0 1965

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2010

Year Africa


FIGURE 5.15

South America

Asia

Oceania

Europe

Former USSR

Consumption of veneer sheet and plywood.

Particleboard World consumption of particleboard grew very fast, from 9 million m3 in 1965 to 70 million m3 in 1997. It is projected that world particleboard consumption would continue to increase to 106 million m3 by 2010 (Figure 5.17). Europe switched from being the largest net importer of particleboard in 1989 to being the largest net exporter in 1994. In 1997 Asia was the largest net importer of particleboard (Figure 5.18). The 2010 projections show North/Central America

107

The Base Scenario


5000 4000 3000 2000 1000 0 1965 −1000

1970

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1995

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−2000 −3000 −4000

Year Africa


FIGURE 5.16

South America

Asia

Oceania

Europe

Former USSR

Net trade of veneer sheet and plywood.


120000 105000 90000 75000 60000 45000 30000 15000 0 1965

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2000

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2010

Year Africa


FIGURE 5.17

South America

Asia

Oceania

Europe

Former USSR

Consumption of particleboard.

would be the largest net exporter, at a slightly higher level than that of Europe. Asia would continue to be the largest net importer, and its net imports of particleboard would increase steadily, reaching 1.66 million m3 by 2010. Fiberboard World consumption of fiberboard increased from 6.4 million m3 in 1965 to 23 million m3 in 1997. Asian consumption of fiberboard was the largest of the regions considered in 1997. The projections suggest that world fiberboard consumption

108



2500 2000 1500 1000 500 0 1965 −500

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2010

−1000 −1500 −2000

Year Africa


South America


FIGURE 5.18

Asia

Oceania

Europe

Former USSR

Net trade for particleboard.

35000 28000 21000 14000 7000 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year Africa


FIGURE 5.19

South America

Asia

Oceania

Europe

Former USSR

Consumption of fiberboard.

would increase rapidly, from 23 million m3 in 1997 to 34 million m3 in 2010 (Figure 5.19). By the year 2010, Asia, North/Central America, and Europe would be the three main consuming regions, in that order. The net exporting regions in 1997 (South America, North/Central America, Oceania, Europe, and the former USSR) are projected still to be net exporters in 2010, with Europe becoming the largest net exporter by quadrupling its net export (Figure 5.20). The main net importing region by 2010 would still be Asia, its deficit in fiberboard trade increasing to about 3,877 thousand tons in those 13 years.

109

The Base Scenario


2000 1000 0 1965 −1000

1970

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2010

−2000 −3000 −4000 −5000

Year Africa


South America

FIGURE 5.20

Asia

Oceania

Europe

Former USSR

Net trade of fiberboard.

FIBER FURNISH Total fiber furnish includes all wood pulps, wastepaper, and other fiber pulps. The GFPM projected that world consumption of fiber furnish, which almost tripled from 1965 to 1997, would reach 461 million metric tons by the year 2010, from 289 million metric tons in 1997. Wood Pulps Wood pulps include mechanical pulp and chemical/semichemical pulp. Wood pulp consumption was expected to grow to 212 million metric tons by 2010, from 158 million tons in 1997 (Figure 5.21). In 1997, Asia was the largest net importer and North/Central America was the largest net exporter. The projections show the same rankings for the year 2010 (Figure 5.22). World imports and exports of wood pulp would increase more slowly in the first decade of the 21st century, and the increases in imports in Asia and Europe would come from increased exports in North/Central America (Figures 5.23 and 5.24). Mechanical Pulp From 1997 to 2010, consumption of mechanical pulp is projected to grow fastest in Europe and slower in other regions (Figure 5.25). Oceania, the largest net exporter in 1997, would continue to expand its world market share and become the largest exporter by 2010 (Figure 5.26). North/Central America would be the second largest net exporter. Asia would continue to be by far the major importer and would increase its net imports by 2010 to about 965 thousand metric tons.

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240000

180000

120000

60000

0 1965

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2010

Year Africa


South America

FIGURE 5.21

Asia

Oceania

Europe

Former USSR

Consumption of wood pulp.


15000 10000 5000 0 1965

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1990

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2000

2005

2010

−5000 −10000 −15000

Year Africa


South America

FIGURE 5.22

Asia

Oceania

Europe

Former USSR

Net trade of wood pulp.

Chemical Pulp Chemical and semichemical pulp consumption was projected to increase from 123 million metric tons in 1997 to 155 million metric tons in 2010 (Figure 5.27). North/Central America would remain the largest net exporter of chemical and semichemical pulp. Asia and Europe would still be the largest net importers, and Europe’s net imports would be virtually the same as those of Asia (Figure 5.28).

111

The Base Scenario


50000 40000 30000 20000 10000 0 1965

1970

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2010

Europe

Former USSR

Year Africa


South America

FIGURE 5.23

Asia

Oceania

Imports of wood pulp.


50000 40000 30000 20000 10000 0 1965

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1990

1995

2000

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2010

Year Africa


South America

FIGURE 5.24

Asia

Oceania

Europe

Former USSR

Exports of wood pulp.

Other Fiber Pulp Figure 5.29 shows the projected rapid growth of consumption of other fiber pulp, such as pulp made of straw and bagasse. Other fiber pulp consumption in Asia was projected to approach 28 million metric tons by 2010, though this was less than the sum of mechanical and chemical pulp consumption in the region, which was about 36 million metric tons. The trade for other fiber pulp is relatively small. Europe and Asia would continue to be net importers and North/Central

112



80000 70000 60000 50000 40000 30000 20000 10000 0 1965

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2000

2005

2010

Year Africa


FIGURE 5.25

South America

Asia

Oceania

Europe

Former USSR

Consumption of mechanical pulp.

800


600 400 200 0 1965 −200 −400

1970

1975

1980

1985

1990

1995

2000

2005

2010

−600 −800 −1000 −1200

Year Africa


South America

FIGURE 5.26

Asia

Oceania

Europe

Former USSR

Net trade of mechanical pulp.

America would continue steadily increasing their exports of other fiber pulp (Figure 5.30). Wastepaper According to the GFPM projections, consumption and production of wastepaper would almost double from 1997 to 2010, and Asia, North/Central America, and South America would double their consumption during this period (Figure 5.31). This results from the exogenous assumption of increased

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The Base Scenario


200000

150000

100000

50000

0 1965

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2010

Year Africa


FIGURE 5.27

South America

Asia

Oceania

Europe

Former USSR

Consumption of chemical and semichemical pulp.


15000 10000 5000 0 1965 −5000

1970

1975

1980

1985

1990

1995

2000

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2010

−10000 −15000

Year Africa


FIGURE 5.28

South America

Asia

Oceania

Europe

Former USSR

Net trade of chemical and semichemical pulp.

wastepaper usage in paper manufacturing (input–output coefficients) and the assumed increase in the recovery rates of wastepaper. The GFPM results indicate that Asia would remain the largest net importer while North/Central America would remain by far the major net exporter. The projections suggest that exports from the region would continue to increase rapidly (Figure 5.32). Asia’s net imports would increase by 50% by 2010, and the net surplus of Europe would increase slightly.

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45000 36000 27000 18000 9000 0 1965

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2000

2005

2010

Year Africa


FIGURE 5.29

South America

Asia

Oceania

Europe

Former USSR

Consumption of other fiber pulp.


400

200

0 1961

1966

1971

1976

1981

1986

1991

1996

2001

2006

−200

−400

Year Africa


South America

FIGURE 5.30

Asia

Oceania

Europe

Former USSR

Net trade of other fiber pulp.

PAPER AND PAPERBOARD The paper and paperboard group includes newsprint, printing and writing paper, and other paper and paperboard. Figure 5.33 shows that Asian consumption is projected to exceed that of North/Central America by 2005. This is due essentially to the faster rate of GDP growth for Asia presumed in the scenario. Figure 5.33 also shows that the world consumption of paper and paperboard would be 470 million

115

The Base Scenario


300000 250000 200000 150000 100000 50000 0 1965

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2010

Year Africa


South America

FIGURE 5.31

Asia

Oceania

Europe

Former USSR

Consumption of wastepaper.


15000 10000 5000 0 1965

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2010

−5000 −10000 −15000

Year Africa


South America

FIGURE 5.32

Asia

Oceania

Europe

Former USSR

Net trade of wastepaper.

metric tons by 2010, compared to 294 million metric tons in 1997. Figure 5.34 suggests that Europe would still be the largest net exporter of paper and paperboard by 2010, reaching 16 million tons. Asia’s net imports would more than double in 13 years. World imports and exports of paper and paperboard would continue to increase but with slower rates in the next decade. Asia’s imports would increase about 74% from 1997 to 2010, while the exports of North/ Central America and Europe would increase about 10% during the same period (Figures 5.35 and 5.36).

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500000 400000 300000 200000 100000 0 1965

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Year Africa


South America

Asia

Oceania

Europe

Former USSR

Consumption of paper and paperboard.

FIGURE 5.33


20000 10000 0 1965

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−10000 −20000 −30000

Year Africa


FIGURE 5.34

South America

Asia

Oceania

Europe

Former USSR

Net trade of paper and paperboard.

Newsprint The growth of newsprint consumption would be faster than that during the last decade in all regions, especially in Asia (Figure 5.37). World newsprint consumption would increase from 35 million metric tons in 1997 to 55 million metric tons in 2010. The largest net exporter would still be North/Central America, followed by Europe and the former USSR (Figure 5.38). Asia would remain the largest net importer and is projected to increase its net imports by 72% from 1997 to 2010.

117

The Base Scenario


120000

90000

60000

30000

0 1965

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FIGURE 5.35

South America

Asia

Oceania

Europe

Former USSR

Imports of paper and paperboard.


120000

90000

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30000

0 1965

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Year Africa


FIGURE 5.36

South America

Asia

Oceania

Europe

Former USSR

Exports of paper and paperboard.

Printing and Writing Paper The GFPM predicted that consumption of printing and writing paper would grow fastest in Oceania and Asia. World printing and writing paper consumption would increase from 88 million metric tons in 1997 to 155 million metric tons in 2010, increasing almost 80% in 13 years (Figure 5.39). The position of Europe as a net exporter of printing and writing paper would strengthen. Asia’s trade deficit in printing and writing paper would reach nearly 5.1 million metric tons in 2010 (Figure 5.40).

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70000 60000 50000 40000 30000 20000 10000 0 1965

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South America

FIGURE 5.37

Asia

Oceania

Europe

Former USSR

Consumption of newsprint.


6000 4000 2000 0 1965

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−2000 −4000 −6000

Year Africa


South America

FIGURE 5.38

Asia

Oceania

Europe

Former USSR

Net trade of newsprint.

Other Paper and Paperboard Other paper and paperboard consumption is projected to grow slower than consumption of printing and writing paper and newsprint: Asia would be the region experiencing most of the increase (Figure 5.41). World consumption of other paper and paperboard would increase from 170 million metric tons in 1997 to 259 million metric tons in 2010. Asia would more than double its net imports by 2010. Net exports from North/Central America would continue to grow almost linearly, while net exports from Europe would more than double from their 1997 level (Figure 5.42).

119

The Base Scenario


200000 160000 120000 80000 40000 0 1965

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2010

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FIGURE 5.39

South America

Asia

Oceania

Europe

Former USSR

Consumption of printing and writing paper.


12500

7500

2500 1965 −2500

1970

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2010

−7500

Year Africa


FIGURE 5.40

South America

Asia

Oceania

Europe

Former USSR

Net trade of printing and writing paper.

PRICE TRENDS Sawnwood and Industrial Roundwood Figure 5.43 presents historical trends and projections according to the base scenario for industrial roundwood and sawnwood world prices in 1997 U.S. dollars. In the GFPM, a commodity’s world price was calculated as the average of all countries’ prices, weighted by each country’s corresponding consumption of

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300000 250000 200000 150000 100000 50000 0 1965

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2010

Year Africa


FIGURE 5.41

South America

Asia

Oceania

Europe

Former USSR

Consumption of other paper and paperboard.


15000 10000 5000 0 1965

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2010

−5000 −10000 −15000

Year Africa


FIGURE 5.42

South America

Asia

Oceania

Europe

Former USSR

Net trade of other paper and paperboard.

that commodity. Historically, the price of sawnwood paralleled the price of industrial roundwood, mainly because roundwood was a major part of the total cost of sawmilling, reaching as high as 60–70% (FAO 1997a). The projections show that the relationship between the two prices would remain strong over the next 13 years. The price projections suggest that the real price of industrial roundwood worldwide would increase by $34/m3 over the next decade and would rise to about $125/m3 by 2010. As a consequence of the rising price of

121

The Base Scenario


350 300 250 200 150 100 50 0 1965

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2010


FIGURE 5.43

Sawnwood & sleepers

World prices of sawnwood and industrial roundwood.

industrial roundwood, the price of sawnwood would increase by $76/m3. The price of sawnwood is projected to reach about $312/m3 in 2010. Wood-based Panels The price of wood-based panels fell between 1965 and 1997, as shown in Figure 5.44. This steady fall in price was made possible mostly by a reduction in costs of production (new processes, economies of scale, cheaper raw material from sawmill residues). Among all panel products, the real price of veneer and plywood declined more than that of particleboard or fiberboard. The projections show that the real prices of veneer and plywood, particleboard, and fiberboard should increase slightly during by 2010, almost in parallel with the increasing price of industrial roundwood (Figure 5.43). Pulp and Paper For the pulp and paper sector, wood costs account for a smaller share of total costs, notably because of high capital costs. However, because the technology is broadly similar all over the world, wood cost is one of the means by which individual companies seek to gain a comparative advantage. Therefore, to some extent, the price trend of paper and paperboard also followed that of industrial roundwood during the last three decades (Figure 5.45). The strong rise after 1973 was due to high energy costs that subsequently declined. The prices of paper and paperboard projected with the GFPM under the base scenario continue

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900 800 700 600 500 400 300 200 100 0 1965

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2005

2010


FIGURE 5.44

Particle board

Fibreboard

World prices of wood-based panels.

to increase steadily from 1997 to 2010. By 2010, the world paper and paperboard prices would recover to their 1990 level. The projected price trend corresponds to the raw material price trend, dampened by the increased use of wastepaper and other fiber pulp in paper and paperboard production worldwide and in Asia particularly.

PAPER RECYCLING TRENDS The recycling trends projected by the GFPM result in part from exogenous assumptions regarding technical change (the substitution of wastepaper for wood fiber in paper manufacturing), the exogenous assumptions in maximum recovery rates of wastepaper in each country, and endogenous calculations determining the competitive advantage of each country within these constraints. Recovery Rates Figure 5.46 shows the historical and projected wastepaper recovery rates (ratio of recovered to consumed paper) for the seven regions and the world. In 1997, the world recovery rate was about 39%, highest in Europe, at 46%, and lowest in Africa, at 26%. According to the base scenario, the projected world recovery rate would be 49% in 2010, and Europe would still have the highest rate, 55%. Africa’s recovery rate would still be the lowest but would increase to 32% by 2010. North/Central America would increase their recovery rate substantially, from 39% to 52%.

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The Base Scenario


2000 1800 1600 1400 1200 1000 800 600 400 200 0 1965

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2010

Year Newsprint



FIGURE 5.45

World prices of paper and paperboard.

60% 50% 40% 30% 20% 10% 0% 1965

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2010

Year Africa Oceania

FIGURE 5.46

North/Central America Europe

South America Former USSR

Asia World

Historical and projected wastepaper recovery rates.

Utilization Rates Figure 5.47 shows the historical and projected wastepaper utilization rate (ratio of wastepaper consumed to paper and paperboard produced). In 1997, the world utilization rate was about 37%, with Asia and Oceania’s the highest rate, 47%, and North/Central America and the former USSR’s the lowest, 29%. By 2010,

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60% 50% 40% 30% 20% 10% 0% 1965

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Year Africa Oceania

FIGURE 5.47



Asia World

Historical and projected wastepaper utilization rates.

the world utilization rate would reach 46%, with Asia’s highest rate of 55% and the former USSR’s lowest rate of 31%. The wastepaper utilization rate in North/Central America would increase from 29% to 40% during this period. Fiber Furnish Figure 5.48 shows the historical and projected shares of wastepaper in total fiber furnish. The trends for these shares follow the trends for wastepaper utilization rates. World wastepaper share would increase from 39% to 47% from 1997 to 2010. Asia’s wastepaper share in total fiber furnish would still be the largest, increasing from 47% to 56% during this period, while the share of North/Central America would increase from 30% to 41% from 1997 to 2010.

5.4 SUMMARY AND DISCUSSION The global forest sector has expanded greatly during the last three decades, and overall trends show rising production, domestic consumption, and trade. The base scenario projections, based on the assumed GDP growth rates, roundwood supply shift rates, and other model parameters, show that the consumption, production, and trade of forest products would continue increasing over the next decade.

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The Base Scenario 60% 50% 40% 30% 20% 10% 0% 1965

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2000

2005

2010

Year Africa Oceania

FIGURE 5.48



Asia World

Historical and projected shares of wastepaper in total fiber furnish.

World roundwood consumption would increase 25% to 4.2 billion m3 by 2010, and world roundwood trade would increase 70% to 173 million m3. Asia would still be the largest net importer, and the former USSR would remain the largest net exporter. World wood-based panel consumption would increase 54% to 225 million m3 and world trade would increase 29% to 57 million m3. Asia would become the largest net importer and the former USSR and North/Central America would be the major net exporters. World paper and paperboard consumption would increase 60% to 470 million tons in 2010, and world trade would increase 25% to 104 million tons. Europe and North/Central America would remain the major net exporters, and Asia would double its net imports during the next decade. Because of the strong demand for forest products, world prices would increase. It is unlikely that the price of industrial roundwood or wood-based panels would again reach their 1980 levels. But prices of sawnwood and papers would recover to near their 1980 levels by 2010. The projected wastepaper recovery rates and utilization rates are close to those projected by Mabee and Pande (1997) and Mabee (1998). The price projections suggest that the real price of industrial roundwood worldwide would increase slowly over the next decade. This confirms the claims in Tomberlin (1999), who suggests that decreasing stocks of large timber in many countries, combined with a trend toward more harvest restrictions, would lead to real price increases. But the future price trajectory of timber is highly uncertain (Boulter and Darr 1996); the prices may fall due to availability of substitute materials, technological change, and the possibility of substantially

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increased timber production from plantations and from Russia (Tomberlin 1999). In market economies, prices determine in part the demand and supply of forest products. Price levels are also critical in determining the feasibility of forestry and industry projects. It is, therefore, useful that the methodology to predict consumption, production, and trade also gives projections of the market-clearing prices. But the prices projected by GFPM are better viewed as long-term trends, because the year-to-year price fluctuations are very sensitive to model parameters and assumptions. The projected consumption, production, trade, and prices depend on the model parameters and assumptions, such as demand elasticities, input–output coefficients, GDP growth rates, and the roundwood supply shift rates. Therefore, the forecasts in this chapter must be viewed as an example of possible market developments under these assumptions and policies. They will serve as a basis of comparison for the next three scenarios, in which the effects on forest product production, consumption, and trade of a slowdown in economic growth, an increase in paper recycling, and tariff liberalization are separately studied.

CHAPTER

6

Effects of the Asian Economic Crisis

6.1 INTRODUCTION In mid-1997, a number of key Asian currencies depreciated significantly, signaling the beginning of a major recession: the Asian economic crisis. In May 1998, the currencies of Malaysia, the Philippines, the Republic of Korea, and Thailand were worth about two-thirds their value of one year before, and the Indonesian rupiah was worth 75% less (FAO 1999b). The Asia-Pacific region is a significant producer of forest products. The region is also a large market for forest products, and this role has been growing rapidly as a result of rising population and income. In the late 1990s the expectations were that Asia would soon be a leader in the global forest sector. The crisis, however, significantly lowered the demand for forest products in Asian markets. Imports of industrial roundwood by Japan decreased markedly in 1998. The importance of Japan as a market for log exports from New Zealand and the International Tropical Timber Organization (ITTO) producer countries meant that these countries’ forest export revenues appreciably decreased (Tissari 2001). At the same time, prices for market pulp dropped sharply, and many Indonesian timber companies were facing an uncertain future (FAO 1999b). 127

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The effects of the Asian economic crisis have been felt in other parts of the world. For example, the softening Asian demand resulted in a drop in Canadian newsprint shipments to Asia. At the same time, the surplus Asian newsprint was diverted into the U.S. market (FAO 1999b). The excess supply of Asian paper and paperboard, combined with lower currency values brought on by the Asian economic crisis, resulted in increased U.S. imports of paper and paperboard. As a result, U.S. paper and paperboard production and exports declined (Advanstar 1999). Beginning in 1998, many insightful discussions about the Asian economic crisis appeared in newspapers and journals, but quantitative analyses have been scarce. For the macroeconomic aspects, OECD (1999) gave a survey of the crisis by economists and policy makers and addressed the measures needed to restore sustained economic growth and stability in the crisis-affected countries. The Asia-Pacific Economic Cooperation (APEC) secretariat indicated recently that throughout the APEC region, countries were progressively recovering from the 1997–98 financial crisis. Nevertheless, the report identified remaining challenges, such as addressing the lack of transparency in the financial and corporate sectors (APEC 2001). For the forestry sector, Lee (1999) described the impact of the crisis in the Asia-Pacific region and the possible effects elsewhere. Lee concluded that Asian economies would eventually recover from the crisis and return to rapid growth trajectories, although the very high historical rates might no longer be achievable. FAO (1999b) summarized the impact of the Asian crisis on the AsiaPacific forest sector as lowering demand, prices, and supply, suggesting that the effects of the crisis would spread over the world. Shield (1998) examined the pulp and paper industry of Japan, arguing that a cause of its crisis was the rigidity of the Japanese financial and economic system, which had supported inefficient enterprises. Routson et al. (1998) concluded that the Asian crisis had lowered U.S. exports of pulp and paper, increased imports, and kept prices low. This shift in the U.S. pulp and paper trade balance resulted in an increase in the U.S. trade deficit in pulp and paper (Thompson 1999). Chang (1999) argued that, because Japan was unlikely to recover quickly, the outlook of the pulp and paper industry in the Asia-Pacific area depended largely on the continuation of China’s economic growth. Adams and Castano (2001) drew the same conclusion for tropical timbers: Japanese imports were lower in 2000 than in 1997; the only positive note was China’s strong growth and continuing imports. In one of the few quantitative studies of the Asian economic crisis, Coyle et al. (1998) used an econometric model to measure its impact on the U.S. agricultural sector. They investigated two scenarios, the crisis confined to Southeast Asia and the Republic of Korea, and a deepening of the crisis in Japan and China. Both scenarios showed that the economic crisis in Asia would


129

reduce U.S. exports but would also reduce the cost of some intermediate inputs important to U.S. agriculture. This would stimulate the U.S. domestic economy and might offset the negative impact of export declines. No similar study seems to have been done for the forest sector. The objective of the study summarized in this chapter was to predict the long-term effects of the Asian economic crisis on the forest sector, in Asia and other regions, up to 2010. The methods used the GFPM to simulate production, trade, and prices, with and without the macroeconomic impacts of the crisis.

6.2 GFPM APPLICATION1 The effects of the Asian economic crisis on the world forest sector were projected for the period 1998–2010. As seen in Chapter 3, the main exogenous variables in the GFPM are the rate of growth of gross domestic product (GDP) and the rate of shift (changes at constant price) of industrial roundwood supply in each country. This application of the GFPM assumed that the Asian economic crisis would affect the forest sector of countries mainly through changes in economic growth, as measured by their GDP growth rates. In particular, changes in the relative nominal value of the currencies of different countries would not have a direct long-term effect on equilibrium quantities and real prices (Buongiorno et al. 1988, Uusivuori and Buongiorno 1990). Figure 6.1 summarizes a simple theory underlying the projections. The figure represents the demand and supply of a single commodity in Asia and in the rest of the world (ROW) in a particular year. Without the Asian economic crisis, the demand in Asia would have been at the level symbolized by the downward-sloping curve DA. The supply would have been at level SA. In the rest of the world, the demand would have been DR and the supply SR. The equilibrium price, P, would have been such that the imports of Asia, I, would equal the exports of the ROW, X. The figure assumes that the single effect of the Asian economic crisis was to decrease GDP in Asia and thus to shift the demand curve to the left, to the position DA′. After adjustment, the new equilibrium would then be such that Asian imports would be I′, equal to ROW exports, X′, and the new equilibrium price would be P′. According to Figure 6.1, the effect of the crisis would be to decrease production in both Asia and the ROW, decrease the price, and decrease imports and exports. Meanwhile, consumption would decrease in Asia and increase in ROW. However, the magnitude and to a lesser extent the direction of the effects could be different for various reasons. There are dynamic effects that 1 The data for this application, and the GFPM software, are available at: www.forest.wisc.edu/ facstaff/buongiorno/book/index.htm

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Asia

P

P

ROW

SA

SR

P′

DA

I

P

X

I′

X′ DA′ DR Q

Q

FIGURE 6.1 Theoretical equilibrium supply, demand, trade, and prices in Asian countries and the rest of the world (ROW).

slow down the adjustment of equilibrium over time; these include the change of capacity of production with profitability and the inertia of trade between countries, both of which are represented in the GFPM. Furthermore, the interaction between products and countries is much more complex than symbolized in Figure 6.1.

PROJECTED ECONOMIC SLOWDOWN Table 6.1, under the heading “Without crisis,” shows the projected GDP growth rates for nine Asian economies, which were applied in FAO (1997a) and in Zhu et al. (1998) as exogenous demand shifts. FAO economists had prepared these growth rate projections in 1996, before the Asian crisis. With the advent of the crisis, the FAO staff revised the world GDP growth projections in the year 2000 (Whiteman 2000a). Table 6.1 shows the effects of the crisis, measured by the difference in GDP growth between the 1996 and 2000 projections, in key economies in the Asia-Pacific region. It is quite possible that other factors have led to the revisions of the projections, but for the purpose of this study the changes were assumed to reflect mostly the exceptional economic events in Asia in 1997 and 1998.

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Effects of the Asian Economic Crisis TABLE 6.1

Projected Effects of the Asian Economic Crisis on Real GDP Growth (%/year) 1998–2000 Without crisis

China Hong Kong Indonesia Japan Korea, REP. Malaysia Philippines Singapore Thailand

8.06 5.22 7.79 3.12 6.26 7.95 5.31 6.88 6.19

2001–2005 Crisis effect −0.53 −4.59 −8.91 −2.67 −2.77 −5.68 −2.64 0.00 −6.82

Without crisis 7.30 5.50 7.53 2.97 6.32 7.68 5.34 6.44 7.00

2006–2010 Crisis effect −0.27 −0.50 −2.88 −1.27 −1.02 −1.72 −0.52 0.00 −1.65

Without crisis 7.30 5.50 7.53 2.97 6.32 7.68 5.34 6.44 7.00

Crisis effect −0.29 0.00 −2.63 −0.98 −0.82 −1.43 −0.06 0.00 −2.00

Source: Whiteman (2000a). GDP growth rates without the Asian crisis were projected in 1996. GDP growth rates with the Asian crisis were projected in 2000.

According to this scenario, for example, due to the Asian crisis, the annual growth rates of GDP in Indonesia dropped from 7.79% to –1.12% for the period from 1998 to 2000; further, the long-term projections were that the GDP growth rates would be lower (4.65% per year rather than 7.53% between 2001 and 2005, and 4.90% per year rather than 7.53% per year from 2006 to 2010). While Japan’s GDP growth rate was reduced significantly, China’s GDP was predicted to continue to grow at about 7% per year throughout the next decade, only slightly slower than without the Asian crisis. The growth rate of Singapore would be unaffected. The modification of GDP growth rates reflects the effects of the Asian crisis on all sectors of the world economy. In the GFPM, the GDP growth rates affect the forest products sector largely via the domestic demand component. To compute the detailed effects of the Asian economic crisis on the forest sector, the GFPM was applied with the alternative GDP growth rates for these nine economies in Table 6.1. The assumptions for all other countries remained the same as in Chapter 4. The difference between the two scenarios gave the measure of the effects of the Asian economic crisis on the global forest sector.

EFFECTS OF THE SLOWDOWN Figures 6.2–6.5 illustrate the yearly effects of the Asian economic crisis, from 1998 to 2010, on production and imports of industrial roundwood and woodbased panels, as predicted by the GFPM, with the assumptions described earlier. The United States is also shown as an example of a single country. The GFPM

132


4%

2%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−2%

−4%

−6%

Year AFRICA SOUTH AMERICA EUROPE

NORTH/CENTRAL AMERICA ASIA FORMER USSR

United States OCEANIA WORLD

FIGURE 6.2 Projected percentage change in industrial roundwood production due to the Asian economic crisis.

40%

30%

20%

10%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−10%

−20%




FIGURE 6.3 Projected percentage change in industrial roundwood imports due to the Asian economic crisis.

133

Effects of the Asian Economic Crisis 5%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−5%

−10%

−15%




FIGURE 6.4 Projected percentage change in wood-based panel production due to the Asian economic crisis.

10%

5%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−5%

−10%


FIGURE 6.5 nomic crisis.



Projected percentage change in wood-based panel imports due to the Asian eco-

134


gives projections for each of 180 countries and 14 forest products. The figures show the percentage difference in annual production and imports with and without the Asian economic crisis. In general, the effects of the crisis increased over the projection period. However, the effects could vary erratically, as in the case of imports of wood-based panels (Figure 6.5). Little significance should be attached to these yearly fluctuations, because they could change substantially with slight changes in model parameters. The results were, therefore, summarized as average percentage differences from 1998 to 2010, as shown in Tables 6.2–6.6.

Industrial Roundwood As shown by the last line of Table 6.2, the world annual production of industrial roundwood would be 1.7% lower from 1998 to 2010 than it would have been without the crisis. This in general agrees with theoretical expectations (Figure 6.1); however, there are considerable regional differences in production. It would decrease relatively more in developing than in developed countries. The largest drop in production would occur in Asia, notably in Indonesia, China, and Malaysia, and in North/Central America, especially in the United States, where production would decrease almost as much as in Asia. At the same time, production would change little in Europe. The relative changes in trade of industrial roundwood due to the Asian economic crisis would be substantially larger than those of production. World imports and exports would be about 5% lower, in agreement with the theoretical expectations shown in Figure 6.1. Imports would be reduced the most in Asia, especially in Japan and Korea, while U.S. imports of industrial roundwood would not change. The large increase of imports in North/Central America is due to an increase in Canadian industrial roundwood imports. Due to the Asian economic crisis, the United States and Africa were expected to export much less industrial roundwood, but the former USSR would export more.

Sawnwood Due to the Asian economic crisis, world sawnwood production would decrease relatively more in developing countries (Table 6.3). The largest negative effects on production would be in Asia, notably in Indonesia, Japan, and Malaysia, and in North/Central America. Production would increase in Europe and would change little in other regions. World trade in sawnwood would be slightly lower with the Asian economic crisis. While imports in both developing and developed countries would be

TABLE 6.2

Projected Effects of the Asian Economic Crisis on Industrial Roundwood (1998–2010) Effects on average annual 1

3

3

1997 (10 m ) Region

Production

AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA China Hong Kong Indonesia Japan Korea, REP Malaysia Philippines Singapore Thailand OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD

64,853 632,100 432,166 131,714 316,414 114,544 824 47,273 23,576 4,890 35,767 3,716 674 5,592 40,939 313,862 108,623 1,116,342 492,163 1,608,505

1

Imports 904 10,563 2,019 29 69,549 7,938 884 178 47,321 9,470 311 785 55 894 11 52,485 815 111,185 23,171 134,356

Production Exports

103 m3

7,902 20,810 18,243 8,980 13,244 3,107 575 683 6 0 6,783 0 26 461 17,091 26,522 25,895 89,595 30,849 120,444

−1,380 −17,986 −10,752 −648 −11,077 −5,577 −46 −3,758 25 −33 −2,072 −83 −12 −4 −689 −1,462 1,690 −19,115 −12,437 −31,552

Data from FAO (1999a); the production of some countries was estimated (see Chapter 4).

Imports 103 m3

−1.9% −2.4% −2.1% −0.4% −2.8% −3.6% −4.2% −6.3% 0.1% −0.4% −5.3% −2.2% −1.4% −0.2% −1.4% −0.4% 1.3% −1.5% −2.1% −1.7%

−21 1,601 0 0 −8,933 −458 −101 −66 −6,579 −1,299 −77 −93 −4 −235 0 −173 0 −5,138 −2,388 −7,526

Exports 103 m3

−1.6% 11.6% 0.0% 0.0% −10.6% −4.6% −11.2% −30.3% −11.4% −12.1% −20.0% −10.2% −3.8% −23.1% 0.0% −0.3% 0.0% −3.9% −8.6% −4.7%

−1,122 −7,608 −7,609 31 −227 −56 −33 7 0 0 0 0 0 0 −495 −350 2,245 −6,915 −610 −7,526

−10.6% −26.1% −27.8% 0.2% −2.2% −3.0% −7.3% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% −2.4% −1.3% 6.0% −6.2% −1.8% −5.1%

135

136 TABLE 6.3

Projected Effects of the Asian Economic Crisis on Sawnwood (1998–2010) Effect on average annual 19971 (103 m3)

Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA China Hong Kong Indonesia Japan Korea, REP Malaysia Philippines Singapore Thailand OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD 1

Production 8,350 181,091 111,425 30,285 96,531 26,969 441 7,238 21,698 4,759 8,382 258 25 322 6,981 89,529 25,819 321,426 117,160 438,586

Imports 4,950 47,814 43,610 702 23,218 3,146 931 33 12,590 985 329 412 602 1,463 847 37,498 632 97,945 17,716 115,661

Production Exports 1,283 56,072 6,776 3,517 6,126 523 781 418 13 20 3,109 142 227 80 1,248 35,305 9,144 101,191 11,504 112,695

103 m3 −82 −2,726 −448 63 −3,446 −685 −23 −1,182 −866 −246 −634 30 −1 153 34 1,329 −81 −2,255 −2,654 −4,909


Imports 103 m3

−0.9% −1.3% −0.4% 0.2% −2.9% −1.7% −4.7% −12.8% −3.7% −4.0% −6.9% −48.4% −0.0% 35.5% 0.4% 1.4% −0.3% −0.6% −1.9% −1.0%

5 492 448 28 −1,383 −225 −31 −5 −557 −78 −40 −54 1 −392 −20 −497 0 −625 −751 −1,376

Exports 103 m3

0.1% 1.2% 1.2% 4.8% −5.4% −5.8% −4.0% −15.8% −4.1% −6.5% −13.2% −8.2% 0.1% −21.0% −1.6% −1.4% 0.0% −0.7% −3.5% −1.2%

−80 −2,234 0 129 44 0 −10 70 0 0 0 0 0 0 14 832 −81 −1,457 82 −1,376

−7.8% −3.5% 0.0% 3.8% 1.2% 0.0% −1.8% 21.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.8% 2.9% −0.8% −1.4% 1.0% −1.2%

TABLE 6.4

Projected Effects of the Asian Economic Crisis on Wood-based Panels (1998–2010) Effect on average annual 1

3

3

1997 (10 m ) Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA China Hong Kong Indonesia Japan Korea, REP Malaysia Philippines Singapore Thailand OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD 1

Production

Production

Imports

Exports

103 m3

1,805 50,756 38,502 6,306 45,837 14,499 41 10,163 6,435 2,485 7,000 435 355 548 2,607 43,629 4,783 108,160 47,563 155,723

709 10,472 8,845 460 18,926 5,089 1,705 44 7,379 1,728 139 244 676 88 254 16,565 469 34,968 12,887 47,855

433 11,363 3,226 2,195 16,933 881 1,135 8,764 52 125 5,285 49 235 252 889 15,876 2,229 30,293 19,625 49,918

1 293 174 74 −4,990 −2,730 −45 −428 −757 −448 −446 −34 −8 −76 −24 −620 −7 −1,105 −4,167 −5,273


Imports 103 m3

0.1% 0.4% 0.3% 1.0% −7.5% −11.0% −34.1% −3.3% −8.8% −12.0% −4.7% −6.5% −1.4% −9.4% −0.7% −1.1% −0.1% −0.8% −6.1% −2.5%

−5 −135 −141 −6 −560 384 −101 −22 −807 12 −23 −21 37 −18 10 437 0 −505 246 −259

Exports 103 m3

−0.7% −1.3% −1.6% −1.2% −2.4% 5.1% −6.4% −23.5% −9.9% 0.6% −16.4% −5.8% 4.3% −15.6% 3.5% 2.3% −0.1% −1.3% 1.5% −0.5%

−10 96 −4 −5 −210 0 −69 4 0 5 −174 0 0 20 −6 −119 −5 −33 −226 −259

−1.7% 0.8% −0.2% −0.2% −1.0% 0.0% −7.8% 0.1% 0.0% 3.6% −2.4% 0.0% 0.2% 5.7% −0.5% −0.7% −0.1% −0.1% −1.0% −0.4%

137

138 TABLE 6.5

Projected Effects of the Asian Economic Crisis on Wood Pulp (1998–2010) Effect on average annual 1

3

1997 (10 t) Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA China Hong Kong Indonesia Japan Korea, REP Malaysia Philippines Singapore Thailand OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD 1

Production

Production

Imports

Exports

103 t

2,253 84,405 56,980 9,413 20,438 2,346 94 2,928 11,320 1,195 201 149 15 555 2,295 41,588 3,935 144,229 20,098 164,327

306 6,291 5,704 783 9,786 2,305 7 847 3,225 1,977 71 96 31 337 214 15,169 69 24,826 7,792 32,618

777 16,656 5,813 4,187 1,500 27 3 1,284 60 2 0 0 16 103 586 9,029 948 27,735 5,948 33,683

−36 −1,625 −953 −237 −1,923 6 1 −191 −1,645 −122 −51 −38 −1 −76 −56 −938 −135 −4,416 −534 −4,950


Imports 103 t

−1.3% −1.6% −1.3% −2.0% −7.5% 0.4% 11.8% −5.2% −11.4% −8.3% −18.7% −18.8% −2.9% −11.9% −1.9% −1.9% −2.8% −2.5% −2.2% −2.4%

6 −368 −364 −35 −1,369 −7 0 −374 −699 −117 −8 7 0 −52 41 41 2 −976 −706 −1,682

Exports 103 t

1.7% −5.3% −5.8% −4.2% −11.1% −0.3% 5.0% −31.3% −21.1% −4.3% −6.7% 4.2% −0.2% −10.6% 15.6% 0.2% 2.7% −3.7% −6.7% −4.5%

−59 −848 −78 −308 165 −1 −1 106 −4 0 0 0 0 2 −19 −488 −126 −1,536 −146 −1,682

−7.1% −4.3% −1.1% −6.3% 12.1% −5.2% −21.8% 8.9% −7.6% 1.0% 0.0% 0.0% −0.5% 2.9% −2.1% −5.2% −11.5% −4.9% −2.2% −4.4%

TABLE 6.6

Projected Effects of the Asian Economic Crisis on Paper and Paperboard (1998–2010) Effect on average annual 1

3

1997 (10 t) Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA China Hong Kong Indonesia Japan Korea, REP Malaysia Philippines Singapore Thailand OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD 1

Production

Production

Imports

Exports

103 t

3,162 109,037 86,274 9,842 85,235 31,583 280 4,930 31,016 8,363 711 613 87 2,271 3,291 83,729 3,823 229,718 68,401 298,119

1,504 18,285 14,381 3,382 18,971 6,583 4,056 199 1,614 548 993 406 750 405 1,390 38,681 1,005 59,827 23,391 83,218

483 25,899 11,153 1,842 9,156 1,070 2,676 1,212 1,149 2,077 98 45 211 525 828 47,815 1,589 77,447 10,165 87,612

52 −2,282 −2,347 63 −9,049 −296 0 −1,626 −5,828 −644 −204 −98 −2 −392 2 −669 −12 −8,650 −3,245 −11,895


Imports 103 t

1.2% −1.4% −1.9% 0.5% −7.0% −0.6% 11.3% −20.2% −13.5% −5.1% −16.0% −10.0% −1.3% −11.0% 0.1% −0.7% −0.2% −2.8% −3.1% −2.8%

27 363 374 −57 −1,508 79 −582 −96 −386 −63 −265 −25 −3 −132 −24 481 5 426 −1,138 −712

Exports 103 t

1.6% 1.9% 2.6% −1.5% −5.5% 0.9% −12.7% −34.1% −21.6% −9.6% −15.3% −4.6% −0.1% −22.1% −1.2% 1.2% 0.6% 0.7% −3.4% −0.7%

24 −858 −1,009 88 331 21 −274 294 177 3 0 4 −1 106 −41 −259 2 −979 267 −712

4.3% −2.6% −7.0% 3.9% 3.5% 3.2% −10.9% 23.4% 13.7% 0.0% 0.1% 7.3% −0.9% 18.0% −4.2% −0.5% 0.1% −1.1% 2.5% −0.7%

139

140


lower, they would increase slightly in the United States. Sawnwood exports would be lower in developed countries but higher in developing countries. The largest relative increase in exports would occur in Indonesia. Wood-based Panels One effect of the Asian economic crisis would be to decrease the annual world production of wood-based panels (plywood, veneer, particleboard, and fiberboard) by about 2.5% (Table 6.4). The production of developing countries, especially in China, Republic of Korea, Malaysia, and Indonesia, would be the most affected, and there would also be large decreases in production in Japan and Europe. World trade in wood-based panels would decrease much less than production in relative terms. The largest decreases in imports would occur in Japan, while China’s imports would increase. Europe would see its exports of woodbased panels increase and its imports decrease slightly. In the United States, both imports and exports of wood-based panels would be lower. Wood Pulp Due to the Asian economic crisis, annual world production of wood pulp would be 2.4% lower from 1998 to 2010 (Table 6.5). Production would be reduced more in developed countries than in developing countries, in both relative and absolute terms. The largest negative effect on production of wood pulp would occur in Japan, the United States, and Europe. Trade of wood pulp would be reduced by about 4.5% due to the Asian economic crisis. In some Asian countries, such as Indonesia, imports would decrease and exports would increase, thus improving their net trade balance. The balance of trade of Japan and the United States would also improve. Instead, European imports of wood pulp would increase and exports would decrease substantially, thus worsening net trade. Paper and Paperboard As a result of the Asian economic crisis, the world annual production of paper and paperboard would be 2.8% lower from 1998 to 2010 (Table 6.6). In relative terms, the production of developing countries would be slightly more affected. In Asia, there would be especially large decreases of production in Japan and Indonesia. Producers in the United States would also be affected negatively and significantly, while production would be relatively little affected in other regions.

141


Total world imports and exports of paper and paperboard would be little affected by the Asian economic crisis, but there would be strong regional differences in the effects. While imports of developing countries would decrease and their exports increase, the reverse would occur in developed countries. A notable improvement in the balance of trade for paper and paperboard would occur for Japan, Indonesia, Thailand, and South America. In contrast, the trade balance would worsen for the United States, where imports would increase while exports would decrease between 1998 and 2010. In the United States the effect of the Asian economic crisis would be to decrease exports of all commodities, except sawnwood, which would not be affected. This decrease in the United States’ exports was due to the lower demand in Asia.

Prices Figure 6.6 shows the annual effects of the Asian crisis on world prices of industrial roundwood and end products, predicted with the GFPM. Table 6.7 shows the effects for selected years and the average effect from 1998 to 2010. These are the differences between the prices in this scenario, and the prices in the base scenario in Chapter 5. For sawnwood, veneer sheet and plywood, and other paper and paperboard, the average price from 1998 to 2010 was slightly lower with 20%

10%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−10%

−20%


Sawnwood & sleepers

Veneer & Plywood

Particle board

Fibreboard

Newsprint



FIGURE 6.6 nomic crisis.

Projected percentage change in forest product world prices due to the Asian eco-

142 TABLE 6.7

The Global Forest Products Model Projected Effects of the Asian Economic Crisis on Real World Prices (1998–2010) Price level

Commodity

1980

1997

Effect in year 2005

2010

Average effect

0.5% −1.6% −0.1% −1.7% −0.9%

0.5% −0.7% 4.1% 6.7% 0.9%

0.2% −0.3% −0.7% 0.9% 0.1%

−1.1% −7.1% −7.0% −10.0% −0.8% −3.0% −7.6% 0.8% −0.9% −0.3% −0.9% 5.8%

14.3% 11.0% −7.3%

1.6% 2.2% −0.2%

1998

1999

2000

US $/m3 Industrial roundwood Sawnwood and sleepers Veneer and plywood Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard

158 91 289 237 789 465 323 208 385 285 US $/t 768 1,313 1,108

562 894 790

0.0% 0.0% 0.0% 0.0% 0.0% −0.8% 0.0% −2.5% 2.1% 0.0% 0.0% 4.5% 0.0% −0.1% 0.0%

than without the Asian crisis; for other products, their average prices were higher. However, during the early stage of the crisis, in 1998 and 1999, all paper and panel prices were lower, and other prices changed little. The reason for the positive average price effect for some products is that due to the early stagnation or decline of production and prices, capacity failed to grow, and the resulting capacity constraint led to price increases when countries recovered from the crisis and demand started to grow again in later years.

6.3 SUMMARY AND CONCLUSION The findings confirm and quantify previous predictions that the impact of the Asian economic crisis on the global forest products sector would be to lower demand and prices (at the beginning of the crisis and to a lesser extent in later years of the projection) and to decrease supply (FAO 1999b). The crisis effects would not be limited to the Asia-pacific region but would impact countries all over the world. The world demand and production of all products would be lower from 1998 to 2010 due to the Asian economic crisis. The annual output of industrial roundwood would be about 1.7% lower worldwide, and Asia’s annual output would be reduced by 2.8%. For wood-based panels, annual world output would be 2.5% lower and Asia’s would be 7.5% less. The largest production drop would occur for paper and paperboard: 2.8% lower worldwide, in Asia 7.0% lower than if the crisis had not occurred. Another predicted effect of the crisis is that Asia’s imports would be much lower for all commodities. The largest effect would be on wood pulp, for which Asian annual imports would be approximately 11% lower. However, the


143

exports of sawnwood, pulp, and papers from Asia would be higher. This would be due to the lower domestic demand in Asia and the accompanying lower prices. For the United States, the Asian economic crisis would increase imports of paper and paperboard and decrease exports, confirming the predictions of Routson et al. (1998) and of Thompson (1999). Thus, the GFPM projections support the conjecture that the effects of the Asian crisis would spread to countries that depend on Asian markets or are in competition with them. A potential consequence is that this may trigger a resurgence of requests for trade protection as the Asian economies increase their exports. There have already been signs that voluntary reductions in forest products tariff within the APEC might be delayed as a consequence of the crisis (Lee 1999).


CHAPTER

7

Effects of Tariff Liberalization

7.1 INTRODUCTION This chapter describes the application of the GFPM to predict the impacts of the Accelerated Tariff Liberalization of the General Agreement on Tariffs and Trade (GATT) on the global forest sector, and especially the United States. The original version of this study was done in collaboration with the USDA Forest Service, in preparation for the meeting of the World Trade Organization in Seattle in 1999 (Zhu et al. 2001). The chapter reviews the Uruguay Round of GATT and the Accelerated Tariff Liberalization (ATL) proposal to remove import tariffs on forest products in countries of the Asia-Pacific Economic Cooperation (APEC) organization. It then describes the GFPM simulations of this scenario. The conclusion is that the ATL would have minor effects on global production, consumption, and prices but some perceptible effects on trade and prices. The welfare of consumers would rise, while that of producers would decrease.

7.2 THE URUGUAY ROUND OF GATT AND THE ATL PROPOSAL International trade in forest products is expanding fast, attracting more attention from policymakers (Esty 1994). As discussed in Chapter 2, historical trends show a change in the composition of global exports from raw materials, 145

146


such as logs, toward higher-value products (panels and papers). Paper and paperboard products now account for about 45% of the value of world forest product exports, while sawnwood accounts for 21% and wood-based panels for 13% (FAO 1999a).

THE GATT The global tariff barriers to trade declined significantly as a result of successive postwar GATT negotiations. The Kennedy Round (completed in 1967) reduced the average tariff by about 35% (Krugman and Obstfeld 1994). After the Tokyo Round, completed in 1979, weighted average tariffs in the most industrialized markets declined from 7% to 4.7%, and $300 billion worth of global trade was liberalized among 99 countries (Esty 1994). Despite the overall tariff reductions implemented in the Tokyo Round, tariffs remained lowest on unprocessed products and rose with increased processing. The most recent important treaty of global tariff liberalization was the 1994 Uruguay Round of the GATT. As a result of the Uruguay Round, the value of world trade could increase by about 9–24% by the year 2005 (GATT 1994). Because U.S. tariffs on most forest products were already quite low before the agreement, the substantial reduction in tariffs by other countries should benefit the U.S. forest industry (Wisdom 1995). The U.S. forest products industry predicted about $10 billion in direct additional exports of forest products attributable to the Uruguay Round agreements (AF&PA 1994). Most import tariffs on industrial products would be reduced by one-third from 1994 to 1999; tariffs on some products, including pulp and paper, would be eliminated completely in major developed country markets over 8–10 years (up to 2005). Tariff escalation by level of processing has been considerably reduced. Developed countries set bounds on all their tariffs, while developing countries did the same for 65% of their tariffs (Barbier 1996). Much attention was given to nontariff barriers, including improvement of trade rules controlling subsidies, and to countervailing duties, and better rules were drafted for assessing technical barriers and standards (Esty 1994). Barbier (1996) summarizes the Uruguay Round agreements for forest products. The tariffs on most forest products would be reduced 33% on a tradeweighted basis. Austria, Canada, the European Union, Finland, Japan, New Zealand, the United States, and several other major importers agreed to tariff elimination on pulp and paper. These countries would halve their 1986 tariffs by the year 2000 and eliminate them by 2004. The major developed countries were also committed to reducing tariffs by 50% on solid wood products over five years, starting in 1995. For developed countries, the average tariff on forest products (wood, pulp, paper, and furniture) would be reduced from 3.5% to 1.1%.


147

In the United States, average tariffs would be cut from about 3.1% to 1.8%. The tariff escalation for forest products in developed country markets would be reduced significantly. For wood, the reduction ranged from 30% to 67%; for paper and paperboard, tariff escalation would be eliminated completely. The Uruguay Round agreement committed all major developed countries and a high proportion of developing countries to binding forest product rates, thus reducing market risk. For the nontariff barriers, the Agreement on the Application of Sanitary and Phytosanitary Measures and the Agreement on Technical Barriers to Trade would improve market access. Nevertheless, while the Uruguay Round reduced tariff rates significantly, its implications for the nontariff barriers increasingly faced by forest products was less clear (Barbier 1999).

THE ATL PROPOSAL Despite the Uruguay Round agreement, tariffs are still a significant barrier to trade in forest products (Bourke and Leitch 1998). Even in OECD countries, tariffs remain high for some products, such as wood-based panels. In other countries, tariffs of between 10 and 20% are common, and they can reach 40%. In 1997, ministers of APEC countries called for the nomination of sectors for early voluntary liberalization (EVSL) (FAO 1999b). The United States, Canada, Indonesia, and New Zealand proposed the forest sector. In 1998, APEC leaders agreed to move the tariff portion of the EVSL initiative to the World Trade Organization, to conclude an agreement by the end of 1999. This study was done to help the U.S. trade representative office evaluate this initiative (Council on Environment Quality 1999). The Accelerated Tariff Liberalization (ATL) proposal covers all forest products, such as logs and wood products (chapter 44), pulp, paper and paper products (chapters 47–49). Parties to the Uruguay Round of GATT zero-for-zero agreement would move up the elimination of tariffs on pulp, paper, and paper products from January 1, 2004, to January 1, 2000. Others would attempt to remove tariffs by the same date but could delay removal until January 1, 2002. The proposal called for the elimination of tariffs on all other products by January 1, 2002.

7.3 PREVIOUS STUDIES The effects of tariff changes on forest products trade have been studied mostly with single-country partial equilibrium models. For example, Prestemon and Buongiorno (1996) predicted the effects of NAFTA on Mexico’s imports with such a model. But Boyd and Krutilla (1992) studied the U.S. forest sector under

148


free trade within a general equilibrium framework. Buongiorno and Manurung (1992) computed the effects of an import tax in Europe on the tropical timber trade with a multicountry partial equilibrium model. Boyd et al. (1993) found that due to the U.S. tariff removals on the North American lumber trade, Canadian softwood exports to the United States would increase by 4.5%. After the 1994 Uruguay Round agreement, the Food and Agriculture Organization of the United Nations made several assessments of its effects on world agriculture, forestry, and fisheries (FAO 1999c). Barbier (1996, 1997) contributed to the forestry part of this work by adapting the partial equilibrium model of Baldwin and Murray (1977). Working with 12 main countries he found that the Uruguay Round of GATT would increase world imports by 0.4–0.5% only. As a supplement to Barbier (1996), Brown (1997a,b) found that the gains of trade due strictly to the Uruguay Round would also be relatively small for Asian Pacific countries.

7.4 GFPM SIMULATIONS1 The GFPM model was used to simulate an accelerated tariff liberalization scenario. All assumptions and parameters were the same as for the base scenario described in Chapter 5. Tariffs were the same as in the base scenario for 1998 and 1999. However, it was assumed that beginning in 2000 the tariffs would be eliminated for all commodities. Changes in tariffs corresponding to each scenario were assumed only for the following countries: In America: Canada, Mexico, United States; Brazil, Chile In Asia and Oceania: Brunei, China, Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Thailand, Australia, New Zealand, Papua New Guinea In the European Union: Austria, Belgium-Luxembourg, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain, Sweden, United Kingdom The detailed data for the assumed changes in tariffs are in Table 7.1. For other countries, it was assumed that there would be no change in tariffs due to the ATL agreement.

THEORETICAL EXPECTATIONS The theoretical effect of a change in tariff can be illustrated by considering trade between two countries for one product, as in Figure 7.1. Both countries have a downward-sloping domestic demand and an upward-sloping domestic supply. 1

The data for the simulations, and the GFPM software, are available at www.forest.wisc.edu/ facstaff/buongiorno/book/index.htm

TABLE 7.1

Country AMERICA Canada

Mexico

United States

Brazil

Tariff Rates in Alternative Scenarios (percent)

Product Veneer and plywood Particleboard Other paper and paperboard Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard Fiberboard Mechanical wood pulp Other fiber pulp Newsprint Printing and writing paper Other paper and paperboard Veneer and plywood Particleboard Fiberboard Printing and writing paper Other paper and paperboard Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard

1997

1998

1999

2000

6 2.5 2 10 10 10 10 15 20 15 5 5 15 10 10 5 0.8 0.6 1.5 2.4 10 10 10 10

5 2.1 1.7 8.3 8.3 8.3 8.3 12.5 16.7 12.5 4.3 4.3 12.9 8.6 8.6 4.2 0.7 0.5 1.3 2.1 8.3 8.3 8.3 8.3

4 1.7 1.4 6.7 6.7 6.7 6.7 10 13.3 10 3.6 3.6 10.7 7.1 7.1 3.3 0.5 0.4 1.1 1.7 6.7 6.7 6.7 6.7

4 1.7 1.1 6.7 6.7 6.7 6.7 10 13.3 10 2.9 2.9 8.6 5.7 5.7 3.3 0.5 0.4 0.9 1.4 6.7 6.7 6.7 6.7

Base scenario 2004 2010 4 1.7 0 6.7 6.7 6.7 6.7 10 13.3 10 0 0 0 0 0 3.3 0.5 0.4 0 0 6.7 6.7 6.7 6.7

4 1.7 0 6.7 6.7 6.7 6.7 10 13.3 10 0 0 0 0 0 3.3 0.5 0.4 0 0 6.7 6.7 6.7 6.7

ATL scenario 2000 2010 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

149

(continues)

150

TABLE 7.1 (continued)

Country

Chile

ASIA and OCEANIA Brunei

China

Product

Base scenario 2004

2010

ATL scenario 2000 2010

1997

1998

1999

2000

Fiberboard Printing and writing paper Other paper and paperboard Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard Fiberboard Mechanical wood pulp Chemical and semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard

2 20 12.5 11 11 11 11 11 11 11 11 11 11 11 11 11 11

1.7 17.1 10.7 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.4 9.4 9.4 9.4 9.4 9.4 9.4

1.3 14.3 8.9 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.9 7.9 7.9 7.9 7.9 7.9 7.9

1.3 11.4 7.1 7.3 7.3 7.3 7.3 7.3 7.3 7.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3

1.3 0 0 7.3 7.3 7.3 7.3 7.3 7.3 7.3 0 0 0 0 0 0 0

1.3 0 0 7.3 7.3 7.3 7.3 7.3 7.3 7.3 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other)

20 20 20 20 20 9 2 6

16.7 16.7 16.7 16.7 16.7 7.5 1.7 5

13.3 13.3 13.3 13.3 13.3 6 1.3 4

13.3 13.3 13.3 13.3 13.3 6 1.3 4

13.3 13.3 13.3 13.3 13.3 6 1.3 4

13.3 13.3 13.3 13.3 13.3 6 1.3 4

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

Indonesia

Japan

Korea

Sawnwood Veneer and plywood Particleboard Fiberboard Mechanical wood pulp Chemical and semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard Sawnwood Veneer and plywood Particleboard Fiberboard Wastepaper Newsprint Printing and writing paper Other paper and paperboard Sawnwood Veneer and plywood Particle Board Fiberboard Printing and writing paper Other paper and paperboard Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood

9 20 22 22 2 2 2 2 15 20 15 10 20 10 10 25 5 15 10 6.8 7.1 6 3.1 2.5 2.1 2 2 2 5 8

7.5 16.7 18.3 18.3 1.7 1.7 1.7 1.7 12.9 17.1 12.9 8.3 16.7 8.3 8.3 21.4 4.3 12.9 8.6 5.7 5.9 5 2.6 2.1 1.8 1.7 1.7 1.7 4.2 6.7

6 13.3 14.7 14.7 1.4 1.4 1.4 1.4 10.7 14.3 10.7 6.7 13.3 6.7 6.7 17.9 3.6 10.7 7.1 4.5 4.7 4 2.1 1.8 1.5 1.3 1.3 1.3 3.3 5.3

6 13.3 14.7 14.7 1.1 1.1 1.1 1.1 8.6 11.4 8.6 6.7 13.3 6.7 6.7 14.3 2.9 8.6 5.7 4.5 4.7 4 2.1 1.4 1.2 1.3 1.3 1.3 3.3 5.3

6 13.3 14.7 14.7 0 0 0 0 0 0 0 6.7 13.3 6.7 6.7 0 0 0 0 4.5 4.7 4 2.1 0 0 1.3 1.3 1.3 3.3 5.3

6 13.3 14.7 14.7 0 0 0 0 0 0 0 6.7 13.3 6.7 6.7 0 0 0 0 4.5 4.7 4 2.1 0 0 1.3 1.3 1.3 3.3 5.3

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

151

(continues)

152


Country

Malaysia

Philippines

Product Particleboard Fiberboard Mechanical wood pulp Chemical and semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard Fuelwood and charcoal Veneer and plywood Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard Fiberboard Mechanical wood pulp Chemical and semichemical wood pulp Other fiber pulp Wastepaper

1997

1998

1999

2000

8 8 2 2 2 2 8 8 8 20 40 20 20 5 15 20 3 3 3 10 20 20 20 3 3 3 3

6.7 6.7 1.7 1.7 1.7 1.7 6.9 6.9 6.9 16.7 33.3 16.7 16.7 4.3 12.9 17.1 2.5 2.5 2.5 8.3 16.7 16.7 16.7 2.6 2.6 2.6 2.6

5.3 5.3 1.4 1.4 1.4 1.4 5.7 5.7 5.7 13.3 26.7 13.3 13.3 3.6 10.7 14.3 2 2 2 6.7 13.3 13.3 13.3 2.1 2.1 2.1 2.1

5.3 5.3 1.1 1.1 1.1 1.1 4.6 4.6 4.6 13.3 26.7 13.3 13.3 2.9 8.6 11.4 2 2 2 6.7 13.3 13.3 13.3 1.7 1.7 1.7 1.7

Base scenario 2004 2010 5.3 5.3 0 0 0 0 0 0 0 13.3 26.7 13.3 13.3 0 0 0 2 2 2 6.7 13.3 13.3 13.3 0 0 0 0

5.3 5.3 0 0 0 0 0 0 0 13.3 26.7 13.3 13.3 0 0 0 2 2 2 6.7 13.3 13.3 13.3 0 0 0 0

ATL scenario 2000 2010 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Thailand

Australia

New Zealand

Newsprint Printing and writing paper Other paper and paperboard Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard Fiberboard Mechanical wood pulp Chemical and semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard Sawnwood Veneer and plywood Particleboard Fiberboard Printing and writing paper Other paper and paperboard Sawnwood Veneer and plywood Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard

10 3 10 1 1 1 5 20 20 20 7 7 7 7 35 35 35 5 5 5 5 5 5 8 8 7.5 6.5 7.5 7.5 7.5

8.6 2.6 8.6 0.8 0.8 0.8 4.2 16.7 16.7 16.7 6 6 6 6 30 30 30 4.2 4.2 4.2 4.2 4.3 4.3 6.7 6.7 6.3 5.4 6.4 6.4 6.4

7.1 2.1 7.1 0.7 0.7 0.7 3.3 13.3 13.3 13.3 5 5 5 5 25 25 25 3.3 3.3 3.3 3.3 3.6 3.6 5.3 5.3 5 4.3 5.4 5.4 5.4

5.7 1.7 5.7 0.7 0.7 0.7 3.3 13.3 13.3 13.3 4 4 4 4 20 20 20 3.3 3.3 3.3 3.3 2.9 2.9 5.3 5.3 5 4.3 4.3 4.3 4.3

0 0 0 0.7 0.7 0.7 3.3 13.3 13.3 13.3 0 0 0 0 0 0 0 3.3 3.3 3.3 3.3 0 0 5.3 5.3 5 4.3 0 0 0

0 0 0 0.7 0.7 0.7 3.3 13.3 13.3 13.3 0 0 0 0 0 0 0 3.3 3.3 3.3 3.3 0 0 5.3 5.3 5 4.3 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

153

(continues)

154


Country Papua New Guinea

EUROPE European Union1

OTHERS South Africa

Product Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard Fiberboard Mechanial wood pulp Chemical and semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard

Base scenario 2004 2010

ATL scenario 2000 2010

1997

1998

1999

2000

40 40 40 40 100 55 55 40 40 40 40 11 11 11

33.3 33.3 33.3 33.3 83.3 45.8 45.8 34.3 34.3 34.3 34.3 9.4 9.4 9.4

26.7 26.7 26.7 26.7 66.7 36.7 36.7 28.6 28.6 28.6 28.6 7.9 7.9 7.9

26.7 26.7 26.7 26.7 66.7 36.7 36.7 22.9 22.9 22.9 22.9 6.3 6.3 6.3

26.7 26.7 26.7 26.7 66.7 36.7 36.7 0 0 0 0 0 0 0

26.7 26.7 26.7 26.7 66.7 36.7 36.7 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

Sawlogs and pulpwood Sawnwood Veneer and plywood Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard

1 2 8.2 8.2 8.2 4.5 7.2 5.7

0.8 1.7 6.8 6.8 6.8 3.9 6.2 4.9

0.7 1.3 5.5 5.5 5.5 3.2 5.1 4.1

0.7 1.3 5.5 5.5 5.5 2.6 4.1 3.3

0.7 1.3 5.5 5.5 5.5 0 0 0

0.7 1.3 5.5 5.5 5.5 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

Sawnwood Veneer and plywood

2 9.4

2 9.4

2 9.4

2 9.4

2 9.4

2 9.4

0

0

Israel

Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard Sawnwood Veneer and plywood Particleboard Fiberboard Newsprint Printing and writing paper Other paper and paperboard

9.4 9.4 5.3 5.3 5.3 2 9.4 9.4 9.4 5.3 5.3 5.3

9.4 9.4 5.3 5.3 5.3 2 9.4 9.4 9.4 5.3 5.3 5.3

9.4 9.4 5.3 5.3 5.3 2 9.4 9.4 9.4 5.3 5.3 5.3

9.4 9.4 5.3 5.3 5.3 2 9.4 9.4 9.4 5.3 5.3 5.3

9.4 9.4 5.3 5.3 5.3 2 9.4 9.4 9.4 5.3 5.3 5.3

9.4 9.4 5.3 5.3 5.3 2 9.4 9.4 9.4 5.3 5.3 5.3

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

All others Fuelwood and charcoal Sawlogs and pulpwood Industrial roundwood (other) Sawnwood Veneer and plywood Particleboard Fiberboard Mechanical wood pulp Chemical and semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard

2 2 2 5 20 20 20 3 3 3 3 10 10 10

2 2 2 5 20 20 20 3 3 3 3 10 10 10

2 2 2 5 20 20 20 3 3 3 3 10 10 10

2 2 2 5 20 20 20 3 3 3 3 10 10 10

2 2 2 5 20 20 20 3 3 3 3 10 10 10

2 2 2 5 20 20 20 3 3 3 3 10 10 10

Source: APEC secretariat Web site, country Harmonized Tariff Schedule, and World Trade Organization. 1 The GFPM treats each country of the European Union individually: Austria, Belgium-Luxembourg, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain, Sweden, and United Kingdom. 155

156 P


P

Importer

P tI

Exporter

t

P0 P tx

It

Xt

Q I0 FIGURE 7.1

Q X0

Effect of a change in tariff when one product is traded between two countries.

With perfect competition, an equilibrium occurs in which one country is a net exporter and another is a net importer. Assuming that the tariff, t, is the only cost of trading, the price in the importing country, PtI, is equal to the price in the exporting country PtX, plus the tariff. With those prices, the net exports of one country, Xt, are equal to the net imports of the other country, It. Elimination of the tariff leads to a new equilibrium with the same price in both countries, P0. Thus, one effect of the tariff cut is to lower the price in the importing counry from PtI to P0. This increases domestic demand and decreases domestic supply in the importing country, resulting in an increase in imports to I0. In the exporting country, the cut in tariff leads to a price increase from PtX to P0. The domestic demand decreases while the supply increases, leading to an increase in exports to X0. At equilibrium, imports equal exports, I0 = X0. In terms of welfare, Figure 7.1 suggests that the welfare of consumers (consumer surplus, equal to the area under the demand curve) in the importing country would increase while that of consumers in the exporting country would decrease. Meanwhile, the welfare of producers (producer surplus, equal to the area between the price and the supply curve) would decrease in the importing country but increase in the exporting country. The total welfare (consumer surplus, plus producer surplus) would increase in both countries. Although the GFPM simulates this symbolic picture in spirit, it differs from it not only in complexity (many countries and products), but also in terms of

157


the dynamics, such as capacity changes and trade inertia, that govern the movement from equilibrium to equilibrium. Therefore, we cannot expect the results for all countries to conform to these theoretical expectations.

GFPM RESULTS Figures 7.2–7.5 summarize the effects of eliminating tariffs, from 1998 to 2010, for industrial roundwood and for paper and paperboard, for the main regions. The United States is also shown as an example of a single country. The GFPM output contains the detailed results for 180 countries and 14 commodity groups. The figures show the percentage difference between production and imports, with and without tariff elimination, for each year. The results imply that the effect of the ATL agreement would expand progressively over time. However, while this development would occur smoothly for some sectors, such as industrial roundwood (Figures 7.2 and 7.3), the pattern would be more erratic for others, such as paper and paperboard (Figures 7.4 and 7.5). Not much significance should be attached to these year-by-year fluctuations, because they could vary substantially for a small change in the model parameters. For that reason, the data are also presented as average annual percentage effects of the ATL, for the main product groups, from 1998 to 2010 (Tables 7.2–7.6). 4%

2%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−2%

−4%


FIGURE 7.2



Predicted effects of tariff reductions on production of industrial roundwood.

158


5%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−5%

−10%

−15%


FIGURE 7.3



Predicted effects of tariff reductions on imports of industrial roundwood.

6%

3%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−3%

−6%


FIGURE 7.4



Predicted effects of tariff reductions on production of paper and paperboard.

159

Effects of Tariff Liberalization TABLE 7.2

Predicted Effects of Tariff Reductions on Industrial Roundwood (1997–2010) 1997 (103 m3) Production Imports Exports

Region

AFRICA 66,366 NORTH/CENTRAL AMERICA 613,534 United States 416,092 SOUTH AMERICA 130,085 ASIA 277,153 OCEANIA 41,285 EUROPE 291,482 FORMER USSR 103,019 DEVELOPED, ALL 1,072,919 DEVELOPING, ALL 450,005 WORLD 1,522,924

Effect on average annual Production Imports Exports

904 7,902 10,563 20,810 2,019 18,243 29 8,980 69,549 13,244 11 17,091 52,485 26,522 815 25,895 111,185 89,595 23,171 30,849 134,356 120,444

0.3% 0.5% 0.4% 0.8% 0.5% −1.6% −0.2% 1.0% 0.3% 0.6% 0.4%

0.1% −2.3% 0.0% 0.0% −4.1% 0.0% 2.7% 0.0% −1.4% 0.1% −1.1%

−0.4% −2.8% −3.0% −1.0% 3.7% −3.0% −2.2% −0.4% −1.9% 0.8% −1.2%

20%

10%

0% 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−10%


FIGURE 7.5



Predicted effects of tariff reductions on imports of paper and paperboard.

EFFECTS ON PRODUCTION The predicted effects of the ATL agreement on world production were quite small, less than 0.5%. For industrial roundwood and paper and paperboard, the world production would increase slightly with the tariff reductions, but world

160 TABLE 7.3

The Global Forest Products Model Predicted Effects of Tariff Reductions on Sawnwood (1997–2010) 1997 (103 m3) Production Imports Exports

Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD TABLE 7.4

4,950 1,283 47,814 56,072 43,610 6,776 702 3,517 23,218 6,126 847 1,248 37,498 35,305 632 9,144 97,945 101,191 17,716 11,504 115,661 112,695

0.7% 0.9% 0.6% 0.6% −1.4% −1.9% 0.3% 1.6% 0.2% 0.2% 0.2%

7.6% 1.8% 0.0% 3.7% 1.0% 0.8% 6.7% 4.4% 3.6% 2.8% 3.5%

−0.1% −1.9% −2.0% −5.9% 11.2% 14.5% 5.0% 0.0% 3.1% 4.9% 3.4%

Predicted Effects of Tariff Reductions on Wood-based Panels (1997–2010) 1997 (103 m3) Production Imports Exports

Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD TABLE 7.5

8,350 181,091 111,425 30,285 96,531 6,981 89,529 25,819 321,426 117,160 438,586

Effect on average annual Production Imports Exports

1,805 50,756 38,502 6,306 45,837 2,607 43,629 4,783 108,160 47,563 155,723

709 10,472 8,845 460 18,926 254 16,565 469 34,968 12,887 47,855

433 11,363 3,226 2,195 16,933 889 15,876 2,229 30,293 19,625 49,918

Effect on average annual Production Imports Exports 1.2% 0.2% 0.3% 4.0% 0.8% 2.0% −1.3% 5.3% −0.5% 2.0% 0.3%

−3.3% −0.5% −1.0% 12.2% 9.7% −3.7% 6.2% −1.6% 4.1% 10.6% 6.2%

2.3% 2.0% 9.5% 16.1% 8.9% 6.7% 3.6% 12.4% 3.7% 9.5% 6.0%

Predicted Effects of Tariff Reductions on Wood Pulp (1997–2010)

Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD

1997 (103 t) Production Imports Exports 2,253 84,550 59,394 9,375 19,431 2,324 37,458 3,879 140,979 18,291 159,270

306 6,291 5,704 783 9,786 214 15,169 69 24,826 7,792 32,618

777 16,656 5,813 4,187 1,500 586 9,029 948 27,735 5,948 33,683

Effect on average annual Production Imports Exports 0.4% 0.2% 0.4% 0.4% −0.8% −0.2% 0.8% 1.1% 0.4% −0.5% 0.3%

1.1% 1.4% 1.5% 5.3% −0.6% −6.1% −0.4% 2.2% 0.4% −1.2% −0.1%

0.7% −0.5% −1.1% 0.0% −1.9% 0.1% 0.8% 3.6% 0.1% −0.6% −0.1%

161


Predicted Effects of Tariff Reductions on Paper and Paperboard (1997–2010)

Region AFRICA NORTH/CENTRAL AMERICA United States SOUTH AMERICA ASIA OCEANIA EUROPE FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD

1997 (103 t) Production Imports Exports 3,162 109,037 86,274 9,842 85,235 3,291 83,729 3,823 229,718 68,401 298,119

1,504 18,285 14,381 3,382 18,971 1,390 38,681 1,005 59,827 23,391 83,218

483 25,899 11,153 1,842 9,156 828 47,815 1,589 77,447 10,165 87,612

Effect on average annual Production Imports Exports 0.4% 0.5% 0.7% 1.1% −1.4% −1.4% 0.7% 0.6% 0.5% −1.6% 0.0%

−0.1% −1.4% −2.0% 0.1% 6.6% 1.1% 0.4% −0.3% −0.2% 5.5% 1.8%

2.3% 2.8% 7.2% 0.6% 0.7% −1.0% 1.5% −0.4% 1.9% 0.5% 1.7%

production of wood-based panels and wood pulp would decrease slightly. For example, while the ATL agreement would cause industrial roundwood production to increase slightly in most regions, it would decrease in Asia and Europe. In the United States, the main effects on production would be an average 0.7% increase in annual sawnwood output from 1998 to 2010. The effects on production of other products in the United States would be negligible. The effects of the ATL agreement on apparent consumption, defined as production plus imports and minus exports, are not shown here since they were similar to the effects on production.

EFFECTS ON IMPORTS The predicted effects of the tariff reductions would be more important on trade, in relative terms, than on consumption and production. As shown in Tables 7.2–7.6, world annual imports of industrial roundwood would be about 1.1% lower under ATL from 1997 to 2010. The main impact (−4.1%) would be in Asia, mostly in Japan. United States imports of industrial roundwood would be unaffected by the agreement. Except for wood pulp, world imports of all manufactured products would increase with lower tariffs. World annual imports of sawnwood and panels would be 3.4–6.2% higher with the ATL agereement, and those of paper would be 1.8% higher. World wood pulp annual imports would be slightly lower by 0.1%. Annual U.S. imports of sawnwood would be about 2% lower, those of panels 1% lower, and those of paper and paperboard 2% lower. Only the annual pulp imports of the United States would be higher, by 1.5% on average between 1997 and 2010.

162


EFFECTS ON EXPORTS At the world level, the effects of the tariff reductions on exports would be the same as on imports, as it should be, after adjustment for the discrepancies in import and export statistics. However, the distribution of the changes by region was quite different, as shown in Tables 7.2–7.6. In particular, while U.S. imports of industrial roundwood were almost unaffected by the ATL, annual exports were 3% lower with the ATL than without it. Further, annual exports of sawnwood from the United States would remain unchanged, while annual exports of pulp would change little (1.1% lower) with ATL. Instead, annual U.S. exports of panels and papers would be substantially higher (9.5% and 7.2%, respectively) with the ATL than without it. The main gains in exports, in relative terms, would occur in South America for panels.

EFFECTS ON PRICES Table 7.7 and Figure 7.6 show the ATL effects on world prices for seven final products and industrial roundwood. Despite some yearly fluctuations, it appears that all product prices would be lower from 2000 to 2010 with the tariff reductions. The major price reduction would occur for papers, ranging from 3.1% to 6.5%. Next would be panels, ranging from 0.7% to 1.1%. The average price of industrial roundwood would 0.8% lower for the period 1997– 2010. Sawnwood prices would be affected only a little, by 0.4%.

TABLE 7.7

Predicted Effects of Tariff Reductions on Real World Prices (1997–2010)

Commodity

Price level 1980 1997

1998

Year of effect 2000 2005

2010

Average1

US$/m3 Industrial roundwood Sawnwood and sleepers Veneer and plywood Particleboard Fibreboard Newsprint Printing and writing paper Other paper and paperboard 1

158 91 289 237 789 465 323 208 385 285 US$/t 768 562 1,313 894 1,108 790

0.0% 0.0% 0.0% 0.0% 0.0%

−0.1% −0.3% 0.3% −0.1% 0.2%

−0.9% 0.3% −1.7% 0.0% −0.6%

−1.7% −1.6% −7.0% −3.7% −2.7%

−0.8% −0.4% −0.9% −0.7% −1.1%

0.0% 0.0% 0.0%

0.3% 1.9% −0.2%

−2.5% −21.1% −3.7% −18.3% 0.6% −5.5%

−5.5% −5.1% −3.1%

Average difference in yearly price from 1997 to 2010.

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Effects of Tariff Reductions on Welfare (1997–2010)

Region

Consumers1

Producers2

Total

0.4% 3.6% 3.8% 0.9% 2.0% 2.0% −0.4% 2.5% 0.1% 2.3%

−0.3% −2.1% −2.3% −0.6% −1.1% −1.6% 0.1% −1.3% 0.0% −1.4%

0.0% 0.0% 0.2% −0.1% 0.3% 0.1% −0.1% 0.2% 0.0% 0.1%

Africa North/Central America United States South America Asia Japan Oceania Europe Former USSR WORLD 1

Difference in consumers’ surplus. Difference in producers’ surplus.

2

5%

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−5%

−15%

−25%


Sawnwood & sleepers

Veneer & Plywood

Particle board

Fibreboard

Newsprint



FIGURE 7.6

Predicted effects of tariff reductions on prices.

EFFECTS ON WELFARE Table 7.8 shows the effects of reducing import tariffs on welfare for major world regions and for the United States and Japan. Effects on the welfare of consumers were measured by the percent difference in consumer surplus, relative to the value of consumption at equilibrium price, averaged from 1997

164


to 2010. Effects on the welfare of producers were measured by the percent difference in producer surplus, relative to the value of production at equilibrium price, averaged from 1997 to 2010. Effects on total welfare were measured by the percent difference in producer surplus plus consumer surplus, relative to the value of production and consumption at equilibrium prices, averaged from 1997 to 2010. At the world level, the tariff reductions induced by the ATL agreement would increase welfare only slightly, by 0.1%, from 1997 to 2010. Consumer welfare would increase by 2.3% of consumption value, while producer welfare would decrease by 1.4% of production value. The largest total welfare gain would occur in Asia, where welfare would be 0.3% higher on average from 1997 to 2010 under the ATL agreement. The largest consumer welfare gain in relative terms would be in North/Central America, 3.6% on average from 1997 to 2010. Only producers in Oceania would see a small welfare gain, of 0.1%; other regions would see losses of producer welfare due to price decrease. In the United States, the welfare of consumers would be 3.8% higher on average from 1997 to 2010, while producers would see their average annual welfare lowered by 2.3%. The overall welfare gain for the United States would be 0.2% only. Similarly, in Japan, the welfare gain of consumers would exceed slightly the losses of producers.

7.5 SUMMARY AND CONCLUSION Brooks (1999) compares the results obtained with the GFPM and the CINTRAFOR Global Trade Model (CGTM) (Cardellichio et al. 1989) in an earlier study of the effects of the ATL (Zhu et al. 2001). The CGTM is a spatial equilibrium model that deals with eight forest products in 43 regions. It provides a detailed description of the solidwood sector (logs, sawnwood, and plywood) of the world forest products market, but it does not cover the pulp and paper sectors. Because of this, and because it does not deal with individual countries, it gave less comprehensive results than the GFPM. But the CGTM has detailed data for regions of the United States and for some more detailed products, such as softwood sawnwood and hardwood sawnwood. With similar assumptions regarding GDP growth and tariff cuts, both models agree on the following main effects of accelerated tariff liberalization: the absence of significant changes in production and consumption at the world scale, significant changes in the commodity composition of trade and in geographic patterns of production and trade, the likelihood of changes in U.S. trade — accompanied by little effect on U.S. production and consumption, and the small net effect on timber harvests at the world level.


165

Based on the projections from the GFPM, by itself, the ATL agreement would cause only small changes in world production and consumption, but the trade would shift toward more processed products. Exports would increase from Europe, Oceania, South America (Chile), and Asia (Indonesia and Malaysia). For the United States, production and consumption would barely be affected, but the trade would change more. It is likely that U.S. exports of logs and pulp would decrease and the exports of most processed products would increase. Similar to the findings by Wisdom (1999) for many countries and for the world, the welfare gain of consumers would exceed the welfare loss of producers due to trade liberalization, but not by much. Policies to partly compensate producers in the forest products sector for their loss may be possible. However, “The question of whether, in fact, [they] should be compensated, and if so, how, is a political question. It is a very important question.” (Wisdom 1999). To judge the environmental implications of further liberalization, changes in timber harvest can be used as a coarse indicator of impacts on the forests. Based on model projections, aggregate global timber harvest would increase little, as compared to the base scenario in 2010. This expected change in world timber harvest is the net effect of both increases and decreases in individual countries. Projected timber harvesting would increase most in the former USSR and South America (by about 1% on an average year) and decrease slightly in Oceania and Europe with accelerated tariff liberalization. Increased harvest in managed secondary forests, especially plantations, is likely to account for much of the net increase in world timber harvests (Sedjo and Lyon 1990, Tomberlin and Buongiorno 2000). Taken together, these projections suggest that, at a broad scale, further tariff liberalization in the forest sector would be neutral with respect to some high-profile environmental consequences (harvesting primary forests). Further tariff liberalization may also lead to positive environmental changes by stimulating increases in manufacturing efficiency in export-oriented developing countries. In addition, trade liberalization in forest products is most likely only as part of a broader set of reduction in tariff and other trade barriers. This may contribute to increasing income and rising standards of living in developing countries, accompanied by decreases in consumption of fuelwood and increases in consumption of other forest products — including forest amenities and attendant conservation measures — along an environmental Kuznet’s curve (Raunikar and Buongiorno 1999). Last, it should be pointed out that the scenario investigated here assumed full and immediate implementation of the initiative by all APEC members, but the initiative allowed developing countries to delay full implementation until 2004.


CHAPTER

8

Impact of U.S. Paper-Recycling Policies

8.1 INTRODUCTION International environmental concerns have been expressed widely in concurrence with increasing trade liberalization, such as the agreements of NAFTA (North American Free Trade Agreement) and the Uruguay Round of the GATT (Esty 1994). The study reported in this chapter1 was meant to test that environmental policies, especially in large countries like the United States, could have significant trade impacts and that as result they could be used, deliberately or not, as trade policies. This case study dealt with paper recycling. It aimed at showing how recycling policies could have significant impacts on the competitiveness of different countries, and therefore on the global forest products trade, and on the welfare of consumers and producers. These policies, like regulations on mandatory minimum recycled fiber content for newsprint, may give, perhaps inadvertently, a cost advantage to U.S. producers.

1

Originally published in Zhu and Buongiorno (2002).

167

168


For example, much more wastepaper is available in the United States than in Canada, because paper consumption is so much higher in the United States. At the same time, Canada is the major exporter of newsprint to the United States, and Canadian consumption is small relative to its production. Canadian exports may, therefore, be hurt by U.S. legislation on minimum fiber content, because increasing recycling in the United States provides a cheap source of raw material to U.S. producers. In this sense, wastepaper-recycling policies may actually serve as a “new” barrier to paper product trade.

8.2 PAPER RECYCLING IN THE UNITED STATES From 1970 to 1985, the wastepaper recovery rate of the United States increased slightly, from 23% to 27%, while the wastepaper utilization rate remained stable around 23–24% (Zhang 1992). After 1988, both the recovery rate and the utilization rate increased rapidly. In 1997, according to the American Forest and Paper Association, the recovery rate was around 45% and the utilization rate approximately 38% (Raymond Com. 1999). Table 8.1 shows the historical changes in U.S. paper product production and trade. From 1988 to 1997, the production of paper and paperboard increased by 24%. As in 1988, the United States was still a net importer of paper and paperboard in 1997. However, the trade deficit was more than halved from 1988 to 1997. Net trade improved the most for other paper and paperboard. It worsened only for printing and writing paper. From 1988 to 1997, wastepaper production increased at almost twice the rate of paper production. The wastepaper recovery rate increased from 30% to 37%, net trade decreased, and wastepaper utilization increased from 23% to 31%. The increasing wastepaper recovery rate in the United States, which accelerated in recent years, was due to the widespread expansion of programs of paper collection and sorting. The programs are a response to long-term environmental, economic, and human health problems associated with landfills, waste incineration, and solid waste management. Based on Raymond Com. (1999), Ince (1999) summarizes the legislation and programs related to paper recycling and groups them into two general categories in terms of their impacts on the supply of or the demand for recovered paper and recycled paper products. On the supply side, the number of local collection programs for recovery of recyclable materials increased from approximately 1000 in 1988 to 9000 mandatory collection programs and 10,000 voluntary programs in 1998. There exist landfill bans for paper in three states and some local bans. On the demand side, the federal government, 47 state government agencies, and 500 local

TABLE 8.1

U.S. Paper Products Production and Trade (103 t) 1988

Product Newsprint Printing and writing paper Other paper and paperboard Total paper and paperboard Wastepaper Data Source: FAO (1999a).

1997

Production

Exports

Imports

Net trade

5,427 19,588 44,572 69,587 23,177

388 235 3,969 4,591 7,528

7,794 2,964 1,094 11,853 217

−7,406 −2,729 2,875 −7,262 7,311

Change

Production

Exports

Imports

Net trade

6,544 24,328 55,402 86,274 33,310

967 1,512 8,674 11,153 6,807

6,502 4,768 3,111 14,381 628

−5,535 −3,256 5,563 −3,228 6,179

Net Production trade 21% 24% 24% 24% 44%

25% −19% 93% 56% −15%

169

170


government agencies have purchasing preferences for recycled products. At least 26 states have some form of recycling tax incentive or credit program. At least 13 states have some form of business loan program to help stimulate recycling. Seven states have enacted laws on minimum mandatory recycled content for newsprint. Such laws generally require newspaper publishers to certify that the newsprint they use contains around 40% recycled fiber, and the implementation time for these laws is usually by the year 2000–2005. This legislation and these programs have increased the recovered paper supply and promoted the demand for recycled paper products substantially in recent years (Ince 1999). There also has been a great improvement since the late 1980s in the economics of using wastepaper instead of virgin fiber in paper production. Iannazzi (1991) compares the operating costs for newsprint manufacture and shows that a process based on 100% old newsprint could save 20% of the manufacturing cost, mostly from power saving. Mabee (1998) points out that the economic advantage of wastepaper as a source of fiber in the paper industry results from reduced effluent discharges, reduced water consumption, and reduced mechanical processing compared with those required for virgin fibers.

8.3 PREVIOUS STUDIES There have been other analytical studies on the impact of increased recycling within the United States and Canada. Zhang (1992) and Zhang et al. (1996) studied the effects of paper-recycling policies with the North American Pulp and Paper Model (NAPAP). Ince (1994) applied the same model to project trends in paper recycling and their effects on markets in the United States and Canada. He concluded that the wastepaper recovery rate in the United States would increase to 52% by 2010 and that the wastepaper utilization rate would reach 43%. Due to paper recycling, U.S. imports of pulp and paper products from Canada would decline while Canadian imports of recovered paper from the United States would increase. There are definite limits to wastepaper recovery. The physical properties of wastepaper limit its usefulness in high-strength and lightweight paper (Haynes 1990), so the content of wastepaper is likely to be higher in newsprint than in other paper grades (Ince 1994). Some paper grades, such as sanitary paper, are not recoverable at all. And, more recovery results in lower paper quality, because the fiber becomes weaker and more contaminated every time it is reused. A likely upper limit on the wastepaper recovery rate is 75% (Ince 1994). Guest (1998), however, argues that since Japan, which has a well-developed wastepaper collection system, is struggling to reach a recovery target of 55% but currently sticking at about 53%, a plausible maximum recovery rate is 53%.

171


8.4 GFPM ANALYSIS2 The international effects of increased U.S. paper recycling were projected for the period 1998–2010 with the Global Forest Products Model (Chapter 3). One of the exogenous variables of the GFPM is the wastepaper recovery rate, which is influenced by recycling policy. The upper bound of wastepaper supply in a given year is a fraction (recovery rate) of paper consumption in the previous year. This application of the GFPM assumed that further paper recycling in the United States would affect the forest sector of countries, mainly through changes in the U.S. wastepaper recovery rate and utilization rate. The other assumptions were the same as for the base scenario described in Chapter 5.

THEORETICAL EXPECTATIONS Figure 8.1 summarizes the main theoretical principles underlying the projections and expectations regarding the direction of changes. The figure symbolizes the demand and supply in the United States and in the rest of the world (ROW) in a particular year for a product for which the United States is a net importer, say, newsprint. Without increased paper recycling, the supply in the United States P

United States

ROW

P A SU SU ′ SR P

C

P′ D

X

B

I

X′

I′ DU

DR Q

Q

FIGURE 8.1 Theoretical demand, supply, net trade, and price in the United States and the rest of the world (ROW) before and after a supply shift due to increased paper recycling in the United States.

2

The data for this analysis, and the GFPM software, are available at: www.forest.wisc.edu/ facstaff/buongiorno/book/index.htm

172


would be at the level symbolized by the upward-sloping curve SU; the demand would be at the level DU. In the rest of the world, the supply would be SR and the demand DR. The equilibrium price, P, would be such that the net exports of the rest of the world, X, would equal the net imports of the United States, I. Price and Quantity Changes Assume that, promoted by various recycling policies, wastepaper recovery in the United States increases and more wastepaper is used to produce papers. Nothing is changed in the rest of the world. Within limits, the increased use of wastepaper lowers the manufacturing cost (Young 1991, Veverka 1991, Iannazzi 1991, Mabee 1998). This translates into a downward shift of the U.S. supply curve from SU to S′U . Other things being equal this leads to a decrease in the equilibrium price from P to P′. To this lower price corresponds a higher demand both within and outside the United States. But while production is also higher within the United States, it is lower in the rest of the world. The consequences for net trade are: a decrease in U.S. imports, from I to I′, and a decrease in exports from the rest of the world, from X to X′, with X′ = I′. Welfare Changes A standard measure of consumer welfare is the “consumer surplus,” that is, the area under the demand curve minus the cost of the amount consumed at the equilibrium price. For example, the welfare (or surplus) of consumers of paper in the United States before the supply shift is the area of triangle ABP. Similarly, a measure of the producers’ welfare is the “producer surplus,” that is, the value of the product sold at the equilibrium price minus the area under the supply curve (the cost of production). For example, in Figure 8.1, the welfare of U.S. producers before the supply shift is the area of triangle PCD. It is seen from Figure 8.1 that the welfare effect of the supply shift due to increased recycling is to increase the welfare of consumers and producers in the United States. It also increases the welfare of consumers in the rest of the world, but it decreases the welfare of producers in the rest of the world. However, the magnitude as well as the direction of these effects could be different from those expected with this simple model, for various reasons. In particular, there are dynamic effects that link demand, production, and capacity changes. This means, for example, that supply from the rest of the world is not fixed as in Figure 8.1. This feedback is recognized in the GFPM, while it is not in the simple comparative static model sketched in Figure 8.1. Also, the theoretical sketch in Figure 8.1 abstracts from the relations between products at different stages of manufacturing, possible substitutions between wood and recycled fiber, and other complexities simulated by the GFPM.

173


TABLE 8.2 Past and Projected Fiber Utilization in the Manufacture of Paper and Paperboard in the United States (metric ton/metric ton) 2010 Output Newsprint

Printing and writing paper Other paper and paperboard

Annual rate of change

Input

1997

Base scenario

Increased recycling

Base scenario

Increased recycling

Mechanical pulp Chemical pulp Wastepaper Mechanical pulp Chemical pulp Wastepaper Mechanical pulp Chemical pulp Wastepaper

0.050 0.620 0.330 0.010 0.690 0.200 0.090 0.550 0.360

0.050 0.464 0.486 0.010 0.580 0.311 0.071 0.453 0.477

0.050 0.308 0.642 0.010 0.469 0.421 0.051 0.394 0.555

0.000 −0.012 0.012 0.000 −0.009 0.009 −0.002 −0.008 0.009

0.000 −0.024 0.024 0.000 −0.017 0.017 −0.003 −0.012 0.015

U.S. RECYCLING SCENARIOS The base scenario simulated with the GFPM model used the same value of the exogenous variables as in Chapter 5. In particular, the maximum wastepaper recovery rate of the United States, which was 44% in 1997, reached 52% in 2010, corresponding to a growth rate of 0.6% per year. Instead, under the increased recycling scenario, the recovery rate would reach 60% by 2010, a growth of 1.2% per year from 1997. In the base scenario, wastepaper utilization would increase at 1% to 1.2% per year from 1997 to 2010, depending on the product (Table 8.2). The utilization of mechanical and chemical wood pulp would decrease accordingly. Total wastepaper utilization in all products would go from approximately 31% of all fiber input in 1997 to 40% in 2010. Under the increased recycling scenario, the wastepaper utilization rate would increase at 1.5–2.4%, depending on the product, and total utilization would reach 50% by 2010. The base scenario corresponded to the projections from Ince (1994) and took into account that there had been a decrease in new recycling legislation in the late 1990s. The high prices of recycled paper in the mid-1990s diminished the rationale for recycling laws aimed at increasing demand for recovered materials. As a result, in the base scenario the recovery and utilization rates in the United States increased more slowly from 1998 to 2010 than from 1988 to 1997. The assumptions of the increased recycling scenario were partially based on Mabee and Pande (1997), reflecting decreasing landfill capacity, increasing consumption of paper products, the implementation of mandatory recycled fiber content in some states after 2000, and the economic incentive for using recycled fiber. Environmental groups are still promoting government purchasing preferences, tax credits, and executive orders to increase recycling (Raymond Com. 1999,

174


Tappi 1998). According to Alig (1993), legislation in favor of recycling will continue to increase up to 2010. In the increased U.S. recycling scenario, the wastepaper recovery rates and utilization rates of the rest of the world were the same as in the base scenario.

8.5 INTERNATIONAL EFFECTS For each projected year, the GFPM computes simultaneously the quantities produced, imported, and exported, and the corresponding prices, for all products and countries. The equilibrium prices are such that all markets clear. For the purpose of exposition, we present first the effects on prices, then the effect on each product, and finally the effects on welfare. However, these are simultaneous results reflecting the equilibrium shift in the entire forest sector, triggered by the increased paper recycling in the United States.

EFFECTS ON WORLD PRICES For internationally competitive industries like forest products, the market signals are transmitted essentially through price changes. Therefore, the effects on prices are primordial in tracing the consequences of accelerated paper recycling in the United States. Table 8.3 shows the effects of increased U.S. paper recycling on average real (net of inflation) world prices from 1998 to 2010. TABLE 8.3 Projected Effects of Increased U.S. Recycling on Real World Prices of Forest Products (1998–2010) Effect in Commodity

Price level 1980 1997

1998

2000

2005

2010

Average (1998–2000)

0.0% 0.0% 0.0% 0.0% 0.0%

0.5% 0.2% −2.4% 0.4% 0.5%

0.0% 1.0% −4.4% −0.4% −0.5%

0.0% 0.1% −0.4% 0.3% 0.1%

0.0% 3.9% −14.0% 0.0% −15.2% −11.1% −0.2% −1.0% 54.2% −0.7% −7.6% −20.2% −0.8% −2.7% −15.3% −0.2% −7.7% 2.7%

−1.1% −10.3% 3.5% −9.3% −6.3% −3.2%

US$/m3 Industrial roundwood Sawnwood and sleepers Veneer and plywood Particleboard Fibreboard

158 289 789 323 385

91 237 465 208 285

0.0% 0.0% 0.0% 0.0% 0.0%

US$/t Mechanical wood pulp Chemical wood pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard

580 813 118 768 1313 1108

331 469 109 562 894 790

0.0% 0.0% −0.1% −0.2% −0.1% −0.1%


175

The average prices of newsprint, printing and writing paper would be significantly lower from 1998 to 2010. These are the differences in price levels between this scenario and the base scenario in Chapter 5. This is consistent with the theoretical expectations based on the simple model in Figure 8.1. Increasing recycling in the United States would decrease the cost of production and reduce prices worldwide. The effects are substantial. For example, increasing recycling in the United States by the stipulated amount would reduce the average price of newsprint from 1998 to 2010 by 9%. Increasing recycling in the United States would decrease the utilization of wood pulp. This in turn would lead to a decrease in the average price of chemical wood pulp (the dominant type of pulp) by nearly 10%. For wastepaper, the price was slightly lower throughout the projection period. However, for the year 2010 the price increased sharply, signaling that production of wastepaper had reached the upper bound set by the recovery rate. The world price of industrial roundwood was hardly affected by increased recycling. This is because the lower price of paper and paperboard would increase demand for these products, and this would maintain the demand for pulpwood and hence leave the price of pulpwood unaffected. As a result of stable wood prices, increased paper recycling would have little effect on the price of other forest products, such as sawnwood and wood-based panels (Table 8.3).

EFFECTS ON THE PAPER INDUSTRY Due to the reductions in prices just observed, increased paper recycling would lead to higher production of paper and paperboard in the United States (Table 8.4). This is consistent with the theory sketched in Figure 8.1. The GFPM predicted that annual U.S. production would be about 6 million tons (5%) higher from 1998 to 2010 than in the base scenario. World annual production would be about 7 million tons higher. Thus, world production outside of the United States would increase by approximately 1 million tons. This result differs from the simple theory in Figure 8.1, because the lower price of paper and paperboard leads to higher world demand, which stimulates investments in new production capacity outside of the United States. In terms of Figure 8.1, this corresponds to a rightward shift of the ROW supply and thus to increased production. The predictions of trade in Table 8.4 are also in general agreement with the theory sketched in Figure 8.1. In particular, the net trade of the United States would improve, due to lower annual imports and higher exports. The increase in net trade of the United States would be matched by a worsening of the trade balance in the rest of the world. However, the effects would vary greatly by region and country. Developing countries, especially in Asia, would bear the brunt of the adjustment, due mostly to a reduction in paper and paperboard production in China and to increased imports of about 1.3 million tons per year. Indeed, the net trade of most Asian countries would worsen.

176


TABLE 8.4 Projected Effects of Increased U.S. Recycling on Average Annual Production and Trade of Paper and Paperboard (1998–2010) Production Region AFRICA NORTH/ CENTRAL AMERICA Canada United States SOUTH AMERICA Brazil ASIA China Japan Korea, Rep. OCEANIA EUROPE Finland France Germany Italy Sweden United Kingdom FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD

Imports

3

Exports

3

10 t

3

10 t

Net trade 103 t

10 t

35 6145

0.7% 4.2%

−16 −301

−0.8% −1.5%

22 482

3.6% 1.5%

38 783

233 6002 190 74 −190 −489 −43 99 −17 904 108 57 431 −8 269 42 10 7147 −69 7078

0.9% 5.2% 1.4% 0.8% −0.1% −0.8% −0.1% 0.7% −0.3% 0.8% 0.7% 0.5% 2.1% −0.2% 2.0% 0.5% 0.1% 2.3% 0.0% 1.7%

10 −357 −23 26 1156 1019 17 1 60 212 0 −41 12 86 0 41 −1 −87 1174 1087

0.6% −2.3% -0.6% 2.3% 4.0% 7.0% 1.3% 0.1% 3.2% 0.5% −0.2% −1.1% 0.2% 2.3% 0.0% 0.5% −0.1% −0.1% 3.4% 1.1%

85 422 7 17 −101 −23 4 −113 24 668 73 88 320 −119 288 4 −14 1211 −124 1087

0.5% 3.4% 0.3% 0.9% −1.0% −3.3% 0.3% −4.3% 2.7% 1.2% 0.6% 3.4% 4.0% −7.5% 2.6% 0.3% −0.8% 1.3% −1.2% 1.1%

75 779 30 −9 −1257 −1042 −13 −114 −36 456 73 129 308 −205 288 −37 −13 1298 −1298 0

In Europe, however, the situation would be quite different: Production and net trade would be higher overall. The reason is that, while wastepaper recovery increases in the United States, its utilization increases even more, due to the rise in world demand. As a result, the supply of wastepaper to Asia decreases, decreasing production of paper and paperboard. But Asian demand increases due to the lower world prices, leading to higher imports, which are supplied in part by the United States and in part by European countries. In total, world imports and exports of paper and paperboard would increase by about 1 million tons per year. While the policy of increased recycling has a positive effect on the trade balance of developed countries, it has the opposite effect on the net trade of developing countries, particularly in Asia.

EFFECTS ON FIBER FURNISH The most direct effect of increased paper recycling in the United States would be an increase in wastepaper production of about 8 million tons per year (Table 8.5). In the rest of the world, production would also be slightly higher. To pursue

177


TABLE 8.5 Projected Effects of Increased U.S. Recycling on Average Annual Production and Trade of Wastepaper (1998–2010) Production Region AFRICA NORTH/ CENTRAL AMERICA Canada United States SOUTH AMERICA Brazil ASIA China Japan Korea, Rep. OCEANIA EUROPE Finland France Germany Italy Sweden United Kingdom FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD

Imports

3

Exports

Net trade

3

103 t

3

10 t

10 t

10 t

13 8355

0.7% 13.6%

−3 −63

−7.7% −4.6%

1 −460

10.0% −4.3%

4 −397

63 8317 75 21 263 −56 −13 82 −19 399 36 52 134 −1 −11 104 4 8769 321 9090

1.1% 15.5% 1.4% 0.6% 0.4% −0.2% −0.1% 1.4% −0.8% 0.8% 1.9% 0.8% 1.0% −0.3% −0.6% 1.8% 0.1% 6.5% 0.7% 5.1%

−26 4 −7 −1 −227 −136 −9 −43 −2 27 −1 −12 −6 0 79 −2 −1 −7 −268 −276

−6.3% 1.6% −7.7% −7.7% −1.7% −2.5% −4.6% −1.4% −7.7% 0.5% −7.7% −3.1% −1.2% 0.0% 7.7% −7.7% −2.8% −0.1% −1.9% −1.3%

38 −499 2 0 90 0 0 0 −9 100 20 0 −91 0 0 85 0 −369 94 −276

26.1% −4.8% 3.9% 0.0% 7.3% 0.0% 0.0%

64 −503 10 1 317 136 9 43 −7 73 21 12 −85 0 −79 88 1 −362 362 0

−4.3% 1.5% 38.7% 0.0% −2.5% 0.0% −0.4% 16.1% −0.9% −2.0% 7.7% −1.4%

this policy of high recycling, the United States would also have to cut its exports. This worsening trade balance would be matched by an improvement in other countries, especially in Asia. Wastepaper is used in combination with wood pulp, according to the technologies described in Table 8.2. As the relative amount of wastepaper utilization increases, that of wood pulp decreases. Thus, the result of increased wastepaper utilization in the United States is a decrease in wood pulp production by about 2 million tons per year (Table 8.6). This is much less than the increase in wastepaper production. Thus, the recycling policy fails to replace wastepaper for virgin pulp. This is due to the higher demand for paper and paperboard resulting from lower world prices. Production of wood pulp also decreases in South America and Asia. However, it increases in Europe, due to the increase in paper demand and the accompanying increase in production. Nevertheless, the net trade of Europe worsens, as does that of Asia and South America, with some variation by country, while the net trade of the United States improves.

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TABLE 8.6 Projected Effects of Increased U.S. Recycling on Average Annual Production and Trade of Wood Pulp (1998–2010) Production Region AFRICA NORTH/ CENTRAL AMERICA Canada United States SOUTH AMERICA Brazil ASIA China Japan Korea, Rep. OCEANIA EUROPE Finland France Germany Italy Sweden United Kingdom FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD

Imports

3

Exports

Net trade

3

103 t

3

10 t

10 t

10 t

24 −1865

0.8% −1.9%

6 −457

1.7% −7.3%

9 347

1.3% 1.8%

3 804

368 −2178 −140 −122 −415 −40 −42 −46 −10 431 166 33 202 −9 163 −15 2 −1390 −584 −1974

1.2% −3.3% −1.3% −1.7% −1.7% −1.3% −0.3% −3.6% −0.3% 0.8% 1.1% 1.1% 3.9% −2.1% 1.3% −1.9% −0.1% −0.8% −2.3% −1.0%

1 −490 46 4 282 −70 23 90 9 23 3 3 −26 −4 1 33 4 −419 333 −86

0.3% −8.7% 6.7% 1.8% 2.6% −1.9% 1.0% 3.6% 2.7% 0.1% 163.2% 0.1% −0.6% −0.2% 0.5% 1.6% 6.5% −1.6% 3.4% −0.3%

171 176 −209 −167 −158 −2 −4 0 3 −77 79 12 −1 0 −33 0 0 285 −371 −86

1.4% 2.6% −4.8% −7.1% −8.9% −10.6% −7.7% −1.9% 0.5% −0.9% 3.6% 4.3% −0.4% 0.6% −1.3% 12.5% 0.2% 0.9% −5.8% −0.3%

170 666 −255 −171 −440 68 −27 −90 −6 −100 76 9 25 4 −34 −33 −4 704 −704 0

EFFECTS ON INDUSTRIAL ROUNDWOOD The GFPM calculates the effects of increased wastepaper recycling in the United States on the complete forest sector, including solidwood products such as sawnwood and wood-based panels. However, the effects on solidwood consumption, production, and trade were small and are not reported here, except for the minimal effects on prices shown in Table 8.3. Thus, the effects on industrial roundwood (Table 8.7) are linked mostly to the effects on the wood pulp market (Table 8.6). With increased paper recycling in the United States, annual industrial roundwood production would be approximately 6 million m3 lower from 1998 to 2010. Furthermore, annual U.S. exports would be approximately 4% higher. Meanwhile, production of industrial roundwood in the rest of the world would increase, but only slightly. Production would be higher in Europe but lower in Asia, in agreement with the changes in wood pulp production in the

179

Impact of U.S. Paper-Recycling Policies TABLE 8.7 Projected Effects of Increased U.S. Recycling on Industrial Roundwood Consumption, Production, and Trade (1997–2010) Production Region AFRICA NORTH/ CENTRAL AMERICA Canada United States SOUTH AMERICA Brazil ASIA China Japan Korea, Rep. OCEANIA EUROPE Finland France Germany Italy Sweden United Kingdom FORMER USSR DEVELOPED, ALL DEVELOPING, ALL WORLD

3

3

10 m

Imports 3

3

10 m

Exports 3

3

10 m

Net trade 103 m3

291 −5977

0.4% −0.8%

−11 234

−0.8% 1.3%

150 678

1.7% 3.4%

161 444

277 −6085 47 267 −965 360 0 −122 −1 1527 444 155 1172 −14 −27 −4 −424 −4275 −1227 −5502

0.1% −1.2% 0.0% 0.3% −0.2% 0.2% 0.0% −1.5% 0.0% 0.4% 0.9% 0.4% 2.0% −0.4% 0.0% 0.0% −0.4% −0.3% −0.2% −0.3%

228 0 0 0 31 0 −78 108 0 387 20 0 0 49 0 18 0 536 105 641

1.4% 0.0% 0.0% 0.0% 0.0% 0.0% −0.2% 1.2% 0.0% 0.6% 0.3% 0.0% 0.0% 0.5% 0.0% 1.7% 0.0% 0.4% 0.4% 0.4%

0 679 −190 −96 67 87 0 0 69 474 0 −186 562 0 0 0 −607 902 −261 641

0.0% 3.8% −1.5% −4.9% 0.8% 5.3% 0.0%

−228 679 −190 −96 36 87 78 −108 69 87 −20 −186 562 −49 0 −18 −607 366 −366 0

0.2% 1.7% 0.0% −4.0% 6.4% 0.0% 0.0% 0.0% −1.7% 0.8% −0.8% 0.4%

two regions (Table 8.6). In terms of net trade in industrial roundwood, the largest losers would be the countries of the former USSR.

EFFECTS ON WELFARE Table 8.8 shows the effect of increased paper recycling in the United States on the welfare of producers, consumers, and both in the main world regions and selected countries from 1998 to 2010. The table shows the average annual differences in consumer surplus and producer surplus due to the recycling policy. These estimates cover the effects of the policy on the entire forest sector, consisting of the supply, demand, and trade of roundwood, sawnwood, wood-based panels, fiber furnish, and paper and paperboard. In all regions, the consumers would gain (the small negative change in consumer surplus for Oceania is negligible). This is a consequence of the decrease in price and increase in consumption of paper and paperboard that result from the increased recycling in the United States. In all countries, producers would

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TABLE 8.8 Projected Average Annual Welfare Differences Due to Increased Paper Recycling in the United States (1998–2010) Consumers Region AFRICA NORTH/ CENTRAL AMERICA Canada United States SOUTH AMERICA Brazil ASIA China Japan Korea, Rep. OCEANIA EUROPE Finland France Germany Italy Sweden United Kingdom FORMER USSR WORLD

6

10 US$

Producers

Total

6

106 US$

10 US$

372 14,013

0.7% 5.6%

−304 −11,110

−0.5% −2.5%

68 2,903

0.1% 0.5%

262 13,510 585 322 2,068 703 351 379 −5 4,647 123 835 724 623 114 809 146 21,825

1.3% 6.2% 1.4% 1.3% 0.6% 0.4% 0.4% 2.4% −0.1% 2.7% 2.1% 3.9% 2.1% 3.0% 2.1% 3.2% 1.1% 2.6%

−1,786 −9,170 −853 −557 −1,215 −510 −245 −183 −34 −3,753 −607 −541 −221 −165 −737 −258 −242 −17,511

−1.7% −2.8% −1.2% −1.3% −0.3% −0.3% −0.2% −0.9% −0.2% −1.4% −1.4% −2.0% −0.5% −0.9% −1.9% −1.5% −0.7% −1.4%

−1,523 4,340 −268 −235 853 193 106 195 −39 894 −484 294 504 458 −623 551 −96 4,314

−1.2% 0.9% −0.2% −0.3% 0.1% 0.1% 0.1% 0.6% −0.1% 0.2% −1.0% 0.6% 0.6% 1.3% −1.4% 1.4% −0.2% 0.2%

lose, due to the decrease in prices accompanied by relatively smaller changes in production. For the world as a whole, the average annual gain in consumer surplus from 1998 to 2010 would be about $US 22 billion, in 1997 purchasing power, while the loss in producer surplus would be about $US 18 billion, leaving a net welfare gain worldwide of approximately $US 4 billion. Thus, in terms of total welfare, a unilateral U.S. policy of increased recycling would have a small positive effect for agents in the world forest sector. But it would not be Pareto optimal for all regions and countries. Not only would some countries gain less than others, but some would be worse off than without the policy. The main total welfare gain would be in the United States, though countries like Italy and the United Kingdom would gain more in relative terms (Table 8.8). The main welfare loss would be in Canada, where producer welfare would decrease more than consumer welfare would increase. In Asia and Europe, however, the welfare gains of consumers would more than compensate for the losses of producers. But within Europe, there would be differences between countries. In particular, a large consuming country like Germany would see its total welfare increase, while large producing countries like Finland and Sweden would face a total welfare decrease.


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8.6 SUMMARY AND CONCLUSION The results of this chapter suggest that environmental policies can affect the competitiveness of countries and, as a result, their net trade and economic welfare. This specific case study examined the effects on the international forest sector of policies that would increase wastepaper recovery and utilization unilaterally in the United States through new regulations and legislation up to 2010. An experiment was conducted by projecting production and trade with the global forest products model, with and without increased recovery and utilization of wastepaper in the United States, other things being held constant. The projections gave detailed results, by country and product, on production, imports, exports, and prices from 1998 to 2010. The results showed that further paper recycling in the United States would affect the pulp and paper markets in other countries significantly, while it would have little effect on industrial roundwood, sawnwood, and wood-based panel production and trade. One main effect would be the reduction in world prices of paper and paperboard. As a result, world demand for paper would increase, attenuating to some extent the substitution of wood pulp by recycled paper. Increased paper recycling in the United States would affect world trade. This result is different from the findings of van Beers and van den Bergh (1997), which suggest that trade flows in forest products are relatively insensitive to environmental regulation. Here it was found that the balance of trade of the United States would improve for paper and paperboard, wood pulp, and industrial roundwood. It would only worsen for wastepaper. In the rest of the world, the worsening of net trade would occur mostly in Asia. Europe would benefit to some extent from the increase in world demand due to lower prices. The total welfare of agents in the world forest sector, measured by consumer and producer surplus, would change little due to increased paper recycling in the United States. However, the welfare of consumers would increase substantially while that of producers would decrease. Net welfare gains would occur in major consumer countries such as the United States, Germany and the United Kingdom, while major producers such as Canada, Finland and Sweden would suffer the largest losses. The GFPM results confirm the general belief that wastepaper recovery and recycling are a way to extend forest resources. Furthermore, they suggest that with this scenario there would be hardly any effect on the price of industrial roundwood. Thus, it is unlikely that the forest stock would be reduced due to forest land conversion to other uses (Darby 1973) or that forest management and timber supplies would decrease (Bourke 1995) due to the increase in recycling considered here.


CHAPTER

9

Effects of Regional Trade Agreements on New Zealand

9.1 INTRODUCTION The purpose of this GFPM application was to predict the potential effects of trade liberalization on the New Zealand forest sector. This study was part of a larger project commissioned by the New Zealand Ministry of Foreign Affairs and Trade to estimate the potential impact of trade liberalization initiatives on the New Zealand forest sector and regional economic development (New Zealand Forest Research Institute Ltd. 2001)1. The utility of the Global Forest Products Model in this type of study is that it provides the ability to analyze the New Zealand forest sector in an international context by considering the numerous and complex links between countries and between industries.

1 Material in this chapter was first published in The New Zealand Journal of Forestry Science (Turner et al. 2001).

183

184


The study considered the effects of two regional trade agreements: AFTACER, between the ASEAN2 Free Trade Area and Closer Economic Relations countries,3 and P5, between the United States, Chile, Australia, New Zealand, and Singapore. Global trade liberalization under the World Trade Organization (WTO) administered Generalized Agreement on Tariffs and Trade (GATT 1994) was also considered. Of New Zealand’s major forest product export markets, by value Australia accounted for over $402 million (28% of the total) in 2000, the Association of Southeast Asian Nations (ASEAN) accounted for nearly $129 million (9%), and the P5 countries (excluding Australia) accounted for nearly $178 million (13%) (New Zealand Forest Owners Association 2001). These countries are important and growing markets for New Zealand forest product exports, particularly of sawn timber, paper and paperboard, and wood-based panels. The GFPM was used to predict the effect of each of the trade agreements on the New Zealand forest sector over the fifteen years from 1998 to 2015. The next section briefly describes the three trade agreements studied. Section 9.3 describes the GFPM analysis used to make the projections. Section 9.4 discusses the results in terms of the predicted effects of each of the agreements on New Zealand production, consumption, and trade. The last section summarizes the main findings of the study.

9.2 TRADE AGREEMENTS The effects on New Zealand forest product production, consumption, and trade of the three trade agreements were computed by comparison with a base scenario that kept tariffs at 1997 levels. This base scenario also included zero tariffs on products traded between New Zealand and Australia under CER. In all other aspects the base scenario used here was the same as that described in Chapter 5, with the exception that gross domestic product (GDP) growth rates reflected the effects of the Asian economic crisis. The effects of the crisis on GDP growth rates are the same as those described in Table 6.1.

AFTA-CER REGIONAL TRADE AGREEMENT The AFTA-CER regional trade agreement requires member countries to grant duty-free access to each other’s markets by 2015. The year 2005 is an optimistic target date for some of the initial liberalization, with further liberalization set to 2 The Association of Southeast Asian Nations comprise Brunei, Darussalam, Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, Laos, Cambodia, and Myanmar. 3 The CER countries are Australia and New Zealand.


185

occur by 2010. The most recent AFTA members—Vietnam, Myanmar, Cambodia and Laos—would have until 2015 to grant duty-free access.

P5 REGIONAL TRADE AGREEMENT The P5 regional trade agreement is likely to seek total tariff elimination for all products by a set date. Tariffs for forest products are not a sensitive issue for these five countries, so the tariffs are likely to be eliminated at the beginning of the agreement, regardless of the rest of its content. The Asia-Pacific Economic Cooperation (APEC) Bogor Goals of free trade among APEC member economies4 require that developed members of APEC— Australia, Canada, Japan, New Zealand, and the United States—achieve free trade by 2010. So for the P5 agreement to be of value, the date of tariff elimination would have to be before 2010. For this study it was assumed that forest products tariff elimination under the P5 agreement would be achieved by 2005. The 1997 tariff schedules under the P5 and AFTA-CER trade agreements are in Tables 9.1 and 9.2, respectively, and represent tariffs applied to other P5 or AFTA-CER countries. Tariffs for all other countries were kept at their 1997 levels. Changes in tariff schedules were set to result in a uniform decline in tariffs to their target levels by the required tariff liberalization date. Target tariff levels and the years in which they are to become effective are given in Tables 9.1 and 9.2.

GENERAL AGREEMENT ON TARIFFS AND TRADE (GATT 1994) The Uruguay Round of GATT (1994) ended with an agreement to reduce tariffs on most forest products by 33% on a trade-weighted basis. The major developed countries were committed to tariff elimination on pulp and paper by 2004 and to reducing tariffs by 50% on solidwood products over five years from 1995. The Uruguay Round agreement also committed major developed countries and most developing countries to set bound rates (maximum tariff rates) on forest products (Barbier 1996). The WTO, established in January 1995, provides an institutional foundation for the GATT and other trade agreements. Here the post-WTO GATT is referred to as GATT 1994 (Bowen et al. 1998). The tariff 4 The APEC member economies are Australia, Brunei Darussalam, Canada, Chile, People’s Republic of China, Hong Kong of China, Indonesia, Japan, Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, Philippines, Russian Federation, Singapore, Chinese Taipei, Thailand, the United States, and Vietnam.

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The Global Forest Products Model TABLE 9.1 1997 Tariff Rates Targeted for Removal by 2005 in the P5 Member Countries, for Selected Commodities1 Country

Commodity

1997 Tariff rate (%)

United States

Plywood/ veneer Particleboard Fiberboard Printing and writing paper Other paper and paperboard

5.0 0.8 0.6 1.5 2.4

Chile

Industrial roundwood Sawnwood Plywood/veneer Chemical pulp Newsprint

Australia

Sawnwood Plywood/veneer Printing and writing paper

5.0 5.0 5.0

New Zealand

Sawnwood Particleboard Fiberboard Newsprint

8.0 7.5 6.5 7.5

11.0 11.0 11.0 11.0 11.0

Source: APEC, World Trade Organization, and country Harmonized Tariff Schedule. Complete tariff data are in Table 7.1. 1

Singapore tariffs on forest products were all zero in 1997.

rates under the GATT 1994 are the same as those used in the base scenario of the accelerated tariff liberalization study (Chapter 7).

9.3 GFPM ANALYSIS5 The main theoretical principles underlying the projections and the expectations regarding the direction of changes due to regional trade agreements are the same as those for tariff liberalization. These theoretical effects are illustrated for trade in a single product between two countries in Figure 7.1. To model the regional trade agreements in this study, a number of modifications were made to the version of the GFPM described in Chapters 4 and 5. The most important was the inclusion of bilateral trade flows, to allow modeling of the regional trade agreements. The interest in the effects of these trade agreements on the New Zealand forest sector in particular meant that additional information regarding the structure of the New Zealand forest industries 5 The data for this application, and the GFPM software, are available at: www.forest.wisc.edu/ facstaff/buongiorno/book/index.htm

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TABLE 9.2 1997 Tariff rates and Target Date for Removing Tariff in the AFTA-CER Member Countries, for Selected Commodities Country

Commodity

1997 tariff rate (%)

Target year for tariff removal

Indonesia

Sawnwood Plywood/veneer Wastepaper Printing and writing paper

10.0 20.0 25.0 15.0

2010 2010 2010 2010

Laos

Industrial roundwood Sawnwood Plywood/veneer Chemical pulp Newsprint

2.0 5.0 20.0 3.0 10.0

2015 2015 2015 2015 2015

Malaysia

Plywood/veneer Newsprint Other paper and paperboard

40.0 5.0 20.0

2010 2010 2010

Thailand

Sawnwood Plywood/veneer Chemical pulp Newsprint

5.0 20.0 7.0 35.0

2010 2010 2010 2010

Australia

Sawnwood Plywood/veneer Printing and writing paper

5.0 5.0 5.0

2005 2005 2005

New Zealand

Sawnwood Particleboard Newsprint

8.0 7.5 7.5

2005 2005 2005

Source: APEC, World Trade Organization, and country Harmonized Tariff Schedule. Complete tariff data are in Table 7.1.

needed to be included in the GFPM, as explained next. The other assumptions were the same as for the base scenario described in Chapter 5 except that the projections were carried out to 2015.

TRADE FLOWS The key adaptation made to the GFPM as part of this study was to describe in detail the trade flows among the AFTA-CER and P5 countries. Thus, total imports and exports for each of these countries were disaggregated by country of origin and destination. Bilateral trade data for 1997 among the AFTA-CER and P5 member countries were taken from FAOStat (FAO 1999a). Data on bilateral trade in wastepaper came from FAO (1999d). Bilateral trade data were adjusted to reconcile differences between trade reported by partner countries and to ensure that total bilateral trade flows did not exceed each country’s reported total imports and exports.

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The Global Forest Products Model TABLE 9.3 (103 t/y)

Assumed New Zealand Capacity of Production for Pulp

Mechanical pulp Chemical pulp

1997–2003

2004–2008

2009–2014

2015

852 748

852 786

852 825

852 866

NEW ZEALAND ASSUMPTIONS Specific assumptions were made regarding New Zealand chemical and mechanical pulp capacity and wastepaper recovery and utilization rates. Capacity Constraints Growth in New Zealand’s chemical and mechanical pulp capacity was constrained, to reflect the limited potential for development of new pulp mills. The areas of plantation forest in New Zealand are dispersed across regions. As such, no single region has a sufficient area of plantation forest to support a new pulp mill. In addition, newly built wood-based-panel plants further reduce the fiber resource available for pulp production (Brown 1997b). It was anticipated that growth in pulp capacity would be due to capacity expansion in existing mills but not to the construction of new mills. The specific assumptions for future pulp capacity in New Zealand are shown in Table 9.3. Wastepaper Recovery Growth in New Zealand’s wastepaper recovery rate was constrained to reflect the poor economics of wastepaper recovery in New Zealand due to the country’s small and dispersed population (Brown 1997b). It was assumed that there would be a 0.7% per annum increase in the amount of paper and paperboard recovered as wastepaper. Under this assumption, New Zealand wastepaper recovery would grow from 22% of paper and paperboard consumption in 1997 to 34% in 2015. It was also assumed that there would be no change in the proportion of recovered paper utilized in New Zealand production of paper and paperboard.

9.4 GFPM RESULTS The effects of the trade agreements on New Zealand forest product production, consumption and exports by the year 2015 are shown in Table 9.4.

TABLE 9.4

Effects of Different Trade Agreements on New Zealand Forest Products Production, Consumption, and Exports in 2015 (%) Production

Commodity Industrial roundwood Sawnwood Plywood/veneer Particleboard Fibreboard Mechanical pulp Chemical pulp Waste paper Newsprint Printing and writing paper Other paper and paperboard

Consumption

Exports

P5

AFTA-CER

GATT 1994

P5

AFTA-CER

GATT 1994

P5

AFTA-CER

GATT 1994

−3.2 −9.3 8.8 −4.4 3.5 0.0 0.6 3.6 15.1 25.8 1.0

−5.0 −4.7 8.7 −2.9 −2.4 0.0 −1.0 36.4 27.5 −19.8 −3.5

11.1 2.7 13.7 14.6 28.8 0.0 0.6 38.1 19.1 24.1 −11.5

−4.0 0.0 0.0 −1.6 5.0 7.3 5.7 7.5 0.0 5.1 0.0

−2.4 0.0 0.0 −4.0 5.0 8.3 3.7 9.3 0.0 5.2 0.0

5.0 0.0 0.0 −5.5 5.0 1.6 −2.1 2.0 0.0 6.6 0.0

−2.2 −17.0 21.5 −7.9 2.8 −11.1 −7.7 0.9 34.5 0.0 13.7

−7.8 −8.5 21.1 −1.0 −5.6 −12.6 −8.5 54.8 62.6 3.8 −7.9

17.9 5.0 33.3 43.8 39.3 −2.4 6.1 62.5 43.4 15.4 35.6

189

190


EFFECTS ON PRODUCTION All three trade agreements, compared with no trade liberalization, led to an increase in the New Zealand production of newsprint, waste paper, and plywood/ veneer. Production of industrial roundwood was lower under the P5 and AFTACER agreements, while production under GATT 1994 was unchanged or increased. The percentage change in production of most forest products was greatest under GATT 1994. The lesser effect of the AFTA-CER and P5 regional trade agreements on New Zealand forest sector production, relative to that of GATT 1994, may arise for several reasons. Firstly, the AFTA-CER and P5 agreements do not include important New Zealand forest product markets, such as Japan and the Republic of Korea, and important emerging markets, such as the People’s Republic of China and India. Additionally, forest product exports from a number of ASEAN member countries and Chile compete directly with New Zealand exports to these markets. Finally, the small effect of the P5 regional trade agreement is a reflection of the already low tariffs on forest products in the P5 countries (Table 9.1).

EFFECTS ON EXPORTS The GFPM results suggested that the impact of the trade agreements on exports would be larger than that on production. Furthermore, the three agreements would affect the composition of New Zealand exports differently (Table 9.4). Exports of more processed commodities—plywood/veneer, newsprint, and printing and writing paper—increased, while exports of raw materials— industrial roundwood and pulp—were lower under all three trade agreements. GATT 1994 resulted in an increase in exports of all products except mechanical pulp and also resulted in the largest change in New Zealand forest product exports as compared with the other trade agreements.

EFFECTS ON CONSUMPTION According to the GFPM, the three trade agreements would result in a moderate increase in consumption of most products in New Zealand. The higher newsprint and printing and paper production (Table 9.4) caused the higherfiber consumption. Generally, however, the three trade agreements had different impacts on the pattern of New Zealand’s forest product consumption.

191


Of particular interest, given the wish to increase New Zealand’s value added to the country’s roundwood harvest, is the impact of trade liberalization on industrial roundwood consumption and product mix. According to the projections, the P5 and AFTA-CER trade agreements had a minor negative effect on New Zealand’s roundwood consumption. The GATT 1994 agreement, however, resulted in a moderate increase in New Zealand roundwood consumption, reflecting increased domestic demand for roundwood to produce and export wood-based panels, and wood pulp.

COMPARISON WITH OTHER STUDIES According to the results of this study, New Zealand would have a comparative advantage in newsprint under AFTA-CER and P5, and in wood-based panels and paper and paperboard under the GATT 1994. The manufacturing cost data for each country used in the GFPM show New Zealand to have relatively low manufacturing costs for wood-based panels and paper and paperboard compared with other countries in the AFTA-CER and P5 agreements (Table 9.5). The manufacturing cost of sawnwood in New Zealand, on the other hand, was comparatively high compared with that in Chile, Indonesia, Malaysia, and Thailand. This result differs from a number of New Zealand Ministry of Agriculture and Forestry (MAF) regional and investment studies (e.g., Ministry of Agriculture and Forestry 1995). These studies suggest that New Zealand could increase its sawmill capacity by utilizing the country’s large inventory of pruned logs and sawlogs. This disparity between New Zealand’s actual production and that TABLE 9.5

Manufacturing Costs1 in the GFPM for Selected Commodities and Countries Manufactured commodity

Country New Zealand Australia United States Chile Indonesia Malaysia Thailand 1

Sawnwood ($/m3)

Plywood/ Veneer ($/m3)

Particleboard ($/m3)

Newsprint ($/t)

105 128 115 78 78 71 90

336 290 377 308 277 308 391

92 52 102 63 63 63 37

186 335 237 195 216 371 324

Excluding wood or wood pulp input.

Printing and Other writing paper and paper paperboard ($/t) ($/t) 634 694 630 682 516 825 565

372 413 431 558 476 732 455

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TABLE 9.6 New Zealand Forest Product Production in the Year 2000, Forecast with the GFPM,1 vs. Actual Production2 Commodity

Units

MAF

GFPM

Difference (%)

Industrial roundwood Sawnwood Plywood/veneer Particleboard Fibreboard Mechanical pulp Chemical pulp Newsprint Other paper and paperboard3

103 m3 103 m3 103 m3 103 m3 103 m3 103 t 103 t 103 t 103 t

18,482 3,862 648 212 819 824 781 378 497

18,328 3,403 342 245 684 794 748 407 549

−0.8 −11.9 −48.8 15.6 −16.5 −3.6 −4.2 7.7 10.5

1

GATT Uruguay Round trade scenario. New Zealand Ministry of Agriculture and Forestry (Source: Ministry of Agriculture and various Forestry Statistical Releases). 3 Includes printing and writing paper.

2

predicted by the GFPM was visible in the differences between early projections and actual outcomes. By the year 2000, the GFPM projections (under the GATT 1994 scenario) had underestimated New Zealand’s actual 2000 production of sawnwood, plywood/veneer, fibreboard, and pulp, and overestimated production of paper and paperboard (Table 9.6). A possible explanation for this disparity is that the GFPM does not distinguish between pruned logs, sawlogs, and pulp logs. Thus, the model does not reflect New Zealand’s relative abundance of material for producing sawnwood and plywood. Constraints to New Zealand increasing its paper and paperboard production, such as those implied by the resource consent requirements of the Resource Management Act of 1991, were also not incorporated into the model. These constraints could, however, be added by exogenously setting New Zealand paper and paperboard capacity in the GFPM. Where the New Zealand forest sector’s competitive advantage actually lies (in paper and paperboard as implied by manufacturing costs in the GFPM or sawnwood as implied by MAF roundwood supply projections) has several implications for the predicted long-term effects of trade liberalization made in this study. If the description of the New Zealand forest sector in the GFPM is erroneous, then the predicted increase in newsprint production under trade liberalization is unlikely to occur, and more sawnwood may be produced instead. Alternatively, if the GFPM is correct, then the profitability of growing pruned logs and sawlogs compared to pulp logs may decrease. If this happens, plantation owners are likely to export pruned logs and sawlogs. This would have the effect of delaying some impacts of the trade agreements by 20–25 years


193

as forest management practices might shift to regimes producing a higher proportion of pulp logs.

9.5 SUMMARY AND CONCLUSION The GFPM has been applied to investigate the effects on the New Zealand forest sector of trade liberalization under the AFTA-CER, P5, and WTO-administered GATT 1994 trade agreements. According to the results, the regional trade agreements (P5 and AFTA-CER) were expected to have a limited effect on New Zealand production, consumption, and trade of forest products. These agreements resulted in lower industrial roundwood production and greater paper and paperboard production and exports and an associated increase in consumption of fiber, compared with the base trade scenario (tariffs remaining at 1997 levels). Tariff reductions under GATT 1994 would have a greater impact on New Zealand forest sector production, consumption, and trade. Paper and paperboard and wood-based panel production and exports were predicted to be higher, with an associated increase in fiber and industrial roundwood domestic consumption. An important policy implication of this study is that if New Zealand wished to increase domestic use of its future roundwood harvest, then supporting the tariff reduction initiative of GATT 1994 would be of greater benefit than pursuing tariff elimination under the P5 or AFTA-CER regional trade agreements. This study has shown that there are some limitations in the detail with which the New Zealand forest sector can be modeled in the GFPM. Nevertheless, the ability to view the New Zealand forest sector in its full international context, with numerous and complex links between countries and between industries, gives the GFPM undeniable value for this type of analysis.


CHAPTER

10

Impacts of U.S. Timber Harvest Restrictions

10.1 INTRODUCTION As indicated in Chapter 2, international trade as a share of global forest sector production has grown considerably in recent decades (Figure 2.4). As a result, domestic forest sector policies have increasing impact on international markets and, through them, on the forest resources of other countries. Log export restrictions implemented in Southeast Asia and North America have substantially affected both forest products trade and timber harvests in the Pacific Rim (Manurung and Buongiorno 1997, Perez-Garcia et al. 1997). The current effort in some countries to institutionalize sustainable forestry, encompassing such measures as green certification, more stringent forest planning requirements, and more environmentally sensitive harvesting techniques, is likely to influence production and trade patterns further (Duinker et al. 1998). Increasing interdependence of national forest sectors complicates the development of domestic forest policy (Brooks 1995) and has led several international organizations to address such issues as deforestation and the role of forests in mitigating global climate change (FAO 1997b).

195

196


This chapter applies a modified version of the GFPM to assess the potential impact of timber harvest restrictions in the United States on global timber harvest and forest products markets. The United States is the world’s largest timber producer, providing some 27% of global production in 1999. In recent years, U.S. timber production has increasingly been restricted by environmental regulations affecting both public and private harvests (Boulter and Darr 1996). To the extent that reductions in domestic production induce, through the international market, timber production in other countries, the intended environmental benefits may be largely counteracted, at least from a global point of view (Sedjo 1995). Estimating the transfer of environmental externalities among countries in such a scenario is difficult, but simple indicators such as change in timber harvest and inventory can contribute to more informed policy deliberations.

10.2 PREVIOUS STUDIES Brooks (1995), in his assessment of reductions in U.S. National Forest harvests, suggests four likely market responses: substitution by timber from other domestic sources; substitution by timber from other countries; substitution by nonwood materials; and reduction in timber use. At least two of these, substitution by other timber sources within the country (e.g., the U.S. South instead of the Pacific Northwest) and substitution from other countries (e.g., Canada), have clearly occurred (Kellison 1995, Martin and Darr 1997). Substitution of timber from other countries is limited by a number of factors, such as increasing harvest restrictions in those countries (Binkley et al. 1994, Hellstrom 1996), physical limits on processing capacities (Lipman 1994), and suitability of wood types (Perez-Garcia 1993, Vincent et al. 1991). Nevertheless, some research indicates that U.S. reductions could increase harvest pressure on forests in other countries. Perez-Garcia (1995), for example, presents simulation results from the CINTRAFOR Global Trade Model showing that for a given area of habitat set aside in the United States, an area 12–80% larger will be harvested in other countries, as rising prices stimulate production in countries where wood processing is less efficient and forest stocking is lower. The international repercussions of U.S. policy are probably most strongly felt in the Pacific Rim (by which we mean Asia, Oceania, and the Americas). North American exports of softwood logs to Asia, primarily Japan and China, grew dramatically during the 1970s and 1980s, exceeding 20 million m3 in 1989. Eighty-five percent of all U.S. coniferous sawlog exports went to Asia in 1995 (FAO 1997c). Japan, which is by far the world’s leading timber importer, imported 25% of its total 1996 wood consumption from the United States, a figure that was even higher in earlier years (Yukutake 1998). Beginning in 1989,


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trade and harvest restrictions in the United States resulted in decreased exports to Japan (Johnson et al. 1995, Perez-Garcia 1993), which shifted toward imports from Canada, Chile, and Oceania (Yukutake 1998). China, meanwhile, has increased hardwood log imports from Southeast Asia as imports from the United States have become more expensive and rising incomes have increased the demand for high-quality hardwood furniture (Zhang et al. 1998). These large consumer markets are thus a key link between competing Pacific Rim timber exporters and those exporters’ domestic forestry policies.

10.3 OBJECTIVES This chapter presents a simulation analysis of the impact of U.S. timber harvest policies on Pacific Rim timber markets and forest resources. The following section outlines current trends in forest policy in the United States. Section 10.5 then describes a modified version of the GFPM to predict how U.S. policies affect other countries’ markets and forests, particularly in the Pacific Rim. Section 10.6 presents results of applying this model to assess the impact of U.S. timber harvest restrictions on the international forest sector through 2010. Section 10.7 tests the sensitivity of the results to assumptions about the trade regime by allowing freer trade flows. Overall, the results suggest that increasing timber harvest restrictions in the United States would shift timber production and forest products manufacturing to other countries and would significantly alter timber trade patterns. Softwood log markets would be most affected. The restrictions would allow the United States to conserve more forest, while other countries would conserve less. Analysis of the associated change in environmental externalities is beyond the scope of this chapter, but results indicate that most of the transfer in timber production would be among developed countries with extensive coniferous timber resources.

10.4 FOREST POLICIES AFFECTING TIMBER HARVEST IN THE UNITED STATES Forest management in the United States is subject to a variety of regulations, both national and local, on both public and private lands. National Forest policy has long recognized the need to provide environmental as well as industrial outputs from public forests. The Organic Administration Act of 1897, for example, stipulated that public forests were to be maintained for water flow purposes as well as for timber production (Cubbage et al. 1993). By 1960, concern that logging on the National Forests threatened nontimber forest values led to passage

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of the Multiple-Use Sustained Yield Act, which added wildlife protection and recreation to the responsibilities of National Forest managers. In the last three decades, debate over the balance between environmental goals and timber production has grown increasingly contentious, resulting in the passage of more laws (Forest and Rangelands Resource Planning Act of 1974, National Forest Management Act of 1976) and to multiple lawsuits against the Forest Service (Flick et al. 1995). Debate has focused most sharply on National Forests in the Pacific Northwest. In this region, most of the remaining old-growth forests are on federal lands (Hellstrom 1996) due to heavy logging on private lands after the Second World War (Haynes et al. 1995). In 1976, under the National Forest Management Act, some land was removed from the timberland base and more environmentally sensitive harvest techniques became obligatory. In 1990, the Forest Resources Conservation and Shortage Relief Act banned the export of coniferous logs from public lands west of the 100th meridian (Boulter and Darr 1996). Then, in 1991, a federal court injunction halted federal timber sales in the Pacific Northwest until adequate protection for the northern spotted owl could be provided under the terms of the Endangered Species Act (Barber et al. 1994). As a result, timber production on the National Forests fell from some 50 million m3 per year in the 1980s to less than 20 million m3 per year in the mid-1990s (Boulter and Darr 1996). While National Forest management has generated the most controversy, private timberlands are also increasingly subject to federal, state, and local regulation (Greene and Siegel 1994). Public lands provided only 18% of the 1991 national harvest. Given the much greater importance of private harvests to timber supply, greater regulation of private forestlands potentially has more impact on international forest product markets than public lands policy. There is no national forestry law governing private timber management in the United States, though some general environmental legislation, in particular the Clean Air Act, the Clean Water Act, and the Endangered Species Act, do limit certain activities on private land (Sedjo et al. 1998). The Endangered Species Act has resulted in timber harvest restrictions on large areas of private timberland, particularly in the South and in the Pacific Northwest (Kellison 1995). Most restrictions on private timberland management originate at the state and local levels. Greene and Siegel (1994) report that 117 state laws and 527 local laws on forestry practices were in effect in the early 1990s. All 50 states had some sort of water quality legislation, and only six had no endangered species legislation. Twenty-one states had formal forest practices laws, though Greene and Siegel (1994) point out that only 10 of these are comprehensive statutes. In the West and North, state legislation is more common; in the South, county regulation is more common. Typical provisions in state and local regulation of forestry include obligatory harvest permits and forest management


199

plans, streamside buffer zones, limits on clear-cuts, and postharvest debris clearing (Greene and Siegel 1994).

10.5 MODIFIED GLOBAL FOREST PRODUCTS MODEL To predict the effects of one country’s domestic forest policies on the forest sectors of other countries, several elements must be explicitly represented: the domestic markets and policies of the countries or regions in question, the links among the different national markets, and the links among different market levels (from raw materials to finished goods). A dynamic spatial equilibrium market model, such as the GFPM described in Chapter 3, is an attractive framework for integrating this information because it allows for a detailed representation of primary, intermediate, and final commodities so that, for example, the impact of timber supply restrictions on pulp and paper production can be captured. In addition, because we are interested in the impact of U.S. policy on forests as well as on markets, this version of the model includes an endogenous timber supply component. Some other changes relative to the standard GFPM of Chapters 3 and 4 are differentiation between softwoods and hardwoods and aggregation of many countries into regions, with only Pacific Rim countries represented individually. Only the model’s differences relative to Chapters 3 and 4 are presented here.

COUNTRIES AND PRODUCTS The scope of this model was chosen to strike a balance between tractability and sufficient detail for the analysis at hand. The major producing, consuming, and trading nations in the Pacific Rim were represented individually, for this is where the major impacts are expected. Other countries were aggregated according to level of development and forest sector characteristics (e.g., Asian Tigers are resource-poor fiber consumers, while Other Latin America is resource rich but lacking in industrial infrastructure) (Table 10.1). The commodities were similar to those listed in Table 4.2, with one main exception. Particularly important to the objectives of this study was the distinction between coniferous sawlogs, nonconiferous sawlogs, and pulpwood, which tend to come from different sources and have different uses. The difficulty in making this distinction is that the FAO stopped reporting trade data for these detailed roundwood types in 1989, so model performance after that year can only be compared to the production data for which a full time series is available. In parallel with sawlogs, sawnwood was also divided into coniferous and nonconiferous sawnwood.

200


TABLE 10.1 Countries and Regions in the Pacific Rim Model Individual countries Australia Brazil Canada Chile China Indonesia Japan Malaysia New Zealand South Africa United States

Regional groupings Asian Tigers (Hong Kong, South Korea, Macau, Singapore) Continental Southeast Asia (Burma, Cambodia, Laos, Thailand, Vietnam) Former USSR (Albania, Armenia, Azerbaijan, Belarus, Bosnia-Herzogovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Georgia, Hungary, Kazakstan, Kyrgyzstan, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russia, Slovakia, Slovenia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan, Yugoslavia) South Asia (Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka) North Africa/Middle East (Algeria, Afghanistan, Bahrain, Djibouti, Egypt, Ethiopia, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Mali, Mauritania, Morocco, Niger, Oman, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, Turkey, United Arab Emirates, Yemen) Other Latin America (Anguilla, Antigua Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bermuda, Bolivia, Cayman Islands, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands, French Guiana, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, St. Kitts and Nevis, St. Vincent, Suriname, Trinidad and Tobago, Turks and Caicos, Uruguay, Venezuela) Other North Asia (North Korea, Mongolia) Pacific Islands (Brunei Darussalam, Christmas Island, Cocos Island, Cook Island, Fiji, French Polynesia, Guam, Nauru, New Caledonia, Niue, Norfolk Island, Papua New Guinea, Philippines, Pitcairn Island, Samoa, Solomon Islands, Tonga, Vanuatu, Wake Island) Sub-Saharan Africa (Angola, Benin, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Comoros, Congo (former Zaire), Congo (Republic), Côte d’Ivoire, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Madagascar, Malawi, Mauritius, Mozambique, Nigeria, Reunion, Rwanda, St. Helena, Sao Tome and Principal, Senegal, Seychelles, Sierra Leone, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe) Western Europe (Andorra, Austria, Belgium/Luxembourg, Denmark, Faeroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Iceland, Ireland, Italy, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom)

BASE YEAR AND PARAMETERS For this study, the base year was 1985. Because FAO data on timber trade by log type ended in 1989, calibrating the model with 1985 as the base year allowed validation of predicted trade flows over the period 1985–89. Input data for the base year were the average of data from 1984, 1985, and 1986, to minimize distortion caused by data-reporting error in a single year.

201


Projections were made up to the year 2010, to allow analysis of mediumterm effects of policy or parameter changes on industrial activity, forest inventory change, and trade flows. The model parameters and exogenous changes were similar to those described in Chapters 4 and 5. A more complete description of the data and parameters is available in Tomberlin (1999).

TIMBER SUPPLY AND INVENTORY CHANGES The main difference between the model used here and that described in Chapter 3 resides in the representation of timber supply. Instead of depending only on price, short-term supply was also assumed to depend on forest inventory, so Equation [3.3] became:  P  Sik = Sik*  ik   Pik,−1 

λik

 Ii  I   i,−1 

γ ik

[10.1]

where i = country, k = product (softwood sawlogs, hardwood sawlogs, pulpwood, or other industrial roundwood), S∗ = current supply at last year’s price, P = price, I = timber inventory, λ = price elasticity of supply, and γ = elasticity of supply with respect to inventory. Note that while timber inventory was not distinguished by log type, the elasticity of log supply with respect to total timber inventory differed by log type. For example, pulpwood supply was more responsive to inventory change because more trees of different size and shape can become pulpwood than can become sawlogs or veneer logs. Table 10.2 shows the price and inventory elasticities used in the model. Econometric analysis of panel data on industrial roundwood production strongly suggested that timber inventory and per capita income positively influence short-term timber supply but did not yield reliable estimates of price elasticities (Tomberlin 1999). Consequently, the judgment of a group of experts in the course of a previous FAO study (Zhu et al. 1998) was used to generate the elasticities in Table 10.2, together with previously published estimates on price and inventory elasticities (see Table 4.6). New Zealand and Chile were assumed to be most price responsive because they have a preponderance of private ownership and well-developed, profitoriented forest industries. Countries dominated by government ownership of forestlands (Canada), with less developed infrastructure (former Soviet Bloc), or with very high production costs (Japan) were assumed to be less price responsive. In all countries, pulpwood was assumed to be more price elastic than were sawlogs.

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TABLE 10.2 Timber Supply Elasticities with Respect to Price and Inventory in the Pacific Rim Model

Australia China Indonesia Japan Malaysia New Zealand South Asia Continental Southeast Asia Pacific Islands Other North Asia Asian Tigers Brazil Canada Chile South Africa United States North Africa / Middle East Former USSR Other Latin America Sub-Saharan Africa Western Europe

Coniferous sawlogs

Nonconiferous sawlogs

Pulpwood

Other industrial roundwood

Price Inventory

Price Inventory

Price Inventory

Price Inventory

0.40 0.20 0.40 0.10 0.40 0.70 0.20 0.40

0.20 0.20 0.40 0.15 0.30 0.60 0.10 0.10

0.40 0.20 0.40 0.10 0.40 0.40 0.20 0.40

0.20 0.20 0.20 0.05 0.30 0.30 0.10 0.20

0.60 0.60 0.60 0.90 0.60 0.90 0.40 0.60

0.60 0.40 0.40 0.15 0.50 0.70 0.20 0.30

0.60 0.60 0.60 0.90 0.60 0.90 0.40 0.60

0.30 0.30 0.30 0.20 0.30 0.30 0.20 0.30

0.50 0.20 0.20 0.50 0.30 0.70 0.60 0.40 0.40

0.30 0.30 0.20 0.40 0.30 0.50 0.50 0.40 0.20

0.70 0.20 0.10 0.80 0.20 0.40 0.40 0.60 0.40

0.30 0.30 0.20 0.50 0.30 0.20 0.30 0.40 0.20

0.80 0.60 0.30 1.00 0.40 0.90 0.70 0.70 0.60

0.50 0.50 0.30 0.70 0.40 0.60 0.60 0.50 0.30

0.80 0.60 0.30 0.90 0.50 0.90 0.70 0.70 0.60

0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30

0.40 0.50

0.40 0.30

0.40 0.70

0.30 0.40

0.60 0.80

0.50 0.50

0.60 0.80

0.30 0.30

0.30 0.40

0.20 0.40

0.50 0.30

0.30 0.30

0.60 0.40

0.50 0.40

0.60 0.40

0.30 0.30

Inventory elasticity estimates are scarce in the literature (Table 4.6) and typically show an inelastic response to inventory changes. Elasticities of timber supply with respect to inventory were centered around the benchmarks provided by econometric estimation (Tomberlin 1999) and other studies in Table 4.6, with pulpwood supply being more inventory elastic than sawlogs in every country (due to fewer restrictions on size and shape of logs). Timber inventory elasticities were also adjusted to reflect differences in wood utilization, industry development, and policy in different countries. For example, Australia’s elasticity of pulpwood supply with respect to inventory was higher than that of China because its industries depend heavily on exports of wood pulp and wood chips. Indonesia was assumed to have a high supply elasticity of nonconiferous sawlogs with respect to inventory because of its vast tropical forest resources and difficulties with concession oversight and smuggling. Japan was assigned a low timber inventory elasticity due to its high production costs and domestic demand for forest amenities.

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Inventory Changes Timber inventory itself was assumed to evolve according to a simple equation of motion: Ii = Ii,−1 + Gi − H i

[10.2]

where I is the total timber inventory in a given year, G is the yearly growth of inventory, and H is the yearly harvest. Harvest H is the sum of harvests for the four log types. They were treated as a single effect on total inventory here because few data exist on timber area, stocking, and inventory by log type. Growth in each country was the sum of natural and plantation forest growth: G = NGn + LGL

[10.3]

where N Gn L GL

= total economically available natural forest area (ha) = average per hectare growth rate of natural forest (m3/ha/y) = area of industrial plantation forest (ha) = average per hectare growth rate of industrial plantation forest (m3/ha/y)

Native forest and plantation area and growth rates were exogenous in the model. The data used to simulate timber inventory change are in Table 10.3. Yearly changes in market equilibrium conditions were brought about by shifts in the timber supply curve that were due in part to changes in income per capita and in part to exogenous policy or resource impacts, so Equation [3.8] became: Sik* = Sik,−1(1 + δ ik )(1 + βik )

[10.4]

where δ is the elasticity of supply with respect to GDP per capita (assumed to be 0.2 in all countries) and β is the rate of shift of supply due to exogenous forces. This exogenous impact on the timber supply curve was assumed to be operative in only two countries, Malaysia and the United States. In Malaysia, the nonconiferous sawlog supply curve was assumed to shift inward at 3% per annum after 1991, reflecting resource exhaustion (see Blanchez 1997). In the United States, as described later, an exogenous inward shift of 2.5% per annum reflected the assumed impact of increasingly stringent timber harvest regulations. U.S. Timber Supply Scenarios Many observers feel that further timber harvest restrictions on both private and public lands in the United States are likely (Reed 1995, WRI 1996). The National Forest planning process may be reviewed, with the possibility that more reductions in public harvests may result. Further restrictions under the

204


TABLE 10.3 Forest Area, Growth, and Inventory Data in the Pacific Rim Model Natural forest Country or region Australia China Continental Southeast Asia Japan Malaysia New Zealand South Asia Indochina Pacific Islands Other North Asia Asian Tigers Brazil Canada Chile South Africa United States North Africa/ Middle East Former USSR Other Latin America Sub-Saharan African Western Europe

Plantation

Area Gross annual Area change increment (103 ha) (%/y) (m3/ha/y)

Area (103 ha)

Area Mean annual Total change increment inventory (%/y) (m3/ha/y) (106 m3)

17,005 138,383 121,840

0.0 −0.4 −1.2

2.5 2.0 1.4

815 26,132 4,466

3.1 3.0 5.0

10.0 6.5 5.0

1,411 13,285 21,809

23,829 19,514 2,060 80,909 83,804 51,437 15,990

0.0 −2.2 0.0 −8.0 −0.9 −0.3 0.0

2.0 1.7 2.5 0.5 1.0 1.2 1.0

10,420 50 1,190 8,000 1,755 305 1,085

0.5 7.0 0.8 4.0 4.0 5.0 5.0

6.3 5.0 15.0 5.1 4.9 4.0 7.5

2,812 4,176 109 4,272 10,051 8,491 703

6,339 584,120 112,077 7,978 7,866 196,635 105,471

0.0 −0.7 0.0 −0.8 −0.9 −0.1 −0.5

1.3 4.8 2.4 4.0 0.0 3.6 1.1

1,750 3,923 3,023 743 888 26,850 1,650

0.0 4.0 8.0 5.0 1.6 3.1 1.0

2.3 10.0 3.5 16.0 10.0 5.6 2.8

463 67,758 13,001 1,580 779 23,203 4,392

499,411 411,445

0.1 −0.5

3.5 2.8

18,800 1,536

0.5 4.0

2.0 12.7

57,921 44,625

448,646

−0.3

1.4

1,431

5.0

9.0

53,340

93,933

0.4

4.0

5,000

1.0

4.0

11,496

Sources: Native and plantation forest areas and rates of area change refer to 1985; area estimates were interpolated from FAO (1995b) estimates for 1990 using the area change estimates (FAO 1995b). Growth rates of natural forest are gross annual increment reported in FAO (1998), assumed to be constant over time. Plantation growth rates are from Brown (1998), also assumed constant over time. Timber inventory in 1985 was calculated from 1985 total forest area and forest stocking (FAO 1995b).

Endangered Species Act are likely on private lands in the Pacific Northwest and the Southeast. State and local regulations also increasingly limit timber production, whether by specifically forbidding some activities or by requiring additional activities that increase production costs (Shaffer et al. 1998). Of the county and municipal laws restricting timber management identified by Greene and Siegel (1994), 58% had been instituted in the five years preceding 1992.


205

Other state and local restrictions on timber harvests, such as the defeated referendum to ban clear-cutting in Oregon (Forestry Source 1998), are possible. Greene and Siegel (1994) report survey results in which respondents’ median expectation of the impact of state and local legislation on private timber harvest volumes varies from −16% for softwood sawlogs in the South to −2% for softwood pulpwood in the Rocky Mountains. The model developed here does not distinguish between private and public timber supply. Rather, the effect of timber harvest restrictions is represented as an exogenous shock to the aggregate U.S. supply curve for each log type (softwood sawlogs, hardwood sawlogs, pulpwood, and other industrial roundwood). Since federal, state, and local regulations affect timber supply, and since these regulations variously affect production costs, access to timber, or both, it is difficult to define a particular change in harvest levels that might result from such regulations. In this study, the status quo 1996 scenario, in which no exogenous inward shift of the U.S. timber supply curves is assumed, is referred to as the “base cut” scenario. The impact of gradually stricter environmental regulation on both public and private lands is referred to as the “low cut” scenario, which is simulated by an exogenous inward shift of 2.5% per year in the aggregate U.S. timber supply curves, beginning in 1996. The magnitude of the exogenous timber supply shift, β, in the low-cut scenario is chosen to correspond to the upper range of values for timber supply reductions suggested by other studies (Binkley et al. 1994, Greene and Siegel 1994, Haynes et al. 1995, Reed 1995). This exogenous shock from increasing regulation is in addition to the endogenous shift in timber supply curves caused by changes in inventory due to harvest and growth, and due to changes in income per capita (see Equations [10.1] to [10.4]).

10.6 EFFECTS OF FURTHER U.S. HARVEST RESTRICTIONS ON THE PACIFIC RIM FOREST SECTOR

EFFECTS ON PRODUCTION AND PRICES Figure 10.1 compares the evolution of U.S. timber harvests under the scenario of increasing harvest restrictions (“low cut”) to the status quo 1996 scenario (“base cut”). The policy-induced negative supply shock in the low-cut scenario caused industrial roundwood production to decline slightly during the projection period, while under the base cut it increased substantially. By 2010, total U.S. industrial roundwood production in the low-cut scenario was 112 million m3, or 22%, lower than under the base cut.

206

The Global Forest Products Model 600

Millions of cubic meters

500

400

300

200

100

0 1985

1990 Low cut

FIGURE 10.1

1995

2000 Base cut

2005

2010

Actual cut

Observed and predicted production of industrial roundwood in the United States.

At the global level, U.S. restrictions resulted in generally higher timber prices and lower production of all log types in 2010 (Table 10.4). The greatest impact was on the coniferous sawlog market, with world production 5% lower and price 47% higher than in the base case. While prices for all roundwood types rose in the lowcut scenario, the overseas’ response in terms of quantity produced was relatively much stronger for nonconiferous sawlogs and for pulpwood than for coniferous sawlogs. Log price increases induced enough production from other countries to offset 33% of the U.S. reduction of coniferous sawlogs, 79% of nonconiferous sawlogs, 62% of pulpwood, and 100% of other industrial roundwood. Coniferous sawlog markets were most affected because the U.S. has a relatively larger share of this market1 and because sawlog supply is less price responsive than pulpwood and other industrial roundwood. In the United States, by 2010 the low-cut harvest was significantly less than in the base cut, and timber prices were much higher. According to the model projections, the regional timber production in 2010 was significantly altered by the U.S. timber harvest restrictions (Table 10.4). Several regions had large increases in coniferous sawlog production, most notably the former Soviet Union, New Zealand, and Western Europe. Canada’s annual allowable cut limited its response to the U.S. policy, while China and Brazil actually had substantially lower coniferous sawlog production in the lowcut scenario. This result reflects changes in trade flows (Table 10.5) and manufacturing (Tables 10.6 and 10.7), which resulted in reduced demand for 1 In 1995, the U.S. share of global production was 28% for softwood sawlogs, 22% for hardwood sawlogs, 36% for pulpwood, and 10% for other industrial roundwood (FAO 1997c).

TABLE 10.4 Main Effects of U.S. Harvest Restrictions on Industrial Roundwood Production and Prices in 2010

Region

Coniferous sawlogs Quantity 103 m3 %

Western Europe Former USSR New Zealand North Africa /Middle East Other Latin America Chile Canada Brazil China United States World

6,829 6,397 6,246 2,155 1,664 1,332 1,260 −2,995 −4,616 −61,119 −40,880

4 5 45 29 18 11 1 −10 −5 −31 −5

Nonconiferous sawlogs Quantity 103 m3 %

Price (%)

Sub-Saharan Africa 4,224 16 China 3,268 6 Western Europe 2,436 8 Malaysia 1,532 6 Pacific Islands 806 7 Other Latin America 571 3 North Africa /Middle East 451 9 Brazil −1,194 −4 South Asia −1,661 −3 United States −14,206 −23 World −3,004 −1

15 19 20 18 15 0 7 5 −16 114 25

Price (%) Region 19 15 70 78 21 23 76 −3 18 82 47

Pulpwood Quantity 103 m3 %

Price (%)

Western Europe 5,720 4 Brazil 4,288 9 Former USSR 3,510 9 Japan 2,453 8 Other Latin America 1,424 8 North Africa /Middle East 776 43 Australia 668 3 South Africa 592 9 New Zealand 509 3 United States −34,515 −15 World −13,275 −2

12 31 19 7 15 14 11 5 17 121 52

Region

207

208 TABLE 10.5 Main Effects of U.S. Harvest Restrictions on Industrial Roundwood Trade in 2010 Coniferous sawlogs Exports Region New Zealand Western Europe North Africa/ Middle East Malaysia Indonesia Chile Canada Asian Tigers Other Latin America Sub-Saharan Africa World

3

3

10 m

%

Nonconiferous sawlogs Imports

Exports

Imports

3

3

3

3

10 m

7,724 337 3,591 75 −697 −7 −60 559 −24 −59 −61 −72 −222 −4 −10,776 −52

87 237 9 6

224

1

Pulpwood

224

% Region Sub-Sahara Africa −37 Pacific Islands 81 North Africa/ Middle East Indonesia Western Europe 49 United States 4 Malaysia 44 Canada Japan 104 Asian Tigers World 1

3

10 m

%

3,639 471 46

27 6 20

−21 −220

−2 −2

−571 −1,171

−86 −15

1,256 2,172

10 5

3

10 m

% Region

−13 −16

591 612 −82 422

−3 2

2,172

5

3

Exports

Imports

3

3

10 m

New Zealand 1,467 Australia 561 Indonesia 23 Sub-Saharan 6 Africa Malaysia 5 Pacific Islands 4 China Japan United States −14,352 Western Europe World −12,287

% 10 m3

%

23 5 6 1 1 0 −1,230 −922 −65 392 −10,527

−34 −21 167 −29

−24 −12,287 −24

TABLE 10.6 Main Effects of U.S. Harvest Restrictions on Sawnwood and Wood-based Panels Production in 2010 Coniferous sawnwood

Nonconiferous sawnwood

Quantity Region Canada Former USSR Western Europe North Africa /Middle East Other Latin America Chile New Zealand Brazil China United States World

3

3

10 m

7,253 3,200 1,800 1,634 931 897 −888 −1,072 −2,625 −29,776 −17,400

% 9 4 2 37 20 25 −13 −8 −6 −34 −4

Price (%) 70 13 64 70 60 70 70 34 103 70 60

Quantity Region Western Europe China Malaysia Asian Tigers Other Latin America North Africa / Middle East Continental Southeast Asia South Asia Indonesia United States World

Wood-based panels

3

%

Price (%)

1,681 1,600 1,434 567 280

14 7 19 28 3

14 14 13 14 −8

248

10

14

226

6

7

−1,029 −1,284 −7,293 −3,319

−4 −9 −21 −2

−8 14 94 23

3

10 m

Quantity Region Former USSR Indonesia China Asian Tigers Chile Japan Europe Western Canada Brazil United States World

3

Price (%)

10 m3

%

2,286 1,159 321 313 286 263 −407

8 15 4 4 15 2 −1

5 12 7 1 0 11 2

−439 −1,229 −8,523 −5,665

−4 −14 −22 −6

6 11 13 11

209

210


TABLE 10.7 Main Effects of U.S. Harvest Restrictions on Pulp and Paper Production in 2010 Wood pulp Quantity %

Price (%)

950 730 570 293 185

9 9 44 9 96

−6 9 5 6 6

147 −315 −353 −1,311 −8,058 −6,855

13 −12 −20 −4 −9 −3

0 4 6 16 33 21

103 t Brazil Former USSR Japan Other Latin America North Africa / Middle East South Africa China New Zealand Western Europe United States World

Paper and paperboard Quantity 103 t Brazil Former USSR North Africa/ Middle East Indonesia Other Latin America Asian Tigers Indochina Japan China United States World

%

Price (%)

1,108 518 432

53 19 45

0 4 4

265 260 194 −298 −1,020 −6,122 −9,400 −13,957

42 35 −35 −12 −9 −13 −9 −3

−1 2 4 0 2 −8 9 4

coniferous sawlogs in these countries. For example, the reduction in China’s coniferous sawlog production in the low-cut scenario stemmed from its reduction in coniferous sawnwood production (Table 10.6). Because rising coniferous sawlog prices reduce competitiveness in coniferous sawnwood manufacture, China was squeezed out of the market by more efficient lumber manufacturers, notably Canada. To support this higher level of softwood lumber processing, Canada reduced its exports of coniferous sawlogs (Table 10.5). Meanwhile, New Zealand’s greater coniferous sawlog production in the lowcut scenario was simultaneous with a decrease in coniferous sawnwood production, because New Zealand exported more logs rather than processing them domestically. The U.S. restrictions thus set off a complex change in the global equilibrium in which total world production and country shares of both timber and downstream products were significantly altered. The impact of the U.S. policy on the nonconiferous sawlog market was more muted, though production increased significantly in Sub-Saharan Africa, China, Western Europe, and Malaysia. Pulpwood production increased most in Western Europe, Brazil, the former USSR, and Japan.

EFFECTS ON INTERNATIONAL TRADE Because trade is a relatively small portion of industrial roundwood production, changes in the pattern of international trade were greater (in percentage terms) than those of timber production (Tables 10.4 and 10.5). Globally, in the low-cut scenario there was more trade in sawlogs (both hardwood and softwood) and less in pulpwood than under the base-cut scenario. The large increases in coniferous


211

sawlog exports from New Zealand and Europe reflected withdrawal of Canada from this market as Canada focused on domestic log processing. United States trade in coniferous sawlogs was unaffected because the trade inertia constraints were binding under both scenarios, as domestic demand drove exports to the lowest level possible and imports to the highest level possible. In the nonconiferous sawlog market, U.S. export reductions were more than offset by increased exports from Sub-Saharan Africa and the Pacific Islands (which include Papua New Guinea), while Malaysia decreased its hardwood sawlogs exports to support more domestic processing. The global pulpwood trade was reduced by 24% as only Australia and New Zealand increased pulpwood exports significantly, while China, Japan, and Western Europe all reduced imports of the more expensive pulpwood in the low-cut scenario.

EFFECTS ON MANUFACTURING In the low-cut scenario, industrial roundwood price increases resulted in higher prices for downstream products, which tended to depress 2010 world manufacturing levels (Tables 10.6 and 10.7). The price difference between scenarios was greatest for coniferous sawnwood, reflecting the price increases for coniferous sawlogs. Price increases were more modest for pulp and paper because wood costs are a smaller fraction of total production costs. Along with the change in global production level came a significant redistribution of manufacturing among countries. Under the low-cut scenario, production of all wood and paper products shifted from the United States to other countries. Thus, the projection results suggested that reductions in raw wood availability in the United States would not be fully offset by increasing roundwood imports and that a reduction in domestic industrial activity would occur. The equilibrium outcomes in other countries varied greatly, depending on trade opportunities and the evolution of manufacturing capacity over the period 1995–2010. Coniferous sawnwood production, for example, concentrated in Canada to such a degree that Brazil, China, and New Zealand manufactured less in 2010 under the low-cut scenario than under the base-cut scenario.

EFFECTS ON TIMBER INVENTORY Table 10.8 shows the effects of U.S. timber harvest restrictions on 2010 timber inventory in countries for which there were significant differences between the two scenarios. The large build-up of timber inventory in the United States resulted in a large net gain in global timber inventory. Canada’s drop in forest inventory was the largest in absolute terms, though in percent the impact on several other regions was comparable. The difference in New Zealand’s final

212

The Global Forest Products Model TABLE 10.8 Main Effects of U.S. Harvest Restrictions on Timber Inventory in 2010 106 m3 Canada North Africa /Middle East Former USSR New Zealand Sub-Saharan Africa Other Latin America Chile Japan Malaysia Australia United States World

−67 −37 −36 −26 −18 −14 −8 −6 −6 −6 536 311

% −0.44 −0.55 −0.04 −13.03 −0.03 −0.02 −0.31 −0.14 −0.16 −0.27 1.47 0.06

inventory was modest in absolute terms, but at 13% it was by far the biggest relative difference in any country. These results suggest that U.S. timber harvest restrictions could have a significant negative effect on the timber stock of other countries.

10.7 SENSITIVITY TO TRADE REGIME Though any model projections are necessarily the result of assumptions, the role of the trade inertia constraints in this analysis bears particular attention. The constraints are in part justified by nontariff barriers to trade, which are common in the forest sector. Brooks (1995), for example, cites phytosanitary impediments to greatly increased U.S. softwood log imports from Chile and New Zealand. However, such factors are difficult to quantify, and the parameters chosen are somewhat arbitrary.2 The global impact of U.S. timber policy depends in part on these trade inertia constraints, since they limit countries’ annual response to price changes. To examine the effect of the choice of trade inertia constraints on the analysis, the two scenarios were run again with trade inertia parameters for logs set 15% higher than the original values. This allowance for faster adjustments of trade flows showed the effects of a “freer trade” regime, and it allowed assessment of the stability of the results to changes in parameters. Table 10.9 compares the differences between the base-cut and the low-cut scenarios under the two trade regimes. Under the freer trade regime, other countries responded more to rising timber prices, so the global reduction in harvest was smaller and the United States reduced its own harvest even more. Freer trade led 2

The sensitivity analysis discussed here refers to the value of the parameters ε in the trade inertia constraints of Equation [3.13].

213


TABLE 10.9 Main Effects of Freer Trade on the Impact of U.S. Timber Harvest Restrictions in 2010 Freer-trade regime Harvest Inventory 103 m3 % (%) Western Europe New Zealand Australia Other Latin America Sub-Saharan Africa Former USSR Chile China Canada United States World

23,243 7,045 6,899 4,822 4,647 3,626 3,558 −106 −791 −114,950 −50,895

7 23 21 10 9 1 13 0 0 −23 −2

−0.4 −17.3 −1.7 0.0 0.0 0.0 −0.5 0.1 −0.3 1.6 0.1

Original trade regime Harvest Inventory 103 m3 % (%) 14,981 6,760 1,844 3,659 4,468 11,456 1,137 −1,233 1,462 −111,376 −57,182

4 22 5 8 8 4 4 −1 1 −22 −3

0.0 −13 −0.3 0.0 0.0 0.0 −0.3 0.0 −0.4 1.5 0.1

to a significant redistribution of the harvest impact: Western Europe, Australia, and Chile were considerably more affected by U.S. policy under freer trade, while countries of the former USSR were less affected. Final global inventory was insensitive to the change in trade regime, though under freer trade the impact of the U.S. harvest restrictions on final timber inventories in Australia, New Zealand, and Western Europe was notably greater. These results show that at the aggregate level the model responds to changes in the trade inertia constraints according to theoretical expectations but that the magnitudes of the effects of U.S. harvest restrictions on individual countries are sensitive to assumptions about the trade inertia constraints. It is worth noting that freer trade (in this model) transfers more timber production, and presumably more production externalities, to other developed countries rather than to developing countries. This is due to the relative dominance of these countries in the coniferous sawlog market, which is most affected by the U.S. harvest restrictions. Increasing substitution of hardwood for softwood timber would tend to mitigate this result and is therefore an area worthy of further research (Vincent et al. 1991).

10.8 SUMMARY AND CONCLUSION The results of this application of the GFPM (a version with more detail on timber supply than the one used in previous chapters) suggest that U.S. timber harvest restrictions are likely to have a substantial impact on international timber prices, timber production, forest sector manufacturing, and trade. In the projections, harvest reductions in the United States shifted timber production to other countries, with a net reduction in global timber harvest. Under freer trade,

214


the magnitude of this shift was greater. Increased roundwood prices resulted in less production of wood and paper products, which also tended to move out of the United States. Trade patterns in both industrial roundwood and downstream products were significantly affected. As a result of U.S. harvest restrictions, the United States (and the world) timber inventory in 2010 was higher, but in most countries other than the United States it was lower. These results have implications for forest conservation. To the extent that timber inventory losses and greater timber production in a country imply negative environmental externalities (e.g., habitat loss or soil erosion), greater forest conservation in the United States may come at the expense of forests in other countries. Globally, the net environmental effects depend on the countries to which timber production shifts. Under both trade regimes explored here, the most aggressive supply response (in percentage terms) came from New Zealand, a country in which virtually no timber harvest is allowed in native forests. Transferring timber production from old-growth forests in Oregon or wetlands in South Carolina to radiata pine plantations in New Zealand would probably result in a Pareto gain, no country being seriously worse off environmentally. In contrast, a shift of harvest to slow-growing native forests in Siberia would probably result in a net global environmental loss. A complete assessment of the global environmental impacts of U.S. policy must also consider the effects of manufacturing relocation, which this analysis suggests also shift from the United States to other countries. Ongoing research into natural resource accounting (Eisner 1988) may provide a tool for addressing some such issues in welfare assessment. By incorporating environmental values into the national accounts, this technique seeks to provide a basis for the welfare analysis of changes in a country’s environmental quality. Linking such accounts to changes in national forest resources as projected in models like the GFPM could provide a complement to traditional welfare analysis. However, many practical issues have yet to be worked out. The international environmental impact of U.S. timber policy depends on other countries’ policy responses and local management decisions. Canada and Sweden have recently adopted new environmental regulations limiting timber production and mandating less destructive harvest practices (Natural Resources Canada 1997, Sedjo et al. 1998). To the extent that environmental regulation increases in these countries, the global response to U.S. policies may be weaker, and the impact on forests may be more concentrated in countries such as Russia that currently lack the institutional capacity for environmental protection. Recognition that domestically sustainable forest management has international implications and that more socially beneficial outcomes might result from policy coordination has led to the establishment of international bodies


215

such as the Intergovernmental Panel on Forests and agreements on sustainable forestry such as the Santiago Declaration (FAO 1999b). Plantation forests have been recognized under the Kyoto Protocol as one element of a broader effort to slow global warming. The success of such efforts in achieving Pareto-improving coordination of national policies, given domestic and international externalities, depends on better forest sector data and models for concerted research in this area.


P AR T

GFPM Software

IV


CHAPTER

11

Using the GFPM Software

11.1 INTRODUCTION This chapter is intended to enable users of the Global Forest Products Model (GFPM) to do the following tasks: 1. 2. 3. 4.

Install and run the GFPM software. Understand the input data requirements to run the GFPM. Change the input data to explore different scenarios with the GFPM. Interpret the summary output created by the GFPM.

As detailed in Chapter 3, the Global Forest Products Model is an economic model of global production, consumption, and trade of forest products. The current version of GFPM has all the data and parameters needed to produce forecasts of markets for 180 countries and 14 different forest product (commodity) categories from 1998 to 2010, or 2015 depending on the application. This chapter goes through the steps required to install the GFPM (Section 11.2), interpret the base-year (1997) input data, which GFPM uses to forecast production, consumption, imports, exports, and prices (Section 11.3), run the GFPM from the GFPM Menu (Section 11.5), interpret the projection output 219

220


from the GFPM (Section 11.6), and carry out a welfare analysis from two GFPM scenarios (Section 11.7).

11.2 INSTALLATION AND CONFIGURATION

HARDWARE AND SOFTWARE REQUIREMENTS The GFPM requires a PC, at least 8MB of RAM, and the Windows NT, Windows 2000, or Windows XP operating system.1 The GFPM uses the PELPS IV (Price Endogenous Linear Programming System) software, a general economic modeling system. PELPS IV itself uses the LINDO linear programming solver to find the solution to each year’s global equilibrium. The GFPM is designed to run with LINDO version 6.0 or later. Data input and output are handled with Microsoft Excel (MS Office 97 or Office 2000).

INSTALLING THE GFPM To install the GFPM (Figure 11.1), carry out the following steps, in order. 1. In the root directory of your C: drive, install LINDO version 6.0 or later. 2. Unzip GFPM.ZIP into the C:\ root directory. 3. Copy the file SNJRT11.DLL from C:\PELPS\COPY-TO-C-DRIVE to the C:\WINNT\SYSTEM32 directory. This file is needed to run Javaexecutable files. 4. Add SET PATH = C:\LINDO; to your C:\AUTOEXEC.BAT file. Reboot the computer after doing this.

11.3 GFPM BASE-YEAR INPUT The base year (currently 1997) is the year from which the projections begin. The data needed to establish the base year are described in Chapter 4. The GFPM uses Microsoft Excel (Office 97 and higher) to organize base-year input data. All input data are in the file WORLD.XLS, in the C:\PELPS\GFPM directory. Data relating to different parts of the model are on 11 worksheets within 1 The GFPM uses the LINDO linear programming solver, version 6.01 (Schrage 1997). LINDO is invoked by the GFPM from DOS, which can be done under the Windows NT, Windows 2000, or Windows XP operating system but not under Windows 95 or Windows 98. The GFPM software and the data used in previous chapters are available at: www.forest.wisc.edu/facstaff/buongiorno/ book/index.htm

221


SNJRT11.DLL file copied into WINNT\SYSTEM32 directory

Root directory directory of of the the C: C: Root drive drive into into which which GFPM.ZIP GFPM.ZIP is is unzipped unzipped LINDO created directory

C:\PELPS directory of files to run the GFPM

AUTOEXEC.BAT file in C:\root directory

Add this to the AUTOEXEC.BAT file

FIGURE 11.1 Explorer.

Files and directories to install and run the GFPM, shown in Microsoft Windows

the WORLD.XLS file (only worksheet with names in bold are used in the current version of the GFPM; they will be discussed in detail later). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Demand Supply Cross Price Manufacture Capacity1 Capacity2 RecyclingD (Recycling Demand) RecyclingS (Recycling Supply) Transportation (Transportation Cost and Tax) Exchange Rate ExogChange (Exogenous Change)

WORLD.XLS contains an additional five worksheets: GDP, RoundwoodSupply, TechChange, Recycle, and Trade. These worksheets may be utilized when preparing data in the ExogChange worksheet, but they are not required for running the GFPM.

222

The Global Forest Products Model Price elasticity of demand A

Country

Commodity Dummy region

B

C

D

E

1

****** DEMAND ******

22 23 24 25 26 27 28 29 30 31 32 33 34

a0 a0 a0 a0 a0 a0 a1 a1 a1 zy zy zy zy

84 85 86 91 92 93 80 82 83 80 84 88 92

Base-year price

561.0 382.0 358.0 552.0 1030.0 1021.0 39.0 65.0 262.0 39.0 414.0 423.0 837.0

97 31 5 31 102 155 5560 1050 12 0.0 0.0 0.0 0.0

-0.22 -0.05 -0.52 -0.18 -0.37 -0.14 -0.1 -0.37 -0.21 0.0 0.0 0.0 0.0

Base-year demand

GDP elasticity of demand F

G

H

0.74 0.65 0.82 1.05 1.11 0.92 0.4 0.19 0.46 0.0 0.0 0.0 0.0

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

I

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

J

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

K

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

L

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lower bound on demand to account for difference between world imports and exports

M

N

O

0 0 0 0 0 0 0 0 0 381 1984 608 2291

97 7 31 1 5 31 102 155 5560 1050 12 0.0 0.0 0.0 0.0

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Pre base-year demand

FIGURE 11.2 Demand worksheet with data for demand equations.

At the top of each worksheet is information describing the data in each column of the sheet (information in grayed-out text is not used by the GFPM). At this point you may find it useful to open the WORLD.XLS file and look at each of the worksheets discussed next.

DEMAND The Demand worksheet (Figure 11.2) contains the data that define the demand equation for each region/country and end product: the quantity consumed in the base year, the corresponding price, and elasticities with respect to price and GDP. Demand prices are set equal to world prices (for net exporters of a commodity) or world prices plus freight costs and tariffs (for net importers of a commodity). Country and commodity codes are defined in Tables 11.1 and 11.2. The pre base-year demand is used to start Equation [3.11], it can be set equal to the base-year demand.

SUPPLY The GFPM has an econometric supply equation for each country and primary product (wood, wood pulps, recycled paper, other fiber). The Supply worksheet (Figure 11.3) contains the data that define the supply equations: the quantity supplied in the base year and the corresponding price, the price elasticity, the elasticity with respect to the supply shifter (1.0 if needed, else 0.0), and the upper bound on supply if needed. Supply prices are set equal to world prices (for net exporters of a commodity) or world prices plus freight costs and tariffs (for net importers of a commodity).

223

Using the GFPM Software TABLE 11.1 Code 80 81 82 83 84 85 86 87 88 89 90 91 92 93

Commodity Codes in the GFPM Commodities

Units

Fuelwood and charcoal Sawlogs and pulpwood Other industrial roundwood Sawnwood Veneer/plywood Particleboard Fiberboard Mechanical wood pulp Chemical/semichemical wood pulp Other fiber pulp Wastepaper Newsprint Printing and writing paper Other paper and paperboard

103 m3 103 m3 103 m3 103 m3 103 m3 103 m3 103 m3 103 t 103 t 103 t 103 t 103 t 103 t 103 t

MANUFACTURE The Manufacture worksheet (Figure 11.4) contains data that define the manufacturing costs and manufacturing [input–output (IO)] coefficients in the base year for commodities that are manufactured into other commodities. The IO coefficients are the quantity of input per unit of output. Manufacturing costs are costs excluding the cost of input explicitly in the GFPM model. Their calculation is explained in Chapter 4. Manufacturing costs are called M records, and manufacturing coefficients are called P records. M records must be entered first, followed by P records. The GFPM uses only one process and one input mix for each output.

CAPACITY — 1 The Capacity1 worksheet (Figure 11.5) contains the data to predict next year’s world manufacturing capacity for each manufactured commodity. The Capacity1 worksheet is not used when running only the base-year model, though it is needed for multiyear forecasts [option 4 of the PELPS IV main menu (Figure 11.15)]. Next year’s world capacity is a linear function of the production in the last three years (as defined in Chapter 3). The expansion parameters are the coefficients in this function.

CAPACITY — 2 The Capacity2 worksheet (Figure 11.6) contains data on base-year manufacturing capacity organized by country and commodity. Capacity growth for each region and commodity depends on world capacity growth, the current national production, and the shadow price of capacity, computed by the GFPM.

224

TABLE 11.2 Code

Country Codes in the GFPM1

Country

Code Country NORTH/ CENTRAL AMERICA

AFRICA A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 B0 B1 B2 B3 B4 B5 B6 B7 B8 B9 C0 C1 C2 C3 C4 C5 C6 C7

Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Congo Cote d’Ivoire Djibouti Egypt Equatorial Guinea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Libyan Arab Jamahiriya Madagascar Malawi Mali

F0 F1 F2 F3 F4 F5 F6 F7 F8 F9 G0 G1 G2 G3 G4 G5 G6 G7 G8 G9 H0 H1

Bahamas Barbados Belize Canada Cayman Islands Costa Rica Cuba Dominica Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Martinique Mexico Netherlands Antilles Nicaragua Panama Saint Vincent Trinidad and Tobago United States

H2 H3 H4

SOUTH AMERICA Argentina Bolivia Brazil

Code Country

Code

EUROPE

ASIA I5 I6 I7 I8 I9 J0 J1 J2 J3 J4 J5 J6 J7 J8 J9 K0 K1 K2 K3 K4 K5 K6 K7 K8 K9 L0 L1 L2

Afghanistan Bahrain Bangladesh Bhutan Brunei Darussalam Cambodia China Cyprus Hong Kong India Indonesia Iran, Islamic Republic Iraq Israel Japan Jordan Korea, DPR Korea, REP Kuwait Laos Lebanon Macau Malaysia Mongolia Myanmar Nepal Oman Pakistan

Country

N5 N6 N7 N8 N9 O0 O1 O2 O3 O4 O5 O6 O7 O8 O9 P0 P1 P2 P3 P4 P5 P6 P7 P8 P9 Q0 Q1 Q2

Albania Austria Belgium-Luxembourg Bosnia-Herzegovina Bulgaria Croatia Czech Republic Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Macedonia Malta Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland

C8 C9 D0 D1 D2 D3 D4 D5 D6 D7 D8 D9 E0 E1 E2 E3 E4 E5 E6 E7 E8 E9

1

Mauritania Mauritius Morocco Mozambique Niger Nigeria Reunion Rwanda Sao Tome and Principe Senegal Sierra Leone Somalia South Africa Sudan Swaziland Tanzania, United Republic Togo Tunisia Uganda Zaire Zambia Zimbabwe

H5 H6 H7 H8 H9 I0 I1 I2 I3 I4

Chile Colombia Ecuador French Guiana Guyana Paraguay Peru Suriname Uruguay Venezuela

L3 L4 L5 L6 L7 L8 L9 M0 M1 M2 M3

Philippines Qatar Saudi Arabia Singapore Sri Lanka Syrian Arab Republic Thailand Turkey United Arab Emirates Viet Nam Yemen

M4 M5 M6 M7 M8 M9 N0 N1 N2 N3 N4

OCEANIA Australia Cook Island Fiji French Polynesia New Caledonia New Zealand Papua New Guinea Samoa Soloman Islands Tonga Vanuatu

Q3 Q4

United Kingdom Yugoslav Fed. Rep.

Q5 Q6 Q7 Q8 Q9 R0 R1 R2 R3 R4 R5 R6 R7 R8 R9

FORMER USSR Armenia Azerbaijan Belarus Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania Moldova, Rep Russian Federation Tajikistan Turkmenistan Ukraine Uzbekistan

ZY ZZ

Dummy region World

Country codes may be any two-digit form. But the dummy region and world must always be “ZY’’ and “ZZ” codes, respectively.

225

226

The Global Forest Products Model Base-year supply A

1 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

B

Supply shifter elasticity

Supply price elasticity, set to zero if supply is horizontal

C

D

E

F

G

H

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

I

J

K

L

M

N

O

****** SUPPLY ****** a0 a0 a0 a0 a0 a1 a1 a1 a1 a1 zy zy zy zy zy zy zy zy zy

80 81 82 89 90 80 81 82 89 90 81 83 85 86 87 89 90 91 93

39.0 93.0 65.0 803.0 89.0 39.0 65.0 65.0 803.0 89.0 65.0 198.0 296.0 277.0 284.0 803.0 89.0 507.0 830.0

2344 153 298 21 1 5560 66 1050 0 0 0 0 0 0 0 0 0 0 0

0.80 0.80 0.80 0.00 0.00 0.80 0.80 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Base-year price

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 13586 1186 165 1062 311 26 3403 130 5823

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Upper bound on supply to account for difference between world imports and exports

FIGURE 11.3 Supply worksheet with data for supply equations.

A 1

Process

Input mix

Country

Input Output FIGURE 11.4 coefficients.

29 30 33 34 35 36 37 38 855 856 857 859 860 861 862 863 864 865 866 867

B

C

D

E

F G

H

I

J

****** MANUFACTURE ****** M M M M M M M M P P P P P P P P P P P P

a0 a0 a0 a0 a0 a1 a1 a1 a0 a0 a0 a0 a0 a0 a0 a0 a0 a0 a0 a0

83 84 88 92 9 93 83 84 88 81 81 81 81 87 88 89 90 87 88 89 90

83 84 85 88 92 92 92 92 93 93 93 93

31 32 36 38 8 39 31 32 36 31 32 33 36 38 38 38 38 39 39 39 39

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

40.17 291.28 177.99 453.45 463.68 77.46 335.95 178.57 2.27 2.72 2.23 3.23 0.00 0.63 0.31 0.06 0.02 0.25 0.26 0.51

Manufacturing cost ($/t) I-O coefficient: Roundwood to sawnwood (m 3/ m3)

Manufacture worksheet with data for manufacturing costs and input–output

227

Using the GFPM Software A

B

C

D

E

F

G

****** CAPACITY -- 1 ******

1 2 === === =========== =========== =========== =========== =========== =========== ======== ======== ======== ======== ======== ======== 10 425997 431634 438586 0.50 0.35 0.25 11 83 61609 59297 60553 0.50 0.35 0.25 12 84 65129 67472 72009 0.50 0.35 0.25 13 85 20375 21580 23161 0.50 0.35 0.25 14 86 36508 34164 35187 0.50 0.35 0.25 15 87 121593 119075 124083 0.50 0.35 0.25 16 88 35380 34838 36047 0.50 0.35 0.25 17 91 81091 82877 89967 0.50 0.35 0.25 18 92 165625 164361 172105 0.50 0.35 0.25 19 93

FIGURE 11.5

Capacity 1 worksheet with data for capacity expansion parameters.

A B C D E 1 19 20 22 23 24 25 26 27

Capacity expansion parameters

world production three years before base year

Commodity

F

G

H

I

J

K

L

M

N

O

-1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

****** CAPACITY -- 2 ****** a0 a0 a0 a0 a0 a0 a1 a1 a1

83 84 85 88 92 93 83 84 88

31 32 33 36 38 39 31 32 36

14.3 27.5 26.4 1.0 37.4 34.1 5.5 12.1 16.5

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0 0 0 0 0 0 0 0 0

Country Commodity

Process

FIGURE 11.6

Base year manufacturing capacity

These parameters should remain set to 0, 1 or –1

Capacity 2 worksheet with data on base-year capacity.

RECYCLING (SUPPLY) The Recycling worksheet (Figure 11.7) contains the data that define the potential recovery of wastepaper from consumed paper and paperboard. For each paper type, this sheet specifies the potential recovery rate of wastepaper in the base year. Within that range, supply of wastepaper responds to price with the elasticity defined in the Supply worksheet (Figure 11.3). In the current version of the GFPM, the wastepaper supply is horizontal at the current price, up to the upper bound defined in Chapter 3.

228

The Global Forest Products Model A B C D E 1 10 11 12 13 14 15

Waste paper recovered

F

G

H

I

J

****** RECYCLING (SUPPLY) ****** a0 a0 a0 a1 a1 a1

90 90 90 90 90 90

Paper consumed

91 92 93 91 92 93

1.00 1.00 1.00 1.00 1.00 1.00

0.00 0.00 0.00 0.00 0.00 0.00

0.17 0.17 0.17 0 0 0

Minimum proportion of newsprint recoverable as waste paper

1.00 indicates identical recycling demand and supply regions

FIGURE 11.7

Maximum proportion of newsprint recoverable as waste paper

Recycling worksheet with data on wastepaper recovery.

Base-year imports A 1

Export country

Import country

B

C

D

E

F

G

H

I

J

K

****** TRANSPORTATION COST AND TAX ******

13 a0 14 a1 15 a1 16 a1 17 a2 840 zy 841 zy 842 zz 843 zz 849 zz 850 zz 851 zz 852 zz 853 zz 854 zz 855 zz 2177 zz 2178 zz 2179 zz

zz zz zz zz zz zz zz a0 a0 a0 a0 a0 a1 a1 a1 a1 zy zy zy

90 80 81 93 83 91 93 81 83 91 92 93 83 84 92 93 84 88 92

0.00 0.00 0.00 0.00 0.00 0.00 0.00 14.75 28.14 34.67 66.71 55.67 28.14 18.90 66.71 55.67 0.00 0.00 0.00

Freight cost

FIGURE 11.8

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.020 0.050 0.100 0.100 0.100 0.050 0.200 0.100 0.100 0.000 0.000 0.000

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Import tariff rate

2 1 2 1 2 130 5823 21 653 8 52 57 6 1 2 1 1984 608 2291

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.46 1.05 1.11 0.92 0.46 0.74 1.11 0.92 0.00 0.00 0.00

0.096 0.060 0.051 0.045 0.050 0.000 0.000 0.051 0.050 0.030 0.051 0.045 0.050 0.052 0.051 0.045 0.000 0.000 0.000

Relative range of annual trade variation

GDP elasticity of trade

Dummy region imports to account for difference between world imports and exports

Transportation worksheet with data on transportation costs and tax rates.

TRANSPORTATION COST AND TAX The Transportation worksheet (Figure 11.8) contains information on the direction and quantity of international trade as well as freight costs and ad valorem tariff rates. In the GFPM applications discussed in Chapters 6, 7, and 8, all countries export to and import from the World (region ZZ). Chapters 9 and 10 use trade flows between countries. The mean change in imports and exports from year to year is driven by GDP growth, reflected by an elasticity. Trade is allowed to vary around this mean change by plus or minus a percentage of the volume of trade. The equilibrium process of GFPM calculates the welfare maximizing trade within these bounds. When solving only for the base year [option 3 of the PELPS IV main menu (Figure 11.15)] all countries’ commodity import and export volumes are fixed.

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Thus, the trade inertia parameters (mean elasticity of trade with respect to GDP and range of variation in trade) are not needed.

11.4 EXOGENOUS CHANGE DATA The ExogChange worksheet (Figure 11.9) contains data that influence the change in production, consumption, trade, and prices from year to year: 1. Shifts of the demand curves due to GDP growth (e.g., 3.47%/ annum) (record type D) 2. Shifts of the supply curves for wood and other fiber pulp due to cost changes or forest policy (record type S) 3. Changes in input–output coefficients, reflecting technological change (record type P) 4. Changes in wastepaper recovery coefficients (record type W) 5. Changes in freight cost, tariff rate, and trade inertia (record type T) The data are organized by year, headed with the name PERIODt, where t (1–13) is the year when changes are to be made. The ExogChange data are not needed when running the model for the base year only.

Year to which exogenous changes apply

Record type A B 1 2 112 113 120 121 122 123 124 1734 1735 1736 1737 1738 2453 2454 2455 2456 3337 3338 3339 3340 3341 3342 3871 3873 4694 4695 4696 4704 4705 4706 6025 6026 6027 10607 10611 10612 10613

C

D

E

F

G

Click to update ExogChange worksheet

GDP growth rate H

I

J

K

L

M

N

O

****** EXOGENOUS CHANGE ****** PERIOD1 D a0 a D a0 D a0 D a1 D a1 D a1 S a0 S a0 S a0 S a1 S a1 P a0 P a0 P a0 P a0 W a0 W a0 W a0 W a1 W a1 W a1 T a0 T a1 T zz T zz T zz T zz T zz T zz PERIOD3 D a0 D a0 T zz T zz T zz

80 2 92 93 80 82 83 81 82 8 89 8 80 81 87 88 89 90 90 90 90 90 90 90 zz zz a0 a0 a0 a1 a1 a1

0 0 0 0 0 0 0 0 Change in 0 0 input-output 0 coefficient 38 38 38 38 91 92 93 91 92 93 90 81 81 83 84 83 84 92

Country

92 92 92 92

80 80 a0 a0 a0

85 86 88

FIGURE 11.9

1 1 1 1

0 .00 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0 .00 0.0000 -0.0020 0.0000 0.0020 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.0358 0.0358 0.0358 0.0245 0.0245 0.0245 0.0183 0.0183 0.0000 0.0046 0.0046

P

Q

Update ExogChange 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

Supply shift rate

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0 -0.005 -0.001 -0.001 0 0 0

0.020 0.020 0.020 0.032 0.032 0.032 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

Change in maximum proportion of newsprint recoverable as waste paper Tariff rate change 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0.00 0.00 0

0 0

0.0358 0.0358

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00 0.00

0 0 0

0.00000 0.00000 0.00000

0 0 0

0 0 0

0 0 0

ExogChange worksheet with data on exogenous changes.

230 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


G H A B C D E F Enter the GDP growth rate in the following table, click the Update ExogChange button on the ExogChange sheet to update data

Country Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Congo

Code a0 a1 a2 a3 a4 a5 a6 a7 a8 a9 b0

1998 PERIOD 1 0.0358 0.0245 0.0521 0.0600 0.0543 0.0175 0.0446 0.0372 0.0421 0.0377 0.0046

1999 PERIOD 2 0.0358 0.0245 0.0521 0.0600 0.0543 0.0175 0.0446 0.0372 0.0421 0.0377 0.0046

2000 PERIOD 3 0.0358 0.0245 0.0521 0.0600 0.0543 0.0175 0.0446 0.0372 0.0421 0.0377 0.0046

2001 PERIOD 4 0.0358 0.0330 0.0500 0.0561 0.0532 0.0436 0.0526 0.0350 0.0484 0.0400 0.0318

2002 PERIOD 5 0.0358 0.0330 0.0500 0.0561 0.0532 0.0436 0.0526 0.0350 0.0484 0.0400 0.0318

2003 PERIOD 6 0.0358 0.0330 0.0500 0.0561 0.0532 0.0436 0.0526 0.0350 0.0484 0.0400 0.0318

GDP annual growth rate

FIGURE 11.10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

GDP worksheet used to update GDP growth rates in the ExogChange worksheet.

K L A B C D E F G H I J Enter the technical change rate in the "Average Change" column, then click the Update ExogChange button on the ExogChange sheet to update data Country a0 a0 a0 a0 a0 a0 a0 a0 a6 a6 a6 a6 b0 b0 b0 b0

Input

Output 87 88 89 90 87 88 89 90 87 88 89 90 87 88 89 90

92 92 92 92 93 93 93 93 93 93 93 93 93 93 93 93

Country Algeria Algeria Algeria Algeria Algeria Algeria Algeria Algeria Cameroon Cameroon Cameroon Cameroon Congo Congo Congo Congo

Input M echPulp C hemPulp O thPulp W astePaper M echPulp C hemPulp O thPulp W astePaper MechPulp ChemPulp OthPulp WastePaper MechPulp ChemPulp OthPulp WastePaper

Output Average Change P&WPaper 0.0000 P&WPaper -0.0020 P&WPaper 0.0000 P&WPaper 0.0020 OthPaper -0.0002 OthPaper -0.0018 OthPaper 0.0000 OthPaper 0.0020 OthPaper -0.0002 OthPaper -0.0018 OthPaper 0.0000 OthPaper 0.0020 OthPaper -0.0002 OthPaper -0.0018 OthPaper 0.0000 OthPaper 0.0020

Annual change in IO coefficient

1997 0.00 0.63 0.31 0.06 0.02 0.25 0.26 0.51 0.16 0.19 0.00 0.65 0.16 0.19 0.00 0.65

1998 0.00 0.63 0.31 0.06 0.02 0.25 0.26 0.51 0.16 0.19 0.00 0.65 0.16 0.19 0.00 0.65

1999 0.00 0.63 0.31 0.06 0.02 0.25 0.26 0.51 0.16 0.19 0.00 0.65 0.16 0.19 0.00 0.65

2000 0.00 0.62 0.31 0.07 0.02 0.24 0.26 0.52 0.16 0.18 0.00 0.66 0.16 0.18 0.00 0.66

2001 0.00 0.62 0.31 0.07 0.02 0.24 0.26 0.52 0.16 0.18 0.00 0.66 0.16 0.18 0.00 0.66

IO coefficients calculated by worksheet

FIGURE 11.11 TechChange worksheet used to update the change in IO coefficients in the ExogChange worksheet.

To speed up the changing of data in the ExogChange worksheet, you can use the GDP, RoundwoodSupply, TechChange, Recycle, and Trade worksheets in WORLD.XLS. They allow the user to change GDP growth rates, roundwood supply shifters, future input–output coefficients, wastepaper recovery rates, and tariff rates and to copy them automatically to the ExogChange worksheet. The GDP worksheet (Figure 11.10) allows you to enter GDP growth rates for some or all countries and future years up to 2015. The corresponding values in the ExogChange worksheet are updated by clicking on the Update ExogChange button on the ExogChange worksheet. The RoundwoodSupply worksheet follows a similar format, and the supply shifter information in the ExogChange worksheet is updated in the same manner. The TechChange worksheet (Figure 11.11) enables you to enter an average annual rate of change in the manufacturing (input–output (I-O)) coefficients for the manufacture of paper commodities. The estimated average annual

231


A B C D E F G H I 1 Enter change in recycling rate in "Average Change" column, then click Update ExogChange button on ExogChange sheet to update data 2 Consumed Consumed Commodity Country Commodity Average Change Base PERIOD 1 PERIOD 2 PERIOD 3 3 Country 91 Algeria Newsprint 0.020 0.17 0.190 0.210 0.230 4 a0 92 Algeria P&WPaper 0.020 0.17 0.190 0.210 0.230 5 a0 93 Algeria OthPaper 0.020 0.17 0.190 0.210 0.230 6 a0 91 Angola Newsprint 0.020 0.00 0.020 0.040 0.060 7 a1 92 Angola P&WPaper 0.020 0.00 0.020 0.040 0.060 8 a1 93 Angola OthPaper 0.020 0.00 0.020 0.040 0.060 9 a1 91 Benin Newsprint 0.020 0.00 0.020 0.040 0.060 10 a2 92 Benin P&WPaper 0.020 0.00 0.020 0.040 0.060 11 a2 93 Benin OthPaper 0.020 0.00 0.020 0.040 0.060 12 a2 91 Botswana Newsprint 0.020 0.00 0.020 0.040 0.060 13 a3 92 Botswana P&WPaper 0.020 0.00 0.020 0.040 0.060 14 a3 93 Botswana OthPaper 0.020 0.00 0.020 0.040 0.060 15 a3 91 Burkina Faso Newsprint 0.020 0.00 0.020 0.040 0.060 16 a4 92 Burkina Faso P&WPaper 0.020 0.00 0.020 0.040 0.060 17 a4 93 Burkina Faso OthPaper 0.020 0.00 0.020 0.040 0.060 18 a4 91 Burundi Newsprint 0.020 0.00 0.020 0.040 0.060 19 a5 92 Burundi P&WPaper 0.020 0.00 0.020 0.040 0.060 20 a5

Annual change in paper recovery rate

Paper recovery rate calculated by worksheet

FIGURE 11.12 Recycle worksheet used to update the change in maximum wastepaper recovery rates in the ExogChange worksheet.

change in the I-O coefficient throughout the forecast period is entered under column G, “Average Change.” It is important to check that the specified “Average Change” will not result in nonsensical I-O coefficients in subsequent projection periods. You should check that the calculated I-O coefficients (column I to column U) do not become negative or greater than 1.00. The annual change in the I-O coefficients in the ExogChange worksheet may then be updated by clicking on the Update ExogChange button on the ExogChange worksheet. If you wish to specify annual changes in the I-O coefficients that differ from period to period, you may do so by entering the change in I-O coefficients in the ExogChange worksheet. The Recycle worksheet (Figure 11.12) enables you to enter an average annual change, over the projection period, in the rate of wastepaper recovery from newsprint, printing and writing paper, and other paper and paperboard. The estimated average annual change in the maximum wastepaper recovery is entered under column E, “Average Change.” As with the I-O coefficients in the TechChange worksheet, the maximum recovery rates calculated for subsequent projection periods must be checked for nonsensical estimates. The annual change in the maximum recovery rate in the ExogChange worksheet can then be updated by clicking on the Update ExogChange button on the ExogChange worksheet. If you wish to specify annual changes in the maximum recovery rates that differ from period to period, you may do so by entering the change in maximum recovery rate in the ExogChange worksheet. The Trade worksheet (Figure 11.13) enables you to enter the change in commodity tariff rates for the period 1998–2010 (column D to column N). As with the I-O coefficients in the TechChange worksheet, the tariff rates calculated for subsequent

232


A B C D E F G H 1 Enter tariff rate change in following table, then click Update ExogChange button on ExogChange sheet to update data 2 Exporter Importer Commodity Base Year Tariff PERIOD1 PERIOD2 PERIOD3 PERIOD4 3 zz a0 81 0.300 -0.05 -0.05 -0.05 -0.05 4 zz a0 83 0.100 -0.02 -0.02 -0.02 -0.02 5 zz a0 92 0.200 0.00 0.00 0.00 0.00 11 zz a0 93 0.200 0.00 0.00 0.00 0.00 12 zz a1 83 0.100 -0.01 -0.01 -0.01 -0.01 13 zz a1 85 0.100 -0.01 -0.01 -0.01 -0.01 14 zz a1 93 0.150 -0.015 -0.015 -0.015 -0.015 15 zz a3 83 0.080 -0.02 -0.02 -0.02 0 16 zz a3 93 0.150 0.00 0.00 0.00 0 17 zz a4 83 0.050 -0.01 -0.01 -0.01 -0.01 18 Change in Algerian sawnwood import tariff in 1998

Figure 11.13

Trade worksheet used to update changes in tariff rates in the ExogChange worksheet.

FIGURE 11.14

The GFPM welcome screen.

projection periods must be checked for negative estimates (import subsidies). The changes in tariff rates in the ExogChange worksheet can then be updated by clicking on the Update ExogChange button on the ExogChange worksheet.

11.5 RUNNING THE GFPM To launch the GFPM, go to C:\PELPS\GFPM and double-click the file GFPM.BAT. This brings up the GFPM welcome screen (Figure 11.14). Pressing any key will bring up the GFPM main menu (Figure 11.15). The menu has the following options.

233


FIGURE 11.15

The main menu of the GFPM.

FIGURE 11.16

The Save Input menu.

1) Create Input Files To change the input data, first make the changes in the WORLD.XLS input file, then: 1. Select 1) Create input files from the GFPM main menu. The Save Input menu will then appear (Figure 11.16). 2. Click the SaveWorld button in the menu to create the new input files. This step may take up to 5 minutes. Then the main menu (Figure 11.15) will appear again. 2) Define Run-time Parameters This option allows a choice of three run-time parameters of PELPS IV: solution range and number of steps in the stepwise approximation of demand and supply

234


functions (see Chapter 3) and projection length (1–18 years). The default runtime parameters are: Solution range: Number of steps: Length of projection:

0.10 4 13

(r in Equation [3.21]) (h in Equation [3.22])

3) Run Base-Period This option solves for equilibrium prices and quantities (production and consumption) by country and commodity in the base year (1997) only. Imports and exports are fixed at their observed value in the base year. Running the base period is a way to check whether the model solution replicates observed production and prices by country and product with reasonable accuracy. The base period may be run without data in the ExogChange or Capacity1 worksheets or trade inertia parameters in the Transportation worksheet. This option takes approximately 10–15 minutes. 4) Run Multi-Periods This option prompts the GFPM to project production, consumption, imports, exports, and price by country and commodity for up to 18 years from the base year. It takes approximately 4 hours to compute forecasts from 1997 to 2010. 5) Get Output This option will produce the Get Output menu2 (Figure 11.17). Click the RetrieveOutput button under the Base Scenario heading to retrieve the results of a GFPM run. Click the Summary button to create the SUMMARY.XLS file from the retrieved results. These steps may each take up to 5 minutes. The program will return to the Get Output menu (Figure 11.17) afterwards. The Welfare Analysis option is described in Section 11.7. Choosing QuitExcel will return to the GFPM main menu (Figure 11.15). 6) Quit GFPM This option closes the GFPM main menu. 2

During the process of opening the Get Output menu, several error messages may appear in the DOS box under the GFPM main menu because GFPM tries to back up results from previous runs, which may not exist. These error messages can be ignored.

235


FIGURE 11.17

The Get Output menu.

11.6 EXAMPLES OF GFPM OUTPUT Individual country projections are stored in the following Excel files in the C:\PELPS\PELPS\OUTPUT directory: 1. 2. 3. 4. 5. 6. 7.

Capacity Consumption Export Import Nettrade Price Production

Selecting RetrieveOutput from the Get Output menu generates these files. Each contains a table of country data organized by commodity (e.g., Figure 11.18). Summary projection results, combined with historical data for Africa, North/Central America, South America, Asia, Oceania, Europe, and the former

236

FIGURE 11.18 TION.XLS.


Example of detailed production table for total roundwood, in file PRODUC-

USSR are stored in the file C:\PELPS\GFPM\SUMMARY.XLS. This file contains results in the form of: 1. Charts of past data (1965–1996) and projections (1997–2010) of production, consumption, imports, exports, and net trade, by commodity (e.g., Figure 11.19). 2. Tables of past data (1965–1996) and projections (1997–2010) of production, consumption, imports, exports, and net trade, by commodity. 3. Charts of real world prices, historical (1965–1996) and projected (1997–2010) (e.g., Figure 11.20). Clicking on the Summary button under the Base Scenario heading generates summary results for the base scenario. This option will generate two Excel files, which are stored in the C:\PELPS\GFPM folder, SUMMARY.XLS and SUMMARYCHANGE.XLS. Having generated the output files associated with the base scenario model run, a second run of the GFPM could be made for the alternative scenario. After running the alternative scenario, select option 5) Get output from the GFPM main menu to open the Get Output menu (Figure 11.17). Click the

237


FIGURE 11.19

Example of summary production chart provided in file SUMMARY.XLS.

Historical FAO data

GFPM forecast

Data for price charts

FIGURE 11.20

Example of summary price chart provided in file SUMMARY.XLS.

238


FIGURE 11.21

Summary table output from SUMMARYCHANGE.XLS.

RetrieveOutput button under the Alternative Scenario heading to get the output for the alternative scenario. The output files created for the base scenario are saved in the C:\PELPS\PELPS\OUTPUT folder with the suffix _OLD, and new output files are created for the alternative scenario. If summary results for the alternative scenario are also required, click the Summary button under the alternative scenario heading. As well as creating summary output in SUMMARY.XLS, the data in the SUMMARYCHANGE.XLS file will also be updated to allow comparison of production, consumption, and trade data between the base scenario and the alternative scenario. The SUMMARYCHANGE.XLS workbook contains the types of tables and charts that have been provided in Chapters 6, 7, and 8 to summarize the impacts of the Asian economic crisis, accelerated tariff liberalization, and increased wastepaper recycling. Summary tables in the SumTable worksheet of SUMMARYCHANGE.XLS provide the average annual percentage difference in aggregate commodity production and trade (Figure 11.21). SUMMARYCHANGE.XLS also contains charts of the regional percentage differences in production, consumption, and trade for aggregate commodities between the base and alternative scenarios for each projection period (Figure 11.22).

239


FIGURE 11.22

Chart output from SUMMARYCHANGE.XLS.

11.7 WELFARE ANALYSIS WITH THE GFPM The welfare analysis component of the GFPM allows estimation of the welfare impacts of an alternative scenario compared with a base scenario. To carry out a welfare analysis, two separate scenarios must be run, with the first scenario being the base scenario. Having run the base scenario (following the steps described in the previous sections), select option 5) Get output from the GFPM main menu to open the Get Output menu (Figure 11.17). Click the RetrieveOutput button under the Base Scenario heading to get the output for the base scenario. The output files created include VALUE_TOTAL.XLS, VALUE_CONS.XLS and VALUE_PROD.XLS, which contain tables of the value of each product consumed, produced, and the total value (the sum of the values of production and consumption). These data are used in the welfare analysis to scale the change in consumer, producer, and total welfare, to calculate relative welfare changes, in percent, as described at the end of Section 3.6. To perform a welfare analysis comparing the welfare levels under the base and alternative scenarios, click the WelfareAnalysis button under the alternative scenario heading. This will create three files, WELFARECHANGE_TOT.XLS,

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FIGURE 11.23 Chart output in WELFARECHANGE_TOTAL.XLS showing the difference in (consumer plus producer) welfare level for all forest products between the base scenario and the alternative scenario.

WELFARECHANGE_CONS.XLS, and WELFARECHANGE_PRO.XLS, which contain tables and charts summarizing total, consumer, and producer welfare impacts, respectively, of the alternative scenario over the base scenario (Figure 11.23). Welfare impacts are described in terms of the percentage difference in producer, consumer, or total welfare between the two scenarios.

11.8 TROUBLESHOOTING GFPM ERRORS

INFEASIBILITY Infeasibility means that LINDO cannot find a solution to the optimization problem described by the GFPM input data. This results in termination of the GFPM with several run-time errors. Infeasibility may occur while calibrating the model for the base year. In several applications of the GFPM (for example, those reported in Chapters 6–8), countries import from the world and export to the world without specification of countries of origin and destination. To this end, the GFPM has a “world” region, similar to a country, with code number “ZZ”. A material balance

241


such as Equation [3.4] applies to the world region, but with trade activities only, thus forcing world imports to be equal to world exports for all commodities. However, world import data are rarely equal to world exports in the base year. To account for the difference, the GFPM has a dummy region, “ZY”. Associated with this dummy region is a material balance equation such as Equation [3.4], with demand and supply fixed to account for the difference between world imports and exports in the base year. To ensure that the base year has a feasible solution, check that the imports of the dummy region are at least equal to its demand (for commodities that the dummy region imports) or that its exports are at most equal to its supply (for commodities that the dummy region exports).

RUN-TIME ERROR Run-time errors are error messages generated by Pascal, one of the programming language of the GFPM. Run-time errors cause the GFPM to terminate. Check the type of run-time error for the first error that occurs, because often an initial error will cause a chain of subsequent errors. Two common types of run-time error are the following. 106 Invalid Numeric Format The format of the data for a variable or parameter differs from the format expected by the Pascal program. This error occurs when data in the WORLD.XLS worksheets have the wrong format or cells are erroneously empty. It is easiest to check for this kind of error in the PELPS input files, such as UPIN.DAT. For example, Figure 11.24 shows how entering a process number as “1.000” instead of “1” in the ExogChange worksheet leads to “##” in the file UPIN.DAT, causing a run-time error.

Incorrectly formatted input

Data resulting in run-time error

FIGURE 11.24 Incorrectly formatted data in the ExogChange worksheet and the corresponding data in UPIN.DAT resulting in a run-time error.

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207 Invalid Floating Point Operation Pascal has attempted to take the log or square root of a negative number or divide by zero. This error most often occurs when zero quantities supplied or demanded are associated with a nonzero elasticity. Either set the supply/ demand quantity to an arbitrarily small number (say, 0.1) or set the elasticity to zero.

CHAPTER

12

GFPM Software Design and Implementation

12.1 INTRODUCTION This chapter describes the structure of the GFPM software. This chapter will help users with access to the GFPM source code to modify it to suit their needs. Other readers may skip this chapter, although even for them it can be useful for model debugging by tracing the model calculations over time.1 An overview of the GFPM software structure is given in Section 12.2 in terms of the overall processing flows and the main components of the GFPM. Section 12.3 describes the role of batch files in controlling the execution of the GFPM programs, and it details the sequence of program execution corresponding to each of the Main Menu options of the GFPM (Figure 11.15). Next, each component is described in detail in Sections 12.4, 12.5, and 12.6, including the main programs in that component, the control mechanism, and the data flow. The description of individual programs, procedures, or functions focuses on their functionality, input/output, and processing mechanism. It does not go through the program code line by line. The chapters on using the GFPM 1

The GFPM executable files, and the data files, are available at: www.forest.wisc.edu/ facstaff/buongiorno/book/index.htm

243

244


(Chapter 11) and GFPM structure and formulation (Chapter 3) should be read before this chapter.

12.2 OVERVIEW OF GFPM SOFTWARE STRUCTURE Figure 12.1 shows the main components of the GFPM: data preparation, data processing, and output retrieval. The data preparation is done with Microsoft Visual Basic Application macros in INPUT.XLS, and the user interface is the Excel spreadsheet WORLD.XLS. Parameters Production, Consumption, Trade, Prices

Data Preparation Assumptions

Timber Supply Trade

Data Processing (Base Period) Year

Manufacture, Final Demand

Shifts: Demand, Supply, Capacity, Technology, Recycling, Trade, Policies

Consumption, Production, Price

Data Processing (Year t)

Output Retrieval

For Each Year

Import, Export, Welfare

data files program FIGURE 12.1

Implementation components in the GFPM.

245

GFPM Software Design and Implementation TABLE 12.1 Correspondence Between WORLD.XLS Worksheets and .DAT Files WORLD.XLS worksheet Demand Supply Crossprice Manufacture Capacity1 Capacity2 RecyclingD RecyclingS Transportation ExchRate ExogChange

.DAT file DEMIN SUPIN CROIN MATDAT MANPAR DEPIN REDIN RECIN TRAIN, TAXIN EXCIN UPIN

You enter the demand, supply, trade, and other data in the formatted spreadsheets in WORLD.XLS and run the macros via a graphical user interface in the INPUT.XLS workbook. These macros convert the data in WORLD.XLS into .DAT files (Table 12.1). These are ASCII files, which are then read by the dataprocessing component. The data-processing component is PELPS IV, an improved version of PELPS III (Zhang et al. 1993), which reads data in ASCII format to construct an MPS file. The MPS file is an industrial-standard input file for linear programming systems.2 The MPS file is then read by the LINDO linear programming software to solve the economic equilibrium problem equivalent to an optimization. The optimization problem is first solved for the base year. For succeeding years, the MPS file is updated based on last year’s solution and exogenous changes. The core of the data-processing component is constructing the MPS input file for LINDO and updating it from year to year. The output-retrieval component consists of Visual Basic Application macros, which convert the ASCII output from the data-processing component to figures and tables in Excel spreadsheets such as SUMMARY.XLS.

12.3 CONTROL PROGRAMS AND PROGRAM EXECUTION Each of the three components of the GFPM software consists of a number of programs, the execution of which is controlled by a DOS batch file control program. The batch files switch control from one program to another, making programs written in different languages work together. Some batch files, however, simply delete unused files or change the current working directory. Table 12.2 lists the main files in the GFPM. 2

The format of MPS files is described in the documentation for LINDO (Schrage 1997).

246 TABLE 12.2

The Global Forest Products Model GFPM Program Files

Pascal files Basecap.pas Baseeqlm.pas Base_rec.pas Baseout.pas Baseyear.pas Chksign.pas Editout.pas Findfile.pas Iter_rec.pas Iter-a.pas Itercap.pas Iterout.pas Menu.pas Sort.pas T_ratio.pas Transfer.pas Update-a.pas Welcome.pas

Java files Basemat.java Itermat.java Job.java ListEmptyException.java NoNextElementException.java Queue.java SortedList.java

DOS batch files Batchfil.bat GFPM.bat Input.bat Output.bat Pelps.bat Welcome.bat

Excel workbooks Input.xls Output.xls Summary.xls World.xls

The batch file GFPM.BAT in C:\PELPS\GFPM launches the GFPM by calling WELCOME.BAT in C:\PELPS\PELPS\, which starts WELCOME.EXE and then switches control to PELPS.BAT, which starts the MENU.EXE program, which generates a BATCHFIL.BAT file, the central controlling batch file. For each main menu option (Figure 11.15), MENU.EXE generates the corresponding DOS batch code in BATCHFIL.BAT to control the execution of programs to perform that option. The most complex menu option is “4) Run multi periods,” for which BATCHFIL.BAT launches Pascal and Java programs and LINDO to make multiyear projections. Another DOS batch file is INPUT.BAT in C:\PELPS\, which launches the INPUT.XLS workbook and then deletes unused files. The batch file OUTPUT.BAT in C:\PELPS\ backs up the old output data and launches the OUTPUT.XLS workbook to retrieve the GFPM output. The following programs correspond to each of the Main Menu options and are controlled by the BATCHFIL.BAT batch file. The programs are described in detail in Sections 12.4–12.6. 1) Create Input Files Choosing this option launches the INPUT.XLS workbook, produces the .DAT files, and splits the UPIN.DAT file into multiple UPIN.I files, one for each projection year.

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2) Define Run-time Parameters Choosing this option runs the Option procedure in MENU.EXE, which sets the solution range, the number of steps in the demand and supply approximation, and the length of projection. The run-time parameters are stored in the PARAMET.DAT file in the C:\PELPS\PELPS directory, along with the years for which exogenous change data are set. 3) Run Base-Period Choosing this option first launches the BASE_REC.EXE program that calculates the lower and upper bounds on recycled paper supply. It then launches BASEYEAR.EXE to generate the different parts of the MPS file for the base year. Next BASEMAT.EXE is launched to combine these parts as an MPS file. It then launches the LINDO optimization program to find the base-year solution. CHECKSIGN.EXE checks the sign of output data to delete negative signs on shadow prices in the output files. BASEEQLM.EXE processes output files to generate ASCII files containing production, demand, trade, and other results (Table 12.3). 4) Run Multi-period Figure 12.2 shows the processing flows for this option. The steps of the “3) Run base period” option are first carried out to generate the base-year results. UPDATE-A.EXE then applies the parameters describing exogenous changes stored in UPIN.DAT to update demand, supply, capacity bounds, trade bounds, etc., from one year to the next. The ITERCAP.EXE program updates the capacity data file DEPOUT.DAT. The ITEROUT.EXE program generates ASCII output files for each aspect of the solution, such as demand, supply, production, TABLE 12.3 .DAT file CAPACITT CAPPRICE DEMAND DEMPRICE PRODUCTT PROPRICE SUPPLY SUPPRICE TRANCOST TRANSHIP

Main ASCII Output Files from the GFPM Content Capacity of each produced commodity in each country Shadow price of each produced commodity in each country Demand of each final commodity in each country Price of each final commodity in each country Production of each produced commodity in each country Price of each produced commodity in each country Supply of each raw material in each country Price of each raw material in each country Transportation cost of each traded commodity in each country Import and export of each traded commodity in each country

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.DAT files (input)

BASE_REC.EXE BASEYEAR.EXE BASEMAT.EXE

MATIN.DAT (MPS file)

LINDO

SORT.EXE CHECKSIGN.EXE

ITER-A.EXE ITERMAT.EXE

ITERCAP.EXE ITEROUT.EXE ITER_REC.EXE T_RATIO.EXE

UPDATE-A.EXE

.DAT files (output) FIGURE 12.2

UPIN.I Processing flows in option “4) Run multi periods.”

manufacturing, capacity, and transportation. These files contain the final solution of the GFPM in text format. These files are used to produce summary figures and tables in the output-retrieval component. ITER_REC.EXE calculates the supply bounds for recycled paper. T_RATIO.EXE updates GDP growth rates for each period, which are used for trade bounds updating in ITER-A.EXE, since the bounds on trade depend on past trade and economic growth according to


249

Equations [3.13]. ITER-A.EXE generates the separate sections of the MPS file used by LINDO for each yearly iteration. ITERMAT.EXE combines these sections into a single MPS file, which is used by LINDO to construct the optimization problem for each projected year. SORT.EXE sorts the capacity output from LINDO by country code and commodity code, and CHECKSIGN.EXE deletes negative signs on shadow prices in the output files. After generating the projection output for year t, UPIN.(t + 1) is copied as UPIN.DAT. Along with other output files from year t, UPIN.DAT is used by UPDATE-A.EXE to update intermediate files for the generation of the next period’s MPS file, via ITER-A.EXE. 5) Get Output Choosing this option launches the OUTPUT.XLS workbook for output retrieval. 6) Quit GFPM Choosing this option simply exits from the GFPM.

12.4 DATA PREPARATION The data preparation component of the GFPM involves converting data in the WORLD.XLS worksheets to ASCII input files for PELPS IV. Data preparation is performed by Excel macros in the INPUT.XLS workbook and by Pascal programs.

DATA PREPARATION PROGRAMS The main Excel macro in C:\PELPS\GFPM\INPUT.XLS (referred to as INPUT.XLS) is “SavePelps.” It saves each spreadsheet in WORLD.XLS as a text file. The main Pascal program in data preparation is TRANSFER.EXE, which creates separate files for each year’s exogenous change data from the ExogChange worksheet in WORLD.XLS.

PROCESSING FLOWS IN THE DATA PREPARATION COMPONENT Figures 12.3 and 12.4 show the processing flows from entering data to generating ASCII data files for the processing component. You enter the GFPM data and parameters in the Excel file C:\PELPS\GFPM\WORLD.XLS, manually or

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Enter model parameters and data

Apply utility macros

WORLD.XLS worksheets

Excel macros (INPUT.XLS)

ASCII text files (in C:\PELPS)

Pascal program (TRANSFER.EXE)

ASCII.DAT files (in C:\PELPS\PELPS) FIGURE 12.3

Processing flows in the data preparation component.

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Data Preparation WORLD.XLS

Demand

…

Manufacture

…

ExogChange

INPUT.XLS

DEMAND.PRN

…

MANUFACTURE.PRN

…

EXOGCHANGE.PRN

INPUT.BAT

Data Processing DEMIN.DAT

…

MATDAT.DAT

…

UPIN.DAT

UPIN.1 TRANSFER.EXE

UPIN.4 UPIN.8

FIGURE 12.4

Data files and programs used in the data preparation component of the GFPM.

using utility macros.3 Chapter 11 describes in detail the format of the input data in the WORLD.XLS worksheets. To allow further processing, the WORLD.XLS worksheets are converted to text files (.PRN files) by the “SavePelps” Excel macro in INPUT.XLS. These .PRN files are then converted to .DAT files via C:\PELPS\INPUT.BAT and are stored in the C:\PELPS\PELPS directory. Table 12.1 shows the WORLD.XLS worksheets and corresponding .DAT files in the C:\PELPS directory. The ExogChange worksheet in WORLD.XLS containing the interyear exogenous changes is saved as the text file UPIN.DAT. This file is then split into separate text files for each year’s exogenous changes by TRANSFER.EXE. Each year’s exogenous change data are saved as an UPIN.I file, where “I” corresponds to the year number.

12.5 DATA PROCESSING AND PROBLEM SOLVING The data-processing component contains programs and data files for converting input text files to an MPS file from which LINDO constructs the optimization problem. There are also programs and data files for saving the solution 3 Utility macros in WORLD.XLS can be used to update data in the ExogChange worksheet. These macros are specific to the GFPM composed of 14 commodities, 180 countries, and base year of 1997. Users must adapt them for new applications.

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from LINDO to output text files and for updating next period’s MPS file using output files and exogenous change data. All executable programs and data files reside in the C:\PELPS\PELPS directory, unless otherwise specified.

DATA FILES FOR DATA PROCESSING The three main data files used in data processing are MATIN.DAT, SOLUTION. DAT, and AUTOLD.DAT. MATIN.DAT contains the optimization problem to be solved by LINDO in MPS format. It is described in more detail in the next section. SOLUTION.DAT is the output file from LINDO containing the results of the optimization. AUTOLD.DAT contains the batch commands to run LINDO. This file also controls output of the LINDO solution to SOLUTION.DAT and the separation of these data into several intermediate files, such as CAPAOUT.DAT and PRODOUT.DAT. For details about the commands used in this file, please refer to LINDO documentation, such as Schrage (1997). Additional data files contain input data, which are combined to produce MATIN.DAT, or output data derived from SOLUTION.DAT via AUTOLD.DAT. Other .DAT files are intermediate files used to temporarily store information between yearly iterations. After the optimization problem is solved by LINDO, the results in SOLUTION. DAT are diverted to different files according to the result type by the batch file AUTOLD.DAT. For example, “K”-type results are diverted to CAPAOUT.DAT, which contains manufacturing capacity results. A particular code is used to identify each row in the .DAT files. It consists of a type code, a country code, and a commodity code. For example, in UPIN.DAT, “D 11 80” represents demand of commodity 80 in country 11; “T 11 zz 80” represents trade of commodity 80 from exporting country 11 to importing country zz.

THE MPS FILE The Mathematical Programming System (MPS) is a common format for describing linear programs (Schrage 1997). In this format the linear program forming the optimization model for the GFPM is described in terms of four components: 1. A ROWS section, which gives the name and the equality or inequality type of each row of the linear program 2. A COLUMNS section, which lists each nonzero in the objective function and constraints along with the row in which it appears 3. An RHS section, which lists each nonzero on the right-hand side along with the row in which it appears 4. A BOUNDS section, which lists simple upper and lower bound constraints on variables.

253

GFPM Software Design and Implementation TABLE 12.4 the GFPM

Naming Conventions Used in the MPS File (MATIN.DAT) of

Code A C D F K L S T U Y ZY ZZ

Representation Lower bound for first trade inertia (with very small penalty) Each commodity for each country Demand Upper bound for first trade inertia (with very small penalty) Capacity Lower bound for second trade inertia (with price as penalty) Supply Trade Upper bound for second trade inertia (with price as penalty) Production Dummy region World market

FIGURE 12.5

Example of a section of the MPS file MATIN.DAT.

The MPS file describing the GFPM optimization problem for a given year is MATIN.DAT, which is the center of the model. Figure 12.5 shows a sample section of this file. Table 12.4 shows the naming conventions for the MPS file in the GFPM.

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Data Preparation Data Processing DEMIN.DAT

UPDATE-A.EXE

MATDAT.DAT

UPIN.DAT UPIN.1 TRANSFER.EXE

UPDATE-A.EXE

UPIN.8

BASEYEAR.EXE

ITER-A.EXE

TRALIST.DAT

UPIN.4

ROWLIST.DAT

COLLIST.DAT

RHSLIST.DAT

BONLIST.DAT

CONSTOUT.DAT

TRANSPOR.DAT

MATIN.DAT

Intermediate .DAT Files

LINDO

AUTOLD.DAT

SOLUTION.DAT UPDATE-A.EXE CAPAOUT.DAT

STEPOUT.DAT

PRODOUT.DAT

Data Retrieval SUPPLY.DAT

FIGURE 12.6

SUPPRICE.DAT

PRODUCTT.DAT

TRANSHIP.DAT

CAPACITT.DAT

Data files and programs used in the data-processing component of the GFPM.

In Figure 12.5 the row label “Ch183” represents country h1 and product 83 (United States and sawnwood) and the row label OBJFUNC represents the objective function. The column label “Yh1833311” represents production of product 83 in country h1 with process 31 (U.S. sawnwood production). The column labels “Th1zz83” and “Tzzh183” represent exports and imports, respectively, of commodity 83 between country h1 and region zz [U.S. sawnwood exports (imports) to (from) the world]. The column labels “Dh183A” and “Dh183B” represent U.S. demand for sawnwood (first and second steps of the stepwise demand). The section of the MPS file shown (Figure 12.5) corresponds to the U.S. material balance for sawnwood: Th1,zz,83 + Yh1,83 − Dh1,83 − Th1,zz,83 ≥ 0 where Th1,zz,83 is U.S. sawnwood imports (exports), Yh1,83 is U.S. sawnwood production, and Dh1,83 is U.S. sawnwood demand. The MPS file also contains


255

information relating to a portion of the objective function: max Z = L +

∫

Dh 1, 83

0

Ph1,83 (Dh1,83 ) dDh1,83 − Yh1,83mh1,83 − ch1,83Th1,zz,83 − L

where mh1,83 = 114.56 is the cost of manufacturing sawnwood in the United States, ch1,83 = 28.14 is the cost of transportation for U.S. imports of sawnwood, and Ph1,83 (Dh1,83) = 416.4 and Ph1,83(Dh1,83) = 299.7 are the prices of sawnwood for two levels of demand. The BOUNDS section in Figure 12.5 contains the bounds on the demand steps: DA ≤ Dmin + Dwid DB ≤ Dwid where Dmin + Dwid = 140486.05 and Dwid = 7412.95. To construct the MATIN.DAT file, four separate files, corresponding to each section of the MPS file, are first created: ROWLIST.DAT, COLLIST.DAT, RHSLIST.DAT, and BONLIST.DAT. These files are then combined to form MATIN.DAT.

DATA-PROCESSING PROGRAMS The executable files for carrying out data processing are located in the C:\PELPS\ PELPS directory, and the corresponding programs written in the Pascal or Java language are listed in Table 12.2. Figure 12.6 shows the main programs and data files and their relationships in the data-processing component of the GFPM. The key programs are BASEYEAR.PAS, ITER-A.PAS, and UPDATE-A.PAS. These Pascal programs are compiled into the executable files BASEYEAR.EXE, ITER-A.EXE, and UPDATE-A.EXE. The program BASEYEAR.PAS organizes the data to create the MPS file MATIN.DAT for the base period. ITER-A.PAS organizes the data to create MATIN.DAT for the iteration periods. UPDATE-A.PAS updates the output from iteration t with the exogenous changes for that iteration to generate the input for iteration t + 1. With the updated input, ITER-A. PAS generates a new MPS file.

BASEYEAR.PAS The main output from the BASEYEAR.PAS program are the text files COLLIST. DAT, ROWLIST.DAT, BONLIST.DAT, and RHSLIST.DAT. The most important procedures in BASEYEAR.PAS are DemandAndSupply and Matdat.

256


DemandAndSupply uses data from input files DEMIN.DAT and SUPIN.DAT to build the stepwise area under the demand and supply curves. The results are formed into four lists corresponding to the MPS input format: Row, Column, RHS, and Bounds. The main subprocedure of DemandAndSupply is Stepwise, which calculates the stepwise area from initial price, quantity, and price elasticity.4 Matdat takes input from MATDAT.DAT, DEPIN.DAT, and TRAIN.DAT to form three lists: Row, Column, and RHS. The main subprocedures of Matdat are ManufacturingCost, ActivityCoefficient, ManufacturingCapacity, and TransportationCost.5 The ManufacturingCost subprocedure builds Row, Column, and RHS lists for each manufacturing cost. The ActivityCoefficient subprocedure builds Row, Column, and RHS lists for each input–output coefficient. The ManufacturingCapacity subprocedure builds Row, Column, and RHS lists for each capacity constraint. The TransportationCost subprocedure builds Row, Column, and RHS lists for each trade flow, including its trade inertia constraints.

ITER-A.PAS This program is similar to BASEYEAR.PAS, but it produces an additional output file, TRALIST.DAT, that contains the column data relating to trade variables. When solving the base-year, trade volumes are fixed, thus there are fewer trade variables than in subsequent years. Therefore, during iterations the column list is broken into TRALIST.DAT for trade variables and COLLIST.DAT for all other variables. The intermediate output files, COLLIST.DAT, ROWLIST.DAT, BONLIST.DAT, RHSLIST.DAT and TRALIST.DAT, are combined to produce the MPS file MATIN.DAT. The major procedures in ITER-A.PAS are DemandAndSupply, CapacityExtension, and ManufacturingAndTransportation. DemandAndSupply is very similar to the DemandAndSupply procedure in BASEYEAR.PAS. CapacityExtension uses data from MANPAR.DAT, PRODOUT.DAT, CAPAOUT.DAT, UPIN.DAT, and DEPIN.DAT and writes results into MANPAR.OUT and MATIN.DAT. MANPAR.OUT receives the updated global production estimates (from Equation [3.9]) and lagged production values. MATIN.DAT receives the updated estimate of manufacturing capacity for each commodity and country (from Equation [3.10]). Capacity is represented as a 4 The CrossPriceElasticity and RecycleDemand procedures are not used in the current version of the GFPM, so the worksheets in WORLD.XLS corresponding to the data handled by these procedures are empty. 5 The ByProduct sub procedure is not used in the current version of the GFPM.


257

record, which contains the capacity name, production, old capacity, new capacity, etc. Capacity records form a linked list,6 mainly to facilitate sorting. For each commodity, the global-level capacity change is first calculated, and then it is distributed to each country according to Equation [3.10]. ManufacturingAndTransportation first updates the exchange rates based on the exchange rates specified in EXCIN.DAT and then updates the penalty for exceeding the trade inertia bounds based on the previous periods’ export prices according to Equation [3.30]. The trade inertia bounds are also recalculated based on Equation [3.13]. Based on the current UPIN.DAT, exogenous changes to manufacturing costs and input–output coefficients are applied and the results stored in COLLIST.DAT and VARIOUT.DAT. The Trade subprocedure sets the name and inequality type of each row of the linear program corresponding to Trade Inertia Constraints [3.27] and [3.29]. Four lines for each trade activity are written to MATIN.DAT in the ROWS section, two for the lower and upper bounds on trade, with row names beginning with an L and U. The other two lines represent the penalty for trade volumes moving from the previous year’s volume, and their row names begin with A and F.

UPATE-A.PAS The main function of the UPDATE-A.PAS program is to update the intermediate data files from one year to another. ITER-A.PAS then uses these intermediate data files to update the MPS file MATIN.DAT. FindString and ChangeRecBound are two procedures to find a particular region-commodity string and to change the bounds for recycled product supply for that region and commodity. ChangeTaxin updates the freight costs in TAXIN.DAT from year to year, based on the exogenous change parameters in UPIN.DAT and data in TAXOUT.DAT. ChangeDemin updates the demand data from period to period based on exogenous change parameters in UPIN.DAT, such as changes in demand elasticities, demand lower bounds, and recycling content. The previous year’s demand data are in DEMIN.DAT, while the current year’s are in DEMAND.DAT. ChangeSupin is similar to the ChangeDemin procedure. It updates supply information, using exogenous change data in UPIN.DAT and the previous period’s supply information from SUPIN.DAT, and temporarily stores it in SUPTEMP1.DAT. 6 A linked list is a data structure that contains smaller data structures, such as records, which are linked together, so a record can easily be inserted or deleted while maintaining the list order.

258


Update is the main procedure in UPDATE-A.PAS; it performs updates on supply, demand, exchange rate, capacity, and recovery rate information from year to year based on the exogenous change information stored in UPIN.DAT. First, the procedure calls the ChangeDemin and ChangeSupin procedures to update the demand and supply data in DEMIN.DAT and SUPIN.DAT. The minimum and maximum recovery rates for recycled products are updated based on the exogenous changes specified by “W”-type records in UPIN.DAT. The previous period’s recovery rates are in RECOUT.DAT, and the updated recovery rates are written to RECIN.DAT. Trade information in TRAIN.DAT is updated based on the exogenous changes specified by “T”-type records in UPIN.DAT. The updated trade information includes the volume of trade, freight cost, transportation cost (freight plus import tariffs), import tariff rates, trade inertia, etc. Trade information in TRAIN.DAT is updated using equilibrium price estimates from CONSTOUT.DAT, freight cost estimates from TAXOUT.DAT, and trade volumes from TRANSPOR.DAT. The previous year’s trade data are stored in TRAOUT.DAT, and the updated trade data are written to TRAIN.DAT. The ChangeTaxin procedure updates the transport cost data. The minimum and maximum supply of recycled paper is updated based on exogenous changes in the recycling rate from UPIN.DAT and the previous period’s consumption of paper and paperboard from SOLUTION.DAT. The necessary data are obtained from RECIN.DAT, SUPTEMP1.DAT, SUPTEMP2.DAT, and DEMIN.DAT. The updates are written to SUPOUT.DAT.

12.6 OUTPUT RETRIEVAL7 Table 12.5 lists the major output-retrieval Visual Basic Application (VBA) programs and their function. The programs are all in the OUTPUT.XLS Excel workbook and are run as different options from the Get Output Menu (Figure 11.17). This section briefly describes the VBA programs for each Get Output Menu option, and their general purpose. The main procedure, MainTables, used to perform output retrieval in the GFPM is then discussed. Figures 12.7 and 12.8 show the processing flows of the output-retrieval component of the GFPM. RetrieveOutput The VBA program associated with the RetrieveOutput option is “World.” Choosing this option creates a set of Excel workbooks in the C:\PELPS\PELPS\ OUTPUT directory containing tables and figures of projected consumption, 7 Most of the VBA programs are specific to the countries and commodities in the GFPM applications described earlier. Users may have to modify them or write others to meet their own output retrieval needs.

259


TABLE 12.5 Major Visual Basic Application (VBA) Programs in the OUTPUT.XLS Workbook and Their Function VBA program AggregatePrice AlternativeSummary BaseSummary Combine_table Commodities Editing MainTables PriceCharts PriceTables QuantityCharts QuantityChartsLine QuantityTables Welfare_analysis WelfareAnalysis WelfareChange ProductValue World WorldCountries

Function Calculates the weighted average price for aggregated products Control program associated with Summary button for alternative scenario Control program associated with Summary button for base scenario Combines tables of different commodities and categories into one sheet in OUTPUT.XLS. Filters data for each commodity based on commodity code Copies tables from different sheets to one sheet Processes the ASCII output data and formats them for further processing Creates price charts for each commodity Calculates the world weighted average price (by consumption) Creates charts (consumption, production, import, export) for each commodity Creates charts (net trade) for each commodity Calculates the totals for each commodity Calculates welfare changes (total, consumer, and producer) Controls program associated with WelfareAnalysis button Calculates welfare change for each commodity Estimates value of products consumed or produced and their sum, to scale welfare changes Controls program associated with GetWorld button for both base and alternative scenarios Adds country names and calculates regional subtotals for each commodity

production, trade, price, capacity, and product value over the projection period, by performing the following steps. (1) The MainTables procedure reads the GFPM output files into the Excel workbooks in the C:\PELPS\PELPS\OUTPUT directory (i.e., CONSUMPTION.XLS, EXPORT.XLS, etc.). (2) The tables and figures of consumption, production, import, export, net trade, price, capacity, capacity price, and product value are created. (3) These tables are combined into a single worksheet in the OUTPUT.XLS workbook by the Combine_tables program. Under the alternative scenario, the RetrieveOutput option creates a new set of Excel workbooks in the C:\PELPS\PELPS\OUTPUT directory. Before adding these files, the original Excel workbooks are saved as _OLD files. This permits the calculation of welfare change between the base and alternative scenarios. Summary Under the Base Scenario, the associated VBA program is “BaseSummary.” It calls the “Summary” program in SUMMARY.XLS and the “getBaseSummary” program in SUMMARYCHANGE.XLS. Under the Alternative Scenario, the

260


ASCII output files from PELPS system

Main Tables

Excel files containing data by category (e.g., import)

Quantity Tables World Countries Editing Quantity Charts, etc.

Excel files containing tables and figures for each commodity

Welfare Analysis

WELFARECHANGE_XXX.XLS (Tables and Figures)

FIGURE 12.7

Combine_table

Base Summary or Alternative Summary

OUTPUT.XLS (detailed tables)

SUMMARY.XLS SUMMARYCHANGE.XLS (Tables and Figures)

Processing flows in the output-retrieval component of the GFPM.

associated VBA program is “AlternativeSummary.” It calls the “Summary” program in SUMMARY.XLS and “getATLSummary” in SUMMARYCHANGE.XLS. WelfareAnalysis The VBA program associated with this option is “WelfareAnalysis.” This program runs the welfare_analysis procedure, which calls the various procedures and functions to calculate welfare change. The main function is WelfareChange. It calculates welfare change for 14 commodities, five aggregated commodities, and the total of all commodities. This function has an argument “filename,” to calculate either consumers’, producers’, or total welfare change.

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Data Processing

Data Retrieval SUPPLY.DAT

SUPPRICE.DAT

PRODUCTT.DAT

TRANSHIP.DAT

…

VALUE_CONS.XLS

…

CAPACITT.DAT

SOLUTION.XLS

MainTables

CONSUMPTION.XLS

PRICE.XLS

PRODUCTION.XLS

CAPACITY.XLS

Base Summary

Alternative Summary

Combine_table

Welfare Analysis

SUMMARY.XLS

SUMMARYCHANGE.XLS

OUTPUT.XLS

WELFARECHANGE_XXX.XLS

FIGURE 12.8

Data files and programs used in the data-retrieval component of the GFPM.

Data in PRODUCTION.XLS, CONSUMPTION.XLS, PRICE.XLS, CAPACITYPRICE.XLS, VALUE_TOTAL.XLS, VALUE_CONS.XLS, and VALUE_PROD.XLS for the two scenarios are used to calculate welfare change according to Equations [3.31] to [3.33]. The data for the value of products consumed and produced in VALUE_CONS.XLS, etc., are used to scale the welfare change. The Excel workbooks, WELFARECHANGE_TOTAL.XLS, WELFARECHANGE_CONS.XLS, and WELFARECHANGE_PROD.XLS in C:\PELPS\PELPS\OUTPUT contain the results of the welfare change calculations. MainTables This procedure is the link between the data-processing component and the output summary (Figures 12.7 and 12.8). The procedure reads nine .DAT files (all files, except TRANCOST.DAT, listed in Table 12.3) and generates 11 Excel workbooks, used to create summary output. The MainTables procedure combines data from different .DAT files into a single Excel workbook, or it splits data from a single .DAT file into several Excel workbooks. SOLUTION.XLS is used to temporarily store data during processing. The resulting Excel workbooks are in the C:\PELPS\PELPS\OUTPUT directory. Each workbook’s filename indicates

262


its content, e.g., NETTRADE.XLS contains net trade data. The MainTables procedure performs the following tasks: 1. Opens the nine .DAT files in Excel workbooks and determines the number of countries 2. Combines the data in SUPPLY.DAT and PRODUCTT.DAT as production data and sorts them by country and commodity 3. Splits the trade flow data in TRANSHIP.DAT into export and import data; sorts them by country and commodity 4. Calculates consumption from import, export, and production data 5. Combines PROPPRICE.DAT, SUPPRICE.DAT, and DEMPRICE.DAT into a single price data set and sorts by country and commodity 6. Gets capacity and shadow price of capacity data from CAPACITT.DAT and CAPPRICE.DAT, respectively, and sorts them by country and commodity code At each step, the results are stored as single sheets in SOLUTION.XLS. The last step of the MainTables procedure is to copy these individual worksheets from the SOLUTION.XLS workbook, paste them to their corresponding workbooks, and save these in the C:\PELPS\PELPS\OUTPUT directory.

P AR T

Conclusion

V


CHAPTER

13

Summary, Evaluation, and Prospects

13.1 SUMMARY OF RESULTS The objectives of the studies presented in this book were to develop a quantitative model of the world forest sector and to apply it in forecasting and policy analysis. Specific applications included predicting the effects of uncontrollable events, such as the Asian economic crisis, of trade policies such as elimination of tariffs, and of environmental policies, such as recycling and timber harvest limits. For this purpose, the Global Forest Products Model, a dynamic linear programming model, was developed by adapting the price endogenous linear programming system (PELPS). The GFPM integrates four major components: demand, supply, processing, and trade. It is designed as a forecasting and policy analysis tool, to project the general future trends in quantities and prices at different stages of transformation under different scenarios.

RESULTS OF MODEL CONSTRUCTION As detailed in Chapter 3, the general principle of the GFPM is that global markets optimize the allocation of resources in the short run. This is simulated by maximizing the sum of consumer and producer surpluses in each projected year. 265

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As a result, for each year, demand, supply, trade, and prices clear markets for all products and in all regions (spatial equilibrium, or static phase). Market forces and political decisions govern long-run resource allocation. To this end, the model parameters are updated to reflect exogenous and endogenous changes in the world forest product markets from one year to the next (dynamic phase). One of the main tasks in developing the GFPM was constructing the database for the global forest products sector. This includes the production, imports, exports, and prices of all products in all countries for the base year (1997 for the projections and 1980 for the validation). Although the data source is the FAOSTAT, considerable work had to be done to remove data inconsistencies and to prepare the data in the format needed for the GFPM. Other elements of the database include the elasticities of demand and supply, the input–output coefficients, the manufacturing costs, the transport costs and tariff rates, and the GDP elasticities of trade. The database also contains the GDP projections by country, up to 2010 or 2015 depending on the application, the supply shifters for industrial roundwood, and the exogenous changes for paper recycling. In terms of methodology, one positive aspect of the GFPM lies in the modeling of trade. The GFPM recognizes that competitive advantage trade theory applies only to interindustry trade. The GFPM also models the intraindustry trade (a country’s imports and exports of the same commodity). To that end, besides being linked to the previous year’s trade via trade inertia parameters, trade quantity is also bound to GDP growth with empirical elasticities. However, this approach is purely empirical and not based on theory that tries to explain intraindustry trade from economies of scale or imperfect competition (Ethier 1982, Krugman 1980, Krugman and Obstfeld 1994). The welfare analysis capability of the GFPM gives useful data on consumers’ welfare change and producers’ welfare change for some scenarios, but it would be useful to develop a more general method. Several technical features of the GFPM software are notable. They allow for the first time the simultaneous treatment of the large number of countries and commodities required, for example, by the FAO timber trend studies. The GFPM software tools have been designed to considerably facilitate the work of the user. They include data processing facilities for the output (giving detailed tables and graphs of the results), and alternative scenario analysis (covering also welfare changes). A particularly important feature of the GFPM is that it can handle extreme conditions, such as might happen from input error, and yet not fail (in the sense of returning an infeasible solution for the equilibrium in a particular year). This was achieved in particular by the goal-programming formulation of the trade inertia constraints described in Chapter 3. This is an important aspect of model operation, for it is otherwise extremely difficult to detect the source of infeasibility in a model of this size.


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To build the model, some model parameters were adopted from other studies, such as the timber supply elasticities. Many more, such as domestic demand elasticities for final products, import demand elasticities, freight factors, and trade inertia parameters, were estimated during the studies described in the book. In particular, as discussed in Chapter 4, new methods were developed to estimate the input–output coefficients and manufacturing costs for each country so that they would be consistent with national statistics while being technologically plausible. Considerable work was done to validate the model. Part of the validation consisted of using the GFPM to predict the evolution of the world forest sector from 1980 to 1994. The validation results showed that the GFPM could not be used to predict detailed year-to-year changes. However, it gives acceptable predictions of the general trends in production, consumption, trade, and prices over long time periods. This, and the agreement of the model predictions with a priori expectations in a variety of conditions, suggests that the model can be used to address the questions covered in the application chapters.

RESULTS OF MODEL APPLICATIONS Base Scenario For a set of “most likely” future GDP growth rates and trade and environmental policies, the base scenario projections in Chapter 5 showed that forest products production and trade would continue increasing over the next decade. World prices would increase in real terms but were not likely to reach their high levels of 1980. Effects of the Asian Economic Crisis In Chapter 6, the GFPM was applied to investigate the impact of the Asian economic crisis on the global forest products sector through 2010. The results suggested that annual world output from 1998 to 2010 will be 1–3% lower, depending on product, due to the crisis. Production will be reduced most in Asia, but the effects will be felt worldwide. Imports and exports will be affected more than production. World prices will be initially lower due to the crisis, but in the long run some prices will increase due to capacity stagnation in the early years. Impact of Tariff Liberalization Under this scenario, simulated in Chapter 7, all tariffs in the APEC and European Union countries would be eliminated starting from year 2000. The projections showed that this would cause only small changes in world production and consumption, but it would cause significant changes in world trade, which

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would shift toward more processed products. All world prices would be lower than without the ATL, and the total world welfare would increase slightly. Effects of U.S. Paper Recycling Chapter 8 reported an application of the GFPM to investigate the international effects of increased wastepaper recycling in the United States. The projections show that the world prices of paper would decrease and demand would increase. As a result, wood pulp consumption would decrease less than the increase in recycled paper. The United States and other major consumer countries would register total welfare gains, while Canada and the main European producers would lose. Regional Trade Agreements and New Zealand In Chapter 9, the GFPM was applied to predict the effects on the New Zealand forest sector of regional and global trade agreements. It was found that the regional agreements (P5 and AFTA-CER) would have limited effects. Tariff reduction under GATT 1994 would have a greater impact. To increase its domestic use of roundwood harvest, New Zealand would benefit more from supporting the tariff reduction initiative of GATT 1994 than the tariff elimination under the P5 or AFTA-CER regional agreement. Pacific Rim Impacts of U.S. Harvest Restrictions This application of a modified version of the GFPM, in Chapter 10, suggests that U.S. timber harvest restrictions have substantial effects on the rest of the world. Although total world timber production decreased, a large part of the U.S. harvest reduction was compensated for by increased production in other countries. And, manufacturing would follow timber production. Thus, greater forest conservation in the United States may come at the expense of other countries. This effect was found to be greater the freer the trade between countries.

13.2 EVALUATION AND PROSPECTS

GENERAL USEFULNESS Several of the GFPM applications presented were commissioned by international or national agencies. For example, the base scenario in Chapter 5 is only one example of the many scenarios that were developed for the FAO for the their global and regional forest products outlook studies. Similarly, the study


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on the effects of global trade liberalization (Chapter 7) was done first at the request of the U.S. Trade Representative, in view of WTO negotiations. And the results of Chapter 9 are part of a larger study commissioned by the New Zealand Ministry of Foreign Affairs and Trade on the economic effects of regional trade agreements. This suggests that there is a real, strong, and continuing need for these kinds of studies and for models to facilitate them. The end of the 20th century and the beginning of the 21st century have witnessed a definite movement toward globalization. This means that it is hardly possible for any individual country to plan a strategy or enact a policy without considering the effects on and the response of other countries. As documented in Chapter 8, for example, these interactions are complex and can lead to unexpected results, such as purely environmental measures having serious trade and welfare implications for other countries. Nevertheless, the results presented in earlier chapters depend critically on the assumptions used in the GFPM. Although it is clear that the detailed implications of the policies examined would be hard to obtain without such a model, it is also obvious that the quantitative estimates produced by the model depend heavily on the values of the parameters of the model and on the trends for the exogenous variables. So at best, the numbers produced by the model must be regarded as orders of magnitudes rather than precise estimates. The issue then does not seem to be whether a model like the GFPM is useful, but rather to decide how to make it even more so.

GEOGRAPHIC COVERAGE In its current form, the GFPM uses 180 individual countries and 14 commodity groups. The choice of both was dictated initially by the requirements of the FAO Forest Products Outlook studies. For other studies, this large number of countries may seem unnecessary. However, there is considerable gain from this level of country detail, both from the point of view of the user and from the point of view of model building, validation, maintenance, and application. First, political decisions are still made at the national level. Consequently, politicians and their advisers want to know the impact of a change in international trade policy on their own country. Second, international data, such as FAO and other international statistics, are available mostly at the national level. This is also true of “soft” data, in the form of expert knowledge. Expertise on Indonesia is generally easier to find, and more reliable, than expertise on the “Asia-Pacific region.” For the model builder, it is far easier to get constructive criticism on results pertaining to a definite country, such as Indonesia, than to a region, such as the Asia-Pacific.

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This means that blatant errors or shortcomings of the model are much easier to detect if the results are presented in terms of individual countries. While building the model, it is also easier to recognize data errors, and to correct them, with individual country data, for example, using some of the methods outlined in Chapter 4. There are good reasons to represent even small countries individually rather than aggregating them. As countries, they are distinct political entities that deserve recognition as much as larger ones. Furthermore, the peculiarities of small countries, such as the possibility of trade activities without production, may lead to aberrant predictions unless the theory embedded in the model is truly general enough to cover extreme cases. It can be said therefore that the ability of the model to work for the smallest as well as the largest countries is another test of the validity of the model. Last, a model built with individual countries is also easier to adapt to different needs. It is easy to produce different regional aggregates if the results are by country. Instead, much time must be spent simply to set up the database to build a special model for a particular set of regions. Nevertheless, there are some possible advantages in working with aggregated regions rather than countries. One is that errors for different countries may cancel out. The other is that having only a few regions facilitates the management of the model output.

COMMODITY COVERAGE What is true for countries also applies to a large extent to commodities. From the point of view of model accuracy and usefulness of results, it seems safer to err in the direction of more rather than less detail. For example, given the different raw material and techniques of production needed for plywood and particleboard, it is better to distinguish between them rather than aggregate them within the very heterogeneous group “wood-based panels.” However, disaggregation is not always feasible. There is a real limitation in terms of the statistics that are available at the national level and in comparable form across countries. The 14 product groups currently used in the GFPM correspond to the classification of forest product statistics used by the FAO. They cover the range of products from roundwood to panels, pulp, and paper and paperboard, for which statistics on production, imports, and exports are available at the national level. Nevertheless, more disaggregation would often be desirable. In particular, the current version of the GFPM has only one category of industrial roundwood, including all species, and sawlogs and pulpwood. Using a modified version of the same model, Trømborg et al. (2000) showed that distinguishing between pulpwood and sawlogs affected the price projections for


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these commodities. Clearly, not distinguishing between pulpwood and sawlogs may result in misrepresentation of a country’s competitive advantage if that country has an abundance of one type of roundwood relative to the other. For example, the study of impacts of regional trade agreements on the New-Zealand Forest Sector (Chapter 9), found unexpected results regarding New Zealand production of sawnwood, which may have been due to the inability in the current specification of the GFPM to adequately represent New Zealand’s relative abundance of sawlogs. Making the distinction between sawlogs and pulpwood in the GFPM would increase the descriptive detail of the model and the quality of the representation of manufacturing activities by distinguishing between the use of sawlogs in the solidwood sector and that of pulpwood in the pulp and fiberboard sectors. Distinguishing between sawlogs and pulpwood would require the estimation of supply equations for these commodities and manufacturing coefficients for their transformation. At a minimum, this would require data on production, consumption, trade, and prices in the base year. Unfortunately, pulpwood trade data are not available in the FAOSTAT database, but data on chips and particles might be used instead. One possible criticism of the GFPM in terms of commodity coverage is that it does not recognize explicitly the use of residues from sawmilling and plywood manufacturing that may be used in the production of particleboard, fiberboard, and pulp. However, the use of the residues is recognized fully, but implicitly, in the input–output coefficients estimated by the method described in Chapter 4.

BASE YEAR, I–O COEFFICIENTS, AND COSTS The base-year data are a fundamental part of the GFPM. Any good projection must start from a solid base. This means a set of statistics on production, imports, and exports that is plausible. Chapter 4 describes the method currently used to estimate production data when they do not exist and to obtain I-O coefficients that are consistent with existing technology, and manufacturing costs in agreement with this technology and with observed product prices. The estimation method used, however, assumes that trade statistics are accurate. Incorporation of bilateral trade flows into the GFPM, as was done in the study of New Zealand forest sector impacts of regional trade agreements (Chapter 9), was hampered by the inconsistency between total country trade flow data and trade flows to individual countries. In a number of cases, the sum of bilateral trade flows exceeded the reported total trade flows. This is a well-known problem, but more work is needed to fix it (Michie and Wardle 1998).

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Country production capacity for each manufactured commodity is another key parameter in setting the base-year data. Currently, production capacities in the GFPM are set at 10% above the base-year production of that commodity, for lack of reliable production capacities (capacity data for pulp and paper are available internationally, but they are not fully consistent with the production statistics in FAOSTAT). Base-year capacities influence future capacity and, hence, future commodity prices. Were a country’s manufacturing capacity underestimated, a small increase in commodity production to meet increased demand could result in a dramatic increase in the price of the commodity if the capacity constraint became binding. More reliable base-year capacity estimates would, therefore, provide more realistic price and capacity expansion trends. The lack of country-level production capacities may be overcome by estimating capacity as the envelope of historical production data. The difficulty is that for many countries, historical production data are inaccurate.

DEMAND ELASTICITY One important set of parameters in the GFPM is the income and price elasticities of demand for the final goods. These parameters determine the consumption changes from one year to another and in turn partially determine the market clearing prices. In most parts of the world, the major measurable factors affecting consumption of forest products are general economic growth and prices. However, many other influences are relevant, such as level of residential investment, technical and economic competitiveness of sawnwood and panels in the building sector, development in electronic media, and competitiveness of paper and paperboard for packaging. Currently, these factors are reflected indirectly in the demand component of the GFPM through the income elasticities and price elasticities of demand. The demand elasticities used in the GFPM were estimated from panel data, as described in Chapter 4. For woodbased panels, pulp, and paper products, the projections of consumption seem high compared to historical trends. This suggests that the income elasticities are too high or the price elasticities too low for those products. Further work is warranted to obtain better values of these elasticities. As discussed in Chapter 4, the current elasticities in the GFPM are based on the analysis of covariance models obtained by pooling cross-sectional and time-series data. However, research should continue to improve these elasticities, for example, using component error or shrinkage estimators. Econometric estimation of the elasticities of imports with respect to GDP is also desirable. In the current version of the GFPM, they are assumed to be the same as the domestic demand elasticities with respect to GDP. Preliminary direct estimation of the elasticities of import demand suggests instead that they


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are larger in absolute value than those of domestic demand. This kind of testing and the attendant correction must go on to improve the model.

TIMBER SUPPLY At present, industrial roundwood supply in the GFPM is responsive to the price of industrial roundwood and an exogenously specified supply shifter. The shifter is an informal assessment of how timber supply would change in a country at current prices. Chapter 10 described a version of the GFPM that added forest stock level as a shift variable and an equation linking forest stock to growth and harvest. Trømborg et al. (2000) experimented with a formulation in which the timber supply was horizontal up to a upper bound and the upper bound shifted over time as a function of price. Neither approach was found sufficiently refined to adopt it in the current “standard” version of the GFPM. Specifying wood supply as a function, in part, of forest stock and forestland area, with intertemporal relations linking current forest stock to past harvest and regeneration decisions and growth, is theoretically appealing. Such a specification would make shifts of roundwood supply price responsive via the effect of timber prices on past harvest and regeneration decisions. There are, however, several difficulties. Firstly, reliable data on forest stocks or forest area for many countries are scarce and inaccurate. Secondly, the effect of forest stock on wood supply is uncertain; for example, the GTM used a unit elasticity for lack of anything better (Binkley and Dykstra 1987, Cardellichio and Adams 1990). Furthermore, political as well as economic forces influence countries’ forest stocks. In that respect, the exogenous supply shifters in the GFPM represent the most likely scenario regarding political and economic influences on forest stock, and it is doubtful that making them endogenous would be possible. Still, the effects of a nation’s forest sector policy might be better represented by using forest inventory and forest area as explicit variables in the GFPM. They would better express policies influencing forest stock than the current exogenous shifts of supply. For example, modeling harvest restrictions as shifting the timber supply of a country to the left does not allow for a possible increase in supply in other countries due to increased roundwood prices encouraging expansion of forest area through increased regeneration. So, in spite of real difficulties, the importance of forest area and forest inventory data for forest policy suggest that the timber supply module of the GFPM should be refined.

MANUFACTURING ACTIVITIES A problem of the processing module, the part of the GFPM that models how raw materials are transformed into intermediate products and then into final products is the infinitely elastic supply assumed by the manufacturing activities

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(which are represented by I-O coefficients and a manufacturing cost). Because the manufacturing cost is constant with production, up to capacity, the production of a particular country may change substantially from one year to another, and so may the shadow price and the welfare of producers. This is one reason why the year-to-year changes cannot be trusted, but only the long-term trends. The cure for this problem would be to treat the manufacturing activities like the supply of raw materials, allowing the manufacturing cost to increase as production increases, up to capacity. The technical difficulty lies in an additional nonlinearity and in the difficulty to quantify how exactly the manufacturing cost changes with output, especially given the fact that we hardly know the current manufacturing cost and must infer it with several assumptions (see Chapter 4).

TECHNICAL CHANGE In the current version of the GFPM, technical change is exogenous, in the form of changes in the input–output parameters for the manufacturing activities. This feature was used in Chapter 8 to describe changes in the utilization of recycled paper due to specific recycling policies. However, it is likely that the input mix in the manufacture of paper or the efficiency of transforming logs into sawnwood depend in part on relative prices. Making the input–output coefficients depend on prices would thus seem like a natural improvement of the GFPM. A serious practical limitation in achieving this, however, is the lack of knowledge of even the current state of technology, let alone past technologies, with which to estimate technical change. Nevertheless, one could in principle apply the methods of base-year calibration (see Chapter 4) to estimate a time series of I-O coefficients and to infer from them the laws of technical change. This is an appropriate area for future research, but not perhaps a first priority to improve the GFPM.

TRADE FLOWS The problem of sometimes unstable year-to-year changes for imports and exports is even more serious than for production. The elasticities of imports and exports with respect to GDP lead to projections that mimic the observed increase in the ratio of global imports to production, but the problem of erratic movements around that trend remains. This is due to the use of bounds (analogous to capacity) on imports and exports within which the linear programming solution varies irregularly from year to year, even though it simulates the correct long-term trend. The correction to this problem would be to introduce functions to simulate the demand for imports and the supply of exports in the


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same way as the domestic demand for final goods and the domestic supply of raw materials. However, the theoretical basis and the numerical techniques for integrating these trade and domestic activities remain to be developed.

WELFARE CHANGE The simple method applied to calculate the welfare changes is general for the welfare of manufacturers only. For the welfare of consumers and suppliers it assumes that the demand and supply curves shift at the same rates between two scenarios. Some more general method of welfare computation, allowing demand and supply curves to shift differently between two scenarios, would improve the welfare analysis capability of the GFPM.

SOFTWARE ISSUES Base-year Calibration Currently, the calibration of the base year of the GFPM has to be done manually (with some semiautomatic programs). Users may set up the model with any number of countries (within limits), any number of commodities, and any number of trade flows. But this involves many calculations, formatting, and input data validation. The main difficulty to automating this process is to write programs to handle some generic usage of the GFPM. It should be possible, although not easy, to build a generic calibration tool for GFPM users to change the GFPM more quickly, such as changing the base year (keeping the model structure unchanged), adding or removing some countries, adding or removing commodities, adding or removing trade flows, and aggregating the input and output data. The ideal calibration tool should set up the base-year model from the raw base-year data (production, trade, and trade values from the FAOSTAT database) and would not require users’ action before they run the model and adjust model parameters and assumptions. The development of such a calibration tool is currently under way. Computational Efficiency Although the GFPM software already handles large models with more than enough countries and products for most applications, its efficiency (problem-solving speed) could be improved. For a model with 180 countries and 14 commodities (implying a linear programming problem with about 10,000 constraints and 20,000 variables for each projected year), it takes about 4 hours to get a 13-years’ projection on a vintage 2000 personal computer. Most of the computing

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time is spent on the MPS file updates from one period to another. The total time used to solve the 13 LP problems alone is not more than one hour. The major cause of the slowness is the intensive disk operations, due to external sorting (not-in-memory sorting) algorithms. The PELPS IV software at the core of the GFPM was, like its predecessors, implemented with the Pascal language. Because of the limitations of the Pascal compiler and the architecture of IBMcompatible computers, only the conventional memory (640 KB) can be used during run time. For large applications, external sorting is necessary but time consuming. Added speed might result by rewriting the major part of the dataprocessing component programs with Java or C++. Regardless, progress in computer speed is likely to reduce each run time considerably in a few years. Major gains in speed are also being achieved currently with an experimental GFPM using quadratic programming and the LINDO-API software. Displaying the Results One difficulty of the GFPM is the mass of data that it produces. Although this detail is useful in many studies, there is often a need for a filter to choose the output and to display it in the most effective way. The form of the current dataretrieval component of GFPM has been dictated by the applications described earlier. Currently, the macros are set for 14 commodities. The output format is also fixed in terms of country groups, commodity groups, table format, and chart format. Although it is relatively easy to retrieve output of a model with different countries, it is difficult to change the commodities, and users may have to reorganize the Excel output format or manually retrieve some desirable output information from the model’s ASCII output files. One way to improve the data-retrieval component would be to build a database application for users to query model results. This would provide more flexibility for users to apply the GFPM.

13.3 CONCLUSION Regardless of possible improvements, the GFPM is already useful as it stands to understand and manage the world forest economy. Forest sector issues have become increasingly complex due to the increase in the goods and services provided by forests and the linkages within the forest sector, and between the forest sector and other sectors. Forest sector issues have also taken a global perspective, due to stronger links between nations through globalization, trade liberalization, and international treaties. Quantitative modeling is necessary to order this complex forestry system, understand its processes, and forecast its changes. The GFPM has enacted this


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quantification by building on the experience of many preceding models, using rigorous theory, econometric parameters, a large amount of hard data, and a fair amount of expert knowledge (particularly on the timber supply side). As a result, the GFPM is one of the few truly international forest sector models, providing a full description of forest production, wood manufacturing, and end product demand, with detailed coverage of individual countries and commodity groups. The GFPM can be used in many different ways. For some users it can be a forecasting tool, to project future production, consumption, trade, and prices with a particular economic growth scenario, as was done in Chapter 5. For others it can be a powerful means of policy analysis, as shown in the applications of Chapters 7–10, for various trade and environmental issues. For analysts and researchers, it can be a way to understand better the interactions between parts of the forest sector, especially between markets and forests, in an international context. Although the data deal with the forest sector, the method is general enough that the model structure can be applied to agricultural or industrial sectors as well.


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INDEX

Note: Page number followed by f or t refer to the figure or table on that page, respectively.

A Accelerated Tariff Liberalization (ATL). see ATL (Accelerated Tariff Liberalization) Accelerator equations, 74 Accelerator theory, 45 AFTA-CER regional trade agreement, 184–185 and New Zealand forest industry, 189t, 190–191 Applications Asian economic crisis, 129–142 base scenario projections, 91–98 modifications for U.S. timber harvest restrictions study, 199–205 New Zealand trade liberalization, 186–188 tariff liberalization, 148–157 U.S. paper recycling, 171–174 ASCII output files, 247t Asia-Pacific Economic Cooperation (APEC), 5 and Global Trade Assessment Project (GTAP) model, 7 and P5 regional trade agreement, 185 and Accelerated Tariff Liberalization, 147 Asian economic crisis and gross domestic product (GDP), 130–131 overview, 127–129 and paper and paperboard, 139t, 140–141 predicted effects, 142–143 and prices, 141–142 projected economic slowdown, 130–131

and roundwood production and import, 132f, 134, 135t and sawnwood, 134, 136t summary of results, 267 theory underlying GFPM projections, 129–130 and wood-based panels, 133f, 137t, 140 and wood pulp, 138t, 140 ATL (Accelerated Tariff Liberalization), 30, 147 and exports, 162 and imports, 161 and prices, 162 and production, 159 summary of effects, 164–165 summary of results, 267–268 and welfare, 163–164

B Base scenario chemical pulp projections, 110 fiber furnish projections, 109, 124 GDP growth rates, 92, 93–94t input-output coefficients, 92 manufacturing cost, 92 model data, parameters, assumptions, 91–98 newsprint projections, 116 paper and paperboard projections, 114–118 price trend projections, 119–122 printing and writing paper, 117, 119f

291

292 Base scenario (continued) pulp and paper price projections, 121, 123f recycling projections, 122–124 roundwood projections, 98–102 roundwood supply shift rates, 92, 95–96t sawnwood projections, 102–104 summary of results, 267 tariff rates, 97 wastepaper projections, 112–113 wastepaper recovery assumptions, 97–98 wood-based panels price projections, 121, 122f wood-based panels projections, 104–108 wood pulp projections, 109 Base year, 42 data, 58–61 data and validation of GFPM, 75–88 input in GFPM program, 220–229 modifications for U.S. timber harvest restrictions study, 200–203 as part of GFPM program, 271 run base-period, 234 run multiple periods, 234 software program, 255–256 used for GFPM projections, 91 BASEYEAR.PAS, 255–256 Bound rates, 29

C Calibration, 57 Canada-U.S. Free Trade Agreement (FTA), 29 Capacity changes, 45–46 data, 227f Capacity worksheets, 223, 227f Capacity Extension procedure, 256 Categories of forest products, 16 ChangeDemin procedure, 257 ChangeRecBound procedure, 257 ChangeSupin procedure, 257 ChangeTaxIn procedure, 257 Charcoal. see Fuelwood Chemical pulp, 110 consumption and net trade projections, 113f New Zealand capacity assumptions, 188 CINTRAFOR Global Trade Model, 8 and U.S. timber harvest restrictions, 196 Climate change, 195, 215 Codes commodities, 223t countries, 224–225t in .DAT files, 252, 253t

Index Commodity codes, 223 Computable general equilibrium (CGE) models, 6–7 Computer requirements, 220 Consumer surplus, 52 Consumers major global markets, 22t–24t per capita consumption, 68 Consumers’ welfare, 52–53 and tariff reductions, 163–164 and U.S. recycling, 172, 179–180 Consumption chemical pulp projections, 113f fiberboard projections, 108f and New Zealand trade agreements, 190–191 newsprint projections, 118f other fiber pulp projections, 114f other paper and paperboard projections, 120f paper and paperboard projections, 84f, 116f particleboard projections, 107f past trends, 15 printing and writing paper projections, 119f roundwood projections, 80f, 99f sawnwood projections, 81f, 103f veneer sheet and plywood, 106f wastepaper projections, 115f wood-based panels projections, 82f, 104f wood pulp projections, 83f, 110f Control programs, 245–249 Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES), 30 Cost of manufacturing, 73–74 as part of GFPM program, 271 reduction due to recycling, 170 Countries, 57–58, 59t codes, 224–225t and geographic coverage of GFPM, 269–270 storage of output data, 235 used for U.S. timber harvest restrictions study, 199, 200t

D Data for capacity expansion, 227f comparison of projected vs. actual, 80–86f conversion to .DAT files, 245t creating input files, 246–247 for demand equations, 222

293

Index example for production, 236f for exogenous changes, 229f get output, 234 invalid numeric format, 241 for manufacturing costs, 226f output example for prices, 237f output example for consumption, 237f output retrieval programs, 258–262 preparation for GFPM analysis, 266 processing programs, 255–258 programs for preparing, 249–251 save input menu, 233f summary graphs, 237f summary tables, 238f for supply equations, 226f used for GFPM projections, 91–98 Data retrieval processing flows, 261f Database construction, 266 Demand calculating shifts, 45 end-product demand calculation, 42–43 in GFPM model, 39–40 linear approximation, 47–49 procedure for updating data, 257–258 recent increases, 16 Demand elasticities, 64–66t in GFPM model, 272–273 Demand equations, 58 elasticity values, 61–66 Demand worksheet, 222 DemandandSupply procedure, 256 Demographics, 34 Directories, 221f Domestic policies, 195

E Economic growth, 4 data for 1999-2001, 33t Economic outlook, 33–34 Elasticity coefficients of demand, 64–65t of supply, 66–68 End-product demand, 42–43 End products, 41 data, 68–69 Endangered Species Act, 198, 204 Endogenous changes, 40 Environment climate change, 195

global warming, 215 and U.S. timber production, 196 Environmental policies and ATL (Accelerated Tariff Liberalization), 165 effects on trade, 5–6, 30 Endangered Species Act, 198, 204 and forest management legislation, 197–199 northern spotted owl, 198 and U.S. timber harvest restrictions, 214 Error messages, 234 infeasability, 240–241 invalid floating point operation, 242 invalid numeric format, 241 run-time errors, 241–242 European Economic Community (EEC), 29 European Free Trade Association (EFTA), 29 EVSL (early voluntary sectoral liberalization), 5, 147 GFPM simulations, 148 Exogenous changes, 40 worksheet, 229–232 Export dominance of developed countries, 25 and GFPM dummy region, 241 global concentration, 26–29 leading exporters by value in 1997, 28f and New Zealand trade agreements, 190 paper and paperboard projections, 117f ratios to production data, 24, 26f regional shares in 1997, 27f roundwood projections, 100f shift from raw materials, 4 and tariff changes, 156f and tariff reductions, 162 volume changes since 1960, 22–25 volumes, 25f wood-based panels projections, 106f wood pulp projections, 111f

F FAO. see Food and Agriculture Organization of the United Nations Fiber furnish base scenario projections, 124 composition changes, 20–21 GFPM projections, 109–114 Global Fiber Supply Model, 11

294 Fiber furnish (continued) global production trends, 17t historical and projected shares of wastepaper, 123f, 124f major global producers and consumers, 24t regional production trends, 19t and U.S. paper recycling, 176–178 Fiberboard, 107–108 Files, 221f ASCII output files, 247t creating input, 233 for data processing, 252 used in data preparation, 251f FindString procedure, 257 Food and Agriculture Organization of the United Nations analysis of Asian economic crisis, 128 data for GFPM projections, 88, 91 trade data for roundwood types, 200 Forest management and ATL (Accelerated Tariff Liberalization), 165 and environmental awareness, 30 global effects, 195 impact of recycling, 21 legislation in U.S., 197–199 of private lands, 198 U.S. timber harvest restrictions, 203–204 Forest product categories, 16 Forest products classification in GFPM, 41 commodity coverage, 270–271 global production trends, 17 and New Zealand trade agreements, 189t recent economic and trade trends, 4 regional production trends, 17–20 transformation in GFPM, 60f used for U.S. timber harvest restrictions study, 199 used in GFPM, 57–58, 60t Forest products sector current challenges, 16 dominance of developed countries, 26–27 factors affecting, 15 response to macroeconomic shocks, 4 Freight cost assumptions for GFPM projections, 75 of forest products in 1997, 61t and transport cost calculation, 46

Index FTA. see Canada-U.S. Free Trade Agreement (FTA) Fuelwood consumption projections, 99 consumption trends, 20–21 global production trends, 17t major global producers and consumers, 22t regional production trends, 18t

G GATT (General Agreement on Tariffs and Trade) about, 4–5, 146–147 effect on trade, 29–30 effects on New Zealand forest industry, 189t, 190–191 and New Zealand trade agreements, 185–186 and tariff assumptions for GFPM projections, 97 GDP. see Gross domestic product (GDP) GDP worksheet, 230 General equilibrium models, 6–7 Geographic coverage, 269–270 GFPM base-year data, 58–61 countries, 57–58, 59t estimating technical change, 274 and extreme conditions, 266 general principles, 40 general usefulness, 263–269 geographic coverage, 269–270 history, 12–13 manufacturing parameters, 68–74 and market dynamics, 44–47 mean absolute relative error (MARE), 79, 87–88 model data, parameters, assumptions, 91–98 overview, 39–40 product transformation, 60f purpose, 3 trade inertia parameters, 74–75 validation, 75, 78–88 GFPM calculations accelerator equations, 74 capacity changes, 45–46 and demand approximation, 47–49 demand elasticities, 61–66

295

Index end-product demand, 42–43 freight cost changes, 46 manufacturing capacity, 44 manufacturing coefficient changes, 45 material balance, 43 objective function, 42 price, 44 primary-product supply, 43 shifts of demand, 45 shifts of supply, 45 spatial global equilibrium, 41–44 and supply approximation, 49–51 supply elasticities, 66–68 tariff changes, 46 trade inertia, 43–44 trade inertia bound changes, 46–47 and trade inertia constraints, 51–52 wastepaper recovery changes, 46 welfare analysis, 52–55 GFPM software. see Software Global Fiber Supply Model, 11 Global Forest Products Model. see GFPM Global trade. see Trade Global Trade Assessment Project (GTAP), 7 Global warming, 215 Goal-programming model for estimating input-output coefficients, 70t, 72 variables and parameters, 71t Gross domestic product (GDP) and Asian economic crisis, 130–131 and base scenario projections, 92 demand elasticity values, 64–65t forest sector contributions, 16 and growth of world economy, 15 growth rate data for GFPM projections, 92, 93–94t and market dynamics, 44–45

H Hardware requirements, 220 History of GFPM, 12–13

paper and paperboard projections, 117f regional shares in 1997, 27f roundwood projections, 100f and tariff changes, 156f and tariff reductions, 161 wood-based panels projections, 105f wood pulp projections, 111f Import elasticity estimates, 74t, 272 Inertia. see Trade inertia Infeasability, 240–241, 265 Information and communication technology, 33 Input files, 245 Input-output coefficients, 68–73 and base scenario projections, 92, 97 data format, 226f for GFPM projections, 92, 97 in manufacture worksheet, 223 as part of GFPM program, 271–272 in TechChange worksheet, 230–231 in U.S. and Ukraine in 1997, 73t Input-output parameters, 68 Installing GFPM, 220 Intergovernmental Panel on Forests, 215 Interindustry and intraindustry trade, 266 Intermediate products, 41 International Institute of Applied Systems Analysis (IIASA), 12 International trade policies, 3, 4–5 trends, 22–25 Intraindustry trade, 47 Invalid floating point operation, 242 Inventory, 201–203, 211–212 ITER-A.PAS, 256–257

J Japan effect of Asian economic crisis, 127–128 forest sector competitiveness, 8 and U.S. timber harvest restrictions, 196

L I Import and GFPM dummy region, 241 global concentration, 26–29 leading importers by value in 1997, 28f

Legislation governing U.S. timber production, 197–199 and U.S. timber supply, 205 LINDO linear programming solver, 220 Linked list, 257

296 M M records, 223 Main menu, 233f MainTables procedure, 261–262 Manufacture worksheet, 223 Manufacturers’ welfare, 54–55 Manufacturing definition in GFPM, 41 effects of U.S. timber harvest restrictions, 211 final product data, 68–69 input-output coefficients, 69–72 parameters in GFPM calculations, 273–274 Manufacturing capacity, 44 Manufacturing coefficients, 45 Manufacturing cost, 42, 73–74 and base scenario projections, 92, 97 data format, 226f and New Zealand trade agreements, 191t ManufacturingAndTransportation procedure, 257 Market dynamics, 44–47 Matdat procedure, 256 Material balance, 43 Mean absolute relative error (MARE), 79, 87–88 Mechanical pulp, 109 consumption and net trade projections, 112f New Zealand capacity assumptions, 188 Memory requirements, 220 Model calibration, 57 MPS file, 251, 252 example, 253f naming conventions, 253t Multi-period application, 247, 248f Multiple-Use Sustained Yield Act, 198

N NAFTA (North American Free Trade Agreement), 6, 147 Net social payoff, 12, 39 Net trade chemical pulp projections, 113f fiberboard projections, 109f industrial roundwood, 102f mechanical pulp projections, 112f newsprint projections, 118f other fiber pulp projections, 114f other paper and paperboard projections, 120f paper and paperboard projections, 116f

Index particleboard projections, 108f printing and writing paper, 119f roundwood projections, 99f sawnwood projections, 103f and U.S. recycling, 172 veneer sheet and plywood, 107f wastepaper projections, 115f wood-based panels projections, 108f wood pulp projections, 110f New Zealand trade liberalization assumptions for GFPM projections, 188 effects of regional trade agreements, 189t, 190–191 GFPM analysis, 186–188 Ministry of Agriculture and Forestry studies, 191–192 regional agreements, 184–186 summary of results, 268 trade flows, 187 Newsprint consumption and net trade projections, 116, 118f GFPM projections, 116 recycled content, 170

O Objective function, 42 Organic Administration Act, 197 Other fiber pulp consumption and net trade projections, 114f GFPM projections, 111–112 Other models CINTRAFOR Global Trade Model, 8 Computable general equilibrium (CGE) models, 6–7 Global Fiber Supply Model, 11 Global Trade Assessment Project (GTAP) model, 7 partial equilibrium models, 8–10 Timber Assessment Market Model (TAMM), 9 Timber supply Model, 10–11 Output ASCII files, 247t examples, 235–239 get output menu, 235f retrieval procedures, 258–262 Output retrieval processing flows, 260, 261f

Index

P P records, 223 P5 trade agreement, 185 effects on New Zealand forest industry, 189t, 190–191 Pacific Rim countries, 200t Paper/paperboard products and Asian economic crisis, 139t, 140–141 base scenario projections, 114–118, 125 consumption, 84f consumption and net trade projections, 116f cost of manufacturing and recycling, 170 export volume since 1960, 23 fiber utilization data and projections, 173t global production trends, 17t historical changes in U.S. production and trade, 168, 169t import and export projections, 117f increasing demand for, 4 major global producers and consumers, 24t price projections, 121–122, 123f price trends, 32–33, 32f production growth between 1960 and 1995, 15 regional production trends, 19t and tariff reduction, 158f, 159f, 161t and U.S. paper recycling, 175–176 U.S. production, 4 and U.S. timber harvest restrictions, 210t PAPYRUS model, 12 Partial equilibrium models, 8–10 Particleboard, 106–107 PELPS, 12 and validation of GFPM, 78 PELPS IV, 12, 220 and data conversion, 245f and run-time parameters, 233 PERIODt data, 229 Plywood consumption, 105 consumption and net trade projections, 107f Population growth and distribution, 34 Previous studies impact of U.S. paper recycling, 170 tariff liberalization, 147–148 U.S. timber harvest restrictions, 196–197 Price of forest products in 1997, 61 GFPM calculation, 44 paper and paperboard projections, 123f

297 projections through GFPM, 40 roundwood projections, 121f sawnwood projections, 121f shadow price, 44 and trade inertia constraints, 51 and U.S. paper recycling, 172, 174–175 wood-based panels projections, 122f Price elasticity of timber supply, 201–202 Price elasticity of demand, 64t, 66t Price elasticity of supply, 67t Price Endogenous Linear Programming System (PELPS), 12, 78 Price trends base scenario projections, 119–122 historical data, 31–33 paper and paperboard projections, 121–122, 123f pulp projections, 121–122 sawnwood projections, 119–121f, 121f and U.S. recycling, 174–175 wood-based panels projections, 121, 122f Prices and Asian economic crisis, 141–142 example of data output, 237f and tariff reductions, 162 and U.S. timber harvest restrictions, 205–210 Primary-product supply, 43 Primary products, 41 Printing and writing paper consumption and net trade projections, 119f GFPM projections, 117 Private timber land management, 198 Producers’ welfare and tariff reductions, 163–164 and U.S. recycling, 172, 179–180 Production. see also U.S. timber production capacity by country, 272 example of data output, 236f global trends, 16–17 growth between 1960 and 1995, 15 major producers, 21–24 and New Zealand trade agreements, 190, 192t ratios to export data, 24–25 regional trends, 17–20 and tariff reductions, 159–161 of timber in United States, 196 and U.S. timber harvest restrictions, 205–210 Products. see Forest products Program execution, 245–249 Pulp. see Wood pulp

298 Q Quit software program, 234, 249

R Reactive programming limitations, 9 Recycling. see also U.S. paper recycling base scenario projections, 122–124 calculating wastepaper recovery changes, 46 and international competitiveness, 5–6 legislation and GFPM projections, 98 and input-output coefficient assumptions, 92 as new trade barrier, 168 and newsprint production in North America, 30 paper recovery rates, 75 summary of GFPM results, 268 trends, 122–124 worksheet, 228, 228f, 231f Resource allocation, 40 Results difficulties in displaying, 276 of model construction, 265–267 summary for models, 267–268 RetrieveOutput procedure, 258–259 Roundwood and Asian economic crisis, 132f, 134, 135t base scenario projections, 98–102, 125 consumption, observed and projected, 80f export and import projections, 100f export volume since 1960, 22 global production trends, 17t industrial consumption projections, 102f major global producers and consumers, 22t net trade, observed and projected, 80f net trade projections, 99f, 102f price trend projections, 119–121 price trends, 31 prices, observed and projected, 85f production growth between 1960 and 1995, 15 regional production trends, 18t supply shift rates, 92, 95–96t supply worksheet, 229 and tariff reductions, 157f, 158f types for U.S. timber harvest restrictions study, 199 and U.S. paper recycling, 178–179 and U.S. timber harvest restrictions, 205–210, 207t, 208t

Index Run base-period, 234, 247 Run-time errors, 241–242 Run-time parameters, 233–234

S Santiago Declaration, 215 Sawnwood and Asian economic crisis, 134, 136t base scenario projections, 102–104, 125 consumption, observed and projected, 81f export volume since 1960, 22 global production trends, 17t major global producers and consumers, 23t net trade, observed and projected, 81f price trend projections, 119–121 price trends, 31, 31t prices, observed and projected, 85f production growth between 1960 and 1995, 15 regional production trends, 18t and tariff reductions, 160t types for U.S. timber harvest restrictions study, 199 and U.S. timber harvest restrictions, 205–210, 209t Shadow price, 44 Social accounting matrix, 6 Software base-year calibration, 275 base-year input, 220–229 commodity codes, 223 computational efficiency, 275–276 error messages, 241–242 files and directories, 221f get output, 234, 235f input files, 246–247 installation and configuration, 220 main menu, 233f quit, 234 run-time parameters, 233–234 running GFPM program, 232–234 save input menu, 233f welfare analysis, 239–240 worksheets, 221 Software design about, 243 control programs and program execution, 245–249 data preparation, 249–251

Index data processing, 251–258 data-processing programs, 255 ITER-A.PAS, 256–257 output retrieval, 258–262 run-time parameters, 247 structure, 244–245 summary programs, 259–260 UPDATE-A.PAS, 257–258 Visual Basic Application (VBA) programs, 259t welfare analysis, 260–261 Software requirements, 220 Spatial equilibrium, 9 Spatial global equilibrium, 40, 41–44 Summary consumption chart, 237f Summary programs, 259–260 Suppliers’ welfare, 53, 54f Supply. see also Timber Supply Models calculating shifts, 45 in GFPM model, 39, 273 and Global Trade Assessment Project (GTAP) model, 8 linear approximation, 49–51 primary-product supply calculation, 43 procedure for updating data, 257 scenarios for U.S. timber harvest restrictions study, 203–205 for U.S. timber harvest restrictions study, 201 Supply equations, 58, 66–68 data format, 226f Supply worksheet, 222

T Tariff liberalization ATL (Accelerated Tariff Liberalization), 145–147 and exports, 162 GFPM projections for early reduction, 157–164 and imports, 161 and paper and paperboard, 158f, 159f previous studies, 147–148 and prices, 162, 163f and production, 159–161 rates in alternative scenarios, 149–155t recent treaties, 4–5 and roundwood production and import, 157f, 158f summary of results, 267 theoretical expectations, 148, 156–157

299 Uruguay Round of GATT, 145–147 and welfare, 163–164 Tariff reductions. see ATL (Accelerated Tariff Liberalization) Tariffs. see also GATT (General Agreement on Tariffs and Trade); Trade agreements assumptions for GFPM projections, 97 and ATL (Accelerated Tariff Liberalization), 147 base scenario projections, 97 calculating charges, 46 rates in alternative scenarios, 149–155t and recent trade agreements, 29 theoretical effect of change, 148, 156–157 updating data, 231–232 Tax rates, 228f updating data, 257–258 TechChange worksheet, 230 Technical change, 274 Timber Assessment Market Model (TAMM), 9 Timber supply. see Supply Timber Supply Model (TSM), 10–11 Time. see Base year Trade. see also Net trade dominance of developed countries, 25–26 effects of environmental policies, 30–31 interindustry and intraindustry, 266 intraindustry, 47 liberalization and GATT, 29–30 and tariff liberalization, 156f trends in international markets, 22–29 and U.S. timber harvest restrictions, 210–211 Trade agreements. see also ATL (Accelerated Tariff Liberalization); GATT (General Agreement on Tariffs and Trade) AFTA-CER regional trade agreement, 184–185 ATL (Accelerated Tariff Liberalization), 30, 145–147 effects on New Zealand forest industry, 189t GATT (General Agreement on Tariffs and Trade), 29–30, 185–186 P5, 185 recent treaties, 4–5 summary of GFPM results, 267–268 Trade flows, 274–275

300 Trade inertia, 43–44 calculation using GFPM, 43–44 changes in bounds, 46–47 constraints upon, 51–52 parameters in GFPM calculations, 74–75 and U.S. timber harvest restrictions, 212 worksheet, 228–229 Trade policies. see Tariff liberalization Trade worksheet, 232f Transport cost, 46 calculating using GFPM, 75 worksheet, 228–229

U UPDATE-A.PAS, 257–258 Update procedure, 258 Uruguay Round, 4–5, 185 effect on trade, 29–30 and tariff assumptions for GFPM projections, 97 U.S. economy, 33–34 U.S. paper recycling base scenario projections, 98 and cost of manufacturing, 170 and fiber furnish, 176–177 global impact, 5, 171 and paper industry, 175–176 previous studies, 170 and prices, 174–175 recent policies, 168, 170 and roundwood, 178–179 scenarios for GFPM simulation, 173–174 summary of effects of U.S. policies, 181 theoretical effect of change, 171–172 and welfare, 179–180 and wood pulp, 178t U.S. timber harvest restrictions impact and freer trade, 213t and international trade, 210–211 and inventory, 211–212 and manufacturing, 211 modifications to GFPM, 199–205 and paper/paperboard products, 210t policies affecting, 197–199 previous studies, 196–197 and price, 205–210 and production, 205–210

Index and roundwood production and price, 206, 207t, 208t and sawnwood, 209t summary of GFPM results, 268 and trade inertia, 212 and welfare analysis, 214 and wood pulp, 210t U.S. timber production policies affecting, 197–199 previous studies, 196–197

V Validation, 57, 75, 78–88, 267 Veneer. see Plywood 105 Visual Basic Application macros, 245 Visual Basic Application (VBA) programs, 259t

W Wastepaper consumption and net trade projections, 115f GFPM projections, 112–113 and U.S. recycling, 177t utilization rates, 123–124 Wastepaper recovery assumptions for GFPM projections, 97–98 base scenario projections, 112–113, 122, 123f calculating rates, 75 changes, 46 data in recycling worksheet, 227, 228f estimated rates for 1997 and 2010, 76–78t historical and projected rates, 122–123 and industrial roundwood trade, 178–179 limits, 170 New Zealand capacity assumptions, 188 and paper industry, 175–176 previous studies, 170 and price changes, 172 and prices, 174–175 in United States, 168–170 updating data, 231 Welcome screen, 232f Welfare analysis, 52–55 capability of GFPM, 266, 275 component of GFPM, 239–240 GFPM programs, 260–261 and tariff changes, 156–157 and tariff reductions, 163–164 and U.S. paper recycling, 172, 179–180 and U.S. timber harvest restrictions, 214

Index Wood-based panels and Asian economic crisis, 133f, 137t, 140 base scenario projections, 104–108, 125 consumption and net trade, observed and projected, 82f export volume since 1960, 23 major global producers and consumers, 23t price projections, 85f price trend projections, 121, 122f price trends, 32, 32f production growth between 1960 and 1995, 15 regional production trends, 19t and tariff reductions, 160t and U.S. timber harvest restrictions, 209t Wood pulp and Asian economic crisis, 138t, 140 consumption and net trade, observed and projected, 83f export volume since 1960, 23

301 GFPM projections, 109 import and export projections, 111f price trend projections, 121–122 production growth between 1960 and 1995, 15 and tariff reductions, 160t and U.S. paper recycling, 178t and U.S. timber harvest restrictions, 210t Worksheets, 221, 222 capacity1, 223 capacity2, 223 demand data, 222 exogenous changes, 229–232 GDP, 230 manufacture, 223 recycling, 227, 231f supply data, 222 TechChange, 230–231 Trade, 232f trade inertia, 228–229 transportation costs, 228–229


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