Bioenergy and Land Use Change 978-1119297345

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Geophysical Monograph 231

Bioenergy and Land Use Change Zhangcai Qin Umakant Mishra Astley Hastings Editors

This Work is a co‐publication of the American Geophysical Union and John Wiley and Sons, Inc.

This Work is a co‐publication between the American Geophysical Union and John Wiley & Sons, Inc. This edition first published 2018 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and the American Geophysical Union, 2000 Florida Avenue, N.W., Washington, D.C. 20009 © 2018 the American Geophysical Union All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions Published under the aegis of the AGU Publications Committee Brooks Hanson, Senior Vice President, Publications Lisa Tauxe, Chair, Publications Committee For details about the American Geophysical Union visit us at www.agu.org. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging‐in‐Publication data is available. ISBN: 978-1-119-29734-5 Cover image: Courtesy of Marty Schmer Cover design: Wiley Set in 10/12pt Times New Roman by SPi Global, Pondicherry, India

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CONTENTS Contributors.........................................................................................................................................................vii Preface ..................................................................................................................................................................ix

Part I: Bioenergy and Land Use Change 1

Bioenergy and Land Use Change: An Overview Pankaj Lal, Aditi Ranjan, Bernabas Wolde, Pralhad Burli, and Renata Blumberg ...............................................3

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An Exploration of Agricultural Land Use Change at Intensive and Extensive Margins: Implications for Biofuel‐Induced Land Use Change Modeling Farzad Taheripour, Hao Cui, and Wallace E. Tyner .........................................................................................19

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Effects of Sugarcane Ethanol Expansion in the Brazilian Cerrado:  Land Use Response in the New Frontier Marcellus M. Caldas, Gabriel Granco, Christopher Bishop, Jude Kastens, and J. Christopher Brown ..............................................................................................................................39

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Biofuel Expansion and the Spatial Economy: Implications for the Amazon Basin in the 21st Century Eugenio Y. Arima, Peter Richards, and Robert T. Walker .................................................................................53

Part II: Impacts on Natural Capital and Ecosystem Services 5

Toward Life Cycle Analysis on Land Use Change and Climate Impacts from Bioenergy Production: A Review Zhangcai Qin, Christina E. Canter, and Hao Cai ............................................................................................65

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Bioenergies impact on Natural Capital and Ecosystem Services: A Comparison of Biomass and Coal Fuels Astley Hastings .............................................................................................................................................83

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Empirical Evidence of Soil Carbon Changes in Bioenergy Cropping Systems Marty R. Schmer, Catherine E. Stewart, and Virginia L. Jin ..............................................................................99

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The Importance of Crop Residues in Maintaining Soil Organic Carbon in Agroecosystems David E. Clay and Umakant Mishra .............................................................................................................115

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Incorporating Conservation Practices into the Future Bioenergy Landscape: Water Quality and Hydrology May Wu and Mi‐Ae Ha ...............................................................................................................................125

Part III: Data, Modeling and Uncertainties 10

Uncertainty in Estimates of Bioenergy‐Induced Land Use Change: The Impact of Inconsistent Land Cover Data Sets and Land Class Definitions Nagendra Singh, Keith L. Kline, Rebecca A. Efroymson, Budhendra Bhaduri, and Bridget O’Banion.............143

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Challenges in Quantifying and Regulating Indirect Emissions of Biofuels Deepak Rajagopal .......................................................................................................................................155

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vi CONTENTS

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Biofuels, Land Use Change, and the Limits of Life Cycle Analysis Richard J. Plevin ..........................................................................................................................................165

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Lost Momentum of Biofuels: What Went Wrong? Govinda Timilsina .......................................................................................................................................181

Index ..................................................................................................................................................................189

CONTRIBUTORS Eugenio Y. Arima Department of Geography and the Environment, University of Texas, Austin, Texas, USA

Gabriel Granco Department of Geography, College of Arts and Sciences, Kansas State University, Manhattan, Kansas, USA

Budhendra Bhaduri Geographic Information Science & Technology Group, CSED, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

Mi‐Ae Ha Energy Systems Division, Argonne National Laboratory, Lemont, Illinois, USA Astley Hastings School of Biological Sciences, University of Aberdeen, Aberdeen, UK

Christopher Bishop Kansas Applied Remote Sensing Program, University of Kansas, Lawrence, Kansas, USA

Virginia L. Jin Agroecosystem Management Research Unit, USDA‐ARS, Lincoln, Nebraska, USA

Renata Blumberg Department of Nutrition and Food Studies, Montclair State University, Montclair, New Jersey, USA

Jude Kastens Kansas Applied Remote Sensing Program, University of Kansas, Lawrence, Kansas, USA

J. Christopher Brown Department of Geography and Atmospheric Science, University of Kansas, Lawrence, Kansas, USA

Keith L. Kline Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

Pralhad Burli Department of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA

Pankaj Lal Department of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA

Hao Cai Energy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA Marcellus M. Caldas Department of Geography, College of Arts and Sciences, Kansas State University, Manhattan, Kansas, USA

Umakant Mishra Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA

Christina E. Canter Energy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA

Bridget O’Banion Department of Environmental Sciences, Ohio State University, Columbus, Ohio, USA

David E. Clay Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, South Dakota, USA

Richard J. Plevin Transportation Sustainability Research Center, University of California, Berkeley, California, USA

Hao Cui Department of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA

Zhangcai Qin Energy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA

Rebecca A. Efroymson Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

Deepak Rajagopal Institute of the Environment and Sustainability, University of California, Los Angeles, California, USA vii

viii CONTRIBUTORS

Aditi Ranjan MYMA Solutions LLC, Lincoln Park, New Jersey, USA Peter Richards Bureau for Food Security, U.S. Agency for International Development, Washington, District of Columbia, USA Marty R. Schmer Agroecosystem Management Research Unit, USDA‐ARS, Lincoln, Nebraska, USA Nagendra Singh Geographic Information Science & Technology Group, CSED, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA Catherine E. Stewart Soil Management and Sugarbeet Research, USDA‐ARS, Fort Collins, Colorado, USA Farzad Taheripour Department of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA

Govinda Timilsina Development Research Group, World Bank, Washington, District of Columbia, USA Wallace E. Tyner Department of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA Robert T. Walker Center for Latin American Studies and Department of Geography, University of Florida, Gainesville, Florida, USA Bernabas Wolde Department of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA May Wu Energy Systems Division, Argonne National Laboratory, Lemont, Illinois, USA

PREFACE Expanding bioenergy production has raised concerns over potential land use change (LUC) and LUC impacts on the environment. To accommodate new or additional bioenergy feedstock production, the use of land changes in the forms of land cover, land use, and land management. These changes are very likely to affect the biogeochemical and biophysical processes, which shape the environment and ecosystem functions. The estimations and interpretations of bioenergy‐ induced LUC have been uncertain and controversial, primarily due to the limitation of ground‐truth data at scale to identify LUC that is directly or indirectly associated with bioenergy development. In recent years, scientists have made progress in understanding the importance and significance of LUC and LUC impacts. New state‐of‐the‐ art techniques have been utilized to clarify certain issues, for instance, remote sensing to quantify LUC observations and economic modeling to relate LUC to specific bioenergy program(s). Various aspects of LUC‐related environmental impacts have been studied in the context of bioenergy production at different scales. This book covers a variety of interdisciplinary topics related to bioenergy development, LUC and LUC impacts, with contributions from agronomy, economics, energy, geography, earth sciences, atmospheric science, and environmental sciences. The book consists of three

major parts: (I) bioenergy and land use change, (II) impacts on natural capital and ecosystem services, and (III) data, modeling, and uncertainties. Each part includes four to five chapters with different focus or perspectives. Part I focuses on bioenergy and LUC‐related definitions, mechanisms, modeling, and estimates. Part II contains individual studies and summaries on various environmental impacts, including carbon stocks, soil health, water quality, and climate impacts. Part III demonstrates methodologies, uncertainties, and challenges. It is not our intention to cover all aspects in this field or to endorse any perspective or statement but rather to report what has been done, what is being debated, and what needs to be further investigated. This book will be a valuable resource for experts and professionals involved in bioenergy and land use change assessment. It provides both high‐level reviews and in‐depth analyses on multidisciplinary topics; therefore, it could be of interest to researchers and students from a wide variety of fields in energy, economics, and environment. Finally, we would like to sincerely acknowledge the chapter authors for their valuable contributions and reviewers for their constructive criticisms. We also gratefully thank AGU and Wiley editorial staff, especially Rituparna Bose, Mary Grace Hammond, and Kathryn Corcoran for their excellent support and service.

Zhangcai Qin, Energy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA Umakant Mishra, Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA Astley Hastings, School of Biological Sciences, University of Aberdeen, Aberdeen, UK

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Part I Bioenergy and Land Use Change

1 Bioenergy and Land Use Change: An Overview Pankaj Lal1, Aditi Ranjan2, Bernabas Wolde1, Pralhad Burli1, and Renata Blumberg3

ABSTRACT Bioenergy production can have direct and indirect land use impacts. These impacts have varied implications, ranging from land tenure, commodity production, urbanization, carbon sequestration, and energy independence to several others. In recognition of its broad and intricate impacts, a growing amount of research focuses on this area, hoping to address the controversies and inform the relevant policies in a way that ensures more sustainable outcomes. In this chapter, we provide a summary of the research around land use change economics and modeling. We examine various concerns, as well as their empirical evidences, and outline the conceptual opportunities and challenges involved in measuring both direct and indirect land use change. We also describe a number of modeling methods that have been used in previous studies, including spatially disaggregated modeling approaches, econometric land use change approaches, and integrated environmental economic approaches. The chapter concludes with an analysis of policy imperatives and suggestions that could form the foundation of a more sustainable bioenergy development pathway. 1.1. INTRODUCTION

Bioenergy encompasses energy produced from biomass and includes fuels such as sugarcane‐ or corn‐based ethanol, biofuels produced from energy grasses, farm residue, and woody materials, as well as energy obtained from other plant‐based sources. Agricultural and forested biomass‐based energy is considered an option to reduce dependency on fossil fuels, increase the current share of the nation’s renewable energy, and improve the sustainability of forests and marginal lands. Cellulosic biomass‐ based energy or second‐generation fuels, for example, fuels produced from energy grasses or woody biomass, have certain advantages over other energy sources, such as first‐generation fuels like corn ethanol, because they limit competition between agricultural food crops and those destined for fuel production [Hill et  al., 2006]. While the development of cellulosic biofuels could result in competition for use of resources, including water, labor, carbon storage, and financial resources, one of the most important issues surrounding bioenergy markets is land use impact [Searchinger and Heimlich, 2015].

The United States is the largest consumer of petroleum products in the world, consuming around 7.45 million barrels per day in 2012 [Energy Information Administration (EIA), 2014]. A significant share of these petroleum products is imported from politically unstable regions of the world. This reliance on fossil fuels has led to economic, social, and environmental concerns that have gained public attention. Bioenergy appears to offer hope by reducing the gap between domestic energy supply and demand, diversifying energy sources, reducing greenhouse gas (GHG) emissions, and by providing socioeconomic benefits in the form of additional income and new jobs. 1

Department of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA 2 MYMA Solutions LLC, Lincoln Park, New Jersey, USA 3 Department of Nutrition and Food Studies, Montclair State University, Montclair, New Jersey, USA

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 3

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BIOENERGY AND LAND USE CHANGE

There are varied answers to the following question: What is the land use impact of using biomass‐based energy, and how does it change over time? Arable land is a scarce resource that is already under pressure from food agriculture, forestry, industry, urban, and other demands. The situation is further complicated by the fact that the land use change (LUC) associated with increased bioenergy production can negate the potential benefits of production and ultimately degrade the environment. The bioenergy market is largely encouraged on the national/ supranational scale by government initiatives; countries with notable biofuel initiatives include the member states of the European Union, the United States, and Brazil. Without taking into account both the economic and environmental factors that surround the bioenergy market, these initiatives can lead to poor land use decisions at the local level [Baker et al., 2010; de Oliveira Bordonal et al., 2015; Vasile et al., 2016]. Economic methods of biomass supply estimation are based on the interplay of demand and supply markets of bioenergy. The bioenergy market is shaped by the interplay between global oil fuel, other types for energy sources, price of substitutes, bioenergy production costs, and alternative uses for arable land. A high fuel price not only increases the demand for biofuels by increasing the incentive for the production of alternative sources but also increases the cost of cultivation or harvesting. Bioenergy production also affects potential profit margins for the sale of biofuels, and if costs of production are high, actual production will necessarily drop [Rajcaniova et al., 2014]. As fossil fuel prices rise, the production of bioenergy becomes more profitable and therefore leads to the conversion, or construction, of agricultural land for biofuel production. For example, Piroli and Ciaian [2012] showed that a one‐dollar increase in per‐barrel price of oil could encourage the planting of between 54,000 and 68,000 ha of bioenergy cropland globally. The same study found that increasing oil prices increased agricultural area globally by 35.5 million ha, out of which biofuel feedstocks were 12.12 million ha/year. Volatility in the oil market encourages the use of first‐generation biofuel crops, such as maize, wheat, and soybean, over second‐ generation biofuels, like perennial grasses, because of the comparatively low turnaround time of first‐generation bioenergy crops. This variable demand for bioenergy, met by quick switches to energy crops over food, as prices fluctuate, can lead to not only fluctuations in supply for conversion plants but also unsustainable biomass cultivation and/or harvesting practices resulting in modified land use, deforestation, soil degradation, and GHG emissions, among others. Often the first lands to be developed are pasturelands and other croplands. However, other land uses are converted to biofuel production with an

increasing demand for biofuels, including forest and swampland [Rajcaniova et al., 2014]. Uncontrolled bioenergy transition can result in increased agricultural runoff because of improper cultivation and harvest methods. This has been observed in the red river basin in the Dakotas. When the primary crops in the area changed to corn with increased biofuel demand, sediments increased by 2.6%, phosphorous by 14.1%, and nitrogen by 9.1% [Lin et al., 2015]. Second‐ generation biofuels tend to increase soil organic carbon (SOC) when planted on cropland but tend to decrease SOC when planted on their native counterparts: forests and grasslands [Harris et al., 2015]. There is also apprehension that the conversion of land to bioenergy can lead to more arable land being opened up to raise supply. This increase in agricultural land has the potential of being environmentally deleterious, as more sensitive natural environments may be repurposed. Land use change to agricultural row systems can also cause habitat loss [Jonsell, 2007]. Land use change from natural forests to forest plantations, including short‐rotation woody crops, is an important area of concern from an ecological point of view [Wear et al., 2010]. Fargione et al. [2008] contend that the conversion of lands, such as rainforests, peatlands, and grasslands, to produce crop‐ based biofuels in Brazil, Southeast Asia, and the United States could potentially release 17–420 times more CO2 than the annual GHG reductions from the use of these biofuels. Meanwhile, Plevin et  al. [2010] estimate emissions associated with indirect land use change for U.S. corn ethanol ranging between 10 and 340 g CO2e MJ−1 for a variety of modeling scenarios and assumptions. Similarly, using a spatially explicit model to project land use changes, Lapola et al. [2010] suggest that indirect land use changes resulting from expansion of biofuel plantations in Brazil could create a carbon debt that would take about 250 years to be repaid. Biomass production might also have negative consequences unless coordinated with breeding and nesting seasons and maintaining cover for overwintering small mammal species [Bies, 2006]. However, interventions focused on ecological restoration or fuel‐reduction activities associated with woody biomass can also benefit wildlife habitat [Janowiak and Webster, 2010]. In the face of a growing bioenergy sector and associated policy incentives, land use analysis is considered critical for the future of bioenergy markets. Many authors have explored this issue; what has been lacking is a systematic analysis of trends, evidence, and complexity in assessing bioenergy market growth and associated land use impacts. Toward this goal, a comprehensive literature review was undertaken. We review the problems, applications of economic techniques, methodological complexities, and certification efforts from the literature, focusing

BIOENERGY AND LAND USE CHANGE: AN OVERVIEW 5

more on the ways in which bioenergy and land use issues have been approached by economists. We focus less on the methodological aspects and rely more on comparative results and outcomes in order to provide the reader a broad understanding of current research in this area. The rest of the chapter is organized as follows. In the following section, we discuss forest biomass supply assessments, focusing on some of their differences and similarities. We then turn to the modeling efforts and methodologies that have been used to estimate land use change impacts associated with bioenergy markets. In the fourth section, we focus on the empirical evidence of land use change analysis and challenges such as uncertainty and modeling challenges. We discuss technological and policy imperatives and bioenergy certification in the fifth section. Finally, we summarize observations and provide perspectives on the future of bioenergy markets and land use impacts. 1.2. LAND USE CHANGE AND CURRENT RESEARCH Land use change is an important component of the use of biofuels, and it is important in terms of delineating effect of biofuels on GHG emissions and carbon sequestration by considering the lifetime GHG effects of the fuels as opposed to their effects when they are only burning. It is increasingly being recognized that land use effects of bioenergy production are linked to net GHG reductions. Assuming that energy crops do not lead to land use changes, life cycle analyses of different biofuels (including woody biomass) suggest overall GHG reductions [Birdsey et  al., 2006; Blottnitz and Curran, 2007; Eriksson et al., 2007; Gustavsson et al., 2007]. However, Searchinger et al. [2008] argue that life cycle studies have failed to factor in indirect land use change effects and suggest that using U.S. croplands or forestlands for biofuels results in adverse land use effects elsewhere, thus harming the environment rather than helping it. To this end, researchers and organizations, including the Intergovernmental Panel for Climate Change [Watson et  al., 2000], the National Wildlife Federation, and the Union of Concerned Scientists have put considerable effort into defining and studying the effects of land use change in biofuels [Watson et al., 2000; Searchinger et al., 2008; Union of Concerned Scientists, 2008; Plevin et al., 2010; National Wildlife Federation, 2014]. 1.2.1. Direct Land Use Change Direct land use change (DLUC), the direct change of land usage due to increased biofuel production, has the most obvious and measurable effect on the land and surrounding areas. DLUC occurs most commonly when

uncultivated areas, such as forests or grasslands, are converted into farmland for the production of biofuel crops. Direct land use change can have considerable consequences for GHG emissions and other environmental concerns [Union of Concerned Scientists, 2008; National Wildlife Federation, 2014]. Destruction of forest ecosystems, for example, causes a significant amount of carbon stored in the forests to be released, which can offset many of the carbon advantages of using biofuels [National Wildlife Federation, 2014]. In addition to this, DLUC can also impact the environmental benefits that these areas could provide, including biodiversity and ecosystem services such as water filtration, erosion control, and ground water recharge. DLUC, when considering these factors, can result in adverse ecosystem trade‐offs. DLUC is an important consideration for any bioenergy project. The environmental and economic cost of clearing land, planting, and growing the bioenergy plants can also influence net GHG emissions. The emissions vary considerably, depending on the crop and the area in which they are planted, so bioenergy crops should be carefully considered for the potential plantation area before planting to ensure that emissions do not outweigh the benefit of the crop. Fueled by some of the above mentioned concerns, DLUC impacts arising because of biofuel production have come under scrutiny in recent years. Sustainability initiatives, both in the United States and abroad, have made it a priority to understand the consequences of DLUC [Stappen et  al., 2011; Jones et  al., 2013]. Because of the nature of land use change and the differing nature of bioenergy crops and cultivation practices, these result in different economic and environmental cost and benefits [Natural Resource Defense Council (NRDC), 2014]. Sugarcane, for example, is a bioenergy crop that has gained popularity over the years and is currently a staple in the United States and Brazil, two countries that together account for around 90% of the world’s ethanol production. In addition to potentially reducing GHG emissions by nearly 85% in comparison to fossil fuels, in areas of Brazil, converting land to sugarcane production can potentially lead to positive benefits, such as local climate cooling [de Oliveira Bordonal et al., 2015]. However, sugarcane cultivation practices for bioenergy production, particularly the burning of plant residues during harvest time, can contribute to GHG emissions. De Oliveira Bordonal et  al. [2015] sought to quantify some of the effects of DLUC and provide an analysis of the effects of sugarcane production on GHG emission. They found that the expansion of sugarcane plantations contributed to significant GHG emissions from agricultural production, but around 57% of this was offset through carbon uptake in the new biomass. However, calculations of GHG emissions can differ depending on the system

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boundaries of the assessment and key drivers of the land use change, coupled with factors such as ecosystem services and carbon pools and sinks, among others. It is also important to note that the GHG emissions from land use can differ wildly, depending on the location in which they are grown. A study by Bailis and McCarthy [2011] found that there was a considerable difference in the carbon debt between jatropha plantations in India and Brazil as a result of the differing climates and soil types. The study by Stappen et al. [2011] confirms this by finding a considerable difference in net GHG emissions between soy grown in the United States (27%) and Argentina (−568%). This has important implications as it indicates that there is no one‐size‐fits‐all bioenergy crop that has superior performance compared to all others in all places and at all times. As such, bioenergy should be considered for each area individually to fully understand how it will perform and how much work in the form of emissions and labor will be produced in their growth. 1.2.2. Indirect Land Use Change Indirect land use change (ILUC), a secondary consideration in land use change for bioenergy crops, includes the effects that are related to, but not immediately caused by, the cultivation of bioenergy crops. Because it is difficult to define where ILUC begins and ends, it is far more difficult to calculate than DLUC. Though some studies attempt to approximate ILUC, it is perhaps more important to understand some of the dynamic factors that influence the proliferation of ILUC and get a more complete understanding as to how a bioenergy operation will affect the surrounding land. Understanding the possible extent of ILUC is critical for making management decisions that can accurately and successfully reduce carbon emissions while simultaneously protecting local ecosystems and ensuring that the operation does not produce an unhealthy amount of GHGs. The ILUC impact of biofuel feedstocks was brought to the forefront during the “food versus fuel” debate. This common criticism of bioenergy refers to the conflict between using food crops for fuel instead of food [Naylor et  al., 2007]. The price of crops, such as corn, that are used for both food and fuel increased in some instances because more of it was being used for fuel. This can often extend to land use change elsewhere, as displaced food‐growing operations in one part of the world may result in land being diverted for growing bioenergy crop elsewhere [Doornbosch and Steenblik, 2008]. Such a change may happen in geographically disconnected areas, as it is largely a result of market mechanisms,

and therefore can encourage a significant change in land use that cannot be traced firmly back to any singular bioenergy operation. This proliferation of changing land use as a result of bioenergy operations can result in increased GHG emissions if carbon‐rich ecosystems are converted to farmlands [Fritsche et al., 2010]. What makes the problem “wicked” is the fact that ILUC is more difficult to quantify, and it is more difficult to identify the drivers resulting in adverse bioenergy‐based land use change. Because ILUC is difficult to quantify, there is some confusion and even skepticism in the scientific community over its relevance. Finkbeiner [2014] posits that ILUC quantification methods are still in their infancy and that there exists no proven method to accurately convey how ILUC affects GHG emissions. He cites wildly varying estimates (from −200% to over 1700%) among studies that try to quantify the effects of this change, far more than other scientific studies of its type, and notes that there are no relevant standards for this type of study at this time. He notes that major international standards, such as the EU Product Environmental Footprint Methodology and ILCD Handbook, do not include ILUC in their calculations, bringing their relevance and reliability into question. He also points out that adding ILUC emissions into the emissions of major biofuels may be misleading and may result in a lopsided comparison with fossil fuels; ILUC can also occur with the production of fossil fuels, but they are never included in fossil fuel emission calculations. Moreover, methodologies including comprehensive life cycle assessment (LCA) and input‐output models can potentially address these challenges by accounting for direct and indirect land use change more precisely [Liang et  al., 2012; Marvuglia et al., 2013; Dilekli and Duchin, 2016]. Though ILUC can represent an important piece of the land use change emissions, it is important to consider the concerns brought up by this study and others as this field of study matures. Only then can the indirect effects of biofuels be accurately represented. Though the study of its effects is imperfect, ILUC can be deemed important to understanding the complete effect of biofuel production on the environment. 1.2.3. Current Research Trends in Bioenergy and Land Use Literature Though the production of biofuels has advanced considerably in recent years, it has also led to some concerns. This is evident from our word‐cloud analysis whereby we quantitatively analyzed large collections of textual information to evaluate some of the most widely cited publications of the previous decade. Text mining

BIOENERGY AND LAND USE CHANGE: AN OVERVIEW 7

1.3. MODELING EFFORTS AND METHODOLOGIES ESTIMATING LAND USE CHANGE AND BIOENERGY MARKETS

Figure  1.1 Word cloud representing the 50 most frequent words in the text analysis.

has become more sophisticated and has benefitted from computational and technological advances in linguistics, computer science, and statistics [Meyer et al., 2008]. We analyzed 46 papers published since January 2006 to August 2016, encompassing the broad theme of bioenergy and land use. These publications were cumulatively cited over 3000 times according to citation statistics compiled using Google Scholar. The analysis was performed using text‐mining features in the programming language R [Williams, 2016]. The word cloud provides a visual of the 50 most frequently used words in the papers analyzed in the text analysis and illustrates some of the important focus areas in previously published land use and bioenergy literature (Figure 1.1). Within the text‐mining algorithm, we exclude numbers, as well as commonly used conjunctions, prepositions, and “stopwords” such as “all,” “almost,” and “largely,” among others. It is interesting to note that along with the obvious focus on biomass, land, and bioenergy, the text analysis identifies “food,” “crops,” “agriculture,” “forest,” and “feedstock” as keywords with a relatively high frequency. This suggests that the food versus fuel argument and conversion of forestland for cultivating bioenergy feedstocks emerge as an important story line. Finally, “oil,” “gas,” “ethanol,” and “fuel” also feature in many publications. While it is plausible to infer that the frequency of words is correlated with the number of publications included in the analysis, enhanced computational and analytical capacities allow us to decipher important trends in the literature in relatively less amount of time.

Several approaches, including land tenure, urbanization, energy production and consumption, climate change, economic growth, and population growth, have been used to model land use [Irwin and Geoghegan, 2001; Lambin et  al., 2001]. These approaches are crucial in helping us understand, quantify, and predict likely social, economic, and environmental outcomes, all of which are valuable in informing relevant decisions and in minimizing potential adverse outcomes while maximizing the potential positive outcomes. The results are useful for making informed decisions regarding land use planning and policy. In order to quantify land use changes, LCA is often used. LCA is a methodology that attempts to bring all the factors of a crop’s life cycle into the equation, from its conception to its disposal, to fully understand their effects. Though many different crops have different carbon‐emission savings when compared to fossil fuels and this number can be calculated [Stappen et al., 2011], large differences in the LCA of these crops generally come from differences in how direct and indirect land use changes are assessed [Marvuglia et al., 2013]. For example, generally more land use contributes to higher GHG emissions and therefore can cause a net GHG increase; studies that assess a larger area of land use, likely by including more land from indirect use, may appear to have higher emissions and therefore lower net savings [Finkbeiner, 2014]. Furthermore, different types of LCA may change the net balance; Marvuglia et  al. [2013], for example, describe the difference between LCA, which aims to describe the impacts of the human economy on the global environment, and consequential LCA (CLCA), which aims to show how the environment will respond to possible decisions. All of these factors can ultimately lead to considerably different results, and thus the major factors, namely, land use change, must be assessed in order to understand how they change the models. The way land use change is modeled depends partly on the drivers, including social, economic, technological, biophysical, political, and demographic, and the implications of land use change one aims to model. The subject matter, actual use, applicability, and type of information available to the model developer also affect the way one models land use change [Adams et al., 1999]. On the basis of the techniques adopted and end use, these approaches can be sorted into various groups, the operational classification for this study being (i) spatially disaggregated approach, (ii) economic approach, and (iii) integrated environmental economic approach.

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Each of these approaches is best suited to handle a specific set of land use change drivers and relevant implications by addressing a given problem from different specialized points of view. Given their unique perspective, these approaches differ in their unit of analyses, land use of interest, intended user, contagion criteria, temporal and spatial considerations, and their ability to model emergent behavior [Agarwal et  al., 2002]. Their respective scopes can be local, regional, national, or even international, and they can either project future trends or describe the processes that resulted in the change that has already occurred [Drummond and Loveland, 2010]. The given approaches and the specific models used can follow a probabilistic or deterministic transition rule for conversions among given land use classes [Agarwal et al., 2002]. While the specific perspective may differ, the models are not always mutually exclusive. For instance, the statistical models and transitional matrices are comparable, whereas hybrid models build on the strengths of one model and overcome inherent weaknesses [Briassoulis, 2000; Gibson et al., 2000]. Quality control of the results can be assessed through the realism in capturing relevant underlying processes, precision of results in describing data, and the generalizability or replicability of the results in other settings [Grimm et al., 2005]. The models also share some common constraints, including the uncertainty and limited availability of historical land use information, which is compounded while modeling feedback loops [Kim and Dale, 2009]. Other factors being the same, variations in input data and related assumptions may affect the estimated land use and cover change and associated impacts [Center for BioEnergy Sustainability (CBES), 2009]. In what follows, we describe each approach and give examples of specific cases where the said approaches are used. 1.3.1. Spatially Disaggregated Modeling Approaches This approach explicitly accounts for the spatial heterogeneity of the area of study. It can also account for the various land use options available to a given land, allowing for the inclusion of relevant neighborhood conditions such as presence and proximity of developed sites, roads, and land features. This is important in assessing correlating land uses and changes, and it increases the chances of correctly predicting the amount, probability of conversion between different land use classes, and type and stability of land use change over time [Brown, 2002]. Moreover, current and historical land use patterns and the contagion specification help to validate model prediction and check the reliability of the results [Clarke et  al., 1997; Pontius and Schneider, 2001]. The unit of analyses affects the data requirement, precision of results, and relevance of the results to varying stakeholders [Walsh et al., 2001].

In the context of bioenergy, regional differences exist in the biomass yield along with the corresponding biofuel yield, both positive and negative. Moreover, different feedstocks have varying agronomic conditions and input requirements [Varvel et  al., 2008]. This approach can prove useful in accounting for such variations while modeling the extent and outcomes of land use change associated with feedstock production. However, the scale and quality of remote‐sensing data and other types of data have to be uniform, making it challenging to model land cover and land use change effects from biomass regrowth [Schulze, 2000]. The reliance on existing and historic land uses is also intractable for emerging land use developments, distant future projections magnifying the problem and showing the need for reasonable and flexible thresholds systems for given land uses [Agarwal et al., 2002]. The more complex spatial models, including spatially representative and spatially interactive models, can incorporate or produce data at up to three spatial dimensions. The area base model, for instance, predicts land use proportions among farmland, forests, and urban or other types of land uses [Hardie and Parks, 1997]. Using counties as units of analyses, it attempts to predict and explain the coexistence of several land uses and conversion among them by using their respective heterogeneous attributes. Alternatively, the spatial dynamic model predicts shifts in cultivation for given topographies on the basis of their proximity to urban centers by using site productivity, ease of clearing, and erosion hazard, among others, as variables [Gilruth et al., 1995]. However, it has a relatively large unit of analyses, 6 km2. Similarly, Chomitz and Gray [1996] use spatially disaggregated information such as wetness, road, and slope to determine land use among natural vegetation and farming. Although it implicitly accounts for human decision making, a more explicit accounting of the human dimension could increase its application. In addition, a longitudinal analysis, in lieu of a cross‐sectional analysis, should be included. The challenge for this approach is that it does not always account for economic agents and policy changes, a challenge that can be addressed by employing econometric approaches. 1.3.2. Economic Land Use Change Approaches This approach identifies and explicitly accounts for multiple economic actors, their interaction, and the drivers they respond to, including tax and energy policies, to determine the probability and magnitude of land use change [Walker et al., 2000; Brown, 2002]. While in some cases the econometric model uses the land’s exogenously determined market price, or an equivalent thereof, selling price, and conversion cost between alternative uses for

BIOENERGY AND LAND USE CHANGE: AN OVERVIEW 9

given land use class [Bockstael, 1996], prices can be modeled endogenously as well. Thus, the land and the physical processes that involve land are treated in economic terms, not in physical terms as in the spatially disaggregated approach. One can also assess consumer and producer surpluses and carbon path under varying policies over time and at the regional level [Adams et al., 1996]. It allows for scenario analyses, where varying policy options are evaluated for potential land use impact along with forcing factors such as population growth and commodity demand. The ability of such approaches to handle different policy scenarios makes it useful in assessing and quantifying trade‐offs associated with varying options available to decision makers. Given the temporal dynamics of the said economic variables, the time span between different land uses can also be determined by using hazard and survival models, respective modeling varying from one another in the smallest temporal unit of analyses for a given change to occur [Irwin and Geoghegan, 2001]. Such approaches combine the probability of land use change with information on the timing of such change and how long given changes last, the time dimension providing an additional contextual layer. Moreover, this approach can account for individuals’ socioeconomic attributes and motivation [Pfaff, 1999]. Given their stake in land ownership and biomass supply decision, the ability to account for such stakeholders is crucial in ensuring more accurate estimations and policy‐ relevant results [Nelson et  al., 1999; Ahn et  al., 2000]. Using agricultural inputs such as agricultural prices and profitability, timber age, site condition, and the lagged values associated with land uses, for instance, such an approach can allocate land for competing uses in a way that maximizes a predefined objective function [Chomitz and Gray, 1996]. Similarly, the production of a given feedstock affects its other uses, the resource allocation to the other types of feedstock, and how that reflects on the relevant market prices. These intricate relationships are important in understanding the dynamics within the bioenergy feedstock production system, and the econometric approach is suited to handle such relationships in modeling land use change [Perlack et al., 2011]. Using a timber model, forage production, and nontimber benefits, Swallow et  al. [1997] simulate the optimal harvest sequence. By including the discounted values of future cash flow from alternative uses for the land, this approach provides a decision support tool. The NELUP extension model [O’Callaghan, 1995], on the other hand, uses linear programming at the farm level to determine the optimal ways to maximize profit for a given set of resources and farm activity. It also explicitly accounts for individual’s risk taking or aversion behavior.

Similarly, the forest and agricultural sector optimization model uses a dynamic nonlinear model to assess the relationship between forestry, agriculture, and terrestrial carbon [Adams et  al., 1996]. It maximizes economic welfare of the decision maker and determines optimal allocation of land to alternative uses. Moreover, it allows for policy effects and has a feedback loop for intertemporal price dynamics. Besides the data intensity, this approach does not fully account for parcel contagion criteria and potential candidates for conversion where all plausible land use options are not accounted for, a challenge addressed by spatially explicit approaches. 1.3.3. Integrated Environmental Economic Approaches This approach couples landscape attributes with the relevant economic agent or economic drivers to determine land use change in a dynamic setting. Its ultimate goal is to understand human‐environment dynamics across space, time, and decision‐making process [Grimm et al., 2000; Agarwal et al., 2002]. It can feature data on relevant development and household. Such models can also predict feedback effects between land use decisions, environmental outcomes, and relevant policies. It is also crucial in handling emerging land use developments, including energy production and progress on the relevant technologies. The way land is managed, before and after the change, may be as important as the amount of land use and cover change, having notable effects, both in the short term and long term, on ecological, biophysical, economic and social, and climatic outcomes [Fargione et  al., 2008; Searchinger et al., 2008]. Thus, interest exists in tracking, evaluating, and monitoring its consequences in a way that accounts for the complex relationship that exists between the physical and the socioeconomic systems [Tesfatsion, 2001]. The integrated environmental economic approach is suited to handle such relationships in modeling land use change. In the case of bioenergy, for instance, the original use of the land can affect the overall energetic and GHG performance of the bioenergy produced. While some feedstocks compete with prime agricultural land, others do not. Instead, they grow on marginal and even contaminated sites, which are otherwise unusable, and in the process, they help restore its functional capacity [Liebig et al., 2005; McLaughlin and Kszos, 2005; Mitchell et al., 2010]. Similarly, woody bioenergy can also improve forest conditions and reduce the fire and disease outbreak risk associated with overstocked forests [Polagye et al., 2007]. These benefits need to be considered for the purpose of comprehensiveness. By using this approach, one can integrate physical information on land use change with policy and economic information, including sustainable land

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management practices and upcoming technologies, such as carbon capture and storage [Barkley, 2007]. However, since they account for varying drivers and impacts simultaneously, such models can be taxing in both data and computation needs and usability by varied stakeholders. The general systems framework uses a recursive linear programming model at the regional and catchment levels and takes into account hydrological and ecological models and inputs, including soil characteristics, species, meteorological data, and input/output farm data [O’Callaghan, 1995; Alberti and Waddell, 2000]. It helps one determine the dynamics between market forces, hydrology, and ecological outcomes. Such frameworks explicitly model choices of decision makers using probability functions and include relevant socioeconomic determinants while having an ecological analysis unit of 1 km2. Similarly, the land use change analyses system model integrates socioeconomic module with landscape and impact modules [Berry et  al., 1996]. It models the transition probability matrix, develops new land use maps, and shows corresponding impacts on species habitat, using 90 m2 as its unit of analyses. 1.4. EMPIRICAL EVIDENCE AND MODELING CHALLENGES Given the contextual considerations, differences in feedstock and technology, and the varied working assumptions used by the studies, direct comparison of the previous studies on land use change is not always possible. However, researchers have attempted to not only evaluate the physical aspects of land use change but also study effects on a variety of variables ranging from GHG emission to soil quality and biodiversity. Moreover, whether or not the switch to biofuels will result in carbon savings could be significantly influenced by the types of land that are used to produce them [Searchinger et  al., 2008; Lapola et al., 2010]. Andersen [1996] analyzed the determinants of deforestation in the Brazilian Amazon using two different measures. The first measure of deforestation was based on satellite photos, whereas the second was based on land surveys. Using regression analysis, the author concluded that local economic forces were more important factors than the government’s development policy at explaining deforestation across 316 municipalities during 1975– 1985. More recent studies, which use spatially disaggregated data, also combine other modeling techniques in an attempt to provide a more in‐depth analysis. Tompkins et al. [2015] present a deforestation and land use change scenario generator model that interfaces with dynamic vegetation models. The model named FOREST‐SAGE disaggregates the regional‐scale scenario to the local grid‐ scale scenario using certain risk rules based on physical

and socioeconomic attributes. Their model successfully reproduced spatial patterns of forest‐cover change as recorded by the Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Field data. Since the expansion of biofuels is a relatively recent phenomenon, there has been limited data and evidence to show that biofuels have resulted in large‐scale changes to land use [Taheripour and Tyner, 2013]. Yet in many cases, the relationship between biofuel production and land use change is a contentious issue. In the United States, Piroli and Ciaian [2012] employed time series analysis to evaluate the relationships between fuel‐price changes and land use change, both direct and indirect, to test for interdependencies and the role of biofuels. They analyzed data for the 1950–2007 time period for five majorly traded agricultural commodities, the cultivated agricultural land area, and crude‐oil prices. The study concludes that the expansion of the bioenergy sector has indeed accelerated land use change in the United States wherein bioenergy crops are being cultivated on lands previously used for food crops. Meanwhile, Taheripour and Tyner [2013] state that the allocation of cropland in the United States has witnessed significant changes over the past two decades, in response to a variety of market conditions and policies. However, the total amount of harvested land has remained relatively unchanged. Rajcaniova et  al. [2014] also estimated the impact of bioenergy on global land use change using econometric techniques. They concluded that increasing energy prices and biofuels’ production had a significant impact on global land use change through both direct and indirect pathways. They estimate a 12.12 million ha increase in global agricultural area due to higher biofuel production, which translates to 0.25% of the total worldwide agricultural area. Furthermore, the results for region‐specific estimates indicate a yearly total land use change increase due to biofuel production in Asia, South America, and North America. Similarly, Al‐Riffai et al. [2010] found that biofuel policies in the European Union resulted in indirect land use changes through deforestation in other parts of the world. In a simulation‐based study, Lal [2011]has applied a modified version of Subregional Timber Supply model [Abt et  al., 2000] spanning 13 southern states in the United States and found that bioenergy markets arrest the decline in private forest lands and in fact lead to an increase if higher opportunity costs gets translated to high bioenergy demand. The results of moderate bioenergy demand scenario suggest that the Southern private forestland acreage increases by 4.6% from 175.39 million acres in 2010 to 183.47 million acres in 2050 (Figure 1.2). This acreage is 18% higher than no woody bioenergy scenario results. This is largely due to the increase in planted pine acreage (20% from 2007 levels), which offsets the

BIOENERGY AND LAND USE CHANGE: AN OVERVIEW 11 (a) 200 180

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Figure 1.2 Private forest acreage change for five forest types in Southern United States comprising 13 states under (a) no biomass diverted to energy scenario and (b) moderate consumption of woody biomass for energy scenario. (See insert for color representation of the figure.)

acreage decline from other forest types. The percentage decrease in the acreage of other four forest types (natural pine, oak pine, upland hardwood, and lowland hardwood) is also much smaller than the low woody biomass scenario or where woody bioenergy markets do not exist. Finally, we consider an example of integrated environmental economic models. Over the years, Brazil has witnessed a substantial increase in acreage allocated for sugarcane cultivation, a large portion of which is used for biofuel production [de Souza Ferreira Filho and Horridge, 2014]. De Sá et al. [2013] empirically evaluated the potential indirect effects of sugarcane cultivation on forest conversion in the Amazon region in Brazil. The impact of factors such as road density, access to markets, credit availability, soil quality, precipitation on deforestation in the Brazilian Amazon was evaluated within the modeling framework. Furthermore, the study also sheds light on the potential shift of cattle ranching from the Sao Paulo region to the Amazon. The authors claim that sugarcane expansion has led to the movement of cattle‐ranching activities form the Center‐South region toward the

Amazon and that the indirect effect owing to displacement is both significant and nonnegligible. They also suggest that the results of their study are consistent with those obtained through studies relying on remote‐sensing evidence. Meanwhile, Elobeid et al. [2011] found that the expansion of sugarcane cultivation came at the expense of other crops and pastures in Brazil. 1.4.1. Modeling Challenges Land use change analyses often focus on the underlying processes that result in the alteration of landscapes and try to evaluate the implications of such changes [USDOE, 2011a]. Reports, including USDOE [2011b], state that while land use models focus on specific sectors of the economy, the complexity of real‐world interactions, such as the effects of biofuel policies in one part of the world on land use influences in another part, is difficult to capture accurately. Furthermore, collaborations between researchers are required to improve methodological uncertainties as well as for improved data

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collection and integration. Some of the most pressing challenges from a data perspective include the need for better quantification of land cover, accurate measurement of environmental impact of land use change, model validation through comparison with empirical data, and accurate evaluation against other energy alternatives. More refined modeling approaches that document and quantify the uncertainty in land use change models and allow for improved integration with economic models are also required. Nassar et al. [2011] note that agricultural activities tend to be one of the primary drivers of land use change at a global level. The authors discuss the most common methodologies used by policymakers to quantify the land use change impacts of biofuel expansion. The paper highlights some of the limitations of current economic models and suggests that the calibration of economic parameters, for example, price elasticity and land supply elasticity in accordance with historical land use data and satellite imagery, could be a step in the direction of improving model accuracy. The authors also conclude that the evaluation of land use change ought to consider the agricultural sector as a whole rather than focusing primarily on the biofuel sector. Although models that predict land use and land cover change are routinely used to perform environmental assessments and inform policy decisions, inherent in these projections are model uncertainties that can influence model results significantly [Prestele et al., 2016]. Prestele et al. [2016] identify hot spots of uncertainty in land use/ land cover models using a regression analysis of 11 global‐scale models. They find that model uncertainty is higher at the edges of “globally important biomes” such as the boreal and tropical forests. The diversity of land use models and the lack of consistent observational data, coupled with multiple definitions of land use and land cover across models, also introduce model uncertainty. Meanwhile, De Rosa et al. [2016] evaluated six land use change models for LCA, which they classified into three main categories: (i) economic, (ii) causal‐descriptive, and (iii) normative models. They contend that the causal‐ descriptive models are better suited for long‐term assessments, whereas the economic models perform better for short‐term assessments of the impacts of land use change. The authors also make important suggestions regarding the need to incorporate economic, biophysical, and statistical information to account for the inherent complexity of land use change models and achieve robust results. Furthermore, improving the precision of data, inclusion of more impact categories, identification of land use, specifically marginal land, are identified as some of the factors that can improve the quality of assessments of land use models. Finally, the authors point out that, in order to perform more holistic assessments of land use

change, models should extend beyond measuring only GHG impacts and include impact categories such as nutrient leaching, water‐resource impacts, biodiversity impacts, and socioeconomic analyses. 1.5. TECHNOLOGICAL AND POLICY IMPERATIVES AND CERTIFICATION Land use change is guided by policy imperatives, and government policies and programs can not only influence the economic market for bioenergy but also significantly influence diversion of feedstocks for energy use. Perhaps the most significant drivers of land use change are green‐ energy initiatives; bioenergy is generally considered as a source of such energy, and therefore, bioenergy initiatives may increase and grow in size. In the United States, a range of policies were introduced and are being implemented to support research and development in bioenergy with a view to enhance the share of biofuels in the overall energy mix. These policies include the Biomass Research and Development Act of 2000, the 2002 Farm Bill, the Energy Policy Act of 2005, and the Energy Independence and Security Act (EISA) of 2007, among others. The 2002 Farm Bill included provisions and incentives to promote the development of biorefineries and financial support to feedstock producers and engaged in educational outreach to highlight the benefits of biofuels. The Energy Policy Act of 2005 established the Renewable Fuels Standard (RFS) and introduced quantitative targets, while EISA 2007 aimed to achieve an increase in biofuel output to the tune of 36 billion gallons by 2022 of which 21 billion gallons would come from advanced biofuels [Food and Agricultural Organization (FAO), 2008]. In addition to these targets, a variety of research grants, tax credits, and other fiscal incentives were implemented to support feedstock producers, biofuel processors, and the end consumers. Meanwhile, several other countries, including Brazil, China, India, as well as several European Union member states, have instituted both mandatory and voluntary targets [Global Bioenergy Partnership (GBEP), 2007]. Other countries, particularly European ones, also have green‐energy or specifically bioenergy‐incentivizing initiatives. The U.S. Department of Agriculture (USDA) has implemented the Conservation Reserve Program (CRP) since 1985 to protect the quality of soil, stabilize land prices, and ensure steady agricultural production by providing technical and financial assistance [Dale et al., 2010]. In Iowa, temporary waivers were granted for harvesting biomass grown on CRP lands for use in research activities while allowing farmers to take the full benefit of CRP payments to encourage switchgrass establishment [Hipple and Duffy, 2002]. Similarly, under the aegis of the

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Biomass Crop Assistance Program (BCAP), the USDA provides “financial assistance to owners and operators of agricultural and non‐industrial private forest land who wish to establish, produce, and deliver biomass feedstocks” [USDA, 2016]. This support comes in the form of matching payments for eligible materials supplied to specified biomass‐conversion facilities or through annual payments for producers who enter into contracts with the Commodity Credit Corporation on contract acres within the BCAP project areas [USDA, 2016]. Some of these policies often offer subsidies or payments for farmers who produce bioenergy or can provide a market where none truly existed before. For example, most cars in the United States can run on 10% ethanol fuel, and others produced in the United States can run on nearly 85% ethanol fuel, which has created a large market for producers of ethanol. This, in turn, has created a new job market in the United States for bioenergy, which has positively affected the economy and encouraged further growth. However, as this causes the industry to grow, it can have a detrimental effect on land use change in some of the negative ways outlined earlier. However, the efficacy of policy incentives is a subject of much debate. In its statement to the House Agriculture Subcommittee on Conservation, Energy, and Forestry in 2012, the Wood Fiber Coalition claimed that the Biomass Crop Assistance Program negatively affected long‐ standing industries as several traditional wood products were diverted toward the bioenergy industry [Wood Fiber Coalition, 2012]. The biofuel industry has come under similar criticism whereby it is claimed that the increased production of biofuels comes at the expense of food grains being used to produce bioenergy, resulting in higher food prices. While such ramifications were unintended, it is crucial for biofuel policies to safeguard against the direct and indirect consequences of higher bioenergy mandates. As a result, one of most important challenges confronting policymakers is to ensure effective policy design and incentives that eliminate or limit any adverse impacts of policies intended to support the biofuel industry. One of the other challenges with regard to the development of biofuels, specifically cellulosic and other advanced biofuels, is that technological innovations have not been attained at a level to make biofuel production economically viable. Mitchell et al. [2008] stated that substantial advancements in agronomics and genetics were needed to enhance feedstock production, particularly for switchgrass. The underlying challenges are similar for other cellulosic feedstocks as well. Meanwhile, Chung [2013] highlights the requirement for advancements in conversion technologies, along with collaboration among stakeholders, to overcome the obstacles and challenges that inhibit the growth of the industry.

1.5.1. Bioenergy Certification and Land Use Where bioenergy crops are grown, different policy criteria may work to ensure that they are produced in a responsible way. Regulatory initiatives such as the European Renewable Energy Directive and German Biofuels Sustainability ordinance work toward such a goal. These include suggestions on quotas on ethanol for fuel, GHG emission saving, and conservation of biodiversity [Stappen et al., 2011]. Furthermore, policies and economics supporting nontraditional biofuels that do not need to replace farm crops and can be grown on marginal lands, such as switchgrass, may ultimately propel the industry to create a more sustainable biofuel‐ based economy. Firbank [2008] highlighted the possible adverse impacts of bioenergy production on land use and biodiversity, while others have emphasized the GHG, socioeconomic, and water‐resource ramifications [Berndes, 2002; Searchinger et al., 2008; German et al., 2011]. It is important to note that the development of biofuels on commercial scales will necessitate the allocation of substantial land area for the cultivation of feedstocks as well as the setting up of processing and conversion facilities [Rinehart, 2006; Mitchell et al., 2012; NRDC, 2014]. In this regard, it will be crucial to evaluate the former land use and land cover of the cultivation and processing sites to ensure that the long‐ term benefits outweigh costs and the land use changes are not detrimental from environmental, social, and economic perspectives. As such, the certification criteria are usually not mutually exclusive, thereby ensuring multiple safeguards from different aspects of biofuel production. Adverse direct land use change effects caused by bioenergy production can be enforced and are already incorporated by some European certifiers of green electricity, such as Eugene (Europe), Bra Miljoval (Sweden), Ok‐ Power (Germany), and Naturemade Star (Switzerland), who specify that biomass used for energy must come from Forest Stewarship Council (FSC) certified forests. Some other certifiers have developed their own land use change indicators. Milieukeur (the Netherlands) and Green Power (Australia) insist that biomass not be sourced from plantations that have been planted after clearing existing old‐growth or native forests. However, North American certifiers such as Green‐e (USA) and Environmental Choice (Canada) do not consider land use changes at all in their certification initiatives. In order to minimize the negative effects of direct land use changes, a cutoff year for conversion of natural forests to plantations could be used (such as 1994, as outlined in FSC 2002 guidelines) to ensure that natural, multiple‐species forests are not converted to energy crops or monocultures [Fritsche et  al., 2006]. Furthermore, standards could incorporate net changes in aboveground carbon, soil carbon stocks, and

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wood products. The NRDC [2014] delineates specific protective performance guidelines for feedstock producers. First, the feedstock producers should not divert productive croplands, or their operations should not result in the conversion of critical habitats that adversely affect vulnerable species. Additionally, feedstock producers should not convert native forests and grasslands for feedstock cultivation as well as not divert federal or  protected lands. Furthermore, feedstock producers should maximize productivity and minimize land use subject to constraints pertaining to sustainable yields. The guidelines also recommend collection of evidence for previous and existing land use and product/land assessments. However, it is surprising to note that most of the bioenergy‐certification systems lack GHG‐reduction indicators. The Roundtable on Sustainable Palm Oil (RSPO) [2007], as well as recent suggestions on bioenergy certification [Richardson et  al., 2005; Lewandowski and Faaij, 2006; Van Dam et al., 2008], suggests having GHG emission reduction but does not elaborate as to how much this reduction should be. The EISA 2007 specifies that a minimum of 50% reduction in GHG emissions from the use of advanced biofuels (such as cellulosic biofuels) should take place compared to fossil fuels sold or distributed as transportation fuel in 2005. From a certification perspective, Burli et al. [2016] list nine impact categories including land use, agrochemical application, waste management, air quality, and monitoring, among others. The stated impact categories can safeguard from potential risks associated with large‐scale adoption of switchgrass‐based biofuels; however, the guidelines can be adapted for other feedstocks as well. Building on the protection of productive farmlands and limiting the impact on food production, the sustainability criteria ensure that the allocation of land to feedstock cultivation does not result in the propagation of monocultures and irrecoverable losses to above‐ or belowground vegetation and carbon sinks. The Roundtable on Sustainable Biomaterials (RSB) [2013] provides specific guidelines for biofuel‐feedstock producers and processers pertaining to the safeguarding of land rights and land use rights. The guidelines require the operator to conduct a Land Rights Assessment in cases where the initial screenings and assessments indicate a negative impact to existing land rights and land use rights as a result of biofuel operations. The guidelines require the operators to settle any disputes through free and informed consent on the basis of negotiated agreements with the affected parties. In addition, the guidelines state that biofuel operations should not be conducted on lands that were acquired as a result of involuntary resettlements and that if there are any disputes about the tenure agreements among the stakeholders, the biofuel operations shall not be approved under the RSB guidelines [RSB, 2013].

In addition, guidelines and biofuel certification criteria also require thorough impact assessments and documentation that show positive net benefits for GHG emissions, favorable crop mix vis‐à‐vis prior land use, and evaluation of local/regional land and food prices and social indicators such as income and employment compared to past trends, among other factors. 1.6. MOVING FORWARD In this chapter, we have provided a summary of the land use change issues arising because of bioenergy market development. On the basis of systematic literature review, we discuss direct and indirect land use change impacts, modeling approaches used, and ensuing challenges. Empirical evidence from the United States and elsewhere has also been outlined. We discuss policy imperatives and bioenergy‐certification initiatives as well. Our analysis suggests that bioenergy production must ensure an efficient allocation of land while limiting both direct and indirect impacts on land, water, and other natural resources as well as cascading impacts on food and feed for local and regional populations. A realistic framework to model such impact requires a considerable integration of concepts, methods, and disciplines. Although recent efforts have shown progress in this area, more needs to be done. Methodologically integrated models can offer new perspective, strengthen the reliability of the results, better capture the real‐world complexities, and build on the strength of each individual model and make up for their respective weaknesses. Moreover, the geographic focus of studies can strive to include previously unexplored areas and topics including urban‐rural interactions. This can reduce replication of efforts that only produce marginally new insights instead of addressing needs that have not been met to date. This can also free up resources that can be allocated to other priorities. Standardizing methods, data collection, analyses, and presentation of findings will also help researchers and users to better focus on the results instead of having to sort out the effects that are of interest and those having to do with the way the results are produced or presented. Ensuring that such models are also available to all interested users can lead to greater use out of such resources, create synergistic benefits, and lead to a quicker collection and processing of data. Results based on such approaches will better serve decision‐making process and contribute toward better land use plans and policies. There are also advancements in LCA and means to estimate net GHG emissions arising from bioenergy production. It is being realized that LCAs include not only direct emissions but also significant indirect emissions from processes such as land use changes. The emission

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reductions from biomass production and harvesting play a significant part in this calculation, and sustainability indicators should ensure that these processes allow for emission reductions across the entire system. Because LCA and GHG accounting for each biomass production site entail significant costs, the challenge is to develop harvesting standards for small‐producer compliance. The ILUC effects are also emphasized in Low Carbon Fuel Standards, which have already been enacted by California [Devereaux and Lee, 2009]. Many states in the northeast are also working toward developing such standards [Sperling and Yeh, 2009]. However, ILUCs are much difficult to assess, and today there is no generally accepted methodology for determining such effects. Fritsche et al. [2006] argue for assessing indirect influence of bioenergy on land use change through measures such as land prices and rents. However, conducting such assessments at site level and translating these to operational constraints is quite resource intensive. A satisfactory methodology might be incorporated into the life cycle GHG emissions of fuels at a later date to account for indirect land use change impacts. It is also worthwhile to note that policy incentives and support for bioenergy can result in not only diversion of feedstocks for energy use but also unintended consequences. Identifying such potential unintended consequences is important toward determining the net effects associated with incentive programs. It is also important to determine if multiple programs can be integrated for synergistic purposes. This may reduce transaction cost, ease the application process, and ensure that all potential enrollees and eligible applicants know about and take advantage of the programs available to them. ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the National Science Foundation CAREER Award 1555123, partial support for this study from U.S. Department of Energy’s International Affairs under award number DE‐ PI0000031 and Department of Agriculture National Institute of Food and Agriculture Grant 2012‐67009‐19742. Thanks go to Taylor Wieczerak, Erik Lyttek, and Mike Fowler (Montclair State University) for their help in data collection and review. REFERENCES Abt, R., F. Cubbage, and G. Pacheco (2000), Southern forest resource assessment using the Subregional Timber Supply (SRTS) model, For. Prod. J., 50(4), 25–33. Adams, D. M., R. J. Alig, B. A. McCarl, J.M Callaway, and S.M Winnett (1999). Minimum cost strategies for sequestering carbon in forests. Land Econ., 360–374.

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sustainable use of biomass from forest residues for energy, Technology Progress Report for IEA Bioenergy Task 31 Biomass production for energy from Sustainable Forestry, Dublin. Rinehart, L. (2006), Switchgrass as a bioenergy crop, National Center for Appropriate Technology. http://attra.ncat.org/ attra‐pub/PDF/switchgrass.pdf (accessed 15 July 2017). Roundtable on Sustainable Biomaterials (RSB) (2013), Roundtable on sustainable biomaterials principles and criteria for sustainable biofuel production. http://rsb.org/pdfs/ standards/11‐03‐08%20RSB%20PCs%20Version%202.1.pdf (accessed 15 July 2017). Roundtable Sustainable Palm Oil (2007), RSPO Principles and Criteria for Sustainable Palm Oil Production Including Indicators and Guidance. RSPO, Kuala Lumpur, 53pp. Schulze, R. (2000), Transcending scales of space and time in impact studies of climate and climate change on agrohydrological responses, Agric. Ecosyst. Environ., 82(1), 185–212. Searchinger, T. and R. Heimlich (2015), Avoiding bioenergy competition for food crops and land, Working Paper, Installment 9 of Creating a Sustainable Food Future, World Resources Institute, Washington, DC. http://www. worldresourcesreport.org (accessed 15 July 2017). Searchinger, T., R. Heimlich, R. A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, and T.‐H. Yu (2008), Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land‐use change, Science, 319(5867), 1238–1240. de Souza Ferreira Filho, J. B., and M. Horridge (2014), Ethanol expansion and indirect land use change in Brazil, Land Use Policy, 36, 595–604. Sperling, D., and S. Yeh (2009), Low carbon fuel standards, Issues Sci. Technol., 2009(2), 57–66. Stappen, F. V., I. Brose, and Y. Schenkel (2011), Direct and indirect land use changes issues in European sustainability initiatives: State‐of‐the‐art, Biomass Bioenergy, 35(12), 4824–4834. Swallow, S. K., P. Talukdar, and D. N. Wear (1997), Spatial and temporal specialization in forest ecosystem management under sole ownership, Am. J. Agric. Econ., 79(2), 311–326. Taheripour, F., and W. E. Tyner (2013), Biofuels and land use change: Applying recent evidence to model estimates, Appl. Sci., 3(1), 14–38. Tesfatsion, L. (2001), Introduction to the special issue on agent‐ based computational economics, J. Econ. Dyn. Control., 25(3), 281–293. Tompkins, A. M., L. Caporaso, R. Biondi, and J. P. Bell (2015), A generalized deforestation and land‐use change scenario generator for use in climate modelling studies, PLoS One, 10(9), e0136154.

Union of Concerned Scientists (2008), Land Use and Biofuels, Factsheet, Union of Concerned Scientists Fact Sheet. http:// www.ucsusa.org/sites/default/files/legacy/assets/documents/ clean_vehicles/Indirect‐Land‐Use‐Factsheet.pdf (accessed 15 July 2017). U.S. Department of Agriculture Farm Service Agency (USDA) (2016), Energy programs. http://www.fsa.usda.gov/programs‐ and‐services/energy‐programs/ (accessed 10 September 2016). U.S. Department of Energy (USDOE) (2011a), U.S. billion‐ton update: Biomass supply for a Bioenergy and Bioproducts Industry ORNL/TM‐2011/224, pp. 227, Oak Ridge National Laboratory, Oak Ridge, TN. U.S. Department of Energy (USDOE) (2011b), Land‐use change and bioenergy. http://www1.eere.energy.gov/bioenergy/pdfs/ land_use_change.pdf (accessed 15 July 2017). Van Dam, J. V., M. Junginger, A. Faaij, I. Jürgens, G. Best, and U. Fritsche (2008), Overview of recent developments in sustainable biomass certification, Biomass Bioenergy, 32(8), 749–780. Varvel, G. E., K. P. Vogel, R. B. Mitchell, R. Follett, and J. Kimble (2008), Comparison of corn and switchgrass on marginal soils for bioenergy, Biomass Bioenergy, 32(1), 18–21. Vasile, A. J., I. R. Andreea, G. H. Popescu, N. Elvira, and Z.  Marian (2016), Implications of agricultural bioenergy crop production and prices in changing the land use paradigm—The case of Romania, Land Use Policy, 50, 399–407. Walker, R., E. Moran, and L. Anselin (2000), Deforestation and cattle ranching in the Brazilian Amazon: External capital and household processes, World Dev., 28(4), 683–699. Walsh, S. J., T. W. Crawford, W. F. Welsh, and K. A. Crews‐ Meyer (2001), A multiscale analysis of LULC and NDVI variation in Nang Rong district, northeast Thailand, Agric. Ecosyst. Environ., 85(1), 47–64. Watson, R. T., I. R. Noble, B. Bolin, N. H. Ravindranath, D. J. Verardo, and D. J. Dokken (Eds.) (2000), Land Use, Land‐ Use Change, and Forestry, Cambridge University Press, Cambridge, UK. Wear, D., R. C. Abt, J. Alavalapati, G. Comatas, M. Countess, and W. McDow (2010), The South’s Outlook for Sustainable Forest Bioenergy and Biofuels Production, The Pinchot Institute for Conservation, Washington, DC. Williams, G (2016), Hands‐on data science with R: Text mining. http://handsondatascience.com/TextMiningO.pdf (accessed 15 July 2017). Wood Fiber Coalition (2012), Testimony to the subcommittee on conservation, energy and forestry. http://www.compositepanel. org/userfiles/filemanager/878/ (accessed 10 September 2016).

2 An Exploration of Agricultural Land Use Change at Intensive and Extensive Margins: Implications for Biofuel‐Induced Land Use Change Modeling Farzad Taheripour, Hao Cui, and Wallace E. Tyner ABSTRACT In recent years many studies used economic partial and general equilibrium models to estimate induced land use change (ILUC) emissions due to biofuel production and/or policy. Previous research assumed that all additional harvested crop area came from cultivation of land converted from pasture or forest. However, recent FAO data suggest that there has also been significant addition to harvested area through double cropping and/or returning unused cropland to crop production. This chapter examines the extent to which land intensification can alter the existing estimates for biofuels ILUC emissions. To accomplish this task we (i) reviewed the associated literature; (ii) collected data at the global scale to examine recent trends in land intensification in crop production across the world; (iii) modified a well‐known computable general equilibrium model (GTAP‐BIO), which has been frequently used in this area to take into account land intensification due to multiple cropping and/or conversion of unused cropland to crop production; (iv) calibrated the model to real world observations, and (v) finally calculated ILUC emissions for several biofuel pathways with and without land intensification. Our results confirm the model with land intensification projects lower ILUC emissions for the examined biofuel pathways. The size of reduction depends on the implemented benchmark database, varies across biofuel pathways, and changes by biofuel production location. 2.1. INTRODUCTION

of the possible changes is conversion of pasture or forest to cropland to help meet the need for additional agricultural production. In reality, though, farmers can increase production either by expanding area of cropland (extensive margin) or by increasing production on the current area (intensive margin). There are at least five changes that may occur because of the imposition of the increased demand for a particular type of biofuel (e.g., corn ethanol): 1. Decrease in consumption of the demanded feedstock in nonbiofuel (e.g., corn consumed in nonethanol uses) 2. Crop switching to produce more of the commodity (e.g., corn) with the new demand and less of other crops 3. Conversion of forest and pasture to cropland (extensive margin)

For more than a decade, analysts have been concerned with estimating the land use change implications of biofuel policies and programs [Hertel et al., 2010a, 2010b; Searchinger et al., 2008; Taheripour and Tyner, 2013a; Tyner and Taheripour, 2012; Laborde, 2011; Laborde and Valin, 2012]. Most of this research has focused on expected changes on what is called the extensive margin. That is, when the demand for an agricultural commodity (and its price) increases because of an exogenous shock such as an expansion in demand for biofuels, then one

Department of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 19

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4. Expansion in crop production owing to improvement in yield per harvested area, greater use of double cropping, and cultivating the existing unused marginal land 5. Changes in global production and trade Often, computable general equilibrium (CGE) models are used to estimate land use changes due to biofuels. The CGE models that have been used for the land use change analysis have attempted to incorporate all five of these changes. However, they have not fully captured changes on the intensive margin. Part of the reason for this discrepancy has been the lack of data that could be used to approximate changes that might occur on the intensive margin. However, in recent years, there has been increased exploration of the extent to which changes in crop production and harvested area come from the intensive versus the extensive margin. In the literature‐review section below, we explore some of the findings of that research and its possible implications for revisiting land use change modeling and analysis. Thus, the main motivation for this research is to take into account what we have learned over the past decade regarding land use change on the extensive and intensive margins and to appropriately modify the GTAP‐BIO (GTAP stands for Global Trade Analysis Project, and BIO represents Biofuels) model, which has been frequently used in this area, to better handle changes on the intensive margin. To accomplish this task, we begin by reviewing the relevant literature on the issues described here. Then we proceed to a detailed analysis of the recent history of global changes (by region) in crop production, harvested area, and cropland cover. That analysis provides us a basis for reformulating the way GTAP‐BIO handles the intensive and extensive margins when exogenous changes such as biofuel shocks are applied. The next section describes and justifies the changes that we have made to the GTAP model to better handle the choice between expansion on the intensive and extensive margins. Following the description of model changes that have been implemented, we provide results using the new model and compare those results with simulations from the previous model. One important uncertainty in this analysis is that we have no evidence that changes that might occur in the future because of expansion in demand for crops would mirror the recent land use history. As will be explained in our statistical analyses, in recent years crop production has increased mainly because of intensification in many regions across the world, with some exceptions. What we have tried to do in this analysis is to strike a reasonable balance between recent history and a longer‐term perspective. In essence, we strike a balance between a literal adoption

of the trends from the past decade and bringing that information into the analysis while keeping open the prospect that large future shocks in biofuel or other production could induce changes at both the intensive and extensive margins. 2.2. BACKGROUND During the past decade, many papers have estimated potential land use consequences of producing biofuels from agricultural resources. These papers can be divided into two broad groups. The first group applies technical methods and biophysical analyses to estimate land use emissions for alternative biofuel pathways. This approach targets a particular biofuel pathway and examines what would be the land use emissions if a certain type of natural land is used to produce the desired biofuel (examples are Tilman et al. [2006], Fargione et al. [2008] and many more). This approach does not include economic analysis and only takes into account technical and biophysical variables such as crop yield, crop to biofuel conversion rate, and carbon content of natural land. While this approach sheds light on potential land use emissions of hypothetical biofuel pathways, it fails to represent reality for a simple reason: this approach ignores market‐mediated responses to biofuel production and assumes that natural land will be converted to cropland to produce each and every drop of biofuels. This assumption contradicts real‐world observations. For example, producing ethanol from corn reduces corn consumption in other uses. Farmers could switch from other crops to corn or invest in intensification technologies to produce more per unit of existing cropland. Intensification may occur because of technological progress such as using better seeds, using optimal mix of fertilizer and other chemicals, implementing more advanced soil‐management techniques, and using advanced irrigation techniques. In addition, in certain areas where the length of growing period is long enough, multiple cropping helps to produce more per unit of land per year. Finally, unused cropland could be used to produce more corn or other crops. The second group of papers have used partial or general equilibrium economic models to take into account market‐mediated responses to provide more realistic estimates for induced land use changes and emissions due to biofuel production and policies. These models were mainly used to estimate induced land use changes due to biofuels (basically ethanol and/or biodiesel) produced across the world, in particular in the United States, Brazil, and the European Union. In what follows we concentrate on several key papers that estimated induced land use changes for U.S. corn ethanol.

AN ExpLORATION Of AGRICULTURAL LAND USE CHANGE AT INTENSIVE AND ExTENSIVE MARGINS 21

Searchinger et al. [2008] published the first peer‐reviewed paper in this area. These authors used a partial equilibrium model and projected that 10.8 million ha of natural land would be converted to cropland across the world to support an increase in U.S. corn ethanol by 14.77 billion gallons (BGs). The model was developed by the Center for Agriculture and Rural Development at Iowa State University on the basis of the models developed by the Food and Agricultural Policy Research Institute (FAPRI) at Iowa State and the University of Missouri. In another scenario, they did take into account possible future yield increases. They concluded that the land requirement for U.S. corn ethanol was about 0.73 ha per 1000 gallons, and that generated 100 g CO2e/MJ of emissions. While these authors used an economic model to bring in market‐ mediated responses to biofuels, their model failed to capture these responses properly. A simple calculation shows that about 10 million ha of new cropland is needed to produce 14.77 BGs of ethanol if we simply assume the following: (i) 2.7 gallons of ethanol per bushel of corn, (ii) corn yield of 146.8 bushels per acre (average of U.S. corn yield in 2000–2013), (iii) no switching across crops, (iv) no change in U.S. exports of corn and/or other crops, and (v) returning one third of corn used for ethanol as  DDGS (Distiller’s Dried Grains with Solubles) to livestock industry. The result of this simple calculation, which ignores market‐mediated responses, is not very different from the output of the model used by Searchinger et al. [2008]. Recent historical observations can be used to evaluate the Searchinger et al. projections as well. These authors projected that U.S. farmers alone would convert 2.3 million ha of natural forest or pasture because of the expansion of corn ethanol by 14.77 BGs. They were also expecting major land conversion in many other countries because of the expansion in U.S. corn ethanol. As an example, they projected a major expansion in cropland in the China‐India‐Pakistan region by 2.4 million ha. Between 2000 and 2013, production of U.S. corn ethanol and biodiesel have increased by 12 BGs and 1 BG, respectively. But, as explained in the next section of this chapter, historical data provided by the Food and Agriculture Organization of the United Nations (FAO) are not in line with these projections. The second key publication that we examine in this section is the paper published by Hertel et al. [2010a]. These authors used a CGE model, named GTAP‐BIO, to bring market‐mediated responses into the estimation process of induced land use changes of U.S. corn ethanol. This model was originally assembled by Hertel et al. [2010b] by combining two different versions of the standard GTAP model developed by Hertel [1997]. The two models were (i) GTAP‐E developed by Burniaux and Truong [2002]

and McDougall and Golub [2007] and (ii) GTAP‐AEZ model developed by Hertel et al. [2009]. Then Taheripour et al. [2010] introduced biofuel by‐products into the model. The original model was built on the GTAP database version 6, which represents the world economy in 2001. These models do have a form of intensification included in that there is a yield response to changes in commodity prices. This yield‐price elasticity, named YDEL, includes both on‐farm technology adoption and off‐farm measures such as new seed varieties and machinery improvements. Hertel et al. [2010a] took into account many of the market‐mediated responses and estimated that 4.4 million ha would be needed to produce 13.25 BGs of ethanol. This paper concluded that the land requirement for U.S. corn ethanol was about 0.29 ha per 1000 gallons, and that generated 27 g CO2e/MJ emissions. These figures are 60% and 73% smaller than the corresponding figures calculated by Searchinger et al. [2008]. While these authors showed that market‐mediated responses jointly eliminated a big portion of the land requirement for corn ethanol, their land use projection did not match recent observations on land use changes across the world, as explained in the next section. The GTAP‐BIO model used by Hertel et al. [2010a] implemented the following set of assumptions: 1. Productivity of each hectare of new cropland is two third of productivity of each hectare of existing cropland across the world. 2. Land can move across its alternative uses (forest land, pasture land, and cropland) with a uniform land transformation elasticity of 0.2 across the world. 3. Cropland can move across alternative crops with a uniform land transformation elasticity of 0.5 across the world. 4. No marginal/unused land is available across the world. 5. No double cropping was included, but intensification (higher productivity per acres of harvested crop) occurs because of higher crop prices. In a series of papers, Taheripour and Tyner [2013a, 2013b; Taheripour et al., 2012] altered the first three assumptions and introduced marginal land (cropland pasture) only for the United States and Brazil. Owing to these modifications, the GTAP‐BIO model now uses the GTAP database version 7, which represents the world economy in 2004. It is now augmented with a set of parameters that measure productivity of new cropland versus the existing cropland in each region by Agro‐ Ecological Zone (AEZ). The land transformation parameters of the model are now tuned to new historical observations by region. These parameters separately govern movement of land between pasture and cropland and between the nest of pasture‐cropland and forest. Finally, cropland pasture (marginal land that is used by

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the livestock industry and can move to crop production) is introduced into the economies of the United States and Brazil. However, still no marginal land is available in other regions, and the model does not take into account possibility of expansion in crop production due to double cropping. Taheripour and Tyner [2013a] showed that the above modifications jointly dropped the land requirement of U.S. corn ethanol to about 0.11 ha per 1000 gallons, and that generated 13.3 g CO2e/MJ emissions. While the land requirement for U.S. corn ethanol projected by these authors is significantly smaller than the projections of other studies, their projection does not fully capture the observed recent intensifications in crop production across the world due to double cropping and/or by cultivating available unused cropland. Pleven et al. [2010] defined and used an index to rank the estimated induced land changes obtained from different economic models for a given biofuel pathway. The index is called net displacement factor (NDF) and equals to the ratio of estimated land requirement (expansion in global demand for cropland) obtained from an economic model for an increase in a particular biofuel over a hypothetical land requirement. The hypothetical land requirement can simply be obtained from three figures: (i) the size of expansion in production of the desired biofuel, (ii) feedstock yield per unit of land, and (iii) biofuel yield per unit of feedstock. Under normal conditions, the size of NDF varies between 0 and 100. The size of this measure for many biofuel pathways (including corn ethanol) dropped largely, as research studies on induced land use changes due to biofuels have relied more on actual observations and used more advanced and realistic models. For example, the value of NDF for corn ethanol produced in the United States is about 72% according to the Searchinger et al. [2008] results, which disregarded the role of market‐mediated responses. But the corresponding figure is about 28% for the results provided by Hertel et al. [2010a], which paid attention to the market‐mediated responses. Finally, the magnitude of this index for corn ethanol dropped to 10.3% when Taheripour and Tyner [2013a] tuned the land transformation elasticities according the actual observations and added the area of available cropland pasture into the land databases for the United States and Brazil. Essentially, all of the changes made in the GTAP‐BIO model so far and explained above led to the considerable drop in induced land use change and emissions, but most of it was improvements at the extensive margin. This chapter goes further and includes much better handling of the intensive margin. Siebert et al. [2010] have shown that cropland use intensity varies across the world. They found that the lowest land cropping intensities were in Southern Africa (0.45),

Central America (0.49), and Middle Africa (0.54) and the highest were in Eastern Asia (1.04) and Southern Asia (1.0) around the year 2000. Several publications, including, but not limited to, Cassman [1999], Brady and Sohngen [2008], Alexandratos and Bruinsma [2012], Alston et al. [2010], Borchers et al. [2014], Byerlee et al. [2014], Ausubel et al. [2012], and Hertel and Baldos [2016], have shown that, in many regions across the world, agricultural output has significantly increased because of technological progress, improvement in yield, and using the existing land more efficiently, owing to multiple cropping with no or limited deforestation, in more recent time periods. Babcock and Iqbal [2014] pointed out the fact that there appears to be more intensification in some regions. Ray and Foley [2013] also suggest there has been a significant increase in multiple cropping on the intensive margin across the world. Numerous papers have shown that there is plenty of unused cropland across the world that can be used to produce food or biofuel feedstock. In a recent study, Lewis and Kelly [2014] examined many of these papers, their method of estimation, and their credibility. This study suggests that while it is difficult to provide a common definition for marginal/unused lands and examine their suitability for biofuel feedstock and/or food production, one cannot deny that there are plenty of these types of lands across the world. In recent decades expansion in agriculture in many regions across the world occurred mainly because of intensification and technological progress, but extensification contributed to increases in cropland in several other regions. Evidence shows that expansion in agriculture (either due to higher demand for food or fuel) did generate deforestation in some regions such as Brazil, Malaysia‐Indonesia, Southeast Asia, and sub‐Saharan Africa in recent years. This indicates that the story of land use change due to higher demand for agricultural products (either for food or biofuels) varies across the world. It is not entirely extensification or intensification. It is a mixture of the two. This chapter aims to enhance the GTAP‐BIO model to take into account intensification in crop production due to double cropping and/or cultivation of unused cropland and examine the extent to which this enhancement alters the projections of induced land use changes of some key biofuels including U.S. corn ethanol, sugarcane ethanol produced in Brazil, U.S. soy biodiesel, and rapeseed biodiesel produced in the European Union. To achieve these goals, we accomplished three tasks: (i) data from FAO are used to examine the extent to which the expansion in crop production occurred because of intensification versus extensification in recent years by region; (ii) several changes were made in the GTAP‐BIO model to handle intensification in crop production due to double

AN ExpLORATION Of AGRICULTURAL LAND USE CHANGE AT INTENSIVE AND ExTENSIVE MARGINS 23

cropping and/or cultivation of unused cropland; (iii) the improved model is tuned to recent observations on land use changes to estimate induce land use changes due to biofuels by region. 2.3. RECENT CHANGES IN CROPLAND COVER AND HARVESTED AREA: STATISTICAL ANALYSIS FAO provides a unique data set that represents cropland cover, harvested area, crop production, and their changes by country over time. FAO shows available crop land under two categories: arable land and permanent crops. Arable land, by definition, includes land under temporary agricultural crops (multiple‐cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens, and land temporarily fallow ( 0 in the base data, it indicates that harvested area grows faster than cropland because of returning unused cropland to crop production. In this case again, 0 1 and, as shown in the tuning section, can be determined according to the observed trend in the ratio of H/L. 4. Rule IV: If H/L has decreased over time in a region, it indicates that harvested area has dropped because of a reduction in double cropping and/or an increase in unused land. While in such a case γ can be determined according to the observed trend in the ratio of H/L, we did not observe this trend in our analyses. Note that when there is no improvement in land intensification in a region, then b 0, and that implies 1 for that region. In this case, the model automatically switches to the old model, where L H. When the area of available land decreases but the area of harvested area increases (i.e., l 0 and h 0) in a region, by definition, it contributes to intensification. However, the contribution could be misleading. For example, suppose 200 ha of cropland was available in a region in the base year (say, 2003). Furthermore, assume that in this region only 150 ha of the available land was cultivated and entirely harvested in the base year. This means that in this example 50 ha of the available cropland

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was unused in the base year. By definition, under these assumptions, the ratio of H /L 1 in the base year. Now consider a case where the harvested area remains the same over time (say, in 2003–2013) in this region, but the unused land transfers to urban areas. In this case, by definition, the ratio of H/L increases from 0.75 (i.e., 150 ha of harvested area over 200 ha of available land) in 2003 to 1 (i.e., 150 ha of harvested area over 150 ha of available land) in 2013, and that demonstrates an artificial bubble in land intensification in this hypothetical example. As a precautionary step and to avoid overestimating the changes in land intensification, we excluded this source of intensification from our tuning process, as described in the next section. When harvested area can be expanded because of the expansion in double cropping and/or returning unused land to crop production, that affects the relationship between cropland and harvested area in the land supply tree. The original GTAP‐BIO model uses a nested constant elasticity of transformation (CET) functional form to trace supply of land across its alternative uses. In this land supply system, a two‐level nest is used to supply land across land cover types (including forest, pasture, and cropland). In this nesting structure, percent changes in pm pl . In this cropland is determined by l m equation is the transformation elasticity between pasture and cropland, m shows percent changes in the mix of pasture and cropland, pm stands for percent changes in the price of the mix, and pl represents percent changes in the price of cropland. When harvested area can be expanded because of multiple cropping and/or using unused cropland, it enters into this equation as a shift factor: l m afs pm pl afs . Here afs represents the shift factor. As mentioned before, we intentionally dropped the indices for region and AEZ for simplicity. A set of coefficients, variables, and equations were introduced into the land use module to calculate the size of this shift endogenously. The original model adds another nest to the land supply tree to allocate cropland across crops. This nest links percent changes in harvested area of each crop (i.e., hj, where j is the crop index) with percent changes in cropland, using the following equation: hj l [ pl ph j ]. In this equation, θ is the land transformation elasticity across crops, and phj shows the percentage changes in land rent used in crop j. When harvested area can be expanded with multiple cropping and/or using unused land, the shift factor enters into this equation as well: h j l afs [ pl ph j ]. Note that the original model determines pm using the following equation: pm pl (1 ) ps . Here β represents the share of cropland in the mix of cropland and pasture land, and ps shows percentage changes in the price of pasture land. It is straightforward to show that the shift factor of afs should be included in the cropland price ( pl afs ) (1 ) ps. equation as follows: pm

2.4.2. Tuning Process We use the results of our statistical analysis to tune two sets of model parameters. The GTAP‐BIO model assumes that crop yields grow as farmers invest in intensification in response to higher crop prices. The model uses a parameter called YDEL to measure the magnitude of yield to price response. YDEL determines the extent to which crop producers improve yield per hectare of land per cultivation in response to higher crop prices, and it has no direct link with γ. The latter parameter affects the frequency of using the available cropland in 1 year. On the basis of some limited empirical estimates, the model uses YDEL = 0.25. This set up implicitly assumes that farmers across the world respond to crop prices to improve crop yields at the same rate. Our statistical analysis indicates that yield has changed at different rates across the world during the time period of 2003–2013. While we cannot rule out the impacts of nonprice factors, these observations make it difficult to defend the assumption of using a uniform YDEL across regions in an environment where crop prices have increased all across the world and countries trade crops. To the best of our knowledge, there is no empirical estimate on variation of YDEL by region. Therefore, in this study we used variation in crop yield by region to approximate variation in YDEL. To accomplish this task, we assume that YDEL deviates around the central value of 0.25 between 0.175 and 0.325 according to the observed regional yield growths in 2003–2013, as shown in Figure 2.2. This figure divides the GTAP 19 regions into six yield growth zones. Those regions that fall in the very low yield growth zone represent the lowest level of yield response (YDEL = 0.175). On the other hand, those regions that fall in the very high growth zone represent the highest yield response (YDEL = 0.325). The rest fall in between these two lower and upper values, as presented in Table  2.2. Here we assume the regional values of YDEL vary between 0.175 and 0.325 symmetrically to not largely move away from the original central value of 0.25, which is supported by the literature. Direct estimate of regional values for YDEL is a major research and goes beyond the scope of this research. We also use the results of our statistical analysis presented in the previous section to determine regional values for parameter γ according to the observed historical trends in the area of available land (henceforth, lˆ) and harvested area (henceforth, ĥ) using the following formula as described in Appendix 2.A: γ = (1/α ) ( lˆ /hˆ ). For those regions where the area of available land and harvested area followed positive trends, the value of γ is determined according to this formula. These regions are Brazil, South America, Malaysia‐Indonesia, the Rest of Southeast Asia, and sub‐Saharan Africa.

AN ExpLORATION Of AGRICULTURAL LAND USE CHANGE AT INTENSIVE AND ExTENSIVE MARGINS 29 Table 2.2 Tuned Parameters According to Observed Trends in the Ratios of HOL and COH in 2003–2013 Region

YDEL

γ

USA European Union Brazil Canada Japan China India Central America South America East Asia Mala‐Indo Rest of Southeast Asia Rest of South Asia Russia Other CEE‐CIS Other Europe Middle East and North Africa Sub‐Saharan Africa Oceania

0.300 0.250 0.325 0.250 0.200 0.325 0.325 0.275 0.175 0.175 0.300 0.300 0.325 0.200 0.275 0.200 0.275 0.250 0.175

0.200 0.200 0.414 0.200 1.000 0.000 0.000 0.200 0.925 1.000 0.852 0.768 0.200 1.000 0.200 1.000 0.200 0.821 0.200

For India, where harvested area has increased largely with no expansion in available cropland, the above for0. For China, where harvested area mula suggests has also increased largely with a reduction in available cropland, since we do not know why the area of available land has decreased, we ignore this reduction and assume 0 as well. As shown in Table 2.1 in several regions, including the United States, the European Union, Canada, Central America, the Rest of South Asia, Other CES‐CIS, Middle East and North Africa, and Oceania, harvested area has increased slightly, but the area of available cropland has decreased. For these regions the data clearly suggest that the observed expansions in harvested areas are due to either double cropping and/or using unused cropland, and that implies 0. However, since we do not know why the area of available cropland has decreased, as a precautionary step and to avoid overestimating the improvement in land intensification, it is assumed that 0.2 in these regions. Table  2.1 shows that the area of available land and harvested area both followed decreasing trends in Japan, East Asia, Russia, and the Other Europe between 2003 and 2013. These observations do not confirm increases in intensification in these regions. Therefore, following the guideline provided in the previous section, we assumed 1 in these regions. Finally, when we started using the 2011 database, we found some discrepancies in the data for Malaysia‐ Indonesia palm oil. We corrected that data and also changed a substitution elasticity to better reflect the

changes in market conditions between 2004 and 2011. Also, since this region is a main source of palm oil production and multiple cropping does not apply to palm 1 for this region to evaluate production, we assume land use emissions due to alternative biofuel pathways. To compare the results of the new model with prior work, we used the version of GTAP‐BIO employed by the California Air Resources Board (CARB). This version of the model uses the benchmark database for 2004. It is important to note that the selected values for YDEL and γ, shown in Table 2.2, represent the observed trends in the ratios of HOL and COH during the time period of 2003–2013, and they may not represent long‐term universal trends in these ratios. If these variables follow different trends in the future, the assigned values to YDEL and γ could be revised accordingly. 2.5. EXPERIMENTS AND RESULTS As mentioned above we made use of the corn ethanol simulation for the tuning process. In addition to this experiment, we examined the induced land use changes for the Brazilian sugarcane ethanol, soybean biodiesel produced in the United States, and rapeseed biodiesel produced in the European Union with the new model. To compare the results of the new and old models, we evaluated the land use changes for the examined biofuel pathways with the old model as well. In total we examined eight experiments, as listed in the following section. 2.5.1. Experiments We did four experiments with the old model and the same four with the new model. The experiment names begin with letter N for the new model and letter O for the old model. The following are the experiment names for the new model, with only the first letter changing for the old model: 1. Experiments N‐I and O‐I. Expansion in U.S. corn ethanol by 11.59 BGs (from 3.41 BGs in 2004 to 15 BGs) 2. Experiments N‐II and O‐II. Expansion in Brazilian sugarcane ethanol by 3 BGs with an expansion in U.S. imports of this biofuel by 1 BGs 3. Experiments N‐III and O‐III. Expansion in U.S. soybean biodiesel by 0.8 BGs 4. Experiments N‐IV and O‐IV. Expansion in EU rapeseed biodiesel by 0.8 BGs Appendix 2.B contains these same simulations using the 2011 database. We chose to use the 2004 model and data for the main text so it would be comparable with previous work published in this area. However, the 2011 results will be more important moving forward.

30

BIOENERGY AND LAND USE CHANGE Table 2.3 Land Use Changes Due to Expansion in Corn Ethanol, Using 2004 Database (in 1000 ha) Original model

New model

Region

Forest

Cropland

Pasture

Forest

Cropland

Pasture

USA European Union Brazil Canada Japan China India Central America South America East Asia Mala‐Indo Rest of Southeast Asia Rest of South Asia Russia Other CEE‐CIS Other Europe Middle East and North Africa Sub‐Saharan Africa Oceania World

−63.2 −15.9 −27.8 −23.0 −5.3 −0.8 −7.4 4.0 35.7 2.1 0.6 −12.4 −3.2 12.6 −7.8 −0.3 0.1 −169.6 −0.5 −282.1

153.4 35.0 117.1 37.0 5.4 82.3 11.6 5.6 55.7 1.7 2.2 14.8 24.8 11.7 29.8 0.5 23.6 446.2 18.3 1076.5

−90.3 −19.1 −89.2 −14.0 −0.2 −81.4 −4.1 −9.6 −91.3 −3.7 −2.9 −2.4 −21.6 −24.4 −22.0 −0.2 −23.7 −276.3 −17.7 −794.2

−12.4 2.1 −21.8 −1.1 −5.5 0.9 −2.2 1.4 −9.0 0.9 −0.8 −11.4 −0.4 10.3 0.9 −0.3 0.3 −102.4 0.1 −150.3

30.2 6.7 26.6 6.8 5.6 0.0 0.0 1.0 46.3 1.7 2.4 10.7 3.1 12.4 5.6 0.5 4.6 343.3 3.5 510.9

−17.8 −8.9 −4.8 −5.7 −0.1 −1.0 2.2 −2.4 −37.2 −2.6 −1.6 0.7 −2.7 −22.7 −6.6 −0.2 −4.8 −240.9 −3.6 −360.8

2.5.2. Results 2.5.2.1. U.S. Corn Ethanol Here we first represent induced land use changes due to the expansion in ethanol production from its 2004 level to 15 BGs obtained from the original and new model (i.e., experiments N‐I and O‐I), as shown in Table  2.3. The new model projects that the expansion in the U.S. corn ethanol by 11.59 BGs increases the global demand for new cropland by 510.9 thousand ha, 52% less than the projected figure obtained from the original model. The imputed NDF values for the corn ethanol experiments obtained from the old and new models are about 10.3% and 4.9%. The new model projects less demand for new land because, compared to the old model, it takes into account intensification due to double cropping and/or cultivation of unused cropland. It also implements larger YDEL values in several regions that reflect the higher yield responses in those regions. The share of forest in land conversion obtained from the new model is 29.4%, which is slightly higher than the corresponding figure obtained from the old model (26%). While the new model projects a slightly higher forest share in land conversion, the size reduction in forest area projected by this model is 150.3 thousand ha, which is 46.8% less than the projected figure by the old model. In our earlier work, as we observed in this experiment, the share of forest increases as the size of land requirement for corn ethanol drops. That is because land rent for forest land grows faster than the land rent of pasture as

the demand for cropland grows. Hence, the lower the land requirement for corn ethanol, the higher the share of forest in land conversion. As we discussed before, the old model assumes that L H. However, the new model permits the values to differ. Table 2.4 represents the results of this change. As shown in this table, the old model projects that the area of cropland and harvested area both go up by 1076.5 thousand ha on the global scale because of the implemented ethanol shock. However, the new model projects an expansion of 510.9 thousand ha for the former and an expansion of 894.3 thousand ha for the latter. This pattern is also visible at the regional level. Table 2.4 reflects another important implication of the tuning process explained above. As mentioned before, during the tuning process, we assigned different values to the parameter of YDEL by region on the basis of the observed pattern in yield improvement across the world. Using the regional YDEL values reduces the expansion in the global harvested area from 1076.5 thousand ha obtained from the old model to 894.3 thousand ha obtained from the new model. The direct impact of this modification varies by region. For example, compare the results for the United States and Brazil. The old model, which uses YDEL = 0.25, projects 153 thousand ha and 117 thousand ha for changes in harvested area in the United States and Brazil, respectively. The new model, which uses YDEL = 0.3 for the United States and YDEL = 0.325 for Brazil, projects 147.3 thousand ha and 65.1 thousand ha for changes in harvested areas in the

AN ExpLORATION Of AGRICULTURAL LAND USE CHANGE AT INTENSIVE AND ExTENSIVE MARGINS 31 Table 2.4 Changes in Cropland and Harvested Area Due to Expansion in Corn Ethanol, Using 2004 Database (in 1000 ha) Original model Region USA European Union Brazil Canada Japan China India Central America South America East Asia Mala‐Indo Rest of Southeast Asia Rest of South Asia Russia Other CEE‐CIS Other Europe Middle East and North Africa Sub‐Saharan Africa Oceania World

New model

Change in harvested area

Change in cropland cover

Difference

Change in harvested area

Change in cropland cover

Difference

153.4 35.0 117.1 37.0 5.4 82.3 11.6 5.6 55.7 1.7 2.2 14.8 24.8 11.7 29.8 0.5 23.6 446.2 18.3 1076.5

153.4 35.0 117.1 37.0 5.4 82.3 11.6 5.6 55.7 1.7 2.2 14.8 24.8 11.7 29.8 0.5 23.6 446.2 18.3 1076.5

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

147.3 33.7 65.1 34.8 5.6 12.4 6.9 4.9 48.5 1.7 2.4 13.6 13.9 12.4 28.1 0.5 22.3 422.0 18.2 894.3

30.2 6.7 26.6 6.8 5.6 0.0 0.0 1.0 46.3 1.7 2.4 10.7 3.1 12.4 5.6 0.5 4.6 343.3 3.5 510.9

117.2 26.9 38.6 28.0 0.0 12.4 6.9 3.9 2.3 0.0 0.0 2.9 10.8 0.0 22.5 0.0 17.7 78.7 14.7 383.4

United States and Brazil, respectively. The sharper reduction in Brazil is due mainly to the larger yield response in this country. Of course, YDEL values in any world region also have an impact. Since the new model projects smaller changes in the regional demand for additional cropland, it leads to lower land use emissions too. To convert the induced land use changes obtained for the old and new model to land use emissions, we used the emissions factor model developed by Plevin et al. [2014] and adopted by the CARB. In order to use the CARB emissions model, it is assumed that changes in the mix of harvested area, cultivation of unused cropland, and expansion in double cropping do not alter the land use emissions. Cultivating unused cropland may generate some land use emissions. On the other hand, expansion in double cropping may increase soil carbon content [Alexandratos and Bruinsame, 2012]. Also changes in the mix of harvested area may increase or decrease soil carbon content. Further research is needed to evaluate the net impact of these changes on land use emissions. It is important to note that the CARB emissions model takes into account land use emissions due to the changes in cropland pasture, which is one type of idled cropland. The result of this calculation indicates that the new model generates 8.7 g CO2e/MJ emissions, which is 35.1% smaller than the corresponding figure for the old model. Note that the percent reduction in emissions is smaller than the percent reduction in demand for new land because the share of forest in total land

conversion obtained from the new model is higher than that obtained from the old model. 2.5.2.2. Other Biofuel Pathways Table  2.5 shows the land use impacts of Brazilian sugarcane ethanol, U.S. soybean biodiesel, and rapeseed biodiesel produced in the European Union obtained from the old and new models. As shown in this table, the new model projects smaller land use changes for all examined biofuels. For the case of sugarcane ethanol produced in Brazil, the global demand for additional cropland drops from 384.5 thousand ha obtained from the old model to 142.6 thousand ha obtained from the new model, a reduction of 63%. For the case of sugar­ cane, global harvested area goes up by 271 thousand hectares, which is 90% larger than the size of the corresponding expansion in the global demand for cropland. Hence, multiple cropping and/or cultivation of unused cropland accounts for a big portion of the additional demand for the case of expansion in Brazilian sugarcane ethanol. The Brazil ethanol case is still being evaluated. This is basically due to major intensification in Brazil (due to improvements in HOL and COH). For this biofuel pathway, the share of forest in total land use change goes up from 20% in the old model to 58% in the new model, while the size of forest conversion remains largely unchanged. For the case of soybean biodiesel produced in the United States, the global additional demand for cropland

32

BIOENERGY AND LAND USE CHANGE

Table 2.5 Land Use Changes Due to Expansion in Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2004 Database (in 1000 ha) Description Brazilian sugarcane ethanol: Experiments O‐II and N‐II

Old model

U.S. soybean biodiesel: Experiments O‐III and N‐III

Old model

EU rapeseed biodiesel: Experiments O‐IV and N‐IV

Old model

New model

New model

New model

Sub‐Saharan Africa

Others

Total

−2.6 10.2 −7.6 −14.7 7.7 7.0

−49.0 89.4 −40.3 −33.6 69.2 −35.6

−12.5 44.8 −32.3 −4.2 9.9 −5.8

−77.7 384.7 −306.9 −82.7 142.6 −59.8

2.3 10.1 −12.4 1.1 2.2 −3.3

8.0 9.3 −17.4 4.4 8.1 −12.4

−10.0 46.3 −36.2 −0.9 34.4 −33.5

0.8 22.5 −23.1 −3.8 12.4 −8.6

−13.3 114.8 −101.2 −2.7 62.2 −59.3

4.6 4.5 −9.1 2.0 1.0 −3.0

6.4 5.9 −12.4 0.7 5.4 −6.2

−44.0 109.9 −65.9 −26.6 86.8 −60.2

−17.9 68.9 −51.0 −2.6 16.8 −14.1

−64.9 222.2 −157.4 −24.2 116.7 −92.3

USA

EU27

Brazil

Forest Cropland Pasture Forest Cropland Pasture

−2.5 7.1 −4.6 −0.2 1.4 −1.2

−5.1 9.2 −4.1 −0.3 1.8 −1.5

−6.0 224.0 −218.0 −29.8 52.5 −22.7

Forest Cropland Pasture Forest Cropland Pasture

−13.3 23.2 −9.8 −4.3 4.5 −0.1

−1.0 3.4 −2.4 0.8 0.7 −1.4

Forest Cropland Pasture Forest Cropland Pasture

−0.6 4.0 −3.4 0.3 0.8 −1.1

−13.5 29.0 −15.5 1.9 5.8 −7.7

drops from 114.8 thousand ha to 62.2 thousand ha when we switch to the new model, a reduction by 46.3%. For the case of soybean biodiesel produced in the United States, global harvested area goes up by about 101 thousand hectares, 63% larger than the corresponding expansion in the demand for cropland. This means land intensification drops a large portion of the additional demand for cropland in this case. For this biofuel the new model projects a smaller share for forest in total land conversion (11.6% of the old model vs. 4.4% of the new model). Finally, Table  2.5 shows that, for the case of rapeseed biodiesel produced in the European Union, the global additional demand for cropland drops from 222.2 thou­ sand ha (obtained from the old model) to 116.7 thousand ha (obtained from the new model). For the case of rapeseed biodiesel produced in the European Union, global harvested area goes up by about 205 thousand hectares, 76% larger than the corresponding expansion in the demand for cropland. This means land intensification drops a large portion of the additional demand for cropland in the case of rapeseed biodiesel as well. For this biofuel the share of forest in the global land conversion goes down from 29% to 21% when we use the new model versus the old model. Since the new model projects smaller land use changes compared to the old model, it generates smaller land use emissions too, as shown in Table  2.6. The land use emissions for Brazilian sugarcane ethanol drops from 5.7 to 4.7 g CO2e/MJ when we switch to the new model, a reduction by 17.3%. For U.S. soybean biodiesel, land use emissions drop from 21.6 to 16.9 g CO2e/MJ (a reduction

South America

Table 2.6 Land Use Emissions for U.S. Corn Ethanol, Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2004 Database (in g CO2e/MJ) Biofuel U.S. corn ethanol Brazilian sugarcane ethanol U.S. soybean biodiesel EU rapeseed biodiesel

Old model

New model

Reduction (%)

13.4 5.68

8.7 4.7

−35.1 −17.3

21.62 26.55

16.9 15.7

−22.8 −40.9

by 22.8%), and for the EU rapeseed biodiesel, it declines from 26.6 to 15.7 g CO2e/MJ (a reduction by 40.9%). 2.6. CONCLUSIONS We believe that the new model does a much better job of reflecting the story told by the data trends on cropland cover and harvested area by region. The new model is now calibrated both to rate of growth in crop yield and the relative degree of extensification/intensification by global region. Given that the data story is one of more intensifica­ tion than had been assumed in the past, it is not surprising that the land use changes and corresponding changes in emissions have fallen, in some cases substantially. The results presented here are quite different from previous results obtained by our team and also by other researchers. The simple reason is that the models to date mainly consid­ ered land use changes at the extensive margin, while in reality there has been much more change at the intensive

AN ExpLORATION Of AGRICULTURAL LAND USE CHANGE AT INTENSIVE AND ExTENSIVE MARGINS 33

margin. Essentially, in the prior work in this area, much of the increase in harvested area at the intensive margin had been attributed to the extensive margin. However, recent historical evidence suggests that much of the change in harvested area, in fact, was at the intensive margin. Correcting the data and model for this change clearly results is lower land use change at the extensive margin. APPENDIX 2.A. WEDGE BETWEEN CROPLAND AND HARVESTED AREA The new model uses l ( H /L )h ( B /L )b to calculate percent changes in cropland. As usual, the new model endogenously determines h using the crop‐sector‐derived demands for cropland due to changes in crop demands. Since there is no theory to define a behavioral equation to determine b endogenously, we defined a module to determine b for a given h according to the recent historical observed links between l and h. When changes in harvested area and cropland are equal in a region (i.e., L H), then b 0, and therefore, l ( H /L )h. However, when b ≠ 0, then L H . In this case we can represent the relationship between l and h with l ( H /L )h , where γ represents the wedge between changes in harvested area and cropland. This means that Ll /Hh L/ H . Under normal conditions, this implies the following: 1. When 1, then L H and b 0. 2. When 0, then l 0 when h ≠ 0, and therefore, ( H /L )h ( B /L )b . 3. When 0 1, then H L and B L H. Using l ( H /L )h ( B /L )b and l ( H /L )h , it is straightforward to show that b ( / 1 )( 1)h, where α represents the ratio of H/L and γ is a parameter between 0 and 1. Clearly, this equation links b to h using two parameters of α and γ. The GTAP database provides regional values for α. For a given α, it is straightforward (1/ )(1/h ). This means that we can find to show that out regional values of γ for a given set of regional values of α, if we know percent changes in cropland and harvested area by region. The FAO database is used to determine percent changes in cropland and harvested area by region for the time period of 2003–2013. We represent estimated percentage changes in these two variable by lˆ and ĥ, respectively. Table 2.1 of the original text represents the regional estimated values of lˆ and ĥ. Then these values are used to find regional values of γ using the following equation: γ = (1/α ) (lˆ /hˆ ). APPENDIX 2.B. EXPERIMENTS BASED ON THE 2011 DATABASE AND THE ASSOCIATED MODELS We have done four experiments in the main text on the basis of the 2004 database and the associated models without and with land intensification. We undertake

similar experiments in this appendix using the 2011 data­ base and the associated models without and with land intensification. It should be noted that while we have four corresponding experiments for 2011 as for 2004 in the main text, the magnitudes of the biofuel shocks in 2011 are relatively small. This is simply because, compared to 2004, biofuel production in 2011 is generally higher, meaning that compared to 2004, we need lower output shocks for 2011 to hit or get close to the same level of fuel targets. The four experiments for 2011 are as follows: 1. Experiments N‐I2011 and O‐I2011. Expansion in U.S. corn ethanol by 1.07 BGs (from 13.93 BGs in 2011 to 15 BGs) 2. Experiments N‐II2011 and O‐II2011. Expansion in Brazilian sugarcane ethanol by 1 BGs 3. Experiments N‐III2011 and O‐III2011. Expansion in U.S. soybean biodiesel by 0.5 BGs 4. Experiments N‐IV2011 and O‐IV2011. Expansion in EU rapeseed biodiesel by 0.5 BGs 2.B.1. RESULTS 2.B.1.1. U.S. Corn Ethanol We present the induced land use changes due to expan­ sion in U.S. corn ethanol in Table 2.B.1. The new model projects that the expansion in U.S. corn ethanol by 1.07 BGs would increase demand for new cropland by 69.4 thousand ha globally, 55.2% less than what the old model projects. This reflects that the new model has taken into account land intensification and thus needs less land for this biofuel expansion. The change in forest area accounts for 32% in total land conversion globally in the new model, smaller than the corresponding share projected using the old model (39%).The new model projects less reduction in forest area (22 thousand ha), nearly 39 thousand ha less than the reduction in forest area in the old model. The major reduction in land demand in the 2011 results happens for the same reason as the 2004 results. In the prior model structure, a change in harvested area was always equal to a change in cropland cover. With the new model, changes in harvested area can be greater than changes in cropland cover due to intensification. This is clearly seen in Table 2.B.2, where the results derived from the old model show that the change in harvested area due to the expansion in U.S. corn ethanol was 154.8 thousand ha, exactly the same as the change in cropland cover. With the new modeling approach, however, the change in cropland cover becomes 69.4 thousand ha, 51% less than the change in harvested area. Table  2.B.2 also shows a reduction in the change of harvested area in the new model, compared to the old model. This can be attributed to the tuned parameters, particularly the yield response parameter YDEL, as

34

BIOENERGY AND LAND USE CHANGE Table 2.B.1 Land Use Change Due to Expansion in U.S. Corn Ethanol, Using 2011 Database (1000 ha) Old model O‐I

New model N‐I

Forest

Cropland

Livestock

Forest

Cropland

Livestock

USA European Union Brazil Canada Japan China India Central America South America East Asia Mala‐Indo Rest of Southeast Asia Rest of South Asia Russia Other CEE‐CIS Other Europe Middle East and North Africa Sub‐Saharan Africa Oceania

−3.4 −9.5 −10.2 −5.0 −0.6 −0.5 −3.8 −1.3 −3.9 −0.4 −2.5 −3.8 −0.7 −0.1 −1.3 −0.1 0.0 −13.7 −0.2

20.3 16.0 29.0 6.6 0.6 1.8 6.5 1.2 7.1 0.8 2.8 3.7 2.2 2.4 4.0 0.1 3.5 43.7 2.6

−16.8 −6.5 −18.8 −1.6 0.0 −1.2 −2.6 0.1 −3.2 −0.4 −0.3 0.1 −1.4 −2.4 −2.8 0.0 −3.5 −29.9 −2.5

3.0 −0.9 −3.8 −0.8 −0.6 0.2 0.0 −0.9 −3.7 −0.4 −2.2 −2.6 −0.1 −0.1 0.0 −0.1 0.0 −8.8 0.0

4.3 3.0 11.8 1.2 0.6 0.0 0.0 0.2 6.8 0.8 2.5 2.4 0.4 2.4 0.7 0.1 0.7 31.0 0.5

−7.3 −2.0 −8.1 −0.4 0.0 −0.2 0.0 0.6 −3.1 −0.4 −0.2 0.2 −0.3 −2.3 −0.7 0.0 −0.7 −22.2 −0.4

World

−61.1

154.8

−93.6

−22.0

69.4

−47.5

Table 2.B.2 Changes in Cropland and Harvested Area Due to Expansion in U.S. Corn Ethanol, Using 2011 Database (1000 ha) Old model O‐I

USA European Union Brazil Canada Japan China India Central America South America East Asia Mala‐Indo Rest of Southeast Asia Rest of South Asia Russia Other CEE‐CIS Other Europe Middle East and North Africa Sub‐Saharan Africa Oceania World

New model N‐I

Change in harvested area

Change in cropland cover

Difference

Change in harvested area

20.3 16.0 29.0 6.6 0.6 1.8 6.4 1.2 7.1 0.8 2.8 3.7 2.2 2.4 4.0 0.1 3.5 43.7 2.6

20.3 16.0 29.0 6.6 0.6 1.8 6.5 1.2 7.1 0.8 2.8 3.7 2.2 2.4 4.0 0.1 3.5 43.7 2.6

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

19.4 15.0 25.8 6.2 0.6 1.5 5.1 1.1 7.1 0.8 2.5 3.2 1.8 2.4 3.7 0.1 3.2 40.7 2.5

4.3 3.0 11.8 1.2 0.6 0.0 0.0 0.2 6.8 0.8 2.5 2.4 0.4 2.4 0.7 0.1 0.7 31.0 0.5

15.1 12.0 13.9 5.0 0.0 1.5 5.1 0.9 0.3 0.0 0.0 0.8 1.4 0.0 3.0 0.0 2.5 9.7 2.1

154.8

154.8

0.0

142.6

69.4

73.2

explained in the main text for the 2004 results. Instead of using a globally constant YDEL as in the old model, the new model has a regional dimension for YDEL, and each region may be assigned a different YDEL value. Regions such as Brazil, China, and India were assigned a higher

Change in cropland cover

Difference

YDEL value of 0.325, higher than the uniform value of 0.25 in the old model. As a higher YDEL value implies a greater yield response to price changes, this leads to less land needed, reflected in the reductions of the change in harvested area in the three regions.

AN ExpLORATION Of AGRICULTURAL LAND USE CHANGE AT INTENSIVE AND ExTENSIVE MARGINS 35

The reduction in induced land use change in the new model also leads to lower land use emissions. Using the emissions factor model developed by Plevin et al. [2014], we estimate that the induced land use change obtained from the new model generates 12 g CO2e/MJ emissions, 48.5% less than the land use emissions estimated using the old model. Similar to the simulation results using the 2004 database, the percent reduction in emissions using the 2011 data (48.5%) is also smaller than the percent reduction in demand for new land (55.2%). That is because of the change in the distribution of land use changes across regions generated by the new model versus the old model. 2.B.1.2. Other Biofuel Pathways The new model also projects reductions in induced land use change in other biofuel scenarios, including Brazilian sugarcane ethanol, U.S. soybean biodiesel, and EU rapeseed biodiesel (Table  2.B.3). A 1 BG of expansion in Brazilian sugarcane ethanol would demand 55.2 thousand ha of additional cropland, a 56% drop compared to the results derived using the old model. While the demand for forest also drops in the new model, the forest share in total land conversion decreases, from 38% in the old model to 30% in the new model. Following a 0.5 BG of expansion in U.S. soybean biodiesel, the new model projects 36.2 thousand ha of global demand for additional cropland, a reduction of 50% compared to the additional demand obtained in the old model. Compared to the old model projecting

20.2 thousand ha of deforestation, the new model pro­ jects small deforestation (3.8 thousand ha). A 0.5 BG of expansion in EU rapeseed biodiesel, the new model projects 40.1 thousand ha of global demand for additional cropland, 57% less than the projected figure using the old model. Similar to the scenarios of Brazilian ethanol and U.S. biodiesel, the EU biodiesel scenario also projects less demand for forest (about 29.7 thousand ha less) when using the new model. As expected, the reduced land use changes obtained from the new model in these biofuel scenarios lead to reductions in associated land use emissions (Table 2.B.4). Compared to the old model, land use emissions obtained from the new model reduce by the range of 28%–75% with Brazilian ethanol having the highest emissions reduction (75%) and U.S. biodiesel experiencing the lowest reduction in emissions (28%).

Table 2.B.4 Land Use Emissions for U.S. Corn Ethanol, Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2011 Database (in g CO2e/MJ)

U.S. corn ethanol Brazilian sugarcane ethanol U.S. soybean biodiesel EU rapeseed biodiesel

Old model

New model

Reduction (%)

23.3 13.0

12.0 3.2

48.5 75.3

25.5 23.7

18.3 13.7

28.2 42.1

Table 2.B.3 Land Use Changes Due to Expansion in Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2011 Database (1000 ha)

Brazilian sugarcane ethanol

Old Model O‐II

New Model N‐II

U.S. soybean biodiesel

Old Model O‐III

New Model N‐III

EU rapeseed biodiesel

Old Model O‐IV

New Model N‐IV

USA

EU27

Brazil

South America Sub‐Saharan Africa

Other

World

Forest Cropland Livestock Forest Cropland Livestock

−0.3 1.3 −1.0 0.1 0.3 −0.4

−3.8 6.4 −2.6 −0.4 1.2 −0.8

−30.9 91.5 −60.6 −9.0 38.7 −29.7

−4.9 1.7 3.2 −4.8 1.6 3.1

−2.1 13.9 −11.8 −0.5 9.9 −9.4

−5.4 10.5 −5.2 −2.1 3.5 −1.3

−47.5 125.2 −77.9 −16.7 55.2 −38.4

Forest Cropland Livestock Forest Cropland Livestock

−4.6 10.0 −5.4 −0.9 2.0 −1.2

−3.8 6.4 −2.6 −0.4 1.2 −0.8

−5.1 13.5 −8.4 −2.0 5.3 −3.3

1.1 4.1 −5.2 1.2 3.9 −5.1

1.7 18.1 −19.9 3.8 12.7 −16.4

−9.6 19.9 −10.3 −5.6 11.1 −5.4

−20.2 72.1 −51.9 −3.8 36.2 −32.4

Forest Cropland Livestock Forest Cropland Livestock

−0.2 1.1 −0.8 0.0 0.2 −0.2

−20.6 34.6 −14.0 −2.2 6.8 −4.6

−2.8 7.9 −5.1 −1.0 3.4 −2.3

−0.5 1.9 −1.4 −0.5 2.1 −1.6

−3.8 24.5 −20.7 −1.2 18.2 −17.0

−11.7 23.5 −11.8 −4.9 9.4 −4.5

−39.5 93.4 −53.8 −9.8 40.1 −30.2

36

BIOENERGY AND LAND USE CHANGE

REFERENCES Alexandratos, N. and J. Bruinsame (2012), World agriculture towards 2030/2050: The 2012 revision, ESA Working Paper 12‐30, U.N. Food and Agricultural Organization, Rome. Alston, J. M., B. A. Babcock, and P. G. Pardey (2010), The Shifting Patterns of Agricultural Production and Productivity Worldwide, The Midwest Agribusiness Trade Research and Information Center, Iowa State University, Ames, IA. Ausubel, J. H., I. K. Wernick, and P. E. Waggoner (2012), Peak farmland and the prospect for land sparing, Population and Development Review, 38(Supplement), 221–242. Avetisyan M., U. Baldos, and T. Hertel (2011), Development of the GTAP version 7 land use data base, GTAP Research Memoranda 19, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette, IN. https://www.gtap.agecon.purdue.edu/resources/download/ 5215.pdf (accessed 21 August 2017). Babcock, B. A. and Z. Iqbal (2014), Using recent land use changes to validate land use change models, Staff Report 14‐ST 109, Iowa State University, Ames, IA. Baldos U. and T. Hertel (2012), Development of a GTAP 8 land use and land cover data base for years 2004 and 2007, GTAP Research Memoranda 23, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette, IN. https://www.gtap.agecon.purdue.edu/ resources/download/6048.pdf (accessed 21 August 2017). Borchers, A., E. Truex‐Powell, S. Wallander, and C. Nickerson (2014), Multi‐Cropping Practices: Recent Trends in Double Cropping, Economic Information Bulletin Number 125, Economic Research Service, U.S. Department of Agriculture, Washington, DC. Brady, M. and B. Sohngen (2008), Agricultural productivity, technological change, and deforestation: A global analysis, Americal Agricultural Economics Association annual meeting, Orlando, FL. Burniaux, J. and T. Truong (2002), GTAP‐E: An energy‐environmental version of the GTAP model, Technical Paper 16, Purdue University, West Lafayette, IN. Byerlee, D., J. Stevenson, and N. Villoria (2014), Does intensification slow cropland expansion or encourage deforestation? Global Food Security, 3(2), 92–98. Cassman, K. (1999), Ecological intensification of cereal production systems: Yield potential, soil quality, and precision agriculture, Proceedings of the National Academy of Sciences, 96, 5952–5959. Fargione, J., J. Hill, D. Tilman, S. Polasky, and P. Hawthorne (2008), Land clearing and the biofuel carbon debt, Science, 319(5867), 1235–8. Foley, J. A., N. Ramankutty, K. A. Brauman, E. S. Cassidy, J. S. Gerber, M. Johnston, et al. (2011), Solutions for a cultivated planet, Nature, 478(7369), 337–342. Hertel, T. W. (Ed.) (1997), Global Trade Analysis: Modeling and Applications, Cambridge University Press, New York. Hertel, T. W., and U. L. C. Baldos (2016), Global Change and the Challenges of Sustainably Feeding a Growing Planet, Springer, New York.

Hertel, T. W., H. Lee, S. Rose, and B. Sohngen (2009), Modeling land‐use related greenhouse gas sources and sinks and their mitigation potential, in Economic Analysis of Land Use in Global Climate Change Policy, edited by T. Hertel, S. Rose, and R. Tol, Routledge Press, Oxford. Hertel, T., A. Golub, A. Jones, M. O’Hare, R. Plevin, and D. Kammen (2010a), Effects of U.S. maize ethanol on global land use and greenhouse gas emissions: Estimating market‐ mediated responses, Bioscience, 60(3), 223–231. Hertel, T. W., W. E. Tyner, and D. K. Birur (2010b), The global impacts of biofuel mandates, Energy Journal, 30(1), 75–100. Laborde, D. (2011), Assessing the Land Use Change Consequences of European Biofuel Policies, International Food Policy Research Institute, Washington, DC. Laborde, D., and H. Valin (2012), Modeling land‐use changes in a global CGE: Assessing the EU biofuel mandates with the MIRAGE‐BioF model, Climate Change Economics, 3(3), 39. Lee H, T. Hertel, B. Sohngen, and N. Ramankutty (2005), Towards an integrated land use data base for assessing the potential for greenhouse gas mitigation, GTAP Technical Paper 25, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University Purdue University, West Lafayette, IN. https://www.gtap.agecon. purdue.edu/resources/download/2375.pdf (accessed 21 August 2017). Lewis, S. M., and M. Kelly (2014), Mapping the potential for biofuel production on marginal lands: Differences in definition, data and models across scales, ISPRS International Journal of Geo‐Information., 3, 430–459. McDougall, R., and A. Golub (2007), GTAP‐E Release 6: A Revised Energy‐Environmental Version of the GTAP Model, Purdue University, West Lafayette, IN. Peña‐Lévano L, F. Taheripour, and W. Tyner (2015), Development of the GTAP land use data base for 2011, GTAP Research Memoranda 28, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette, IN. https://www.gtap.agecon. purdue.edu/resources/download/7744.pdf (accessed 21 August 2017). Plevin, R. J., M. O’Hare, A. D. Jones, M. S. Torn, and H. K. Gibbs (2010), Greenhouse gas emissions from biofuels indirect land use change are uncertain but may be much greater than previously estimated, Environmental Science and Technology, 44(21), 8015–8021. Plevin, R. J., H. K. Giggs, J. Duffy, S. UYui, and S. Yeh (2014), Agro‐ecological zone emission factor (AEZ‐EF) model (v47): A model of greenhouse gas emissions from land‐use change for use with AEZ‐based economic models, GTAP Technical Paper 34. https://www.gtap.agecon.purdue.edu/resources/ download/6692.pdf (accessed 21 August 2017). Ray, D. K., and J. A. Foley (2013), Increasing global crop harvest frequency: Recent trends and future directions, Environmental Research Letters, 8(4), 044041, 10 pp. Searchinger, T., R. Heimlich, R. A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, et al. (2008), Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land use change, Science, 319(5867), 1238–1240.

AN ExpLORATION Of AGRICULTURAL LAND USE CHANGE AT INTENSIVE AND ExTENSIVE MARGINS 37 Siebert, S., F. T. Portman, and P. Dill (2010), Global patterns of cropland use intensity, Remote Sensing, 2(7), 1625–1643. Taheripour, F., and W. E. Tyner (2013a), Biofuels and land use change: Applying recent evidence to model estimates, Applied Sciences, 3, 14–38. Taheripour, F., and W. E. Tyner (2013b), Induced land use emissions due to first and second generation biofuels and uncertainty in land use emission factors, Economics Research International, 315787, 12 pp. Taheripour, F., T. W. Hertel, W. E. Tyner, J. Beckman, and D.  K. Birur (2010), Biofuels and their by‐products: Global economic and environmental implications, Biomass and Bioenergy, 34(3), 278–289.

Taheripour, F., Q. Zhuang, W. E. Tyner, and X. Lu (2012), Biofuels, cropland expansion, and the extensive margin, Energy, Sustainability and Society, 2, 25. Tilman, D., J. Hill, and C. Lehman (2006), Carbon‐negative biofuels from low‐input high‐diversity grassland biomass, Science, 314, 1598–600. Tyner, W. E., and F. Taheripour (2012), Land‐use changes and CO2 emissions due to U.S. corn ethanol production,  in Encyclopedia of Biodiversity, vol. 4, second ed., edited  by S. A. Levin, pp. 539–554, Academic Press, Waltham, MA.

3 Effects of Sugarcane Ethanol Expansion in the Brazilian Cerrado: Land Use Response in the New frontier Marcellus M. Caldas1, Gabriel Granco1, Christopher Bishop2, Jude Kastens2, and J. Christopher Brown3

ABSTRACT Global demand for ethanol has increased in the last decade. In Brazil, the rise of ethanol production has been more significant in the Brazilian Cerrado, specifically in the states of Goiás and Mato Grosso do Sul. Although the Cerrado is not a traditional sugarcane producing region, it has become the new frontier for sugarcane ethanol. Under these circumstances, the rapid development of sugarcane in the region has promoted a discussion over the impact of sugarcane production on land use in the Cerrado. There is a need for accurate measurement of land use response and deeper understanding of its dynamic in order to comprehend the impact of sugarcane ethanol expansion to the Cerrado. We investigate two land uses responses (LUR) promoted by expansion of sugarcane ethanol: extensification (conversion of noncropland) and intensification (conversion of cropland). First, we identified the occurrence of each type of LUR from a time series of land use maps developed for the region. Second, we employed a statistical method to examine which factors influence farmers’ decisions of replacing the previous land use by sugarcane, be the previous land use cropland (intensification response) or noncropland (extensification response). Our results indicate that extensification was the predominant response for all the period analyzed (2006–2013) though intensification represented approximately 1/3 of all the sugarcane expansion. The estimates of marginal effects from our statistical model shows that occurrence of extensification in a field is positively affected by distance to the mill (0.085%) and to roads (0.131%). 3.1. INTRODUCTION

a growing and wealthier global population. At the same time, agricultural production needs to incorporate practices that mitigate its impacts on the environment [Rudel et al., 2009]. Two alternative land use responses (LUR) have been proposed as a way to address this challenge. On the one hand, land users may engage in an intensification response. This response is based on the idea that biofuel crops can replace nonbiofuel crops on existing cropland concurrent with offsetting increases of nonbiofuel crop production on remaining cropland, thus sparing natural vegetation from conversion and reducing impacts on the environment [Stevenson et al., 2013; Brown et al., 2014; Cohn et al., 2014; Strassburg et al., 2014]. On the other

The increasing demand for biofuels has stimulated the expansion of bioenergy crop production worldwide, adding new land uses to agricultural sectors [Strassburg et al., 2014]. Land use competition has been intensified by the need to produce more food, fiber, and now fuel, for 1 Department of Geography, College of Arts and Sciences, Kansas State University, Manhattan, Kansas, USA 2 Kansas Applied Remote Sensing Program, University of Kansas, Lawrence, Kansas, USA 3 Department of Geography and Atmospheric Science, University of Kansas, Lawrence, Kansas, USA

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 39

40

BIOENERGY AND LAND USE CHANGE

hand, land users may engage in an extensification response. This response consists of an increase in cropland at the expense of converting noncropland; thus, it spares the competition with food production but may prompt conversion of natural vegetation [Arvor et al., 2012; Brown et al., 2014]. Each of these LUR reacts differently to proximate and underlying factors of land change, such as agricultural conditions (soil, slope, and climate), agricultural trade, governance, and market prices, among others [Walker et al., 2009; Ceddia et al., 2014]. An important case for the discussion about LUR is how Brazilian farmers have responded to increased demand for biofuel production in Brazil. The rise in demand for ethanol has stimulated the Brazilian ethanol industry to expand its production into areas of the Brazilian Cerrado (Figure 3.1) [Sant’Anna et al., 2016; Granco et al., 2015].

The challenge to achieve the demand for food and fuel, with less impact on the environment, is a reality in the Brazilian Cerrado. The region is a global biodiversity hot spot [Myers et al., 2000], an important producer of grain and livestock [Rada, 2013], and is rapidly becoming a major source of sugarcane ethanol in Brazil, especially in the states of Goiás and Mato Grosso do Sul [de Cerqueira Leite et al., 2009; Shikida, 2013]. The production of ethanol has increased by 400% in these states, with a more than 400% increase in production areas for the period of 2006–2013 [Granco et al., 2015]. In addition, the discussion on biofuels’ ability to reduce greenhouse gas (GHG) emission requires consideration of the land use response to biofuel expansion [Searchinger et al., 2008; Lapola et al., 2010, 2014]. Conversion of cropland to biofuel crop results in a relatively small

Figure 3.1 Sugarcane fields in the states of Goiás and Mato Grosso do Sul.

EffECTS Of SUGARCANE ETHANOL ExpANSION IN THE BRAzILIAN CERRADO 41

carbon deficit (or no carbon deficit), while conversion of natural vegetation to biofuel crop produces a carbon deficit large enough to potentially offset the GHG savings of biofuel usage [Fargione et al., 2008; Tilman et al., 2009]. This difference is very significant in Brazil, where the conversion of natural vegetation in the Amazon or the Cerrado can result in a carbon deficit [Lapola et al., 2010]. However, some studies have demonstrated that conversion of degraded pastureland can have a negative carbon deficit [Cohn et al., 2014; Mann et al., 2014; Graesser et al., 2015]. Previous works also suggest that sugarcane expansion over pastureland (extensification response) may not affect the production of beef cattle because the cattle‐ranching sector in Brazil can release land by increasing its cattle stockage by hectare [Cohn et al., 2014; Alkimim et al., 2015]. Moreover, prior research on land use response in Brazil has focused on agricultural intensification to avoid defor­ estation in the Amazon [Arvor et al., 2012; Gibbs et al., 2015; Kastens et al., 2017] and on cattle‐ranching‐inten­ sification scenarios with pastureland being converted to sugarcane production [Goldemberg et al., 2014; Strassburg et al., 2014; Alkimim et al., 2015]. What is not understood yet is which factors influence farmers’ land use decisions leading to land use intensification (conversion of crop­ land) or extensification (conversion of noncropland) responses. In this context, there exists a need for accurate measurements of LUR and a deeper understanding of its dynamics in order to comprehend the impact of sugarcane ethanol expansion into the Cerrado, concerning not only GHG mitigation but also land use competition and conservation of natural vegetation. Therefore, the assessment of LUR is extremely important for the biofuel industry, policymakers, and society. With this knowledge, policies aiming at developing agriculture to meet the demand for more food and fuel can be put in place, mini­ mizing environmental impacts [Dias et al., 2016]. The purpose of this chapter is to examine sugarcane expansion into the states of Goiás and Mato Grosso do Sul. More specifically, we seek to identify farmers’ LUR regarding sugarcane production and determine which factors promote each outcome. To achieve this goal, we analyze the evolution of sugarcane‐producing areas in the states of Goiás and Mato Grosso do Sul, for the period of 2006–2013, through remotely sensed imagery. In doing this, we estimate a statistical model to understand the contribution of each factor toward each LUR. The chapter encompasses five sections beyond this introduction. The following section presents our con­ ceptual framework of LUR to sugarcane expansion. The next section discusses the study area and land use data, followed by a presentation of the methods employed to identify the LUR and the statistical method used to determine the influence of each factor to LUR. With the

methods and data explained, we present our results and the discussion that lead us to our conclusions. 3.2. CONCEPTUAL FRAMEWORK Given our interest in understanding the factors moti­ vating farmers’ LUR, we develop an economic model of latent discrete choice for sugarcane LUR. It is important to note that we are not modeling sugarcane adoption by farmers but farmers’ LUR to their decision to grow sugarcane. First, let us assume that farmers have already decided to grow sugarcane. Once this decision is made, farmers face a follow‐up LUR decision regarding which land use they should replace by sugarcane. Under these assumptions, Equation 3.1 represents the LUR decision for a two‐land use case: E

E E

sc

i j

E E

E

sc i

,

j

(3.1)

where E(πsc) is the expected profit for sugarcane produc­ tion, E(πi) is the expected profit for land use i, and E(πj) is the expected profit for land use j. m

Rk Ck

a

Sk ,

(3.2)

k 1

where πa is the profit function considered in Equation 3.1, with a assuming the values of (sc, i, j); Rk is the revenue from a specific land use; Ck is the production cost of a specific land use; and Sk is the transportation cost for a specific land use. Equations 3.1 and 3.2 demonstrate how this decision process can be related to profit. In this example, an area dedicated to i would be converted to sugarcane production because the expected profit from land use j was greater than the expected profit of i, assuming sugar­ cane’s profit is the same in both regions. This framework can be expanded to an n‐land use case. We cannot observe, however, the true decision process. Rather, we just observe the realization of the process by identifying the LUR. Researchers can only observe which land use was converted to sugarcane and the characteris­ tics of a given location. Let us define LR* as a latent choice variable as given in Equation 3.3: LR*

j

i

x

,

(3.3)

where the researcher can observe β′x, with x a column vector of variables and β a column vector of parameters, and the researcher cannot observe one component given by ε. The farmer will engage in LUR when LR* makes available a net benefit (Eq. 3.4):

42

BIOENERGY AND LAND USE CHANGE

LR

1, LR*

0

0, LR*

0

,

(3.4)

where LR represents the observed LUR decision by the farmer. When LR* ≥ 0, it means the potential profit gains associated with other locations are higher than at loca­ tion i, and the farmer will choose to convert that field i to sugarcane. Analogously, when LR* < 0, then the profit gains associated with other locations are smaller than at location i, and thus, the farmer will prefer not to convert i to sugarcane. Therefore, the probability of LUR being prompted by sugarcane is Pr LR 1 1 F

Pr LR* x

e 1 e

0

Pr

x x

,

x (3.5)

where ε is distributed logistic, and F is a cumulative proba­ bility distribution function. The probability can be defined as the closed‐form expression of a binary logit model. The advantage of this approach is to incorporate a set of explanatory factors to indicate the odds of intensification or extensification taking place at a chosen location. 3.3. METHODS 3.3.1. Study Area Goiás and Mato Grosso do Sul are neighboring states in west‐central Brazil. Goiás shares borders with the states of Tocantins to the north, Bahia to the northeast, Minas Gerais to the east, and Mato Grosso to the west. Mato Grosso do Sul also neighbors Mato Grosso and Minas Gerais; additionally, it neighbors São Paulo and Paraná (Figure  3.1). The study area encompasses 324 counties: 246 in the state of Goiás and 78 in the state of Mato Grosso do Sul. Together both states have 60 mills (36 in Goiás and 24 in Mato Grosso do Sul) in 33 and 21 counties, respectively. Originally, the Cerrado covered 98% of Goiás and more than 60% of Mato Grosso do Sul. The Cerrado in these states exhibits a variety of vegetative cover, ranging from open grassland to closed woodland in a soil that is deep, well drained, and resistant to compac­ tion (although it is acidic, with poor nutrient content and a high concentration of aluminum) [Klink and Machado, 2005; Brannstrom et al., 2008]. The main agricultural products are cattle, soybeans, and corn. Pastureland is the main anthropogenic use covering more than 26 million ha in 2013 [Brasil, 2015], followed by soybeans with close to 5 million ha, while sugarcane covers around 1.4 million ha [CONAB, 2016]. The Cerrado’s natural vegetation is pre­ sent in more than 20 million ha [Brasil, 2015].

3.3.2. Detecting Land Use Responses To determine if sugarcane expansion occurred in the intensive or extensive margin, we require information on the land use converted to sugarcane. Consequently, we developed a time series of thematic maps for Goiás and Mato Grosso do Sul. This data set classifies land use/land cover (LULC) from 2005 to 2013 into six classes: (i) annual single crop, (ii) annual double crop, (iii) pasture/cerrado/ forest, (iv) sugarcane, (v) urban, and (vi) water. However, for the present study, only the first four classes were used. During the summer of 2014, research team members conducted interviews with sugarcane farmers in both Goiás and Mato Grosso do Sul. Following the protocols described by Brown et al. [2007] and Brown et al. [2013], land cover histories from 137 sugarcane field sites (76 from Goiás and 61 from Mato Grosso do Sul) were acquired, many of which extended back to the 2005 crop year. From these data, 464 pre­sugarcane ground reference samples were obtained (2005: 70; 2006: 65; 2007: 52; 2008: 46; 2009: 45; 2010: 47; 2011: 40; 2012: 34; 2013: 34; 2014: 31). Sixty‐eight of the samples repre­ sented annual single crop, 191 represented annual double crop, and 205 represented pasture/cerrado. The original imagery consisted of the 250 m, 16 day composite MOD13Q1 Normalized Difference Vegetation Index (NDVI) data from the Moderate Imaging Spectro­ radiometer (MODIS), covering the study area for crop years 2005–2014, downloaded from the Land Processes Distributed Active Archive Center (LP DAAC; https:// lpdaac.usgs.gov/data_access). These data were reprojected to the WGS84 projection with a grid size of approximately 240 m. Applying the pure pixel approach utilized by Wardlow et al. [2006, 2007], Wardlow and Egbert [2008], and Brown et al. [2013], annual MODIS NDVI profiles were extracted that corresponded with the ground reference data. Using the 23‐date MODIS profiles as independent variables and ground reference cover class as the dependent variable, a random forest (RF) classification model [Breiman, 2001; Clark et al., 2010; Kastens et al., 2017] was developed using the “treebagger” function in MATLAB®. A total of 1000 trees were included in the forest, with each developed using a 5‐element random subset of the 23 candidate predictors (MODIS time periods). To estimate the expected error of the full RF model, 10 iterations of a 1 year holdout cross‐validation (CV) exercise were used. For each iteration, 10 RF models were independently developed using unique 9 year subsets of the 10 year ground reference data set, followed by an application of each model to ground reference data from its respective holdout year. Aggregating the results across all 10 holdout years for each CV iteration, overall classification accuracies across the 10 iterations for the 3‐class model outputs ranged from 80.8%–81.9%, which

EffECTS Of SUGARCANE ETHANOL ExpANSION IN THE BRAzILIAN CERRADO 43

increased to 90.3%–91.4% when grouping the single‐crop and double‐crop classes. Annual forest‐cover layers were developed using Global Forest Change data (2000–2014), which are in 30 m raster format available from http://earthenginepartners. appspot.com/science‐2013‐global‐forest [Hansen et al., 2013]. This data set contains tree canopy cover for the year 2000 and year‐specific forest‐cover gain and loss data for the years 2000–2014. The Global Forest Watch interactive map (www.globalforestwatch.org), the col­ laborative map for this data set, identifies forest/defores­ tation as greater than 30% canopy cover. This threshold was used to identify forest pixels at the resolution of the MODIS‐based LULC grid. These data were burned into the LULC maps obtained from the RF model. Forest pixels, which comprised a marginal fraction of the pre­ sugarcane area, were grouped with the pasture/cerrado class to obtain the pasture/cerrado/forest class. Information on the location of sugarcane fields was developed by the Canasat Program of the Brazilian National Institute for Space Research (INPE) [Rudorff et al., 2010], and these data were kindly provided by the stewards of that data set. Static urban and water layers were obtained from the Brazilian Institute of Geography and Statistics (IBGE) and burned into the maps. From this data set, each cell can be represented as cl,t, where l is the cell location, t is year (2005–2013), and its value corresponds to one of the land use classes defined above. The annual sugarcane expansion (el,t) is defined by the following raster calculation: el ,t

1 if cl ,t 4 and cl ,t 1 4 sugarcane expansion . otherwise no su ugarcane expansion 0 (3.6)

This procedure creates annual sugarcane expansion masks for 2006–2013. We identified the land use response of each field converted to sugarcane by overlaying the mask on the land use map for the previous year. Using the definitions of LUR, each new sugarcane area falls into intensification or extensification response for each year:

LRl ,t

0 if cl ,t 1 1or 2 intensification response . 1 if cl ,t 1 3 exttensification response (3.7)

Once we detect and identify LUR to sugarcane expan­ sion, we focus our attention on modeling this process. 3.3.3. Statistical Model of Land Use Responses To examine the intensification or extensification response to sugarcane expansion, given the new ethanol mills located in the states of Goiás and Mato Grosso do Sul, we developed a statistical analysis of the relationship between observed land use responses and farmers’ profit maximization behavior. Given that we cannot observe the true decision process described in the conceptual frame­ work, the method used here is a logit regression with dependent variables given by extensification (y = 1) or intensification (y = 0) (see Eq. 3.7) and a set of independent variables representing economic, physical, and locational factors that are hypothesized to influence land use response. Because our focus is the LUR prompted by the expansion of sugarcane, each observation is unique for each year, resulting in a cross‐sectional model with 267,794 observations (y = 1 for 181,063 observations). Our data set is formed by land use maps with a cell size of about 240 m, corresponding to an area of 5.8 ha per pixel. Even though our unit of analysis is a sugarcane field, the unit of decision is the county because we do not have access to data representing all the farms located in the study area. The county level is a good scale for the problem at hand because it accentuates the comparison between cropland and noncropland inside the same county. Although this limitation is important, our results still hold as other studies facing the same data constraints demonstrate that the county can serve as a surrogate for farmers’ decisions [Wu and Brorsen, 1995]. This limita­ tion implies estimating a logit regression with clustered standard errors at the county level (Eq. 3.8). Explanatory variables selected for this model are presented in Table 3.1.

Table 3.1 Statistics Summary of Explanatory Variables Variables LUR Dist mill (km) Dist roads (km) Slope (degree) Soybeans yield (ton/ha) Soybeans area (1000 ha) Herd (1000 head of cattle) Year

Mean

Standard deviation

Minimum

Maximum

Observation

0.68 27.31 8.55 1.56 2.64 51.07 173.51 2009.98

0.468 19.268 6.923 0.945 0.749 61.877 113.898 2.140

0 0 0 0 0 0 12 2006

1 222.935 42.0665 20.4306 4.5 290 657.781 2013

267,794 267,794 267,794 267,794 267,793 267,793 267,793 267,794

44

y

BIOENERGY AND LAND USE CHANGE 0

Distanceto mill 2 Distanceto paved road Slope 5 Soybean area 4 Soybean yield . Herd 7 Year (3.8) 1

3 6

One important element in our conceptual model is farmers’ ability to differentiate between profits generated by cropland or by pastureland. To implement this treatment, we include variables Soybean yields and Herd. Soybean yields is a proxy for the revenue that could be achieved with a soybean field. Its value is equal to the total soybean production in a county divided by the area under land use classes Annual single crop and Annual double crop. County‐ level soybean production data are from Pesquisa Agrícola Municipal [Instituto Brasileiro de Geografia e Estatística, 2016b]. Herd is a proxy for revenue from pastureland and is the total cattle herd in a county. Data on cattle herd are from Pesquisa da Pecuária Municipal [Instituto Brasileiro de Geografia e Estatística, 2016a]. Each sugarcane area was also characterized by its slope. Steeper areas (>12°) are not suitable for the mech­ anized production of sugarcane. The slope was calcu­ lated from 30 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data. We further aggregated this layer to match the spatial resolution of our land use maps (cell size 240 m). Sugarcane fields need to be located close to mills, given the crop’s loss of sugar content once harvested. Therefore, we included a variable describing the dis­ tance of each sugarcane area to the nearest mill (Figure 3.2), which is defined as the Euclidean distance from the nearest mill to the centroid of each sugarcane field for each year. The mill location data were collected from CONAB [2013]. The infrastructure in the producing area is represented by the distance between each sugarcane area and the nearest paved road (Figure 3.3). The calculation of this variable included the minimum Euclidean distance from the roads to the centroid of each sugarcane field. The original roads data are from the Banco de Informações e Mapas de Transportes [Ministério dos Transportes, 2010]. 3.4. RESULTS 3.4.1. Measuring the Land Use Responses The importance of sugarcane can be assessed by com­ paring the evolution of production areas from 2006 to 2013 (Figure 3.4). The year of 2009 presented the biggest expansion with 264,000 ha of new sugarcane fields. The next 2 years had smaller area expansion, followed by a strong growth in 2012 and 2013. This variation indicates the installation phases of the new mills: from the first

year’s focus on the development of a core of suppliers to later expansion of the suppliers’ number in order to reach the full crushing capacity of the mill. Throughout this expansion process, we found evidence that sugarcane has converted cropland and noncropland for every year examined. The measurement of LUR indicates that extensification was the dominant response, consistently representing more than 68% of all the LUR. The results show that in 2006 and 2007, extensification had a small advantage in relation to intensification. However, since 2008 there is a strong trend of more exten­ sification. Figure 3.4 shows that 2010 was the year with the largest difference, though this year is not the one with the largest expansion. In addition, the results indicate that 2010 was a turning point with extensification being more than twice the size of intensification, a pattern that persisted for the last three years of the study. Furthermore, intensification is losing importance throughout the period of analysis. This result offers support to the claim that sugarcane has expanded over pastureland in order to avoid competition with food production. Nevertheless, intensification was the response in approximately one third of the new producing areas across the entire study period. Notably, there is the difference in LUR between Goiás and Mato Grosso do Sul with a larger presence of inten­ sification response in Goiás, while extensification has been the more predominant LUR in Mato Grosso do Sul (Figure 3.5). To explain these responses, and to further explore the patterns of sugarcane’s LUR, we implemented a statistical analysis of binary choice. With this model, we discuss the impact of different factors on the LUR decision process. 3.4.2. Sugarcane’s Land Use Responses Model The previous section identified and quantified the intensification and extensification actions to incorporate sugarcane into the agricultural production landscape of the Cerrado. The importance of this measure goes beyond a simple identification exercise. By estimating a statistical model of LUR, we investigate some of the factors that impact the new sugarcane frontier of Goiás and Mato Grosso do Sul. Table  3.2 presents the results for the estimation of Equation 3.8. All variables are statistically significant, and the model correctly estimates the response in approx­ imately 68% of the sample, indicating that the model is a good representation of the original processes. The inter­ pretation of the coefficients is not straightforward, given the model was estimated using the logit link function. The analysis of the marginal effects (Table 3.3) provides for more direct interpretation of the effects of each independent variable on the LUR.

EffECTS Of SUGARCANE ETHANOL ExpANSION IN THE BRAzILIAN CERRADO 45

Figure 3.2 Distance from sugarcane fields to mills, 2013.

The computation of marginal effects advances our understanding of the LUR processes by estimating the  impact of a marginal change in an independent variable in the occurrence of extensification. We are reporting the marginal effects at the mean of the observation sample. All variables are statistically significant, and only Dist roads is not significant at 1%; it is significant at 5%. The interpretation of the effects in Table 3.3 indicates that an increase of 1 km in the distance to nearest mill

(Dist mill) raises the probability of extensification response by 0.085%, while an increase in the distance to nearest paved road (Dist roads) raises the probability of extensi­ fication response by 0.131%. Slope is also positively cor­ related to extensification, as is Herd. Variables Soybeans yield and Soybeans area are negatively correlated, thus indicating an increase in the probability of intensifica­ tion response. The trend (Year) is an important covariate to explain LUR, and it is positively correlated to extensification.

Figure 3.3 Location of paved road and presence of sugarcane fields, 2013.

Intensification

Extensification

Total expansion

300 250

1000 ha

200 150 100 50 0

2006

2007

2008

2009

2010

2011

2012

2013

Figure 3.4 Annual total expansion of sugarcane area and the classification into intensification and extensification responses, in Goiás and Mato Grosso do Sul.

EffECTS Of SUGARCANE ETHANOL ExpANSION IN THE BRAzILIAN CERRADO 47

Figure 3.5 Cumulative intensification and extensification promoted by sugarcane expansion in the states of Goiás and Mato Grosso do Sul, 2006–2013: (A) calling attention to the southeast of Goiás, a region with a large presence of intensification; (B) calling attention to the southeast of Mato Grosso do Sul, a region with a large presence of extensification. Table 3.2 Results for the Statistical Logit Model of Land Use Responses Variables Constant Dist mill (km) Dist roads (km) Slope (degree) Soybeans yield (ton/ha) Soybeans area (1000 ha) Herd (1000 heads) Year

Coefficient −183.1250*** 0.0043*** 0.0065** 0.2382*** −0.4367*** −0.0062*** 0.0050*** 0.0916***

Standard error

z

23.917 0.001 0.003 0.014 0.035 0.000 0.000 0.012

−7.66 3.79 2.06 16.71 −12.65 −15.55 22.09 7.68

95% Confidence interval −230.001 0.002 0.000 0.210 −0.504 −0.007 0.005 0.068

−136.249 0.006 0.013 0.266 −0.369 −0.005 0.005 0.115

Note: *** and ** indicate significance at the 1 and 5% level, respectively. Table 3.3 Estimation Results for the Marginal Effects, at Means Variables Dist mill (km) Dist roads (km) Slope (degree) Soybeans yield (ton/ha) Soybeans area (1000 ha) Herd (1000 heads) Year

Marginal effects

Standard error

z

0.00085*** 0.00131** 0.04758*** −0.08723*** −0.00124*** 0.00100*** 0.01829***

0.00022 0.00064 0.00286 0.00689 0.00008 0.00004 0.00238

3.79 2.06 16.66 −12.66 −15.75 22.59 7.68

Note: *** and ** indicate significance at the 1 and 5% level, respectively.

95% Confidence interval 0.0004 0.0001 0.0420 −0.1007 −0.0014 0.0009 0.0136

0.0013 0.0026 0.0532 −0.0737 −0.0011 0.0011 0.0230

48

BIOENERGY AND LAND USE CHANGE

3.5. DISCUSSION These results show that, during the 2006–2013 period, farmers planted more sugarcane on noncropland (extensification) than on cropland (intensification) and that both LUR are influenced by the presence of mills. Arguably, intensification and extensification responses to sugarcane expansion are co‐occurring responses. Sugarcane mills seek installation locations that offer access to large areas to develop their fields. From the LUR map (Figure 3.5), the prevalence of noncropland land uses leading to extensification is noticeable. On the other hand, mills also want to reduce transportation cost, thus locating closer to roads and better transporta­ tion infrastructure [Granco et al., 2015]. These are the same factors that attract grain producers and industrial facilities to the region. The statistical model confirms the influence of mill proximity on the LUR, with fields farther away sup­ porting extensification. This process can be explained as a cost‐minimization strategy by the mill. By going farther away to secure sugarcane, mills would give preference to larger farms in order to gain scale and reduce harvest cost, and such areas are more commonly used as pastureland. The presence of paved roads is vital for the sugarcane mill’s workflow because harvested sugarcane is transported to the mill by large trucks (Figure  3.3). Even though unpaved roads can be used, paved roads are preferred, given the speed and lower cost of maintenance on trucks. These same advantages also attract crop farmers to be located close to paved roads; thus, intensification is expected to be higher closer to roads because cropland density typically is higher in these areas as well, while extensification frequency increases with distance to roads. Farmers who adopted sugarcane in the Cerrado were already aware of the requirements for the mechanized harvest of sugarcane, mainly the restriction on slope being less than 12° [Aguiar et al., 2011]. The implementa­ tion of mechanized harvest has two goals: (i) reducing carbon emission from the preharvest burning, thus improving sugarcane’s ethanol life cycle carbon budget, and (ii) reducing the labor cost and litigation from hiring a large contingent of workers during harvest season and not offering adequate work conditions [Capaz et al., 2013]. Most ethanol companies that own mills in Goiás and Mato Grosso do Sul have already operated in the state of São Paulo, where mechanized harvest legislation is enforced; thus, they carry this knowledge to their new facilities. Also, the international market for sugar and ethanol demands certification to select products that use nonburned sugarcane. The slope requirement indicates another point of com­ petition with oilseed and grain production because the

slope is also an important factor for those land uses. The statistical model shows that a steeper slope is correlated with an increase in the probability of extensification. This positive correlation may come from the fact that most croplands (as most sugarcane fields) are located in small‐ slope areas, while pastureland can be found in a larger gradient. The variables related to previous land use, Soybean yield, Soybean area, and Herd, are important not only to indicate if one use is important in that county but also to give the trajectory of the previous land use at the county level. The negative correlation between soybean yields and area with respect to extensification is expected because counties with higher yields can reflect that croplands are located in better soils and probably have a better management of soil fertility, which can attract the interest of sugarcane mills to those areas, while large areas dedicated to soybean production can indicate that the best agricultural lands have already been converted from pastureland to cropland by soy producers. The fact that variable Herd is positively correlated to extensifica­ tion indicates that sugarcane may be related to intensifi­ cation of cattle production because more pastureland has been converted from counties with large herds [Cohn et al., 2014; Alkimim et al., 2015]. As our analysis of the LUR showed, extensification has become more dominant throughout the period, a result that is confirmed by the statistical analysis of the trend variable (Year). This dominance reflects two conditions: (i) the prevalence of pastureland as the main land use in the study area, and (ii) oilseed and grain production, especially double‐crop rotation of soybeans and corn, can generate more profit for farmers than pastureland. Notably, sugarcane expansion has prompted both intensification and extensification; however, the statistical model indicates that extensification is more likely to con­ tinue in the future. This is in agreement with the Brazilian government’s desire to stimulate sugarcane expansion on degraded pastureland, thus avoiding land use competition with cropland and reducing GHG emission from degraded pastureland [Manzatto et al., 2009]. Nevertheless, these benefits will only occur if we assume that cattle ranching is actually intensifying its production. A displacement of cattle production to new areas inside the Cerrado, or even to the Amazon biome, could result in more GHG emission due to deforestation [Lapola et al., 2010]. One observation that needs to be made is the definition of LUR regarding only the fields that are converted to sugarcane. One can argue that our definition of intensi­ fication does not consider if more land is converted else­ where to cropland and that extensification over pastureland can be a result of a gain in cattle‐stocking rate, thus promoting intensification of cattle production and releasing land to sugarcane. Although we are aware

EffECTS Of SUGARCANE ETHANOL ExpANSION IN THE BRAzILIAN CERRADO 49

of the limitations of the definitions used, we maintain the validity of our study despite the lack of data on cattle stocking and the technical difficulties in identifying indirect land use change. Inclusion of more explanatory variables, such as cattle stocking and commodities price, can contribute to improve the statistical model in future research. 3.6. CONCLUSION This study is the first to examine the land use responses prompted by the expansion of sugarcane ethanol industry into the states of Goiás and Mato Grosso do Sul. The period of 2006–2013 covers the booming period of the industry, a useful moment to understand the introduction of a new agricultural land use to the Cerrado biome. To accomplish our goals, we developed a procedure to identify intensification and extensification responses at the field level. We then use this new information in a statistical model to examine how different factors affect each LUR. For the period analyzed, our results indicate that sugar­ cane prompted both intensification and extensification responses, but extensification accounted for more than 68% of all LUR. These findings corroborate previous research results pointing to extensification of sugarcane over pastureland in other regions of Brazil [Adami et al., 2012]. In addition, we identified a trend toward extensifi­ cation, which was further validated by the statistical model. This tendency is consistent with the general notion that pastureland is less profitable than cropland, thus being selected more frequently to be replaced by sugarcane. Furthermore, our model examined factors that impacted LUR, focusing on extensification. The highlight is the influence of sugarcane mill location and existing infra­ structure on the LUR decision process, where increasing distance to the mill and to roads supports an extensifica­ tion response. This dynamic can be an object of policies aiming to avoid land use competition between sugarcane and soybeans or to stimulate the development of sugar­ cane areas in counties with degraded pastureland. The extensification response may actually be capturing an intensification of cattle ranching. In this case, farmers would intensify their cattle stockage and release a por­ tion of pastureland to sugarcane production. This would allow farmers to maintain their culture and status as cattle owners while being able to capture new income from sugarcane production. In fact, the Brazilian government is most interested in supporting this land use response to sugarcane expansion because it has the potential to reduce GHG from degraded pastureland and increase productivity of the cattle‐ranching sector without interfering with food production. However, more research is needed on this topic to test if sugarcane

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4 Biofuel Expansion and the Spatial Economy: Implications for the Amazon Basin in the 21st Century Eugenio Y. Arima1, Peter Richards2, and Robert T. Walker3

ABSTRACT  This chapter considers the theoretical conditions in the spatial economy that may lead to indirect land use change (ILUC) in Brazil. We analyze the expansion of soybean and sugarcane agriculture for biofuel production and their encroachment on existing pastures, displacing them to the Amazonian frontier. ILUC can arise (i) by market impacts on commodity prices and (ii) from land appreciation in settled agricultural areas, which stimulates the out‐migration of capital and ranchers to forest frontiers. We explain how those two mechanisms can cause ILUC and present data that seem to support the second mechanism. We conclude that a shift to biofuels will put more pressure on tropical forests. Despite the many advantages of biofuels, recent research suggests that expansion of biofuel feedstocks, mainly soybean and sugarcane, may also fuel tropical deforestation, particularly in the Brazilian Amazon region. The literature recognizes two pathways by which this could occur. The first and most obvious is the direct conversion (DLUC) of tropical forests to feedstock crops. Although direct effects have been documented [Brown et al., 2005; Morton et al., 2006], pasture expansion for cattle raising remains by far the primary direct cause of Amazonian deforestation. Pasturelands occupy 80% of the total deforested area, whereas annual crops cover only about 5% [Embrapa and Inpe, 2011]. The percent value for pasturelands includes pastures under different classifications, from clean pastures to advanced stages of secondary vegetation succession. Given the importance of cattle ranching in the overall deforestation numbers, a second pathway to deforestation has been proposed, which involves an indirect effect, whereby pasturelands are converted to mechanized agriculture in one region only to be reconstituted in Amazonia [Searchinger et al., 2008; Walker et al., 2009; Lapola et al., 2010]. Under this explanation, Amazonian deforestation is partly due to

4.1. INTRODUCTION Biofuels have long been lauded as a potential solution to many of our most pressing economic, geopolitical, and environmental problems, particularly for countries with abundant land resources. An expanding biofuel contribu­ tion to the total energy supply portfolio could help miti­ gate climate change, decrease reliance on fossil fuels, and provide a stable and secure source of energy [Goldemberg, 2007]. As a result, demand for biofuels is expected to soar in the coming decades, as countries transition to more sustainable energy sources. Brazil, with its vast reserves of farmland and advanced biofuel and agricultural tech­ nology, is well positioned to play an important role as a supplier of this form of energy.

1

Department of Geography and the Environment, University of Texas, Austin, Texas, USA 2 Bureau for Food Security, U.S. Agency for International Development, Washington, District of Columbia, USA 3 Center for Latin American Studies and Department of Geography, University of Florida, Gainesville, Florida, USA

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 53

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agricultural development elsewhere, a mechanism known as indirect land use change (ILUC), the main focus of this chapter. Although research to date has tried to seek empirical evidence of ILUC through computation [Walker et al., 2009], simulation models [Lapola et al., 2010], statistical analyses [Arima et al., 2011; Richards et al., 2014], and field surveys [Richards, 2015], the theoretical mechanisms through which these land use systems rearrange spatially from one place to another are still underdeveloped, with a few notable exceptions [e.g., Walker, 2001, 2011, 2014]. The objective of this chapter is to help fill this gap by deploying von Thünen’s locational rent theory to explain how the expansion of biofuels in south‐central Brazil could potentially push cattle ranching to other regions,

Figure 4.1 Brazilian Amazon forest and states.

particularly to Amazonia. Rather than providing a formal mathematical derivation of the theory à la Walker [2014], this chapter accomplishes its objective by using a simple graphical representation of the processes at play, in the interest of reaching out to a general audience. We begin the chapter by briefly discussing Brazil’s biofuel policies and its reliance on sugarcane and soybeans as feedstocks. We also present empirical evidence for the growth of sugar­ cane and soybean plantation in south‐central Brazil in the last 20 years, in tandem with the expansion of cattle ranch­ ing in Amazonia (Figure 4.1). In the next section, potential ILUC mechanisms are presented, followed by a discussion of the actual mechanism that might be operating in Brazil. We conclude the chapter by discussing the implications of these expansions for Brazil’s climate mitigation policies.

BIOfUEL ExpANSION AND THE SpATIAL ECONOMY 55

plantations covered 2.7 million ha [Nunberg, 1986]. By 1990, the area planted had increased by 60% and reached 4.3 million ha and continued a steady expansion until 2005 when it reached 5.8 million ha [IBGE, 2016a]. In the following decade, the area planted expanded more quickly, and by 2014 sugarcane covered approximately10.5 million ha (Figure 4.2). Today, the country produces over 17 million m3 of ethanol per year, a volume two orders of magnitude greater than in the early stages of the program in 1975 [Geller, 1985; ANP, 2015]. Sugarcane plantations have expanded geographically in the last 25 years but not nearly as much as soybean and cattle ranching (as explained in what follows). In the 1990s, plantations were concentrated in the state of São Paulo and along the northeast coast of Brazil (Figure 4.2, top left panel). By 2014, sugarcane consolidated its

4.2. SUGARCANE, SOYBEAN, AND CATTLE‐RANCHING EXPANSION Ethanol production in Brazil from sugarcane is argu­ ably one of the most successful biofuel programs in the world. Large‐scale production began after the oil crisis of the 1970s when a mandate to blend ethanol into gasoline (20% blend) was established [Goldemberg, 2007]. In the following decades, that proportion increased to 25%, and engines that worked with any ethanol‐gasoline proportion, including 100% ethanol, were developed (i.e., flex‐fuel engines) [Moreira and Goldemberg, 1999]. By 2014, flex‐fuel engines powered 88% of all passenger and light commercial cars sold in Brazil [ANFAVEA, 2015]. As a result, demand for ethanol increased, and the sugarcane planted area followed suit. In 1980, sugarcane

1990

2014 0°

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Area planted (ha) 1 Dot = 10,000 30° S States 70° W

60° W

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30° W

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40° W

30° W

Sugarcane 1000

800

600

400

200 1990

1995

2000

2005

2010

2015

Year Planted area (× 10,000 ha)

Production (× 1,000,000 tons)

Figure 4.2 Sugarcane planted area, production, and geographic distribution in Brazil, 1990–2014 (Source: IBGE [2016a]).

56

BIOENERGY AND LAND USE CHANGE

In less than a decade, in 2014, production of biodiesel reached 3.4 million m3. Soybean is by far the most impor­ tant biodiesel feedstock, responsible for 2.6 million m3, or almost 77% of the total biodiesel production [ANP, 2015]. Brazil already had a vast area planted with soybeans in 1990, covering 11.5 million ha that extended from the southern part of the country to latitude approximately 13° S, which included parts of the cerrados (Figure  4.3, top left panel). In the following 25 years, that area almost tripled, reaching more than 30 million ha in 2014 [IBGE, 2016a]; much of this expansion occurred in the cerrados [Lapola et al., 2014] and on the fringes of the Amazonian forest in the state of Mato Grosso, which became the largest soybean producer (Figure 4.3, top right panel). While both sugarcane and soybean experienced growth in area planted and, to a certain extent, geographic expansion into the cerrados and Amazonia, nothing is

footprint in São Paulo, while expanding to neighboring states (e.g., Minas Gerais and Mato Grosso do Sul). The advance of sugarcane over the cerrado areas of Goiás state and its relative absence in Amazonia is also noticeable (Figure 4.2, top right panel). In the case of soybean, for decades Brazil has been one of its largest producers in the world. Soybean is used mostly as a high‐protein animal feed and a source of cooking oil in the food industry. Soybean feedstock for biofuel production in Brazil is more recent and only took off after 2008 when the country launched its national climate change policy [Brazil, 2008]. The plan formalized a long‐term strategy to gradually increase the proportion of biodiesel in the fossil fuel diesel blend, beginning at 2%, with the goal of reaching 20%. As of 2014, the mandatory proportion is at 7%. Production of biodiesel was incip­ ient in 2005 and amounted to only 736 m3 [ANP, 2015].

1990

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Soybean 800

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0 1990

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2000

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Year Area planted (× 100,000 ha)

Production (× 100,000 tons)

Figure 4.3 Soybean planted area, production, and geographic distribution in Brazil, 1990–2014 (Source: IBGE [2016a]).

BIOfUEL ExpANSION AND THE SpATIAL ECONOMY 57

The empirics of these land system dynamics led researchers to posit that the expansion of soybean and sugarcane into pastures pushed the latter to reestablish in Amazonia. Yet the exact mechanisms that explain these landscape cascades are only now being developed. The following section inves­ tigates how these land displacements might occur.

comparable to the march of cattle ranching up north into the Amazonian forests. Brazil already had a sizable cattle herd in the mid‐1970s with 92 million animals, mostly concentrated in the grasslands of the cerrado and in the southern pampas (Figure 4.4, top left panel). Since then, the herd has grown at a steady pace, reaching 170 million in 2000 and 212 million animals by 2014 [IBGE, 2016b]. In terms of spatial distribution, two aspects are worth noting. First, Amazonia became the most important cattle‐raising region in the country, hosting a third of the total herd, with Mato Grosso, Pará, and Rondônia head­ ing the numbers (Figure  4.4). Second, cattle ranching occurs more often than not in places where soybean and sugarcane are absent. This is true even in Mato Grosso, where soybean and cattle‐ranching regions within the state are spatially exclusive.

4.3. LAND RENTS AND LAND USE DISPLACEMENT To illustrate the mechanisms at play that may explain cattle‐ranching displacement into Amazonia, we adopt the classical von Thünen locational rent model because of its explicit depiction of spatial allocation of production and its ability to include different land uses in an integrated framework [von Thünen, 1966]. This stylized conceptual model is illustrated in Figure 4.5, where we portray three

2014

1974 0°

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2000

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1000 1970

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Figure 4.4 Growth and geographic distribution of the cattle herd in Brazil, 1974–2014 (Sources: cattle herd from IBGE [2016b]; pasture area from FAOSTAT [2016]).

58

BIOENERGY AND LAND USE CHANGE $/ha

$/ha

iv ns te In e

iv ns te In e

Ex

ten

Agriculture tm

Exte

nsiv

siv

e te

tm Distance

1 tm

e te

Distance

Pasture Forest

Figure  4.5 Von Thünen’s conceptual model of locational land rents. (See insert for color representation of the figure.)

land uses organized in concentric rings irradiating out from the core, where the hub of consumption, transpor­ tation, and industrial activity of a given region is located. In the case of Brazil, this well‐defined core is the southeast region (e.g., São Paulo and Rio de Janeiro), responsible for 60% of the country’s GDP, more than 40% of its population; it is also endowed with a substantial infra­ structure, including road networks and ports for export of agricultural products [IBGE, 2016c]. Everything else being equal, farmers located closer to this core obtain higher rents (i.e., profits) from their land than those at a distance; this is due to proximity, which translates into savings in freight costs (or higher farmgate prices for their products). The rent gradient established thereby is illustrated by the negatively sloped lines in Figure  4.5, midpanel. The model assumes that landowners are profit maximizers and will devote their land to the use that returns the highest profit. Without loss of generality, we assume two distinct uses. The first is intensive agricultural land uses (red line), which in our case include biofuel crops, either sugarcane or soybean plantations. The second use, called the extensive use (black line), comprises planted pastures for extensive cattle ranching. We observe intensive use of the land whenever profits from agricul­ ture are higher than those from cattle ranching (Figure 4.5, midpanel). The point tm in the graph, called the intensive margin, delimits the transition from agriculture to cattle ranching, whereas the point te is the extensive margin, beyond which rents are negative, and the natural envi­ ronment (i.e., Amazonian forests) is left undisturbed by market‐oriented land uses. The different slopes of the rent lines reflect variation in freight rates among different products, be it soybean, sugarcane, or beef. In its original conception, the von Thünen model does not explain the cascading effect of indirect land use change. Suppose, for instance, that an external shock occurs in the form of technological advances in soybean or sugarcane cultivation (e.g., new, more productive vari­ eties) or higher prices for these commodities driven by internal or external markets. This positive shock has the effect of shifting the intensive rent line outward to the right as agriculture becomes more profitable (Figure 4.5,

right panel). As a result, pasturelands between tm and the new intensive margin t1m are converted to intensive agri­ cultural uses (Figure 4.5, right panel). However, the von Thünen model by itself does not contemplate any change in the extensive margin when shocks occur in the intensive land use system; the rent gradient line for cattle ranching remains unchanged, and therefore, no new deforestation can be attributable to the expansion of soybean/sugarcane. In the next subsections, we investigate two potential market‐based mechanisms that may drive indirect land use, using von Thünen’s insightful concepts. 4.3.1 Capital Constraints and the Extensive Margin Figure 4.6 exemplifies one mechanism whereby changes in the intensive frontier may cause cattle ranching to expand into forested lands. In this particular setup, we assume that capital is scarce in the economy and in the  cattle‐ranching sector in particular. Although cattle ranching is a profitable activity up to the extensive margin te, the actual frontier where deforestation is observed is set at tk because of capital scarcity that constrains the full use of the land available. When the intensive land system rent line shifts outward, land prices between the core and t1m increase because of the higher profitability of soybean/ sugarcane. In theory, land prices (P) are the present value of the sum of the perpetual flow of rents and can be cal­ culated by dividing the annual rent (L) by the discount rate (r): Le rt dt

P 0

L . r

Under this new rent scenario, cattle ranchers located between tm and t1m, assuming they are profit maximizers, are faced with two choices. First, they can convert their pastures into soybean/sugarcane fields, in which case no effect will be observed in the extensive land system. However, if ranchers choose to remain in the cattle business, they can sell their lands at this higher price and buy land beyond tk, where land is available and much

BIOfUEL ExpANSION AND THE SpATIAL ECONOMY 59 $/ha

$/ha

$/ha

tk

e

e

ive ns

iv ns te In

iv ns te In

e Int tm

tm

te Distance

1 tm

tk

te

1 tm

Distance

tk

tk1

te Distance

Figure 4.6 ILUC from capital transfers to the extensive margin. (See insert for color representation of the figure.) $/ha

$/ha

$/ha

ive ns te In

e

e

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iv ns te In

Ex

ten

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Ex

ten

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e te

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siv

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Figure 4.7 ILUC from price elasticity effects. (See insert for color representation of the figure.)

cheaper and ranching is still profitable. With this new capital influx, deforestation for cattle ranching expands to the new frontier edge tk1. 4.3.2. Price Elasticity Effects A second mechanism through which changes in the intensive system may cause changes in the extensive one was fully developed by Walker [2014], and here we provide a graphical description of the same model. Unlike the previous case, lands are fully utilized to their highest rent‐ generating activity at the onset, with tm and te delimiting the boundaries of the intensive and extensive land systems, respectively (Figure  4.7, left panel). After the intensive system rent line shifts to the right, the pasturelands bet­ ween tm and t1m are converted to agriculture (e.g., soybean or sugarcane), because it is now the most profitable activity. Everything else being equal (e.g., no change in produc­ tivity), this loss of pastureland also implies a reduction in cattle production (Figure  4.7, midpanel). If demand for beef is inelastic, this reduction in production will lead to an increase in the selling price of cattle by producers. This higher price will make cattle ranching more profitable, resulting in a shift of the extensive land system rent gra­ dient also to the right. The area between te and te1 that was previously outside the profitability range is now brought into production, resulting in new deforestation. 4.4. DISCUSSION The regression models of Arima et al. [2011] and Richards et al. [2014] reveal a declining, yet strong, statistical signal for ILUC in the Amazon as a result of Brazil’s expanding

soybean sector. In addition, simulation models by Lapola et al. [2010] estimate that 60,000 km2 of projected future deforestation by 2020 could be attributed to sugarcane ethanol and soybean biodiesel. What mechanism, if any, will contribute to indirect land use change in the Amazonian case? There is little empirical evidence to support the price‐effect mechanism on land change. The only state with a significant decline in cattle herd was São Paulo, where numbers peaked at 14 million animals in 2003 but declined steadily to 10 million in 2014, with a negative overall trend between 1990 and 2014 (Table 4.1). In the states of Rio Grande do Sul, Paraná, and Mato Grosso do Sul, where soybeans and sugarcane expanded considerably, the cattle herd has shown a small positive trend since 1990 (Table 4.1). Thus, the overall numbers suggest that the potential impact of herd losses in São Paulo in particular is very small in magnitude compared to the overall cattle herd of Brazil and that the impact is minimal, if any, on beef prices. This same conclusion was reached by Richards [2015] in a localized study in Mato Grosso. The empirical evidence for the capital‐constraint mech­ anism is stronger. Supporting this mechanism is the huge price differential that exists between pastureland prices in intensive regions versus forests in cattle‐ranching regions in Amazonia (Table  4.2). In the extremes, a rancher in Ribeirão Preto, a region where sugarcane is the dominant cropping system, could sell 1 ha of pastureland and pur­ chase as many as 61 ha of forest lands in acre. Pastureland prices in Ribeirão Preto are high because they reflect the potential rent of their alternative use (i.e., sugarcane). The same phenomenon can be observed in soybean regions of Paraná, Rio Grande do Sul, and Mato Grosso

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BIOENERGY AND LAND USE CHANGE

Table 4.1 Cattle Herd, Sugar Cane, and Soybean Trend Regression t‐Statistics for Selected States State

Cattle herd (1990–2014)

São Paulo Paraná Rio Grande do Sul Mato Grosso do Sul Acre Pará Mato Grosso Rondônia

Sugarcane area1 (1990–2014)

−3.60*** 2.20** 1.70* 1.76* 20.08*** 15.57*** 20.97*** 19.79***

14.07*** 23.67*** — 7.61*** — — 24.45*** —

Soybean area1 (1990–2014) 1.38 21.45*** 7.42*** 7.88*** — 7.43*** 19.48*** 12.42***

Source: Authors’ calculations; original data from IBGE. Note: Reported t‐statistics of the linear trend parameter of the regression of cattle herd number or planted area on year. ***significant at 0.01, **significant at 0.05, and *significant at 0.1. 1  For states with planted area greater than 50,000 ha in any year between 1990 and 2014. Table 4.2 Price of Pastureland in Regions Dominated by Agriculture and Prices of Forested Lands in Cattle‐Ranching Regions in Amazonia (2011) State (region)

Land use

São Paulo (Ribeirão Preto) Paraná (Cascavel) Rio Grande do Sul (Passo Fundo) Mato Grosso do Sul Acre (Rio Branco) Pará (São Félix do Xingu) Mato Grosso (Comodoro) Rondônia (Ariquemes)

Pasture Pasture Pasture Pasture Forest Forest Forest Forest

Price/ha ($)

Price of SP (%)

14,283 9,750 6,883 5,358 232 1,067 1,325 570

100 68 48 38 2 7 9 4

Dominant land system Sugarcane Soybean Soybean/wheat Soybean Cattle Cattle Cattle Cattle

Source: Informa FNP [2012].

do Sul; here, pastureland prices are high because they are also suitable for soybean, the dominant regional cropping system. With this discrepancy in the price, ranchers in intensive land management regions may choose to sell their lands and buy larger estates in Amazonia, where land prices are much lower. This migratory movement in search of cheaper lands was also supported by the field survey conducted in the early 1990s by Arima and Uhl [1997]. The authors found that 44% of small ranchers and 28% of the medium‐ scale ranchers had sold their ranches in other states (mostly in São Paulo, Minas Gerais, and Goiás) to invest in southern Pará. In a more recent study in west­ ern Pará, Richards [2015] found little evidence of a local displacement of cattle ranchers from nearby northern Mato Grosso, where soybean plantations have been encroaching on former pasturelands. He did find, how­ ever, that a portion of ranchers purchasing properties in western Pará were arriving from consolidated regions (intensive system regions) elsewhere in Brazil, suggest­ ing that ranchers would migrate to augment the size of their holdings. Although this capital‐relaxation mechanism seems to be the one at play, it is important to note that deforestation

would have happened in the extensive frontier regardless of changes in the intensive land system. This is because those forested lands between tk and te are profitable to ranching (Figure  4.6). In the long run, capital would eventually find its way to the region in search of profits, and deforestation would ensue. Nevertheless, land appre­ ciation in intensive system areas likely accelerated the process of deforestation by providing a readily available source of capital for investment in Amazonia. This “premature” deforestation has negative consequences to climate change because of long permanence of CO2 in the atmosphere, which makes a molecule emitted today more harmful than one emitted a decade from now. 4.5. CONCLUSIONS This chapter has considered ILUC as it affects Brazil, in the context of biofuel production and implications for the Amazonian forest. To this end, we have deployed the classical location rent model of von Thünen to gain insight into interactions among land use systems and their spatial demands, on the national scale. To date, research has advanced two mechanisms to explain links between sugarcane and soybean agriculture on the one hand and

BIOfUEL ExpANSION AND THE SpATIAL ECONOMY 61

cattle production on the other. From a theoretical per­ spective, ILUC can arise (i) by virtue of market impacts on commodity prices, which drive up the value of beef and therefore the rent potential of marginal lands, and (ii) by land appreciation in settled agricultural areas, which stimulates the out‐migration of capital and ranchers to forest frontiers. The chapter presents data that appear to be consistent with the capital migration hypothesis. In the Amazon, the ILUC that has occurred results primarily from booming agriculture in other parts of the country and not from rising beef prices due to supply shortfalls. ILUC can be problematic in countries attempting to address fossil fuel consumption by reliance on biofuels or feedstocks, particularly sugarcane and soybeans. Such is especially the case in countries with forest frontiers and cheap land. For Brazil, and Amazonia more generally, the list of deforestation drivers should be expanded to include the complex manner in which coupled land use systems interact. As we have argued here, such coupling can even reach across continental and global expanses (i.e., teleconnections). This greatly complicates policies directed at forest conservation, of which the Brazilian soybean moratorium is a case in point. Just because soybean farmers have managed to control incursions into primary forest, it does not mean that soybean agriculture no longer poses a threat to the forest. In fact, threats associated with ILUC are likely to intensify, with growing demands to end our societal addiction to fossil fuels by a shift to ethanol and biodiesel. As important as this is to mitigating global warming, we must not solve one environmental problem by aggravating another. ACKNOWLEDGMENTS The authors would like to acknowledge support from NSF project “Territorializing exploitation space and the fragmentation of the Amazon forest” and from CNPq Science Without Borders project 400964/2014‐7. We thank Kaitlin Tasker (UT‐Austin) for doing the von Thünen art­ work and the reviewers for their comments. Peter Richards serves as an economist with the Bureau for Food Security at the U.S. Agency for International Development (USAID). The views and opinions expressed in this chapter are those of the authors and not necessarily the views and opinions of the USAID or of the funding agencies. REFERENCES Agência Nacional do Petróleo (ANP) (2015), Anuário estatístico brasileiro do petróleo, gás natural e biocombustíveis 2015, ANP, Rio de Janeiro, Brazil. Arima, E. Y., and C. Uhl (1997), Ranching in the Brazilian Amazon in a national context: Economics, policy practice, Society and Natural Resources, 10, 433–451.

Arima, E., P. Richards, R. Walker, and M. Caldas (2011), Statistical confirmation of indirect land use change in the Brazilian Amazon, Environmental Research Letters, 6, doi: 10.1088/1748‐9326/6/2/024010. Associação Nacional dos Fabricantes de Veículos Automotores (ANFAVEA) (2015), Carta da ANFAVEA July 2015, São Paulo, Brazil. Brazil (2008), Plano nacional sobre mudança do clima – PNMC – Brasil. Brasília, Brazil. Brown, J. C., M. Koeppe, B. Coles, and K. P. Price (2005), Soybean production and conversion of tropical forest in the Brazilian Amazon: The case of Vilhena, Rondonia, Ambio: A Journal of the Human Environment, 34, 462–469. EMBRAPA and INPE (2011), Levantamento de informações de use e cobertura da terra na Amazônia ‐ sumário executivo. http://www.inpe.br/cra/projetos_pesquisas/sumario_ executivo_terraclass_2008.pdf (accessed 15 July 2017). FAOSTAT (2016), Food and Agriculture Organization of the United Nations data portal. http://www.fao.org/faostat/ en/#data (accessed 15 July 2017). Geller, H. S. (1985), Ethanol fuel from sugar cane in Brazil, Annual Review of Energy, 10, 135–164. Goldemberg, J. (2007), Ethanol for a sustainable energy future, Science, 315, 808–810. Informa FNP (2012), Anuário Anualpec 2012, FNP, São Paulo, Brazil. Instituto Brasileiro de Geografia e Estatística (IBGE) (2016a), Produção Agrícola Municipal. Banco de dados SIDRA. https:// sidra.ibge.gov.br/home/lspa/brasil (accessed 15 July 2017). Instituto Brasileiro de Geografia e Estatística (IBGE) (2016b), Produção Pecuária Municipal. Banco de dados SIDRA. https:// sidra.ibge.gov.br/home/lspa/brasil (accessed 15 July 2017). Instituto Brasileiro de Geografia e Estatística (IBGE) (2016c), Censo demográfico 2010. Banco de dados SIDRA. https:// sidra.ibge.gov.br/home/lspa/brasil (accessed 15 July 2017). Lapola, D. M., et al. (2010), Indirect land‐use changes can over­ come carbon savings from biofuels in Brazil, Proceedings of the National Academy of Sciences, 107, 3388–3393. Lapola, D. M., et al. (2014), Pervasive transition of the Brazilian land‐use system, Nature Climate Change, 4, 27–35. Moreira, J. R., and J. Goldemberg (1999), The alcohol program, Energy Policy, 27, 229–245. Morton, D. C., et al. (2006), Cropland expansion changes defores­ tation dynamics in the southern Brazilian Amazon, Proceedings of the National Academy of Sciences, 103, 14637–14641. Nunberg, B. (1986), Structural change and state policy: The politics of sugar in Brazil since 1964, Latin American Research Review, 21, 53–92. Richards, P. (2015), What drives indirect land use change? How Brazil’s agriculture sector influences frontier deforestation, Annals of the Association of American Geographers, 105, 1026–1040, doi:10.1080/00045608.2015.1060924. Richards, P., R. T. Walker, and E. Arima (2014), Spatially com­ plex land change: The indirect effect of Brazil’s agricultural sector on land use in Amazonia, Global Environmental Change, 29, 1–9, doi:10.1016/j.gloenvcha.2014.06.011. Searchinger, T., et al. (2008), Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land‐use change, Science, 319, 1238–1240.

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Von Thünen, J. (1966), The Isolated State English translation of the original work from 1826, Pergamon Press, New York. Walker, R. (2001), Urban sprawl and natural areas encroach­ ment: Linking land cover change and economic develop­ ment in the Florida everglades, Ecological Economics, 37(3), 357–369. Walker, R. (2011), The impact of Brazilian biofuel production on Amazônia, Annals of the Association of American Geographers, 101, 929–938.

Walker, R. (2014), Sparing land for nature in the Brazilian Amazon: Implications from location rent theory, Geographical Analysis, 46, 18–36. Walker, R., R. DeFries, M. de Carmen Vera‐Diaz, Y. Shimabukuro, and A. Venturieri (2009), The expansion of intensive agriculture and ranching in Brazilian Amazonia, in Amazonia and Global Change, Geophys. Monogr. Ser., vol. 186, edited by M. Keller, M. Bustamante, J. Gash, and P. Dias, pp. 61–81, AGU, Washington, DC.

Part II Impacts on Natural Capital and Ecosystem Services

5 Toward Life Cycle Analysis on Land Use Change and Climate Impacts from Bioenergy production: A Review Zhangcai Qin, Christina E. Canter, and Hao Cai

ABSTRACT Land use change (LUC) associated with bioenergy production can result in significant climate impacts. This chapter reviews LUC and climate impacts in a bioenergy life cycle analysis (LCA) setting. We first review current understanding regarding bioenergy feedstock production and its influence on changes of land cover, land use, and land management. Then by examining biogeochemical and biogeophysical processes associated with LUC, we identify major impacts that can be significant for bioenergy crop production, including carbon stock changes in vegetation and soil, nitrous oxide emissions, and surface albedo change. Finally, we introduce a bioenergy LCA that incorporates LUC impacts and also provide an example of integrating LUC‐induced biogeochemical and biogeophysical climate impacts in a biofuel LCA. Major challenges and future needs are highlighted at the end of the chapter. Further efforts should focus on improving LUC estimation, quantification, and integration of LUC‐induced biogeophysical impacts, including but not limited to surface albedo effects, and examining the importance of ecologically sensitive regions in overall LUC estimates. 5.1. INTRODUCTION

2012 the forestry sector provided 87% of the biomass supply, with fuelwood as the largest contributor. Agricultural sources (i.e., agricultural and animal by‐products and energy crops) and waste (i.e., municipal solid waste and landfill gas) contributed another 10% and 3% of the total global biomass supply, respectively. Bioenergy has been commonly used in human history, for example, making fire with wood. Traditionally, bioenergy, primarily wood, charcoal, and agricultural residues, is used in open fires or simple stoves for cooking and heating. This type of energy is combusted at very low efficiencies, with only 10%–20% of primary energy is converted to useful heat [IEA, 2014; WBA, 2015]. Currently, biomass is widely used in many different ways, even though heating and cooking still share a large portion of the total biomass use in developing countries. Biomass can be used to generate electricity, produce biofuels, and provide heat with high efficiency. For instance, Europe used most of its biomass for electricity production. In the Americas, mainly USA and Brazil, over three fourth of

Bioenergy derived from biomass is becoming increasingly attractive to many countries looking for alternative energy resources. One of the major advantages of bioenergy is that many biomass feedstocks available on a “renewable or recurring basis” can become accessible, which can include crops, agricultural residues, woody crops, trees, algae, and many other biological materials [U.S. Department of Energy (USDOE), 2016]. It is estimated that, in 2011–2012, bioenergy was the largest renewable energy source in the global energy mix, providing about 10% of world total primary energy supply (50 exajoule (EJ)) with other renewable sources contributing only a total of about 3% [International Energy Agency (IEA), 2014; World Bioenergy Association (WBA), 2015]. According to a recent report [WBA, 2015], in

Energy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 65

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the world’s biofuels (2012) have been produced from biomass sources such as corn and sugarcane. The use of biomass is also increasing in heating and combined heat and power plants, where higher efficiency can be achieved [WBA, 2015]. During the past decade, the global biomass fuel supply has been steadily increasing [IEA, 2014; WBA, 2015], and bioenergy is playing a significant role in shap­ ing future global renewable energy use [Chum et al., 2011; IEA, 2014]. Bioenergy production may result in alternative use or additional production of biomass that is already being used for other purposes (e.g., corn and sugarcane) or considered a residue material (crop residues). Changes in land use may occur under these circumstances [Berndes et al., 2011; Chum et al., 2011]. Taking corn ethanol as an example, vegetation or land cover (e.g., cropland, forest, and grassland) can be changed to grow corn. These types of changes are directly related to production of corn for ethanol [Berndes et al., 2011]. Also, land use change (LUC) may take place elsewhere just to compensate for land loss caused by bioenergy production. This LUC is indirectly associated with corn ethanol production [Berndes et al., 2011]. The significance of land use and land use change (LUC) has been discussed since the 1990s, and LUC has become a much‐debated issue regarding bioenergy [Chum et al., 2011]. Both direct LUC (DLUC) and indirect LUC (ILUC) have been discussed in many contexts regarding their connection with bioenergy development and their impacts on bioenergy’s environmental attractiveness [Searchinger et al., 2008; Kim et al., 2009; Melillo et al., 2009; Chum et al., 2011; Qin et al., 2011; Dunn et al., 2013; California Air Resources Board (CARB), 2014]. Given that greenhouse gas (GHG) emission reduction is regarded as one of the most important drivers for recent bioenergy expansion, many researchers and policymakers are very concerned that LUC may not be well understood and that its impacts on GHG emissions may not be properly accounted for when planning bioenergy projects and evaluating it’s climate change mitigation benefits [Berndes et al., 2011; Broch et al., 2013; CARB, 2014]. Without LUC effects, most modern bioenergy systems have much lower GHG emissions per unit of energy output, compared with their counterpart fossil systems [Chum et al., 2011]. However, when LUC effects are included, additional GHG emissions may be released from the bioenergy system, specifically from biomass production associated with land conversion. Recent studies highlighted this potential negative impact and estimated LUC‐related GHG emissions, especially for transportation biofuels [Searchinger et al., 2008; Hertel et al., 2010; Tyner et al., 2010; Wang et al., 2012; Dunn et al., 2013; Plevin and Kammen, 2013]. Under certain circumstances, for example, when large areas of natural

ecosystems (e.g., forest and grassland) are converted to grow biofuel feedstocks (e.g., corn), the LUC emissions can be high enough to offset all GHG‐emission reductions over fossil fuel alternatives, or in a few instances, the total GHG emissions may be even higher [Chum et al., 2011]. However, it is ambivalent as to what aspects of LUC‐ related climate impacts should be included and whether these impacts should be estimated in a bioenergy system setting [Kim et al., 2009; Chum et al., 2011; Bright et al., 2012]. In general, LUC can impact climate via two major processes: biogeochemical and biogeophysical. When land use is changed, the circulation and dynamics of certain chemical elements in a previous or historical land use system will change to accommodate a new system. Carbon (C) and nitrogen (N) are two major elements that are often studied because of their significance in contributing to overall GHG emissions and climate forcing [Intergovernmental Panel on Climate Change (IPCC), 2006]. Changes in vegetation carbon (e.g., aboveground and belowground biomass) and soil carbon can be significant for certain LUCs [Dunn et al., 2013; Harris et al., 2015; Qin et al., 2016b]. Additional nitrous oxide (N2O) may be emitted owing to land use change, which affects overall bioenergy GHG emissions [Drewer et al., 2012; Qin et al., 2015a]. In biogeophysical processes, however, there are no physical element changes necessarily associated with LUC. Rather, LUC affects processes such as surface albedo, surface roughness, and evapotranspiration (ET), which eventually influence local and/or global climate [Luyssaert et al., 2014; Bright et al., 2015]. The LUC‐related climate impacts, both biogeochemical and biogeophysical, can be significant for assessing life cycle environmental impacts (predominantly GHG emissions) associated with bioenergy production [Bright, 2015; Devaraju et al., 2015; Zhu et al., 2016]. Life cycle analysis (LCA) is commonly used to estimate total GHG emissions of bioenergy over the course of each of its life cycle stages, which includes LUC emissions as well as emissions from other processes associated with bioenergy production, transportation, and use. In LCA, it is essential to consider key aspects for analysis regarding LUC emissions assumptions, coproducts, conversion process, technology, system boundary, and reference system [Chum et al., 2011; Dunn et al., 2015]. Both DLUC and ILUC can be considered in a bioenergy LCA. When LUC was first introduced to bioenergy LCA, most studies only incorporated biogeochemical impacts associated with LUC such as carbon stock changes in vegetation and soil [e.g., Searchinger et al., 2008; Melillo et al., 2009; Plevin et al., 2014; Qin et al., 2016a]. For example, forest clearance reduces aboveground biomass, which decreases vegetation carbon stocks. If forest or grassland with carbon‐rich soils is converted to grow annual crops (e.g.,

TOWARD LIfE CYCLE ANALYSIS ON LAND USE CHANGE AND CLIMATE IMpACTS 67

corn), soil carbon may be lost and can cause GHG emissions [Don et al., 2012; Qin et al., 2016b]. As our understanding advances and LCA evolves, many studies attempt to include LUC biogeophysical impacts into the bioenergy LCA framework. For example, surface albedo change [Anderson‐Teixeira et al., 2012; Caiazzo et al., 2014; Cai et al., 2016] and changes in latent heat flux [Anderson‐Teixeira et al., 2012] associated with LUC can significantly affect bioenergy’s climate impacts. In this chapter, we discuss LUC in a bioenergy LCA context, with a focus on biogeochemical and biogeo­ physical impacts. First, we introduce LUC (both DLUC and ILUC) and its quantification with regard to biomass production. Then, we discuss how to estimate climate impacts associated with LUC, including both biogeo­ chemical (mainly C‐ and N‐related) and biogeophysical impacts (primarily on albedo and evapotranspiration [ET]). Furthermore, we explore the existing and potential approaches that can be used to incorporate LUC climate impacts into bioenergy LCA estimation. Uncertainties and limitations of current approaches and estimates, as well as future needs, will also be discussed. Examples will be presented to demonstrate the estimation of LUC impacts in an LCA framework for corn‐, switchgrass‐ and Miscanthus‐based ethanol. It is not our intention in this chapter to judge any single method; rather, we believe the overall LUC‐related LCA approach is still open to

discussion, and future work is desired to further understand and improve LCA methodology for specific bioenergy pathways. 5.2. BIOMASS PRODUCTION AND LAND USE CHANGE 5.2.1. Land Use Change Land, by definition, is earth’s solid surface that is not permanently covered by water. Land can have different uses and support various life forms and natural resources (Figure 5.1). Land use change (LUC), or sometimes called land use/land cover change (LULCC), has been widely adopted to describe allocation of lands (e.g., mainly forest, grassland, and cropland; Figure 5.1), however, its definition is not always consistent among different studies and/or for different purposes [Fisher et al., 2005]. Land use and land cover are often used interchangeably, but each actually has a very different meaning. Land cover is the physical earth surface [Fisher et al., 2005]. It describes the cover types of a region (e.g., forest, grassland, wet­ land, agriculture, and impervious surfaces) and can be classified by analyzing remote‐sensing imagery [National Oceanic and Atmospheric Administration (NOAA), 2016]. Land use, however, describes how humans use the land (e.g., urban vs. agricultural land). It is interpreted by its

(a)

(b)

(c)

Figure 5.1 Land with different uses. (a) Forest in fall, upper peninsula of Michigan. (b) Grassland/pastureland in spring, Indiana. (c) Cropland in fall, South Dakota. Photos courtesy of Dr. Wen Sun. (See insert for color representation of the figure.)

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socioeconomic activities on that land [Fisher et al., 2005]. Land cover and land use classifications do not match one another; instead they have a many‐to‐many relationship suggesting one land cover can have multiple uses, and vice versa [Fisher et al., 2005]. When LUC is used in a bioenergy LCA context, most studies referred to changes of land cover with specific land cover classifications, and many of them include the concept of land use to clarify how land is utilized [Searchinger et al., 2008; Kim et al., 2009; Tyner et al., 2010; Njakou Djomo and Ceulemans, 2012; Plevin et al., 2014]. For example, cropland in general is treated as a major land cover type in bioenergy LUC studies, but it can be further specified as cropland and cropland pasture or even further classified by different uses and crop types (e.g., corn, wheat) [Taheripour and Tyner, 2013]. As far as LUC climate impact is concerned, biogeo­ chemical and biogeophysical patterns can be affected by both land cover change and land use change [Melillo et al., 2009; Cherubini et al., 2013; Qin et al., 2016a]. Recent

studies also suggest that land management (e.g., residue harvest and tillage) can be a critical factor influencing climate impact estimation in bioenergy LCA [Anderson‐ Teixeira et al., 2012; Bright et al., 2015; Qin et al., 2015b]. Table 5.1 lists three common LUC concepts concerning land cover, land use, and land management and potential LUC impacts on biogeochemical and biogeophysical regulations. Land management change (LMC), in particular, can act in different forms. Tillage practice (e.g., conventional vs. conservation tillage) changes soil carbon content [Kim et al., 2009; Van Groenigen et al., 2011; Qin et al., 2016a] and surface albedo [Davin et al., 2014]. Forest [Repo et al., 2012] or crop residue removal [Sharratt, 2002; Liska et al., 2014], cover crop applica­ tion, and organic amendments [Kim et al., 2009; Qin et al., 2015b] may change soil biomass inputs and land surface, which affect carbon/nitrogen cycling and albedo, respec­ tively (Table 5.1). To fully evaluate the climate impacts resulting from bioenergy production, it is important to include all aspects of land use/land cover/land

Table 5.1 Various LUC Concepts and Their Climate Impacts Reported Under Relevant Definitions Climate impacts Land use change (LUC) category Land cover Forest, grassland, cropland, and others

Land use Bioenergy versus other uses (e.g., wood energy vs. wood products and crop residue ethanol vs. residue burning) Switch among different crops (e.g., annual vs. perennial) Rotation change (e.g., crop rotation and forest clear cut) Land management Residue reallocation (e.g., harvest)

Species change (e.g., tree species)

Cover crop application Tillage practice Soil amendments (e.g., manure and biochar)

Biogeochemical

Biogeophysical

Soil carbon [Lapola et al., 2010; Qin et al., 2016a], vegetation carbon stocks [Njakou Djomo and Ceulemans, 2012; Dunn et al., 2013], and nitrous oxide (N2O) emissions [Melillo et al., 2009; Kirschbaum et al., 2013]

Albedo [Caiazzo et al., 2014; Cai et al., 2016] and evapotranspiration [Anderson‐ Teixeira et al., 2012]

Carbon emissions allocation [Nepal et al., 2014]

Albedo [Cherubini et al., 2013; Bright et al., 2015]

Soil carbon [Zan et al., 2001; Qin et al., 2016b]

Albedo [Miller et al., 2016]

Biomass carbon (e.g., yield) [Gentry et al., 2013] and soil carbon [West and Post, 2002]

Albedo [Bright et al., 2015, 2016]

Soil carbon [Blanco‐Canqui and Lal, 2007; Zhao et al., 2015] and N2O emissions/avoidance [Congreves et al., 2016] Biomass carbon (e.g., yield) and soil carbon [West and Post, 2002; Gentry et al., 2013]

Albedo [Sharratt, 2002]

Soil carbon [Poeplau and Don, 2015; Qin et al., 2015b] and N2O impacts [Jarecki et al., 2009] Soil carbon [Kim et al., 2009; Qin et al., 2016a] Biomass carbon (e.g., yield), soil carbon, and N2O impacts [Fronning et al., 2008; Case et al., 2014; Kauffman et al., 2014; Qin et al., 2015b]

Albedo (e.g., coniferous vs. deciduous forest) [Bright et al., 2015] Albedo (crop vs. bare land) [Anderson‐Teixeira et al., 2012] Albedo [Davin et al., 2014]

Note: The table only includes a few examples for the three common LUC concepts. It is not a full list of reports on either LUC definitions or climate impacts.

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management change that can affect either biogeochemical or biogeophysical impacts (Table 5.1). 5.2.2. Direct and Indirect Land Use Change Associated with Bioenergy Feedstock Production Because of bioenergy feedstock production, land use change may occur both directly and indirectly (Figure 5.2). To produce enough biomass feedstocks, additional land may be required from either existing managed land (e.g., cropland) or unmanaged land (e.g., forest, grassland), which results in DLUC (Figure  5.2c). ILUC may occur when land is converted to make up for the production of food, feed, or fiber that was displaced by bioenergy feed­ stock production owing to DLUC [Fargione et al., 2008;

(a)

Searchinger et al., 2008]. ILUC is an unintended conse­ quence of bioenergy feedstock production and can happen both domestically (Figure  5.2c) and internationally (Figure  5.2d) [Taheripour and Tyner, 2013; Dunn et al., 2015]. There is not necessarily a one‐to‐one relationship between land area converted to bioenergy and the area converted to make up food, feed, or fiber production [Berndes et al., 2011]. The actual area change depends on many economic, social, and environmental factors [Berndes et al., 2011; Taheripour and Tyner, 2013]. More often than not, LUC studies, either in legislative policies or academic research, treat DLUC and ILUC jointly but separate domestic and international LUC [Taheripour and Tyner, 2013; Dunn et al., 2015]. For instance, in both the U.S. Renewable Fuel Standard

(b) Domestic land use

International land use

Before

Managed land

Managed land

Unmanaged land

Unmanaged land

(d)

ct re di In

re di

Bioenergy

In

After

ct

LU

LU

C

C

(c)

Direct LUC

Figure 5.2 Examples of direct and indirect LUC due to bioenergy development. Generally, the land consists of managed (e.g., cropland) and unmanaged land (e.g., forest and grassland) in both (a) domestic and (b) international domains. (c) After bioenergy is introduced, the managed/unmanaged land may be converted to grow crops for energy use (direct LUC). Indirect LUC may occur in a (c) neighboring region or (d) even other countries in response to market shocks. (See insert for color representation of the figure.)

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Program (RFS2) Regulatory Impact Analysis prepared by U.S. Environmental Protection Agency (EPA) [2010] and the LCFS (Low Carbon Fuel Standard) Land Use Change Assessment report prepared by California Air Resources Board (CARB) [2016], DLUC and ILUC are not separated. However, GHG emissions are analyzed for both domestic and international LUC, which include DLUC and ILUC associated with biofuel production. The EU‐Renewable Energy Directive (RED) examined ILUC impacts for the purpose of calculating GHG emissions from biofuel production; however it does not require ILUC accounting at this point [Dunn et al., 2015]. An earlier review looked into 15 primary studies regarding biofuel LUC and found that two addressed solely DLUC, nine investigated ILUC, and four assessed both DLUC and ILUC [Njakou Djomo and Ceulemans, 2012]. Further examination into these studies revealed that, among the nine ILUC only studies, some still included both DLUC and ILUC but labeled them as domestic versus international LUC [Tyner et al., 2010] or simply indirect LUC [Hertel et al., 2010]. The latter often refers all LUC as indirect impacts compared with direct GHG emissions from processes such as industrial processing of corn ethanol [Hertel et al., 2010]. Recent efforts tend to look into both DLUC and ILUC impacts rather than any single one of them [Taheripour and Tyner, 2013; Qin et al., 2016b]. However, very few studies show ILUC or international LUC for some bioenergy feedstocks. For example, algae production normally requires land to be converted to algae ponds, which may result in LUC (DLUC), but it is not yet assessed as to what extent would LUC extend to ILUC or international scale [National Alliance for Advanced Biofuels and Bioproducts Synopsis (NAABB), 2014]. 5.2.3. How to Measure Land Use Change LUC impact analysis always starts with an LUC assessment to determine the direction and area change of each LUC category (e.g., from forest to bioenergy cropland) [Hertel et al., 2010; Dunn et al., 2015]. However, it is difficult to measure actual large‐scale (e.g., nationwide) LUC with conventional survey methods or remote‐ sensing approach, not only due to the high cost of such measurements but also due to the uncertainty of attributing any change in land area (especially ILUC) to specific bioenergy pathways [Njakou Djomo and Ceulemans, 2012; Ahlgren and Lucia, 2014]. For instance, National Land Cover Database (NLCD) [Multi‐Resolution Land Charac­ teristics consortium (MRLC), 2016] and Cropland Data Layer (CDL) [U.S. Department of Agriculture (USDA), 2016] are two land use data sources frequently used to analyze LUC in the United States. Both sources are based on remote‐sensing data, providing time‐dependent spatial

land cover/land use information, which allow estimates of land area changes under specific land categories [Wright and Turhollow, 2010; Lark et al., 2015]. On the basis of CDL, Wright and Turhollow [2010] assessed 2006–2011 land conversions and identified elevated grassland to corn/soy conversion in the Western U.S. Corn Belt. Lark et al. [2015] tracked crop‐specific land conversion, with both NLCD and CDL, and found overall cropland expansion from grasslands and nonagriculture lands. Even though both studies linked LUC to biofuel expansion that occurred at approximately the same time period, it is hard to establish a quantitative cause‐effect relationship between them [Wright and Turhollow, 2010; Lark et al., 2015]. Instead of using actual measurement data, another approach assessing LUC is involved with modeling with complex tools [Njakou Djomo and Ceulemans, 2012; Dunn et al., 2015]. Models, including agroeconomic models, combined models, biophysical models, and deterministic (or simplified) models, are particularly helpful for estimating ILUC that cannot be observed at a specific location [Njakou Djomo and Ceulemans, 2012]. For instance, the Global Trade Analysis Project (GTAP) model is a computable general equilibrium (GE) model that has been widely used to assess domestic and international LUC associated with biofuel production [Hertel et al., 2010; Tyner et al., 2010]. GTAP uses economic data and considers biofuel‐related technologies and policies. It has been used by CARB to assess LUC impacts in the LCFS [CARB, 2016]. To estimate LUC‐related GHG emissions in RFS2, the EPA utilized a combination of models to assess domestic and international LUC [EPA, 2010]. The Forest and Agriculture Sector Optimization Model (FASOM), a partial equilibrium (PE) agricultural sector model, is used for domestic analysis. The Farm and Agricultural Policy Research Institute (FAPRI) model, a worldwide agricultural sector economic model, is used to predict crop areas and livestock production globally. Because of intrinsic shortcomings of GE and PE economic models, other models (e.g., combined models, biophysical models, and deterministic models) have also been proposed to explore the possibility of LUC prediction [Njakou Djomo and Ceulemans, 2012]. However, economic modeling is still by far the most commonly used approach in national and/or global‐scale LUC estimates, especially for biofuel production [Ahlgren and Lucia, 2014]. 5.3. LAND USE CHANGE AND CLIMATE IMPACTS Certain climate impacts can occur locally and/or globally because of changes in land cover, land use, and even land management (Figure 5.3). On the one hand, with LUC, the associated ecosystems, either natural

TOWARD LIfE CYCLE ANALYSIS ON LAND USE CHANGE AND CLIMATE IMpACTS 71

Global impacts

Other measures?

AGTP ΔTair(global)

GWP,AGWP

Local impacts

Radiative forcing

∆CO2

∆C pools, fluxes

∆N2O

Others

∆N pools, fluxes

Others

Ecosystem change and associated change of biogeochemical cycling

∆Net radiation

∆Sensible ∆Latent heat heat (∆ET)

Albedo change

Biogeophysical impacts

Biogeochemical impacts

ΔTair(local)

Land surface change

Land use change (LUC)

Figure 5.3 Land use change can affect climate via changes in biogeochemical and biogeophysical processes in the terrestrial ecosystems. The biogeochemical and biogeophysical changes can result in local and global impacts (see upper right legend). AGTP, absolute global temperature change potential; GWP, global warming potential; AGWP, absolute global warming potential. AGWP is cumulative radiative forcing of emission over a given time horizon. Δ indicates change.

(e.g., forest) or managed (e.g., cropland), may change dramatically, including changes of biotic and abiotic components through energy flows and nutrient cycles. Compared with an original ecosystem (before LUC), the energy flow and nutrient cycle changes eventually alter biogeochemical cycling in new ecosystem and result in differences of net carbon exchange (NCE) and/or N‐related emissions (Figure  5.3). Carbon and N2O emissions are commonly assessed for LUC associ­ ated with bioenergy feedstock production to account for biogeochemical impacts in terms of GHG emissions [Melillo et al., 2009; Njakou Djomo and Ceulemans, 2012; Qin et al., 2016a].On the other hand, terrestrial ecosystems regulate climate via fluxes of heat or radia­ tion. Land surface roughness, surface conductance, and albedo can change because of LUC, which causes

biogeophysical impacts. The change of biogeophysical regulation can eventually be translated into a local temperature change or global impacts of radiative forcing (Figure 5.3). As far as bioenergy feedstock production related cli­ mate impacts are concerned, many studies report global warming potential (GWP) or global temperature change potential (GTP) to integrate both biogeochemical and biogeophysical impacts into specific climate impact indi­ cators [Caiazzo et al., 2014; Cai et al., 2016]. GWP and GTP can include radiative forcing resulting from GHG emissions (biogeochemical) and net radiation change (biogeophysical) from LUC (Figure 5.3). In what follows, we will discuss biogeochemical and biogeophysical impacts in detail with regard to LUC associated with bio­ energy feedstock production.

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5.3.1. Biogeochemical Impacts 5.3.1.1. Carbon Balance in the Terrestrial Ecosystems and Beyond The terrestrial ecosystem NCE accounts for primary production and loss via respiration and disturbance. It  indicates whether an ecosystem is a net CO2 sink or source [Qin et al., 2015a]. For carbon accounting asso­ ciated with bioenergy feedstock production, the NCE is normally broken into several major components to estimate carbon pools/fluxes changes due to LUC, for example, biomass production (both aboveground and belowground biomass), soil organic carbon, and biomass harvest [Harris et al., 2009; Dunn et al., 2013; Plevin et al., 2014]. Deforestation normally results in reduction of vegetation carbon stocks, both aboveground and below­ ground biomass [Harris et al., 2009; Dunn et al., 2013] and is also likely to decrease soil carbon content especially when forest is converted to cropland [Post and Kwon, 2000; Don et al., 2011; Qin et al., 2016b]. Biomass harvest is a key disturbance factor affecting NCE in bioenergy ecosystems. For example, the amount of crop residues har­ vested from cropland (e.g., corn) can significantly affect soil carbon dynamics [Blanco‐Canqui and Lal, 2007; Qin et al., 2015b]. As an extreme case, total corn residue removal can reduce soil carbon content and increase overall corn ethanol GHG emissions [Liska et al., 2014]. For a full bioenergy production analysis, the system boundary for carbon accounting is beyond biomass pro­ duction and the bioenergy ecosystem. Foregone carbon sequestration and harvested wood products are two

major components estimated in a bioenergy production system but not normally included in a natural ecosystem NCE framework. Foregone carbon sequestration refers to additional carbon accumulation that could have hap­ pened if LUC did not occur. It can be considered as an analogical impact to carbon emissions [Koponen and Soimakallio, 2015]. Deforestation, for instance, eliminates potential vegetation carbon accumulation due to annual tree growth (especially young forest), which suggests a reduction of carbon stocks in the newly established eco­ system (e.g., bioenergy crop system). Foregone carbon sequestration is often assessed for LUC originating from a forest [Harris et al., 2009; Dunn et al., 2013]. Unlike crop residues, a forest timber harvest does not necessarily result in immediate carbon loss to the atmosphere. Carbon contained within harvested wood products such as furniture can remain sequestered for a long time [Harris et al., 2009; Dunn et al., 2013]. The proportion of harvested timber that ends up in long‐lived wood products varies by region and changes with time [Harris et al., 2009]. 5.3.1.2. Nitrous Oxide Emissions Because of LUC, some existing N dynamics may change (e.g., biomass and soil N) and some additional N pools and fluxes (e.g., fertilization, residue removal, and amendments application) may be added into bioenergy ecosystems (Figure 5.4). The change of N cycling due to LUC can eventually result in changes in N2O emissions. According to IPCC Guidelines for National Greenhouse Gas Inventories (2006), N2O emissions may come from both direct and indirect pathways [IPCC, 2006]. For instance,

Direct emissions

Indirect emissions

N2O emissions not necessarily associated with land use change

N2O emissions resulted from land clearing with fire where applicable N2O emissions associated with land use change N2O emissions not necessarily associated with land use change

Synthetic fertilizer

Crop residue Others (e.g., manure)

SOM loss

Biomass burning

SOM loss Synthetic Synthetic Crop residue Others (e.g., via leaching fertilizer via fertilizer via via leaching manure) and runoff volatilization leaching and and runoff runoff

Figure  5.4 Direct and indirect N2O emissions sourced from agricultural management practices and land use change. Darker colors indicate N2O emissions associated with land use change, while lighter ones indicate emissions normally treated as farming‐related emissions. The bar charts reflect relative amount of N2O emissions. (See insert for color representation of the figure.)

TOWARD LIfE CYCLE ANALYSIS ON LAND USE CHANGE AND CLIMATE IMpACTS 73

compared with unmanaged natural ecosystems such as forest and grassland, cropland releases more N2O directly from agricultural management related sources such as synthetic N fertilizer, crop residue return, and organic N additions (e.g., manure). In some cases, N2O emissions may also result from land clearing with fire (e.g., forest biomass burning) or N mineralization associated with soil organic matter (SOM) loss due to LUC [IPCC, 2006]. Indirect N2O emissions can result from fertilizer, residue, organic amendments (e.g., manure), and other sources (e.g., biomass burning and chemical industry), via processes such as volatilization, leaching, runoff, or biomass harvest [IPCC, 2006]. Even though all of the N2O can be linked to bioenergy feedstock production, only a portion is attributable to LUC (Figure  5.4). From a bioenergy life cycle perspec­ tive, direct and indirect N2O emissions from sources including synthetic fertilizer, crop residue, and others (e.g., manure) are normally treated as farming‐related emissions, and they are closely related to per unit of bioenergy production (e.g., per unit of corn ethanol) [Wang et al., 2012]. These emissions are dependent on agricultural management practices; they can happen with or without LUC. However, the three emissions indicated in Figure 5.4, direct and indirect emissions due to SOM loss and direct emissions from biomass burning, are associ­ ated with LUC and often included as part of bioenergy LUC related GHG emissions [Harris et al., 2009; Hiederer et al., 2010]. SOM loss may result from land converted from SOC‐rich ecosystems to bioenergy ecosystems, for example, from forest to cropland (e.g., corn for ethanol production) [Don et al., 2011; Harris et al., 2015; Qin et al., 2016b]. The release of N mineralization of SOM acts as another source of N2O emissions [IPCC, 2006]. Biomass burning is not a common land clearing practice in the United States, but it is indeed used as a means of site preparation for land conversion to cropland, for example, in many countries in the tropic region [Harris et al., 2009]. N2O emissions from burning can become important for regions with significant DLUC or ILUC [Harris et al., 2009]. 5.3.1.3. Other Emissions CO2 and N2O are two major sources of GHG emissions considered in an LUC‐related analysis [IPCC, 2006]. However, other sources may become significant for a specific region under certain LUCs. Major land types normally considered in bioenergy LUC analyses include forest, grassland, and cropland, but wetlands may be an important land category that should be included in the LUC framework if a considerable amount of wetlands are cleared and drained as a result of bioenergy production [Harris et al., 2009]. For instance, palm oil plantations can expand into peat swamp forests in Southeast Asia,

which directly changes the existing landscape. In this case, methane (CH4), as well as CO2 and N2O, should be included as sources of GHG emissions caused by LUC [Harris et al., 2009; Farmer et al., 2014]. CH4 emissions may also be accounted for in cases where LUC causes changes of livestock production and rice paddy plantations [EPA, 2010]. Land use change may also affect the tropospheric ozone (O3) concentration directly and indirectly [Foley et al., 2005]. Forests, for example, can emit biogenic volatile organic compounds that may oxidize in the atmosphere and generate O3; forest clearing, in this sense, reduces the risk of increasing O3 from these processes [Bright, 2015]. However, the relationship between bioenergy LUC and tropospheric O3 radiative forcing impacts is less clear, and so far O3 is not considered as a major contributor to bioenergy GHG emissions. 5.3.2. Biogeophysical Impacts 5.3.2.1. Change of Albedo and Evapotranspiration Until recently, biogeophysical impacts have been highlighted and quantified for LUC associated with bioenergy feedstock production [Anderson‐Teixeira et al., 2012; Caiazzo et al., 2014; Cai et al., 2016]. Albedo change and ET change (associated with latent heat flux change) are two major impacts that have been emphasized in various studies because of their significance in changing local and/or global climate (Figure  5.3) [Bagley et al., 2014; Bright, 2015; Devaraju et al., 2015]. Vegetated land sur­ faces (e.g., forest) have a relatively lower albedo than bare ground or less vegetated surfaces (e.g., grassland), which therefore absorb more incoming solar shortwave radiation [Anderson‐Teixeira et al., 2012; Bright, 2015]. However, forested land can also release more moisture into the atmosphere via ET and therefore increase the associated latent heat flux because of its easier access to soil mois­ ture and higher atmosphere boundary layer turbulence [Anderson‐Teixeira et al., 2012; Bright, 2015]. The coun­ teracting effects of net radiation and latent heat flux vary among different ecosystems under different land cover, land use [Anderson‐Teixeira et al., 2012; Cai et al., 2016], and land management [Sharratt, 2002; Bright et al., 2016]. The relative importance of biogeophysical climate impacts depends on LUC types and varies by spatial scales and by geographic region [Anderson‐Teixeira et al., 2012; Bright, 2015]. The climate impacts from albedo change and ET change associated with LUC are normally local with global implications. LUC‐induced albedo change, for example, can modify local net radiation and therefore influence local surface temperature. However, the change of surface albedo can also impact the planetary albedo, which affects the global energy balance via radiation change [Bright, 2015]. Anderson‐Teixeira et al. [2012] extrapolated changes due to albedo‐induced

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net radiation and ET associated latent heat flux to the global scale by dividing local effects by global surface area. It is determined that tropical forests, in general, have a higher change of surface energy (relative to bare ground) than grassland, cropland, and other forest types. Land surface models have often been used to represent biogeophysical impacts of land use on water and climate [Bagley et al., 2014; Devaraju et al., 2015]. Anderson‐ Teixeira et al. [2012] modeled biogeophysical processes using IBIS and AgroIBIS for natural ecosystems and agroecosystems, respectively. Zhu et al. [2016] recently used a community land model to simulate carbon, water, and energy fluxes in bioenergy ecosystems and to estimate land use related biogeophysical effects. However, from the perspective of large‐scale bioenergy feedstock production, the effectiveness of models may be questioned as to their ability to simulate various bioenergy species growing in different geographic locations [Bagley et al., 2014; Cai et al., 2016]. To evaluate albedo impacts on climate for discrete LUC scenarios, Caiazzo et al. [2014] used satellite‐measured albedo at multiple geographic locations, trying to capture variability in surface and meteorological conditions. The sampled data (n = 4–14) are then used to calculate potential climate impacts (change of CO2 equivalent) for specific bioenergy pathways (e.g., switchgrass ethanol), assuming that certain types of crops (e.g., corn) are replaced by bioenergy feedstocks (e.g., switchgrass) [Caiazzo et al., 2014]. Recently, Cai et al. [2016] evaluated LUC‐induced albedo change impacts from a biofuel life cycle perspective by using economic model predicted LUC information (LUC types, locations, and acreages) to select representative geographic locations for albedo retrieval. Over a million samples are used to quantify LUC‐induced albedo effects for corn ethanol, switchgrass ethanol, and Miscanthus ethanol production in the United States [Cai et al., 2016]. While albedo change is dominating the global‐scale LUC biogeophysical impacts in terms of radiative climate forcing, ET change can be important for local nonradiative forcing such as surface or atmospheric temperature change (Figure 5.3) [Bright, 2015]. 5.3.2.2. Other Impacts Because of land roughness change due to LUC, the so‐ called roughness length (equivalent to the height where wind speed becomes zero) changes [Devaraju et al., 2015]. In general, a smoother surface has a smaller roughness length, and vegetated land has a larger one, which enables more efficient exchange of heat, momentum, and water between the surface layer and atmosphere. Change of roughness length therefore affects boundary layer turbulence. Converting forest to cropland, for example, will decrease roughness length and turbulence, which results in a less diffusive heat flux and consequently a warming

impact [Devaraju et al., 2015]. Forest emitted biogenic volatile organic compounds can directly and indirectly affect earth’s energy budget and climate system via processes such as radiation absorption and cloud formation, and therefore any LUC resulted in change of forest can potentially impact global climate [Unger, 2014; Bright, 2015]. So far, studies on biogeophysical impacts associated with bioenergy LUC are focused mostly on albedo change, not only because it is better understood scientifically [Bright, 2015] but also because it has relatively higher impact on global climate than other factors [Devaraju et al., 2015; Cai et al., 2016]. Currently, many studies often measure biogeophysical impacts from a radiative forcing perspective, for example, in terms of GWP. Alternative measures may be introduced in future studies to accommodate different biogeochemical and/or biogeophysical impacts, for example, using GTP to include ET and possibly roughness length effects [Bright, 2015] or using a time‐dependent emissions equivalent to indicate temporal scales of GWP for different GHG emissions [Peters et al., 2011; Bright et al., 2016]. 5.4. CLIMATE IMPACTS IN BIOENERGY LIFE CYCLE ANALYSIS 5.4.1. Life Cycle Analysis for Bioenergy Production Life cycle analysis (or life cycle assessment, LCA) is a tool often used to assess a product’s environmental footprint over its lifetime. It is a commonly used method to evaluate bioenergy’s energy consumption and environmental impacts associated with all stages of bioenergy production [Davis et al., 2009; Creutzig et al., 2015; Dunn et al., 2015]. Traditionally, LCA estimates bioenergy production associated with processes such as feedstock production, feedstock logistics, storage and transportation, feedstock to bioenergy conversion, bioenergy transportation and distribution, and bioenergy end uses (Figure 5.5). Only recently has bioenergy LCA started to include LUC impacts as part of GHG‐emission estimates, and most LCA reports only consider LUC biogeochemical impacts in terms of CO2, N2O, and other GHG emissions [Searchinger et al., 2008; Melillo et al., 2009; Qin et al., 2016a]. LUC‐induced biogeophysical impacts are newly incorporated in a few studies to explore their relative importance in a LCA context [Caiazzo et al., 2014; Cai et al., 2016]. Figure  5.5 depicts an example of the recent biofuel LCA evolution with regard to LUC impacts. To estimate climate impacts in terms of GHG emissions associated with each unit (e.g., gallon or MJ) of biofuel produced (e.g., corn ethanol), traditional LCA (Part 1) looks into all energy and material flows within the feedstock‐bioenergy system boundary and accounts for all possible

TOWARD LIfE CYCLE ANALYSIS ON LAND USE CHANGE AND CLIMATE IMpACTS 75 1: LCA excluding LUC impacts 1+2: LCA including LUC biogeochemical impacts 1+2+3: LCA including LUC biogeochemical and biogeophysical impacts

1 Fuel combustion

3

Energy and material inputs

Fuel transportation and distribution

Feedstock conversion

Biogeophysical forcing (∆albedo) Climate impacts (CO2e) 2

Feedstock logistics, storage, and transportation Feedstock production

Biogeochemical forcing (C, N)

Land use change

Land use change (LUC) Figure 5.5 An example overview of a biofuel life cycle GHG‐emission estimate incorporating biogeochemical and biogeophysical impacts from LUC. Carbon (C) and nitrogen (N) cycling is commonly considered in LUC‐ related biogeochemical processes, while albedo is recently considered for LUC‐related biogeophysical processes.

sources of GHG emissions (e.g., fossil fuel use in feed­ stock production) (Figure 5.5). Then, with LUC impacts introduced, biogeochemical emissions, mainly from carbon stocks change (e.g., biomass carbon stocks and soil carbon) and nitrogen oxides (predominantly N2O) are assigned to each unit of energy produced in the fuel (Part 2). Some biogeophysical impacts, for example, albedo change related to LUC, can be calculated at a GHG emission equivalent basis (e.g., net radiation induced GWP impact) and attributed to biofuel produc­ tion (Part 3). However, as noted in section 5.3.2, not all biogeophysical impacts can be translated into global GWP impacts; other measures may be needed to harmo­ nize GHG emissions from biofuel production (Part 1) and from LUC (Parts 2 and 3). 5.4.2. Climate Impacts in Bioenergy Life Cycle Analysis Biogeochemical impacts have been largely implemented in bioenergy LCA GHG‐emission estimates. Depending on factors such as the purpose of the study, differences of LUC definitions, and significance of certain LUC categories, different LCA studies may include different sources of GHG emissions using different approaches [EPA, 2010; Hiederer et al., 2010; Dunn et al., 2013; CARB, 2016]. For example, Table 5.2 lists several LCAs

examining LUC impacts in biofuel production. Most studies reported CO2 emissions from changes of carbon stocks in biomass and soil, but only some considered potential emissions from processes such as foregone carbon sequestration (e.g., CCLUB and LCFS), long‐ term C storage in wood products (e.g., CCLUB, RFS2), and possible peat clearing in certain ILUC regions (e.g., RFS2). In most LCA studies, LUC‐induced N2O emissions are often associated with SOM loss, mainly due to N mineralization (direct emissions) and N leaching and runoff (indirect emissions; Table 5.2). N volatilization is not normally linked with SOM loss [IPCC, 2006] but may still be included in some LCAs (e.g., LCFS; Table  5.2). Biomass burning and peat clearing can also contribute to N2O emissions and be included in studies that predict significant LUC undergoing such practices (Table  5.2). CH4 emissions may also be considered for regions that are LUC hot spots where land is cleared by fire or experi­ ences peat drainage. As mentioned earlier, biogeophysical impacts associ­ ated with LUC have not been well studied and incorpo­ rated in current bioenergy LCA. Many studies have emphasized the importance of considering albedo change related to LUC and some even highlighted major approaches that can be used to assess such impacts on bioenergy life cycle GHG emissions (Table 5.2). However,

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Table 5.2 Examples of LCAs Used for Biofuel Production that Include LUC Impacts LUC impacts Examples

Domestic LUC

Biogeochemical impacts GREET/CCLUB CO2 [Dunn et al., 2016; Qin et al., 2016a]: SOC change Wood products Foregone sequestration N2O [Dunn et al., 2016]: N mineralization due to SOM loss (direct) N leaching and runoff due to SOM loss (indirect) EPA/RFS2

CO2 [EPA, 2010]: SOC change Forest products (wood and paper) Biomass decay N2O [EPA, 2010]: Soils (based on modeling) CH4 [EPA, 2010]: Paddy rice

CARB/LCFS

JRC

CO2 [CARB, 2016]: Biomass carbon stocks Wood products Soil carbon change Foregone sequestration N2O [CARB, 2016]: N mineralization due to SOM loss (direct) N leaching and runoff due to SOM loss (indirect) N volatilization due to SOM loss (indirect) (?)1 CH4 [CARB, 2016]: Biomass burning CO2 [Hiederer et al., 2010; Marelli et al., 2011]: Soil carbon change Biomass carbon stocks N2O [Hiederer et al., 2010; Marelli et al., 2011]: N mineralization due to SOM loss (direct) N leaching and runoff due to SOM loss (indirect)

Biogeophysical impacts GREET/CCLUB Albedo change (estimated for corn, switchgrass, and Miscanthus ethanol) [Cai et al., 2016] Caiazzo et al.2 Albedo change (estimated for switchgrass, soy, palm rapeseed, and salicornia‐sourced fuels) [Caiazzo et al., 2014] Albedo change Bright and et al. ET change [Bright, 2015; Bright et al., 2015] (qualitative analysis)3

International LUC CO2 [Dunn et al., 2016; Qin et al., 2016a]: SOC change Wood products Foregone sequestration N2O [Dunn et al., 2016]: N mineralization due to SOM loss (direct) N leaching and runoff due to SOM loss (indirect) Biomass burning CO2 [EPA, 2010; Harris et al., 2015]: SOC change Biomass carbon stocks Foregone sequestration Peat clearing where applicable N2O [EPA, 2010; Harris et al., 2015]: Peat clearing where applicable Biomass burning where applicable CH4 [EPA, 2010; Harris et al., 2015]: Peat clearing where applicable Biomass burning where applicable CO2 [CARB, 2016]: Biomass carbon stocks Wood products Soil carbon change Foregone sequestration N2O [CARB, 2016]: N mineralization due to SOM loss (direct) N leaching and runoff due to SOM loss (indirect) N volatilization due to SOM loss (indirect) (?)1 Biomass burning CH4 [CARB, 2016]: Biomass burning CO2 [Hiederer et al., 2010; Marelli et al., 2011]: Soil carbon change Biomass carbon stocks N2O [Hiederer et al., 2010; Marelli et al., 2011]: N mineralization due to SOM loss (direct) N leaching and runoff due to SOM loss (indirect) Not available Not available

Not specified

Note: This table lists only major LUC impacts included in several example LCA studies. GREET/CCLUB: the GREET model (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) and its module CCLUB (Carbon Calculator for Land Use Change from Biofuels Production), by Argonne National Laboratory. JRC, Joint Research Centre. 1  An emission factor of 1.325% is used in the AEZ‐EF model [CARB, 2016], so supposedly N volatilization is included. 2  It is not a full LCA. The analysis estimated biogeophysical impacts, but biogeochemical impacts are based on other studies. 3  It is not a full LCA. Bright et al. provided a series of methodology discussions regarding biogeophysical impacts in LCA and laid out LCA frameworks.

TOWARD LIfE CYCLE ANALYSIS ON LAND USE CHANGE AND CLIMATE IMpACTS 77

only a few recent studies actually quantify biogeophysical impacts in a bioenergy life cycle setting, for example, the estimates by Caiazzo et al. [2014] and Cai et al. [2016]. The biogeophysical impacts analysis has yet expanded to international LUC (Table 5.2). From the climate change perspective, both biogeo­ chemical and biogeophysical impacts can vary by specific bioenergy pathways in determining either warming or cooling effects. Take biofuel LCA, for example. Figure 5.6 illustrates GHG emissions, on a per unit energy basis, for ethanol produced from three different feedstocks, corn, switchgrass, and Miscanthus. This example based LUC estimates on CGE modeling [Qin et al., 2016a]. Excluding LUC impacts, all sources of ethanol indicate positive GHG emissions (Figure  5.6a), meaning net warming effect resulted from processes such as feedstock produc­ tion (Figure 5.5). LUC biogeochemical impacts show net positive GHG emissions for corn ethanol but can become negative GHG emissions (net GHG sink) for switchgrass‐ and Miscanthus‐based ethanol under certain circumstances (Figure  5.6b). For example, SOC sequestration due to LUC, for example, from cropland pasture to bioenergy crop‐based cropland, significantly reduces Miscanthus ethanol’s GHG emissions [Qin et al., 2016a]. With albedo change considered, the biogeophysical impacts show a mild cooling effect for corn ethanol (−2 g (a)

(b)

CO2e MJ−1) but a warming effect for switchgrass‐ (12 g CO2e MJ−1) and micanthus‐based ethanol (3 g CO2e MJ−1; Figure  5.6b). Land conversions from cropland pasture to feedstock land contribute significantly to the overall stronger warming effect for switchgrass ethanol [Cai et al., 2016]. Comparisons between LCA‐3 (including both LUC impacts; Figure 5.6d) and LCA‐1 (excluding LUC impacts; Figure  5.6a) reveal that corn ethanol is least affected by LUC impacts because biogeochemical and biogeophysical impacts offset one another. However, for switchgrass ethanol, LUC impacts add an additional 40%–120% GHG emissions to each unit of energy pro­ duced, which significantly lowers its GHG‐emission reduction ability. For Miscanthus ethanol, LUC impacts reduce its GHG emissions to nearly zero. It should be noted that the estimate of biofuel LUC GHG emissions is feedstock specific and highly dependent on LUC estimates and specific land use/land management assumptions [Qin et al., 2016a]. 5.4.3. Challenges and Future Needs LUC quantification is beyond the scope of this review, but it is indeed the basis for estimating LUC climate impacts associated with bioenergy LCA. Depending on the purpose of the study, LUC estimates can vary dramatically

(c)

(d) Gasoline

20% Reduction

30–49

47

50% Reduction

0

3

–4–2

12 –2

–20– -6

2–9

13

–3–13

60% Reduction 22

GHG emissions (g Co2e MJ–1)

57

57–64

94

100% Reduction

0

–28

C

S LCA-1

M

C

S

M

Biogeochemical

C

S

M

Biogeophysical

C

S

M

LCA-3

Figure 5.6 An example showing estimated life cycle GHG emissions for corn‐ (C), switchgrass‐ (S) and Miscanthus‐ based ethanol (M). The estimates are derived from GREET parameters on the basis of Qin et al. [2016a] (LCA‐1 and biogeochemical) and Cai et al. [2016] (biogeophysical). (a) LCA‐1 is LCA approach without considering LUC effects, while (d) LCA‐3 is LCA considering both (b) biogeochemical and (c) biogeophysical impacts of LUC; see Figure 5.5. The dots show only the approximate range of each estimate in the emission chart. The percentage reduction (%) is relative to gasoline GHG emissions.

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BIOENERGY AND LAND USE CHANGE

between different studies and even in the same study with different land use assumptions using different modeling techniques [EPA, 2010; Hiederer et al., 2010; CARB, 2016]. The use of general equilibrium versus partial equilibrium models highlights different spatial/sector focus and results in different LUC values [Njakou Djomo and Ceulemans, 2012; Ahlgren and Lucia, 2014]. Because of differences in modeling structures, input data, and assumptions and uncertainties in each modeling work, it is difficult to harmonize LUC estimates for studies with different purposes and focuses [Ahlgren and Lucia, 2014; Creutzig et al., 2015]. However, it can be very helpful to compare studies with different approaches and identify the critical gaps between different models/methods and between scientific studies and policy‐related analysis [Njakou Djomo and Ceulemans, 2012; Ahlgren and Lucia, 2014]. Recent studies have started to adopt some commonly accepted assumptions and data sets (e.g., census and remote‐sensing data) to improve models and simulate LUC [Taheripour and Tyner, 2013; Hellwinckel et al., 2016]. In particular, forest conversion to bioenergy crops is either constrained [Qin et al., 2016a] or not allowed (e.g., Billion‐Ton study) [USDOE, 2016]. As discussed earlier, NLCD and CDL are two important U.S. LUC data sources. NLCD provides land cover products assessing the spatial land cover changes and trends across the United States. It covers almost all land cover classes such as forest, grassland, and agriculture, on the basis of the Anderson Land Use and Land Cover Classification System [Anderson et al., 1976]. CDL, however, emphasizes crop‐specific land cover categories in the agriculture sector [USDA, 2016]. With different levels of land classifications in NLCD and detailed crop types in CDL, specific LUC can be identified for certain time period [Wright and Turhollow, 2010; Lark et al., 2015] and become a valuable historical reference for LUC model validation [Taheripour and Tyner, 2013]. However, caution should be exercised when using these databases. The land cover/land use data may not be accurate enough to identify regional specific LUC, and it is also difficult to attribute certain LUCs to a single cause (e.g., corn ethanol production). Current efforts to determine bioenergy LUC climate impact are more focused on several major biofuels that are in large‐scale commercial production, such as corn ethanol in the United States and European Union and sugarcane ethanol in Brazil [Njakou Djomo and Ceulemans, 2012; Ahlgren and Lucia, 2014]. Future work will inevitably include LUC impacts for other feedstocks (e.g., cellulosic biomass, forestry wood and residues, and algae) and bioenergy products (e.g., biodiesel, cellu­ losic ethanol, heat and power). It is more challenging to estimate LUC and associated climate impacts for these relatively new bioenergy pathways, which have less

empirical evidence that can aid LUC modeling and LCA. For instance, switchgrass and Miscanthus are two major cellulosic crops that may be used in the United States to produce biofuels (e.g., ethanol). Since these crops are not historically or currently widely grown, LUC estimates cannot rely on historical LUC data to verify or improve economic modeling in a similar way for corn [Taheripour and Tyner, 2013]. Their yields are normally based on limited experimental observation or biomass modeling [Tyner et al., 2010; Qin et al., 2016a] and the estimates of biogeochemical and biogeophysical impacts are often limited by the representativeness of regional observations [Qin et al., 2015a; Cai et al., 2016]. As more observations become available, however, the LUC estimates and cli­ mate impact modeling needs to be updated and improved. Future studies should also highlight some critical regions that are ecologically sensitive but may sometimes be neglected in a global‐scale LCA analysis. For example, the Southeast Asian wetlands and the Amazon rainforest are particularly important for local ecosystem services and even the global environment [Harris et al., 2009; Lapola et al., 2010; Anderson‐Teixeira et al., 2012], but their conversion might be limited due to policy restriction. However, if conversion is included, then necessary biogeochemical (e.g., wetland clearing induced GHG emissions) and biogeophysical impacts (e.g., forest albedo change) should be appropriately incorporated into the LCA. Another area that needs further investigation is LUC biogeophysical impacts. For bioenergy LUC impacts estimated in a LCA context, biogeochemical impacts have been well accounted for in many cases, but biogeophysical impacts so far mainly focused on albedo change with DLUC [Bright, 2015; Cai et al., 2016; Zhu et al., 2016]. Quantification of biogeophysical impacts associated with ILUC could be an immediate next step for better understanding of albedo effects globally [Cai et al., 2016]. Even though the scientific understanding about biogeophysical climate impacts is advancing, the inclusion of meaningful biogeophysical climate metrics for bioenergy LUC analysis is rare [Bright, 2015]. Further efforts should be sustained to examine the importance of other biogeophysical impacts such as ET change and incorporate significant local climate impacts into bioenergy LCA that often assesses global climate impacts. For example, Bright [2015] suggested that local impacts such as ET change could be precharacterized for various LUC scenarios and mapped globally for LCA lookup use. Other climate impact measures such as GTP may be used in combination with GWP to characterize LUC impacts, especially local biogeophysical impacts (e.g., air temperature change) [Anderson‐Teixeira et al., 2012; Bright, 2015]. While LMC (land management change) has often been analyzed for its biogeochemical impacts in LCA (mainly SOC change; Table 5.1), its biogeophysical climate

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impacts are not fully evaluated even though they can be significant. In terms of the surface temperature change due to changes in albedo and ET, the impacts due to LMC can be similar to those from land cover change [Luyssaert et al., 2014]. Considering that bioenergy eco­ systems are largely impacted by land management, increasing efforts are required to integrate LMC biogeo­ physical impacts into bioenergy LCA. 5.5. CONCLUSIONS Bioenergy feedstock production can directly or indirectly affect existing land cover, land use, and land management and result in so‐called LUC. Therefore, the climate impacts associated with LUC, both biogeochemical and biogeophysical, should be assessed in bioenergy LCA. Currently, many LCA studies realized the importance of LUC and incorporated related biogeochemical impacts such as carbon change in vegetation and soil and N2O emissions. In general, LUC biogeophysical impacts, including albedo change and ET change, can be significant but have only been recently integrated in bioenergy LCAs. Further efforts are needed to improve LUC estimates, assess LUC impacts for more bioenergy types, and quantify and integrate biogeophysical impacts into bioenergy LCA. ACKNOWLEDGMENTS The authors thank Dr. Jennifer B. Dunn for valuable comments on an earlier version of this manuscript. This work was supported by the Bioenergy Technologies Office (BETO) of the Office of Energy Efficiency and Renewable Energy of the U.S. Department of Energy, under contract DE‐AC02‐ 06CH11357. REFERENCES Ahlgren, S., and L. D. Lucia (2014), Indirect land use changes of biofuel production  –  A review of modelling efforts and policy developments in the European Union, Biotechnology for Biofuels, 7(1), 35, doi:10.1186/1754‐6834‐7‐35. Anderson, J., E. Hardy, J. Roach, and R. Witmer (1976), A Land Use And Land Cover Classification System For Use With Remote Sensor Data, U.S. Government Printing Office, Washington, DC. Anderson‐Teixeira, K. J., P. K. Snyder, T. E. Twine, S. V. Cuadra, M. H. Costa, and E. H. DeLucia (2012), Climate‐regulation services of natural and agricultural ecoregions of the Americas, Nature Climate Change, 2(3), 177–181, doi:10.1038/ nclimate1346. Bagley, J. E., S. C. Davis, M. Georgescu, M. Z. Hussain, J. Miller, S. W. Nesbitt, A. VanLoocke, and C. J. Bernacchi (2014), The biophysical link between climate, water, and vegetation in bioenergy agro‐ecosystems, Biomass and Bioenergy, 71, 187–201, doi:10.1016/j.biombioe.2014.10.007.

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6 Bioenergies impact on Natural Capital and Ecosystem Services: A Comparison of Biomass and Coal fuels Astley Hastings

ABSTRACT Bioenergy is one energy technology that is being used to decarbonize energy provision in order to reduce greenhouse gas (GHG) emissions. However, all energy value chains use natural capital (NC) other than the atmosphere and impact ecosystem services (ES) other than climate regulation in different ways. In this chapter the consumption of all natural capital categories by various energy systems is discussed along with their impact on the ecosystem services provision. Metrics that can be used to quantify the positive and negative contribution of energy systems to each ecosystem service are investigated, and methods of comparing the overall impact of each energy system are proposed. These impacts and comparison metrics are discussed using the example of coal‐ and biomass‐fired thermal electricity generation. Coal is very detrimental to ES unless the GHG can be stored by carbon capture and storage (CCS) and clean coal technology is used. Biomass firing, on the other hand, can have a positive impact on ES as long as the feedstock is grown on land that does not compete with other land use vital to ES. However, the total contribution that bioenergy can make to a low‐carbon economy is constrained by the land available. 6.1. INTRODUCTION

biofuels produced from biological feedstocks was seen as a way to reduce reliance on imported oil to improve the balance of trade, as in the development of sugarcane ethanol in Brazil, or for energy security by developing maize ethanol and soybean and rapeseed biodiesel in the United States [EPA, 1992]. However, now in the drive to reduce anthropogenic greenhouse gas (GHG) emissions from energy use, bioenergy is seen as a low‐carbon and sustainable replacement to burning fossil carbon based fuels that increase atmospheric concentrations of carbon dioxide. Since the first International Panel on Climate Change (IPCC) assessment report (AR1) in 1990, research and policy development into ways of mitigating climate change has continued and is documented in a series of assessment reports, the latest being AR5 [IPCC, 2014]. China, the European Union, and the United States, the three largest emitters of GHG, are enacting policies to  reduce GHG emissions to tackle climate change. These included supporting the United Nations Framework

All human activities have an impact on natural capital and the services they provide to sustain our existence. From the dawn of the human use of fire, the provision of energy has been a key ecosystem service that has allowed the human population to grow to its current level of 7.4 billion (July 2016) by facilitating and powering industrialization, urbanization (housing and transport), food production, and the growth of the communications and the data‐ driven civilization. Today, bioenergy provides about 10.4% [IEA, 2015] of world primary energy use and is currently used mainly in Africa, Asia, and China through the use of wood and dung as a fuel for heating and cooking [Sims et al., 2006]. In the late twentieth century, the use of

School of Biological Sciences, University of Aberdeen, Aberdeen, UK

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Convention on Climate Change (UNFCCC) from 1992, ratifying international agreements from the Kyoto Protocol in 1997 to the current COP 21 in 2016. In the just‐concluded Paris COP 21 meeting of the UNFCCC in December 2015, the 197 countries present agreed on the global objective of limiting the global temperature to below 2.0°C of the preindustrial level and to further reduce the temperature increase to 1.5°C afterwards. The agreement set the target of reducing global greenhouse gas emission to at least 80% by 2050 [COP21 Paris Agree­ ment, 2015]. Subsequently 142 countries have ratified the agreement, including China and the United States. The use of biobased fuels is one of the possible pathways to produce low‐carbon energy. This has led to a refocusing of the research on biofuels and their production to ensure that they really have low GHG emissions and to reduce any negative impact of their widespread use on the environment. This has led to policy changes, such as the European Union Renewable Energy [2009/28/ EC, 2009; 2009/30/EC, 2009], Fuel Quality Directives [2015/1513/EU, 2015], and the U.S. Energy Policy Acts of 2007 [EPA, 2007]. These promote the addition of ethanol to gasoline and of biodiesel to diesel for transportation fuels and set standards for their GHG emissions. In addition to liquid biofuels that are predominantly used for powering transportation, the use of biomass of various kinds for district heating or heat and power systems is common in Scandinavian countries [Vattenfall, 2017] and as a fuel for large‐scale thermoelectric generation facilities such as DRAX in the United Kingdom. The latter substitutes the original coal fuel and has a generation capacity from biomass firing of 1980 MW, producing approximately 3.5% of the United Kingdom’s electricity. This was facilitated by government subsidies in the form of the United Kingdom’s Renewable Obligation Certificates (ROCs) [DECC, 2013b] for the first 660 MW unit at DRAX and Contracts for Difference (CfD) for the next two 660 MW DRAX units. These guarantee a wholesale electricity price of £100 MW h−1. The three DRAX units consume 7.5 million tonnes of wood pellets a year, which are mainly sourced in North America, equivalent to 7500 km2 of NPP, twice the area of Rhode Island. These are produced from yellow pine forest waste and sawmill residues and pelletized in the United States before shipment to the United Kingdom. Initially up 200,000 tons y−1 of UK‐produced pelletized biomass, mainly straw and Miscanthus, was used in this facility, but it will not be continued as it is easier to manage a single uniform biomass fuel than a multiplicity of fuels with differing characteristics. The straw and Miscanthus fuel displaced is now being used in thermoelectric plants specifically designed to burn straw and Miscanthus in Heston bale format, such as Glanford Brigg 40 MW generator in Lincolnshire, United Kingdom.

Biomass is also used as a feedstock for the generation of biogas/electricity in anaerobic digesters. Initially these digested sewage and farm manure, but they are now being supplemented with biomass as a carbon substrate for the production of methane. This has led to the widespread cultivation of forage maize in Europe, particularly in Germany but to a lesser extent in the United Kingdom and Italy. In Germany the government has encouraged the widespread development of anaerobic digesters by offering a feed‐in tariff of between €60 and €134 MWh, depending on installation size, with over 2500 in operation generating the equivalent of 5 million tons of oil equivalent (Mtoe) of methane a year [NIA 52/13, 2013]. This requires 33 million tonnes of forage maize to be grown in Germany as feedstock using over 5 Mha of arable land, estimated from the Food and Agriculture Organization (FAO) statistics. Certainly with current technology, biomass electricity generation would not be economically viable without subsidies as the supported cost is higher than the market rate for gas and coal generation (e.g., ~£44.00 MWh in the United Kingdom [OFGEM, 2016]). If mitigating GHG emissions is the primary objective for developing renewable energy, then its effectiveness has to be evaluated by quantifying the amount of GHGs it emits per unit of energy produced. The GHG saved by substituting the same unit of energy created from fossil fuel can then be evaluated. This is done by a process known as life cycle assessment (LCA) [ISO14040]. This considers the global warming potential of all GHG emissions related to the entire life cycle of each energy production method. This includes the building, opera­ tion, and decommissioning of the facility as well as all emissions related to the fuel production cycle. Bioenergy LCA includes the impact of land use change on vegeta­ tion and soil carbon balances, the displacement of food crops and the associated land use change required, crop management, and other biomass production costs. This is in addition to fuel processing and transportation to the end‐use facility. An example of the process system boundary, inputs, and outputs considered in an LCA of generic bioenergy is shown in Figure  6.1. LCA usually only considers the impact of bioenergy on one aspect of natural capital, the atmosphere, and one aspect of ecosystem services, that of regulating climate. To fully evaluate sustainability, the impact on all categories of natural capital (NC) and the ecosystem services (ES) they provide must be considered. This enables bioenergy systems to be quantifiably compared to other energy‐generation system such as solar, wind, tidal, and nuclear and to be benchmarked against fossil energy systems such as coal‐ fired thermal systems (the counterfactuals). For true sustainability, energy provision should have a  symbiotic relationship with other ecosystem services

BIOENERGIES IMpACT ON NATURAL CApITAL AND ECOSYSTEM SERVICES 85

Fuel

Seed production Soil GHG emissions Soil preparation planting

Machinery Crop management Fertilizer

Manpower

Harvesting

Fuel GHG emissions

Waste heat

Transporting Coproducts Feedstock to fuel process

Chemicals Energy use

Salable energy

System boundary

Figure 6.1 A schematic of the processes considered in a life cycle assessment of bioenergy, showing the system boundary and the input and outputs considered in the process.

provisions. However, energy‐related anthropogenic activities are more often attributed to causing degradation of the natural capital, which provides ecosystem services other than that of energy provision [Fisher et al., 2009]. The human population has increasingly appropriated a larger proportion of earth’s natural resources for its own use, in particular land area for housing, transportation, industrial processes, mining, timber, and food production [Don et al., 2012]. The increasing population and goals to increase prosperity and reduce inequality, like the UN’s Millennium Goals and the Sustainable Development Goals, mean that the trend of increasing energy consumption per capita will continue alongside the increased energy consumption due to population increase. The International Energy Agency (IEA) estimates that by 2040 the world’s primary energy consumption will increase by 33% with their new policies scenario, reflecting current global political and economic conditions. It also increases by 15% in their scenario to keep atmospheric CO2 below 450 ppm [IEA, 2015]. Both IEA scenarios foresee a large increase in nuclear and renewable energy, which will include bioenergy, hydro, wind, and solar energy generation. In addition, the oil and gas production will increasingly target harder‐to‐produce resources, such as tar sands and shale reservoirs. All these sources of energy require a substantial land footprint and thus will impact natural capital and could conflict with the provision of other ecosystem services [Nayak et al., 2010; Bond et al., 2014; Santangeli et al., 2016a, 2016b]. A saying attributed to Mark Twain, “Buy Land, there’re not making it anymore,” highlights the fact that land is the limiting natural capital resource for which most ecosystem services compete. The question is, how do various sources

of energy impact on natural capital and the other ecosystem services that they provide and how can they be ranked for comparison purposes to determine the relative impact and value of each and rank their sustainability? In this chapter the impact of energy systems on natural capital and the ecosystems they provide is discussed using thermal electricity generation fueled by biomass and coal as examples, coal being used as the counterfactual to biomass fuel. Metrics available to evaluate the impacts are discussed, and the two fuel systems are compared. 6.2. NATURAL CAPITAL AND ECOSYSTEM SERVICES IMPACTS OF THE ENERGY SECTOR 6.2.1. Natural Capital, Ecosystem Services, and Energy Provision Natural capital is defined as nature’s store of resources such as species, ecological communities, soils, land, fresh­ water, coasts, oceans, atmosphere, minerals, and subsoil assets, from which the bulk of human needs including food, clean air, clean water, energy (wood, fossil fuel, and nuclear), and mineral resources are provided [Natural Capital Committee, 2014]. Ecosystem services are described as the benefits we receive from a healthy functioning ecosystem [Haines‐ Young and Potschin, 2012]. The millennium ecosystem assessment (MA) classification system for ecosystem services splits these into supporting, regulating, provi­ sioning, and cultural services. It is the most widely used classification. Another classification is also used, based on the common international classification of ecosystem

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services (CICES), which classifies ecosystem services into provisioning (supply of goods such as food and fuel), regulating (climate and pollution control), and cultural services (recreation and spiritual benefits). In this chapter, the MA classification system is used. Provisioning ser­ vices include fuel, livestock, crops, aquaculture, wild fish, timber, peat, water, atmosphere, and genetic resources. Supporting services include soil formation, nutrient cycling, primary production, habitat, and the production of atmospheric oxygen. Regulating services include hazard regulation, climate regulation, disease regulation, pest regulation pollination, water purification, air quality, and soil quality. Cultural services include spiritual and religious belief, education and inspiration, and recreation and aesthetic values. All energy systems consume natural capital in the form of minerals, land, water, and so on and, while providing energy, impact other ecosystem services in distinct ways. Thermal power stations and their infrastructure have a land footprint and consume minerals for their construction, transpor­ tations, and systems infrastructure. During operation they consume water, emit waste heat, and exhaust GHG and other pollutants into the atmosphere. The mining of coal can have a large land footprint, releases quantities of methane into the atmosphere, and can create large quan­ tities of spoil waste and pollute water bodies. Burning coal releases fossil carbon into the atmosphere. Growing biomass for fuel, although recycling atmospheric carbon through photosynthesis and combustion, has some GHG associated with its production but uses large areas of land and quan­ tities of water, competing with food and fiber provision. To quantify impacts, the entire system and life cycle should be considered. This includes construction and decommissioning of the infrastructure and machinery, the operating and maintenance costs, and the fuel provision and use. This is analogous the capital expenditure (CAPEX) and operating expenditure (OPEX) in economic jargon. In the case that a fuel is required for the operation, the entire fuel cycle also needs to be considered from the provision of the raw material to fuel production and use. This includes any transport costs incurred up to the final use of the fuel. 6.2.2. Method and Metrics for Evaluation of Impacts of Energy System on Natural Capital Any metric that is used to evaluate energy value chains for their interaction with environmental systems must have quantifiable units for each interaction and the resulting impact. These impacts must be translatable into a single unit if a comparison is to be made between each energy system and to enable positive environmental impacts to be weighed against negative ones in order to understand the overall situation. Economists would

argue that the only way to make a comparison is to use a cost‐benefit analysis in monetary terms; however, many ecosystem services such a cultural and biodiversity are currently very difficult to value in economic units, whereas the provision of energy might be easier to quantify in monetary terms. Economic cost‐benefit analysis has other challenges as the following example of providing electrical energy from fossil fuel demonstrates. The cost of the exploration for, production of, and processing and use of coal, oil, and gas is measurable in the currencies of the day, and the quantity of oil, gas, and coal produced, transported, and used can be measured as well as the economic benefit of its use. However, exchange rates, discount rates, and market prices for the coal, oil, and gas commodities and electricity produced all vary with market forces in an unpredictable way during the lifetime of a project. This means the economic benefit value of the energy produced has significant unknowns and a temporal component. On the deficit cost side, the quantity of greenhouse gasses emitted can be measured or calculated, but the monetary cost of the resulting climate disruption is difficult to quantify. Emissions of particulates, NOx and SOx, can be quantified, and amount of these pollutants in the air can be measured, but their impact on human health, the eutrophication of water bodies, and the resultant impact on the provision of clean water and biodiversity are more difficult to quantify. To begin such an evaluation, the first step is to quantify what is measurable and known in hard metrics, and then comparison schemes or comparison metrics can be developed from these. The quantitative metrics will relate to the consumption of natural capital or the quantity of pollutants added to them. The creation of a comparison metric that will allow all these impacts to be weighed against each other is still the subject of research in projects such as ADVENT, funded by the UK Natural Environment Research Council [UKERC, 2016]. Many politicians, policymakers, NGOs, and members of the public mistakenly believe that the words “sustainable” and “renewable” when applied to energy systems mean the same thing. In terms of metrics, they are quite distinct. Sustainable energy systems are able to continue to be used for an indefinite time, without exhausting the natural capital on which they depend or destroying other ecosystem services. Renewable energy systems use a natural capital or resource that is continuously replaced. For example, fossil energy can be used until the economically available natural capital of coal, oil, and gas is depleted or until GHG or pollution seriously impacts humanity by degrading the ecoservice provisions of climate regulation, clean air, water purification, and food provision. This energy system is sustainable up to a point in time, which can be prolonged if the GHG emissions are captured and stored geologically (CCS), but the natural

BIOENERGIES IMpACT ON NATURAL CApITAL AND ECOSYSTEM SERVICES 87

capital of fossil resources is not renewable, and arguably CCS also uses natural capital. Similarly nuclear energy is sustainable as long as there is nuclear fuel to burn and the waste is stored or disposed of in a way that does not have a negative impact on ecosystem services such as soil renewal, food production, clean water provision, or ge­ netic biodiversity. It is certainly not renewable. Solar, tidal, and wind energy systems are renewable as the source of energy is the sun (and in the case of tidal, the moon) and sustainable as long as the sun shines and the intermittent nature of the power generated can be managed to match demand. On the other hand, burning firewood has been sustainable for thousands of years as long as the wood growing each year balances the wood burnt in the same period. So this practice is both sustainable and the natural‐ capital renewable. However if wood is burnt at a faster rate than it grows, then it is unsustainable as the wood supply will eventually run out, but in time the forests will regrow, and therefore, it is still renewable. These examples show that metrics are required to measure both the amount and rate of consumption of natural capital to quantify the impact on ecosystem services and should be both quantitative and have a spatial and a temporal component. As one of the main objectives of using renewable resources is to reduce greenhouse gas emissions by reducing the burning of fossil fuel that releases the carbon dioxide into the atmosphere, the metrics must be able to be used to compare the overall impact of the various renewable energy value chains to fossil fuel ones. This can be achieved by deriving a series of metrics that measure the consumption of natural capital by each energy system in terms of the energy output as listed in Table 6.1. 6.2.3. Method and Metrics for Evaluation of Impacts of Energy System on Ecosystem Services While the consumption of natural capital and polluting emissions are relatively easy to quantify, the impact of

energy systems on ecosystem services is less so. The economic impact of disrupted ecosystem services is easier to measure when the impact is severe and the ecosystem service fails, but it is less easy to quantify in the early stages when the impact is minor. The impacts can also be divided into those that are a continuous function of the energy system operation, such as atmospheric emissions, and those resulting from specific incidents. For example, if a source of ground water is polluted by an oil spill or a nuclear‐waste leak, the clean‐up costs and the creation of alternate water supplies have a financial cost and are readily quantifiable. To quantify the impact of possible incidents, both the probability of their occurrence and severity of impact have to be taken into account using a matrix, as shown in Figure 6.2. Taking continuous atmospheric emissions as an example, diesel or coal particulate and NOx emissions in cities can be quantified by the measured concentration of the pollutants in the atmosphere, but the impact of the degeneration of clean air provision is less easy to quan­ tify, and future impacts can only be estimated using models. It is possible to associate cost with an increase in lung disease or asthma by estimating the workdays lost or cost of medical treatment. However, causation must be established as a mere statistical association is speculative. Here in European cities, the impact of NOx and particulate emissions has not yet reached the point where banning diesel cars is seen to have an economic benefit. Whereas in London in the 1950s and Beijing recently, coal‐burning emissions caused air pollution to reach a crisis point, where health effects were obvious and the economic cost of the failed provision of clean air quantifiable. In both locations burning coal was the known cause, and on the basis of economics, policies were imposed to reduce coal‐ burning emissions. When cause and effect are not proven, the economic cost‐benefit is hard to quantify. As a result the impacts on ecosystem services by energy systems are often classified

Table 6.1 Key Metrics for Quantifying the Impact of, or Consumption by, Contrasting Energy Value Chains on Natural Capital Key metrics Natural capital Land Soil Species Ecological communities Atmosphere Minerals Subsoil assets Water Oceans

Coal thermal

Biomass thermal −1

−1

Land for mining (kWh ha y ) Soil removal for mining and damage by acidification Damage by emissions Damage by emissions Emissions (kWh−1) include SOx, NOx, GHG, and particulates Coal consumption Ground water contamination Mining operation disruption/pollution Mining runoff

kWh ha−1 y−1 Soil carbon and fertility impact Biodiversity reduction due to monoculture Modified by land use change Emissions (kWh−1) include SOx, NOx, GHG, and particulates Fertilizer consumption Nitrates leaching Biomass production water consumption (l kWh−1) Agricultural runoff

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BIOENERGY AND LAND USE CHANGE

Likelihood of occurrence

Small

Large

Medium

SOx emissions from burning coal

Emitting GHG to atmosphere

High

Medium

Nuclear reactor meltdown

Tractor accident oil leak

Low

Severity of consequence

Figure 6.2 Example of a matrix of likelihood of an event occurring from low to high compared to the severity of the consequence from small to large of that event for four energy‐related example events.

qualitatively as (very) positive or (very) negative or having a negligible affect as shown in Table 6.2. This table lists all the ecosystem services and indicates the positive and negative impact of two contrasting energy value chains, namely electricity generation using coal and bio­ mass as fuels. The impact of the two energy systems on each eco­ system service is distinct, so comparing the contrasting impacts facilitates a qualitative comparison. This type of analysis, although qualitative, is frequently used in impact studies such as in the Milner et al. [2015] and Holland et al. [2015] analyses of the impact of second‐generation bioenergy crops on ecosystem services. These were metastudies and also included statistics on the number of papers reporting both positive and negative impact for each category. 6.2.4. Metrics to Compare Energy Systems Table  6.1 demonstrates that use of natural capital can be compared between energy systems as both the consumption of and emissions to each category of NC can be directly compared quantitatively. However, Table 6.2 shows that comparing the impact on ecosystem services is more qualitative, unless the impact is severe enough to become economically measurable. The key environmental metrics to compare energy systems should relate to the total useful energy generated as suggested in Table 6.1. Fuels can be compared in terms of their specific energy content, MJ kg−1, and impacts per MJ, whereas electricity production is compared in terms of the impact per kWh. Many of the factors can be expressed in terms of their economic cost or the GHG emissions per unit of energy, called carbon intensity (CI). Other comparisons using the ratio of useful energy produced

Table 6.2 List of Ecosystem Services and the Impact of Electricity Generation Using Coal and Biomass as a Fuel Impact matrix Ecosystem services Provisioning services Livestock Crops Wild fish, aquaculture Timber Peat Water Genetic resources Supporting services Soil formation Nutrient cycling Primary production Provision of habitat Production of atmospheric oxygen Regulating services Hazard regulation Climate regulation Disease regulation Pest regulation Pollination Water purification Air quality Soil quality Cultural services Spiritual and religious believe Education and inspiration Recreation and aesthetic values

Coal thermal Biomass thermal electricity electricity = = − = = −− −

−− −− = −− = = −

− = = − −−

= + ++ + ++

−− −− − = = −− −− −

= ++ = = = + − +

− = −

= = −

Note: =, no impact; +, positive impact; ++, large positive impact; −, negative impact; −−, large negative impact.

BIOENERGIES IMpACT ON NATURAL CApITAL AND ECOSYSTEM SERVICES 89

divided by the energy to produce all the inputs, called energy use efficiency (EUE), can be made. Water con­ sumption per unit of energy (WUE) can also be used. For bioenergy systems the useful energy produced per hectare of land, called land use efficiency (LUE), can be used for comparison. Other environmental impacts such as atmospheric, water and land pollution, impact on habitat, biodiversity, or visual amenity are more diffi­ cult to compare directly as they will vary by the energy system. However, these should be translated into an economic cost or gain for comparison, which is better understood by policymakers and the public. Pollution load to the atmosphere, water, or land can be quantified in suitable units of polluting substances emitted per MJ or kWh if the emissions are continuous, as result of operations such as burning a particular fuel. Environmental impact can be estimated as the dilution level or concentration of the pollutant in the environ­ ment, expressed in ratios of mass or volumetric concen­ tration levels. Impact on receptors is quantified as the concentration, or dose, that causes harm, which could be carcinogenic, mutagenic, or pathogenic. In the case of polluting discharges that are the result of unplanned lack of containment or accidents, the impact has to be consid­ ered in terms of the potential consequence of the event and the likelihood or probability of its occurrence. This can be depicted in a severity and likelihood matrix depicted in Figure  6.2. Here the continuous emissions

such as NOx, SOx, and GHG are continuous and have a high probability of occurrence with differing severity of consequences, which are time scale dependent. Accidental discharges have a low likelihood of occurrence but can have consequences from small to catastrophic. In the case of additions of substances to the environment, their volume can be estimated, and the concentration in the air, water, or soil can be quantified. The impact can be  estimated by an analysis of the ease with which the pollutant reaches vulnerable parts of the ecosystem by source pathway to receptor modeling. The cost can be estimated by relating the impact of the exposure of the receptor to the pollutant. This can be expressed in terms of toxicity, inflammation, allergy, fertility, and carcinogenic indicators such as L10 or L50, which are the concentra­ tions that affect 10% or 50% of the receptor population, respectively, for the particular harm. 6.3. NATURAL CAPITAL AND ECOSYSTEM SERVICES IMPACTS OF BIOENERGY 6.3.1. Impacts During Various Stages of Bioenergy Value Chain The entire value chain for the production of bioenergy from seed production to combustion has to be accounted for in assessing its use of natural capital and impact on ecosystem services as indicated in Figure 6.3.

Land

Minerals Biomass production

Soil Transport

Species

Ecological communities

Generation

Distribution

Subsoil assets

Atmosphere

Fresh water bodies

End use Genetic diversity

Oceans

Figure 6.3 Example of the natural capital exchanges involved in generating electricity by a biomass‐fired power station. Black ovals are categories of natural capital, and gray boxes are the processes involved in the energy value chain. Solid lines show interaction, and dotted lines show potential interaction without pollution mitigating equipment.

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BIOENERGY AND LAND USE CHANGE

6.3.1.1. Feedstock Production Currently most feedstocks used to produce bioenergy are derived from terrestrial sources although there is current research into the use of marine biological resources. Indeed, before the mid‐nineteenth century, oil derived from whale blubber was the predominant fuel for oil lamps until kerosene became cheaper and displaced whale oil for this use. Feedstocks for bioenergy can be derived from trees or annual or perennial crops. Annual crops, which are usually food crops used for energy production, are considered first‐generation energy crops, and new annual or perennial crops, developed specifically as energy feedstocks, such as the short‐rotation coppice (SRC) species of willow and poplar or perennial grasses such as switchgrass, reed canary grass, and Miscanthus, are considered second‐generation feedstocks. Forested wood is cultivated to produce wood for manufacturing and construction, the residues from which can be used for energy. Some species have historically been grown for firewood by long‐rotation coppicing, for example, beech and ash, and other species can be grown in short‐rotation forestry (SRF) as a feedstock for paper or as fuel such as poplar and eucalyptus. In Europe poplar is considered as a candidate for bioenergy in both SRF and SRC production methods. As bioenergy feedstocks grow in soil, they contribute to soil formation, nutrient cycling, primary production, and habitat formation. They produce atmospheric oxygen during photosynthesis, which is recycled when the bioenergy carbon is burnt to produce carbon dioxide. The degree of contribution to the provisioning ES depends on both the bioenergy crop and the preceding land use. If SRF or SRC wood (willow or poplar) replaces a forest, then the contribution to provisioning ES is similar to the original forest; if grown on grassland, there is a slight increase; and if grown on arable land, then soil formation, nutrient cycling, and habitat provision increase significantly. Low input C4 perennial energy grasses show a similar pattern [Milner et al., 2015]. If annual food crops are grown for bioenergy, then the impact on arable land is similar to the food production use. However, if land use is changed from forestry or pasture to grow the annual crops, then the provisioning ES is degraded for soil formation, nutrient cycling, and habitat provision [McCalmont et al., 2015; Pogson et al., 2016; Richards et al., 2016]. In all cases, due to the economic necessity to maximize yield by optimizing agronomy and genotypes, primary production is enhanced by growing any crops industrially. The carbon cycle of annual and perennial crops, which are harvested each year for bioenergy, like Miscanthus, SRC willow, switchgrass, wheat straw, and maize stover, is that the carbon absorbed by photosynthesis each year is recycled to the atmosphere as CO2 in the same year by

burning in the fuel cycle. This is considered carbon neutral over the annual cycle. For forestry, trees take up to 100–500 years to grow to maturity, and even SRF takes up to 30 years. Therefore, the carbon cycle is somewhat different. To be carbon neutral in an annual time frame, if a 100 year old tree is harvested and burnt as fuel, then there must be 100 other trees absorbing CO2 in that year. So to be carbon neutral, in a 101 ha forest, only 1 ha can be harvested each year. Or to express it another way, to be carbon neutral, no more biomass than is represented by the annual net primary production (NPP) of the forest area can be harvested each year; this is known as a harvest intensity of 1. If forests are mainly harvested for timber used for construction and manufacturing wooden items, then these items will store the carbon until discarded, and only the forest waste and offcuts will be burnt as fuel in the year. Therefore, the carbon cycle of the long‐life products also has to be considered in the GHG accounting of fuel derived from forestry. Increasing bioenergy provision requires large areas of land to be dedicated to growing the feedstock, which requires land use change from other purposes, such as agriculture, natural habitat, grazing, or forestry. However, the impact varies according to the actual land use change and in some cases could actually increase the productivity of the land and its natural capital value. MacKay [2009] estimated the energy density per area of land of various renewable energy sources and showed that it depended on the geographical area. Bioenergy was highest in areas with a combination of good soil, sufficient rainfall, a warm and sunny growing season, and mild winters. For example, the United Kingdom has an energy density for bioenergy of between 1.5 and 0.5 W m−2, depending on whether the photosynthesis type is C3 of C4. This is equivalent to producing an average of 11 tons of harvest­ able biomass per hectare [Hastings et al., 2008, 2013]. Renewable energy sources are assumed to have a positive impact on the atmosphere and the climate regulation and energy provision that they provide; however, they all con­ sume land, which may have negative impacts on food pro­ duction, water provision, biodiversity, and cultural well‐being. These factors need to be quantified in some way so that the relative merits and detriments of each can be compared and a balanced assessment can be made. This means that metrics used to evaluate each use of natural capital, the value of the service provided, and the cost to other ecosystem services should be comparable in a quantitative way as by Day et al. [2013]. Although all crops use water to grow, plants with C4 photosynthesis pathways such as Miscanthus, switchgrass, sugarcane, or maize use less than those with C3 photosynthesis, which include most temperate crops such as wheat, barley, rice, bean, sugar beet, oil seed rape, and all woody trees. This means that replacing C3 with C4 vegetation reduces water

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use by the crop. Water quality is impacted by leaching from the soil [Gassman et al., 2007]. If large inputs of her­ bicides, pesticides, fungicides, and fertilizers are used, then they can leach into subsurface and surface water bodies used for the provision of water. Contaminants in water can be measured in concentrations, and their impact on receptors can be quantified. For example, nitrates in con­ centrations above 50 mg l−1 can cause blue baby syndrome, and high phosphate concentrations can cause the eutro­ phication of water bodies, affecting fish production. Low input plants such as forest trees, short‐rotation and coppice trees, and perennial C4 grasses such as Miscanthus are less likely to impact water provision or fish production. 6.3.1.2. Crop and Forest Management and Transportation When biomass fuel is derived from forests grown predominantly for timber, the impact of management on ES during the forest life cycle is small. The ground is prepared with a deep plow furrow, and the nursery trees are planted with minimal input, and then, apart from thinning the trees after two or three decades of growth, it is left undisturbed until harvested. During harvesting the trunks suitable for timber are transported to saw mills, and the offcuts, unsuitable round wood, and branches are chipped and transported to the pellet mills. Harvesting a forest is very disruptive for NC and ES as the habitat is destroyed and soil is damaged, causing erosion, runoff, and turbidity in streams and rivers. To minimize this damage, it is important that the replanting takes place as soon as possible after harvest. This enables a faster reestablishment of the natural environment. Harvesting the forest in patches, rather than clear‐felling large areas, so that the overall forest area is a mosaic of patches of different aged tree stands, allows a macroscale ecosystem to be maintained. This type of management is also compatible to keeping the harvest intensity below unity and eliminating positive GHG emissions from forestry. Maintaining forest cover in riparian areas will minimize soil erosion. ES and NC impacts can also be minimized in a similar way with dedicated biomass fuel SRF plantations. The management of perennial grasses and SRC willow as biomass fuel crops requires the soil to be prepared for planting in a similar way to grain crops by plowing and harrowing to a fine tilth. Planting willow involves planting cuttings or whips, about 30 cm long in winter using a plug‐planting machine. Willow requires fertilization in the first year and again after each harvest following the 3 year growth period to balance the nutrients lost in the harvest offtake. Harvesting is made in winter, after leaf fall and is a clear cut using a modified forage harvester that chips as it harvests. Spring growth restores the habitat, which is in place during the 3 year growth cycle. Nitrogen, potassium, and phosphor (NPK) removed by

the harvest are replaced by fertilizer applications in the first spring of each 3 year growth cycle. The chipped wood has a moisture content of around 40%. During the 3 year growth period, the stand provides a habitat for mammals, invertebrates, and birds, add carbon to the soil by leaf fall, and resists soil erosion; in addition, an understory of plants can be allowed to develop increasing biodiversity. The stands can also be used as a cover crop for game birds to add economic value. The perennial grass Miscanthus can be propagated by rhizomes or seed, depending on genotype. After preparing the soil to a fine tilth, in early spring rhizomes can be planted using a modified potato planter. Seed propagation is achieved by germinating seeds in plugs, which are grown in glass houses for 6 weeks. The plugs are then planted in the field using planting equipment developed in the horticultural industry [Clifton‐Brown et al., 2016]. NPK fertilizer is applied only in the case that the initial soil NPK loading is insufficient for the first year’s growth. After the growing season, the crop is allowed to senesce so that the nutrients are repartitioned to the rhizome and the plant material ripens and dries in the field until the following spring. Over the winter most of leaves fall, and the remaining stems are harvested in early spring as soon as equipment can get on the field before the first shoots emerge. The first year’s crop will be uneconomic, so the crop is cut and chopped with a forage harvester and left in the field as mulch, which suppress further weed growth. In the subsequent 20 years of the crop’s life span, the harvest is made each spring, either by a forage harvester and chipped or cut in swathes in the field. The swathes are air dried in the field until the moisture content is less than 14%; it is then baled using a large Heston Baler. Chipped Miscanthus has a moisture content of 14%–50%. During the growth and senescence periods, the stand provides a habitat for birds, mammals, and invertebrates and carbon input into the soil, while the root/rhizome system protects against soil erosion. However, owing to the recalcitrant nature of the leaf litter, weeds and other plants are suppressed, creating a monoculture eliminating plant diversity. The stands can also be used as a cover crop for game birds to add economic value. 6.3.1.3. Processing and Transportation Processing of biomass fuel depends on the end use and the transportation distance and mode. To store biomass for any length of time, it has to have less than 14% moisture content. Higher values allow the biomass to decompose, and if confined, this can generate sufficient heat for spontaneous combustion to occur. Therefore, the biomass has to be dried to this level for storage and transport. This uses energy equal to the latent heat of the water removed, reducing the EUE, and depending on the source of heat, it increases the CI of the fuel. This gives an advantage to

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perennial grasses that can be air dried in the field and then baled, dry enough to transport and store. Biomass fuel can be accepted by thermal power stations and heat plants in chip, bale, or pellet form. To reduce transport costs, the most compact form is pellets with a bulk density of approximately 650 kg m3; bales have a density of approximately 170 kg m3, and chips have a density of approximately 90 kg m3. For example, a fully loaded 44 Mg standard European tractor‐trailer rig can carry a full load of pellets but is limited by volume to approximately 21.5 Mg of bales and approximately 10 Mg of chips. Economic truck transportation of chips and bales is limited to farm/forest to pelleting plant or small‐scale thermal installation. Large‐scale pelleting mills of above 1,000,000 Mg y−1 capacity use approximately 3% of the energy in the biomass to make pellets, while small mills around 5000 Mg y−1 capacity use about 10% [Hastings et al., 2017]. So pellets from large mills have a lower CI. If average annual production of biomass is approxi­ mately 10 Mg ha−1, then a large pellet mill will need 100,000 ha to keep it supplied from a dedicated perennial crop and 10,000,000 ha of forest. This implies that the mean transport distance from a dedicated biomass crop to the mill is approximately 31 km, and for forestry it is approximately 316 km. This increases the CI and reduces the EUE of the pellets. Transportation of the pellets to the thermal plant can be more efficiently conducted using rail, barge, or ship. Indeed, the 7.5 million tons of pellets, to fire the DRAX power station in the United Kingdom each year, are shipped from Louisiana in the United States to UK ports and thence by rail to the power plant with a corresponding increase in CI. For comparison, UK‐produced Miscanthus bales will have a CI of 1 g CO2 e C MJ−1, while the U.S.‐produced wood pellets used in DRAX have a CI of 6. This is much lower than coal with a CI of approximately 33 g CO2 e C MJ−1 [Hastings et al., 2012]. 6.3.1.4. Combustion Fuel quality is important for any combustion process. It has to be easily conveyed into the burner and should have a high energy density. Solid fuels are usually trans­ ported by airflow or augers. Pellets are ideal for the purpose, while biomass in bales and chip form has to be ground into smaller particles before being transported in this way. The biomass fuel must have a high energy density and low moisture content to avoid energy loss in water latent heat. Combustion temperature should be as high as possible to increase the thermal efficiency of any heat conversion to work in the steam turbine. However, the combustion temperature must be low enough to avoid any ash melting or slagging and damaging of the boiler’s heat‐transfer surfaces and to avoid producing excessive NOx. To achieve this, the ash content, which is mainly silica, should be as low as possible, both to have a high

energy density and to reduce the ash handling. The concentration of elements that reduce the ash melting point such as potassium should be as low as possible; in addition chlorine and sulfur that combine with flue gasses to form corrosive compounds that damage boiler components, such as SOx and HCl, should be low. There are international standards for biomass fuel pellets. The European one is EN14961‐2, specifying the following values: Particle size Moisture content Caloriic value Ash content (three standards) Bulk density Sulfur content Chlorine

10–12 mm >10% >4.7 kWh kg−1 Low >1%, standard >3%, and high > 6% >600 kg m3 >300 ppm >800 ppm

Only additives allowed are lignin and trace amount of vegetable oil as die lubricant. In the case that reactive elements exist in the feedstock biomass, small amounts of limestone could be added to the pellets to reduce corrosive compounds forming and increase the melting point of the ash. However, although this would perform satisfactorily in boilers, it would not conform to the current EN pellet standard. The waste products of the combustion of biomass are flue gasses, waste heat, bottom clinker, and fly ash. Flue gasses are predominantly carbon dioxide and water vapor. Due to the very small percentage of sulfur in the fuel, SOx emissions are negligible and do not require scrub­ bing. NOx is reduced by ensuring that the combustion temperature is below 1300°C and low gas resident time at high temperatures, which is controlled by recirculating flue gasses, air staging, or water injection. Produced NOx can be scrubbed from the flue gas by the injection of sorbents such as powdered limestone or aluminum oxide. This is then removed with filtration or electrostatic pre­ cipitation. Fly ash is also removed in the same way. These solid residues are recycled to produce cement and building materials. Carbon and ammonia can also be used to scrub NOx, but these produce a waste stream that cannot be recycled. Due to lower combustion temperature, the thermal efficiency of biomass‐fired power stations is around 35% conversion of heat energy to electricity. The waste heat is either vented to the atmosphere through air‐cooled towers or to the sea or rivers. In the case of discharge into sea or river, the water must first be cooled to a value that does not damage the aquatic environment. If the power station is located near an industry or settlement that can use the low‐level heat for process or space heating, then the waste heat can be used, and the thermal efficiency of the installation can be improved up to 85%. The power station must be scaled to the heat demand to achieve this efficiency.

BIOENERGIES IMpACT ON NATURAL CApITAL AND ECOSYSTEM SERVICES 93

6.3.2. Metrics and Method of Evaluation and Integration Bioenergy is certainly renewable, but bioenergy feed­ stock production requires a large amount of land. Most of the impacts of bioenergy are related to the type of land used and are proportionate the area used. Hastings et al. [2009] showed that if high yielding Miscanthus grass was grown on 10% of EU27 arable land, which was set aside to reduce food production in 2005, it would only produce the equivalent of 3.6% of the primary energy consumption of EU27 in that year. A similar study of the UK identified land that could be used to grow biomass for energy in the United Kingdom, without impacting natural capital by displacing forests, national parks, sites of scientific interest, peatlands, and natural pasture and not using the most productive land suitable for food production, was between 0.9 and 3.6 Mha. If the most productive type of biomass feedstock for each land type (considering eight species of SRF and two of SRC or Miscanthus) was grown on this land, the total bioenergy production of the United Kingdom would be between 0.21 and 0.83 PJ y−1 [Hastings et al., 2013]. This is only 2%–8% of primary energy consumption in the United Kingdom. This shows that bioenergy is not a panacea for reducing GHG emission as it is limited by the land area available. The study highlights the need for the most

productive bioenergy feedstocks to be grown to suit the climate and land available and in so doing have the least impact on natural capital. At present growing either woody or giant grasses for biomass combustion produces the most energy per hectare and has the lowest CI and highest EUE when compared to the energetic use of food crops [Hastings et al., 2012]. 6.4. NATURAL CAPITAL AND ECOSYSTEM SERVICES IMPACTS OF COAL 6.4.1. Impacts During Various Stages of Coal Value Chains The entire value chain for the production of coal‐derived energy from coal mining to combustion has to be accounted for in assessing its use of natural capital and impact on ecosystem services as indicated in Figure 6.4. 6.4.1.1. Coal Mining Fossil‐based energy needs land for extracting the raw material and for the processing and end use of the fuel. The land area required for conventional oil and gas production and subsurface coal mining is small, but for unconventional oil and gas [Bond et al., 2014] and surface coal mining, it is large. The impact of the land use change is disruptive to NC as the area becomes an industrial

Land

Minerals Coal mining

Soil Transport

Species

Ecological communities

Generation

Distribution

Subsoil assets

Atmosphere

Fresh water bodies

End use Genetic diversity

Oceans

Figure  6.4 Example of the natural capital exchanges involved in generating electricity by a coal‐fired power station. Black ovals are categories of natural capital, and gray boxes are the processes involved in the energy value chain. Solid lines show interaction, and dotted lines show potential interaction without pollution mitigating equipment.

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complex. Provisioning services are affected by the removal of soil and vegetation, disruption to habitats, spoiling visual amenity, and adding pollutants to the air, soil, and water. The impact magnitude varies with the type of mining. In the case of subsurface coal production, the surface facilities are the shaft entrances and mine shaft conveyance equipment, coal processing and washing facilities, and mine support offices and workshops. A large area is required for the spoil (waste rock) heaps and surface loading and transporting facilities such as rail yards or ship loading facilities. Subsurface coal is mined by removing the coal layer at the coalface and allowing the rock behind to collapse, closing the gap left by the coal. The coalface follows the coal seam, and the mine can extend underground in all directions for many kilometers and even extend under the sea. The rock collapse behind the seam causes small seismic events (like fracking). The mines usually have to be pumped to evacuate subsurface water, which is frequently saline, has a high iron content, and frequently contains heavy metals. The disposal of this water can cause surface and ground water pollution, impacting the provision of water and aquatic organisms. Surface mining of coal requires the stripping of soil and rock overburden of the coal seam and storing the material in heaps. Then coal seam is then dug out and transported to the processing area. Ideally the over­ burden is then replaced, and the surface soil is restored and replanted. The land use impact is much larger than subsurface coal mining, and the area affected is as large as the produced coal seam and can extend many kilome­ ters in all directions. The area of land affected per unit of energy extracted is orders of magnitude higher than sub­ surface mining. The land is frequently left barren until the extraction is complete, and the land is restored only when mining is complete. This can cause problems of erosion and dust emissions to the atmosphere. In addition water provision can be disrupted by the mining activity by diverting water courses, disrupting aquifers close to the surface, and by the introduction of pollutants, origi­ nating from the mining process, into the water, rendering it unsuitable for human provision or making it more difficult to purify. Financial provision for land restoration must be included in the mineral extraction license and is thus a quantifiable cost. Both types of coal mining cause direct methane emis­ sions to the atmosphere as coal naturally absorbs methane under subsurface conditions, and when the pressure is reduced by mining, the methane is released. In subsurface mining wells are usually drilled into the coal seam ahead of the coalface progression in order to drain the methane. This reduces the risk of explosion due to methane release at the coalface into the mine. This methane is usually used to power the surface facilities of the mine. However, some

methane is released into the mine, which is kept below explosive concentrations by forced air circulation through ventilation shafts. This methane is released into the atmosphere with its climate forcing impact. In surface mining methane is seldom harvested through boreholes, and so is all released to the atmosphere during coal digging. Different coals have different methane contents, with higher carbon content coals having the potential to store more methane. Actual methane contained depends on the burial history and pore pressure of the coal seam [Crosdale et al., 1998]. Coal has a CI of approximately 33 g CO2 e C MJ−1,including production costs, but methane emission are not included and can increase this by more than approximately 20% for very gassy coals in the case that the methane is not captured and used. 6.4.1.2. Processing and Transportation At the mine coal is washed and sorted to remove non­ coal minerals. Coal has a higher density (~0.9 g cm3) than that of biomass pellets (~0.6 g cm3) and a higher energy density (~30 MJ kg−1) than that of biomass (~18 MJ kg−1), and so transportation uses less energy and GHG emis­ sions per MJ km than biomass pellets. Coal is usually transported by a combination of rail, canal, or sea from the mine to the power station. Historically in the United Kingdom and European Union, coal‐fired power stations were either built near coal mines or close to ports for ease of coal transportation. These power stations tended to be large facilities of approximately 1 GW, usually as multiple facilities. Electricity was distributed to users by high voltage grids. However, as the mines have been worked out and become uneconomic, the coal has been sourced on a large scale from mostly overcast mines in Russia, the United States, Canada, and Australia. This has led to the development of very large bulk coal ships, port facilities, and rail connections to the existing power station sites in the United Kingdom and European Union. This uses existing infrastructure and avoids further land use impact. In addition, economies of scale minimize transport costs and energy use and hence GHG emissions. 6.4.1.3. Combustion Prior to combustion, the coal is usually milled to a fine powder that can be blown into the furnace for efficient combustion, usually on fluidized beds. Combustion temperature should be as high as possible to increase the thermal efficiency of any heat conversion to work in the steam turbines. This can be up to 45% in large modern coal‐fired power stations. However, the combustion temperature must be low enough to avoid producing excessive NOx, and this is achieved by recycling exhaust gasses. Coal has up to 10% ash content, but this is mainly silica, which is removed as clinker from the furnace bed to be used as an aggregate for construction or by electrostatic

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removal of fly ash, which is used in the production of cement. Ash slagging is not usually a problem as the K content is low. However, coal has a high sulfur content, whose combustion product is SOx. In the European Union, SOx must be removed by scrubbers using sorbents like limestone powder, which react to become gypsum, which is used for making plaster board. This scrubber also removes NOx. Full SOx, NOx, and particulate removal is known as clean coal technology. Failure to remove SOx will cause acid rain with severe impacts on ES such as lake eutrophication and forest degradation. Failure to remove NOx and particulates have a severe impact on air quality and public health, like London in the early 1950s and some Chinese cities today. Clean coal technology does not remove GHG emissions. This requires CCS technology, which involves capturing the CO2, transport­ ing it, and storing it in a geological repository. All thermal power stations release waste heat to the environment. As coal‐fueled power stations tend to be very large and located away from centers of population (e.g., DRAX in the United Kingdom), the waste heat cannot be used. This means that although large coal‐fired power stations have a high thermal efficiency, up to 45%, the waste heat has to be disposed of. If located on the coast, seawater can be used as the coolant, provided the temperature of the effluent seawater is not increased more than 2°C to protect the marine environment. In the case of land‐locked power stations, where river water has to be used, large quantities of heat have to be removed in cooling towers by evaporation of water, as the river cannot provide a sufficient thermal sink for all the energy. A 1 GW power station will have to remove at least 1.2 GW of heat through the evaporation of up to approximately 1900 Mg H2O h−1. This water use has to be considered in terms of the water source availability to avoid impacts on human water supply and aquatic life and can have detrimental effects in dry seasons with low river volumes. 6.4.2. Metrics and Method of Evaluation and Integration Electricity from thermal power stations fueled by coal is currently the cheapest available [OECD‐IEA, 2005]. This is due to cost of the coal fuel and the efficient extrac­ tion and transportation system, as well as the use of exist­ ing generating infrastructure. For example, in the United Kingdom, the CAPEX and OPEX cost is approximately £24 MWh, and the fuel cost is approximately £20 MWh. The major impact of coal‐fired thermal power stations on ES is emissions of GHG and other pollutants to the atmosphere; 1 MWh of electricity will emit approximately 850 kg of CO2 to the atmosphere. It will also use approximately 1900 kg of water for evaporative cooling. If the power station is not using clean coal technology, then

there will also be SOx, NOx, and particulate emissions, which are proportional to the ash and sulfur content of the coal. All modern power stations use clean technology, and older ones can be retrofitted. Concerns about urban air pollution has driven this change as the cost to human health in urban areas has approached epidemic proportions in Chinese cities, as it did in the United Kingdom in the 1950s. At this time in the United Kingdom, either clean technology was fitted or the stations were closed. Another driver to clean coal in Europe was the eutrophication of lakes and forest decimation by acid rain from coal combustion in the late twentieth century. The impact of water use on ES will depend on the amount of available water and season and can be accounted for by pricing water consumption. GHG emissions are more difficult to price as at present no accepted way of pricing climate disruption has been developed by the global community and current traded carbon prices are too low, approximately $5 to have any impact on fuel use. In contrast Norway imposed a tax of $40 per Mg CO2 on oil companies operating in the Norwegian sector or in the North Sea, which reduced GHG emissions for this sector significantly. If this tax level was applied to electricity generation by coal, it would double the cost of electricity but would make it equivalent to the supported price of biomass electricity generation. 6.5. COMPARISON OF IMPACTS AND DISCUSSION A comparison of the impacts on NC, and the ES they provide, of using either biomass or coal for thermal electric generation of electricity must also consider legacy systems. In OECD countries the electricity generating systems were built around large coal‐fired power stations located near coal deposits, which distributed electricity to conurbations by high voltage grids. The disturbance caused by the installation of this infrastructure is embedded into the environment, and current ecosystems have absorbed the impact. However, even with clean coal technology, the climate impact of continued GHG emission is unsustainable to most aspects of NC. Either the GHG emissions have to be captured and stored underground or the use of coal has to stop. The projected cost of damage caused by the current climate change trajectory makes future GHG emissions very costly [Field et al., 2014]. More expensive than the current projected cost of coal‐CCS electricity of £110 MWh [DECC, 2013a] and the current UK‐supported price of biomass thermal electricity of £100 MWh. Biomass fuel is not a panacea for low‐carbon electricity generation. It uses land and has a relatively low energy yield of approximately 200 GJ ha−1; however, this is much higher than the net energy yield of biodiesel and ethanol

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from crops. Solar and wind systems have greater energy yields per hectare but are intermittent, whereas biomass electricity is dispatchable. Biomass power generation can be provided in smaller distributed units, close to the biomass growers and close to facilities and commu­ nities that can utilize the waste heat as combined heat and power units. If the correct feedstocks are grown, the plantations can have a beneficial impact on NC and ES, by providing habitats, conserving water, and improving air quality. However, competition with other land uses limits the amount of energy that can be produced from biomass. In Europe this is estimated to be around 10% of current primary energy consumption. So while careful siting of biomass production is beneficial to NC, it can only be considered one of the contributory technologies to decarbonize energy supply. Bioenergy can also provide one of the few means of providing negative GHG emis­ sions if its use for heat and power is combined with CCS technology to store carbon taken from the atmosphere by photosynthesis into geological repositories. ACKNOWLEDGMENT This work forms part of the ADVENT project funded by the UK Natural Environment Research Council (NERC). REFERENCES Bond, C. E., J. Roberts, A. Hastings, Z. K. Shipton, E. M. João, J. Tabyldy Kyzy, and M. Stephenson (2014), Life‐cycle assessment of greenhouse gas emissions from unconventional gas in Scotland. http://www.climatexchange.org.uk/files/9214/ 1258/6142/Life‐cycle_Assessment_of_Greenhouse_Gas_ Emissions_from_Unconventional_Gas_in_Scotland_Non‐ Technical_Summary.pdf (accessed 12 September 2016). Clifton‐Brown, J., A. Hastings, M. Mos, J. P. McCalmont, C. Ashman, D. Awty‐Carroll, J. Cerazy, Y.‐C. Chiang, S. Consentino, W. Crachroft‐Eley, J. Scurlock, I. S. Donnison, C. Glover, I. Golab, J. M. Greef, J. Gwyn, G. Harding, C. Hayes, W. Helios, T.‐W. Hsu, L. S. Huang, S. Jezowski, D.‐S. Kim, A.  Kiesel, A. Kotecki, J. Krzyzak, I. Lewandowski, S. H. Lim, J. Liu, M. Loosely, H. Meyer, D. Murphy‐Bokern, W. Nelson, M. Pogrzeba, G. Robinson, P. Robson, C. Rogers, G. Scalici, H. Schuele, R. Shafiei, O. Shevchuk, K. U. Schwarz, M. Squance, T. Swaller, J. Thornton, T. Truckses, V. Botnari, I. Vizir, M. Wagner, R. Warren, R. Webster, T. Yamada, S. Youell, Q. Xi, J. Zong, and R. Flavell (2016), Progress in upscaling Miscanthus biomass production for the European bio‐economy with seed based hybrids, GCB Bioenergy, doi:10.1111/gcbb.12357. COP21 Paris Agreement (2015), https://treaties.un.org/doc/ Publication/MTDSG/Volume%20II/Chapter%20XXVII/ XXVII‐7‐d.en.pdf (accessed 18 July 2017). Crosdale, P. J., B. B. Beamish, and M. Valix (1998), Coalbed methane sorption related to coal composition, International Journal of Coal Geology, 35, 147–158.

Day, B., I. J. Bateman, A. R. Harwood, G. M. Mace, R. T. Watson, D. J. Abson, B. Andrews, A. Binner, A. Crowe, and S. Dugdale (2013), Bringing ecosystem services into economic decision‐making: Land use in the United Kingdom, Science, 341(6141), 45–50. DECC (2013a), CCS cost reduction task force: The potential for reducing the costs of CCS in the UK. https://www.gov.uk/ government/groups/ccs‐cost‐reduction‐task‐force (accessed 31 January 2017). DECC (2013b), Investing in renewable technologies  –  CfD contract terms and prices. https://www.gov.uk/government/ uploads/system/uploads/attachment_data/file/263937/Final_ Document_‐_Investing_in_renewable_technologies_‐_CfD_ contract_terms_and_strike_prices_UPDATED_6_DEC.pdf (accessed 18 November 2016). DIRECTIVE (EU) 2015/1513 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 9 September 2015. Amending Directive 98/70/EC relating to the quality of petrol and diesel fuels and amending Directive 2009/28/EC on the promotion of the use of energy from renewable sources. DIRECTIVE 2009/28/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 April 2009. On the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. DIRECTIVE 2009/30/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 April 2009. Amending Directive 98/70/EC as regards the specification of petrol, diesel and gas‐oil and introducing a mechanism to monitor and reduce greenhouse gas emissions and amending Council Directive 1999/32/EC as regards the specification of fuel used by inland waterway vessels and repealing Directive 93/12/EEC. Don, A., B. Osborne, A. Hastings, U. Skiba, M. S. Carter, J. Drewer, H. Flessa, A. Freibauer, N. Hyvönen, M. B. Jones, U. Mander, A. Monti, S. Njakou Djomo, J. Valentine, K. Walte, W. Zegada‐Lizarazu, and T. Zenone (2012), Land‐use change to bioenergy production in Europe: Implications for the greenhouse gas balance and soil carbon, Global Change Biology. Bioenergy, 4(4), 372–391. Energy Policy Act (EPA) (1992), Effective October 24, 1992 (102nd Congress H.R.776.ENR, abbreviated as EPACT92). Energy Policy Act (EPA) (2007), Energy Independence and Security Act of 2007 (Pub.L. 110–140, H.R. 6). Field, C., et al. (Eds.) (2014), Climate change 2014; Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK/New York. Fisher, B., R. K. Turner, and P. Morling (2009), Defining and classifying ecosystem services for decision making, Ecological Economics, 68(3), 643–653. Gassman, P. W., M. R. Reyes, C. H. Green, and J. G. Arnold (2007), The soil and water assessment tool: Historical development, applications and future research directions, Transactions of the ASABE, 50(4), 1211–1250. Haines‐Young, R. and M. Potschin (2012), Common International Classification of Ecosystem Services (CICES V4): Consultation Briefing Note, Center for Environmental Management, University of Nottingham.

BIOENERGIES IMpACT ON NATURAL CApITAL AND ECOSYSTEM SERVICES 97 Hastings, A., J. Clifton‐Brown, M. Wattenbach, P. Stampfl, C. P. Mitchell, and P. Smith (2008), Potential of Miscanthus grasses to provide energy and hence reduce greenhouse gas emissions, Agronomy for Sustainable Development, 28, 465–472. Hastings, A., J. Clifton‐Brown, M. Wattenbach, P. Stampfl, C. P. Mitchell, and P. Smith (2009), Future energy potential of Miscanthus in Europe, Global Change Biology. Bioenergy, 1(2), 180–196. Hastings, A., J. Yeluripati, J. Hillier, and P. Smith (2012), Biofuel crops and greenhouse gasses, in Biofuel Crop Sustainability, edited by B. P. Singh, pp. 383–406, John Wiley & Sons, Ltd, Chichester. Hastings, A., M. Tallis, E. Casella, R. Matthews, S. Milner, P. Smith, and G. Taylor (2013), The technical potential of Great Britain to produce lingo‐cellulosic biomass for bioenergy in current and future climates, Global Change Biology. Bioenergy, 6(2), 108–122. Hastings, A., M. Mos, J. Yesufu, J. McCalmont, R. Shafiei, C. Ashman, C. Nunn, H. Schüle, S. Cosentino, G. Scalici, D. Scordia, M. Wagner, and J. Clifton‐Brown (2017), Economic and Environmental assessment of seed based Miscanthus in the UK, Frontiers in Plant Science, doi:10.3389/fpls.2017.01058. Holland, R. A., F. Eigenbrod, A. Muggeridge, G. Brown, D. Clarke, and G. Taylor (2015), A synthesis of the ecosystem services impact of second generation bioenergy production, Renew. Sust. Energ. Rev., 46, 30–40. IEA (2015), World energy statistics and balances. http://www. oecd‐ilibrary.org/energy/data/iea‐world‐energy‐statistics‐ and‐balances_enestats‐data‐en (accessed 30 August 2016). IPCC (2014), Climate change 2013: The physical science 697 basis, Fifth Assessment Report, Intergovernmental Panel on Climate Change, Geneva. MacKay, D. (2009), Sustainable Energy – Without the Hot Air, UIT Cambridge Ltd, Cambridge. http://www.inference.eng. cam.ac.uk/sustainable/book/tex/sewtha.pdf (accessed 16 June 2016). McCalmont, J., A. Hastings, P. Robson, I. Donnison, G. Richter, N. McNamara, J. Scurlock, J. Woods, and J. Clifton‐ Brown (2015), The environmental credentials of Miscanthus as a bio‐energy crop in the UK, GCB Bioenergy, doi:10.1111/ gcbb.12294. Milner, S., A. Lovett, R. Holland, G. Sunnenberg, A. Hastings, P. Smith, and G. Taylor (2015), A preliminary assessment of the ecosystem service impacts of bioenergy in GB to 2050, Global Change Biology. Bioenergy, 8, 317–333, doi:10.1111/ gcbb.12263.

Natural Capital Committee (2014), The State of Natural Capital: Restoring our natural assets. Second Report to the Economic Affairs Committee, Natural Capital Committee. Nayak, D. R., D. Miller, A. Nolan, P. Smith, and J. Smith (2010), Calculating carbon savings from wind farms on Scottish peatlands. Mires and Peat, 4, Article 09, http://www. mires‐and‐peat.net/ (accessed 18 July 2017). NIA 52/13 (2013), Anaerobic digestion across the UK and Europe. http://www.niassembly.gov.uk/globalassets/documents/ raise/publications/2013/environment/5213.pdf (accessed 12 August 2016). OECD‐IEA (2005), Projected costs of generating electricity 2005 update, SBN 92‐64‐00826‐8, International Energy Agency Publication Service. OFGEM (2016), Wholesale energy gy markets in 2016. https:// www.ofgem.gov.uk/system/files/docs/2016/08/wholesale_ energy_markets_in_2016.pdf (acessed 29 January 2017). Pogson, M., M. Richards, M. Dondini, E. O. Jones, A. Hastings, and P. Smith (2016), ELUM: A spatial modelling tool to predict soil greenhouse gas changes from land conversion to bioenergy in the UK, Environmental Modelling and Software, 84, 458–466. Richards, M., M. Pogson, M. Dondini, E. O. Jones, A. Hastings, D. N. Henner, M. J. Tallis, E. Casella, R. W. Matthews, P. A. Henshall, S. Milner, G. Taylor, N. P. McNamara, J. Smith, and P. Smith (2016), High‐resolution spatial modelling of greenhouse gas emissions from land‐use change to energy crops in the United Kingdom, Global Change Biology. Bioenergy, 9(3), 627–644. Santangeli, A., T. Toivonen, F. M. Pouzols, M. Pogson, A.  Hastings, P. Smith, and A. M. Ilanen (2016a), Global change synergies and trade‐offs between renewable energy and biodiversity, Global Change Biology. Bioenergy, 8(5), 941–951. Santangeli, A., E. Di Minin, T. Toivonen, M. Pogson, A.  Hastings, P. Smith, and A. Moilanen (2016b), Synergies and trade‐offs between renewable energy extraction and biodiversity conservation  –  A cross‐national multi‐factor analysis, Global Change Biology. Bioenergy, 8(6), 1191–1200. Sims, R. E. M., A. Hastings, B. Schlamadinger, G. Taylor, and P. Smith (2006), Energy crops: Current status and future prospects, Global Change Biology, 12, 1–23. UKERC (2016), ADVENT addressing the valuation of energy & nature together. http://www.ukerc.ac.uk/programmes/ advent.html (accessed 10 September 2016). Vattenfall (2017), Facts about bioenergy. https://corporate. vattenfall.com/about‐energy/renewable‐energy‐sources/ biomass/ (accessed 22 August 2017.)

7 Empirical Evidence of Soil Carbon Changes in Bioenergy Cropping Systems Marty R. Schmer1, Catherine E. Stewart2, and Virginia L. Jin1

ABSTRACT Biofuels are seen as a near‐term solution to reduce greenhouse gas (GHG) emissions, reduce petroleum usage, and diversify rural economies. Accurate accounting of all GHG emissions is necessary to measure the overall carbon (C) intensity of new biofuel feedstocks. Changes in direct soil organic carbon (SOC) can have a major impact on estimating overall GHG emissions from biofuels. Even though SOC represents a small portion of a soil’s mass, it plays an essential role in soil functioning and C cycling. Currently, there are limited long‐term data sets that can be used to evaluate SOC changes of perennial energy crops. However, certain recommendations can be made. Conversion of native ecosystems to annual bioenergy crops will likely result in significant SOC stock loss. The expected use of agricultural residues for bioenergy and its effect on SOC use will largely be dependent on residue removal amounts, climate, management practices, previous land history, topography, and soil type. Perennial bioenergy crops have the ability to significantly increase SOC stocks while providing substantial biomass quantities on degraded cropland or idle land under proper management. A multifeedstock, landscape approach minimizes environmental risks in meeting feedstock demands for bioenergy production by providing sufficient feedstock production while maintaining or increasing SOC. 7.1. INTRODUCTION

Biomass sources for bioenergy are globally diverse with specificity by climate and region. Current bioenergy sources are largely comprised of first‐generation biofuels (e.g., corn grain (Zea mays L.), soybean (Glycine max (L.) Merr.), rapeseed (Brassica napus L.), sugarcane (Saccharum sp), oil palm (Elaeis guineensis Jacq.)), but projected utilization of second‐generation biofuels or advanced biofuels will require changes in land use. This change in land use may affect soil organic carbon (SOC) stocks and overall sustainability of these agroecosystems. Second‐generation biofuels will rely primarily on agricultural residues, woody crops, and perennial, herbaceous grasses with characteristics that allow for adequate feedstock availability, low fossil fuel input requirements, and reduced environmental impacts compared with first‐generation biofuels. The overall environmental benefits and GHG mitigation potential of advanced biofuels will rely heavily on

Low‐carbon biofuel sources are being developed and evaluated globally to partially offset petroleum‐based transport fuels resulting in near‐ and long‐term climate benefits [Unger et al., 2010]. Depending on feedstock source and management practices, greater reliance on cropping systems that are bioenergy centric may improve or worsen long‐term sustainability of arable land. The primary indicators for climate change mitigation potential of bioenergy crops or feedstocks are the changes in SOC and direct emissions of GHG from the soil surface.

1 Agroecosystem Management Research Unit, USDA‐ARS, Lincoln, Nebraska, USA 2 Soil Management and Sugarbeet Research, USDA‐ARS, Fort Collins, Colorado, USA

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production practices to establish, grow, and harvest these feedstocks. Land use changes and land management decisions can lead to negative changes in SOC, which further result in increased GHG emissions. Historically, land use changes from human activities have been a major contributor to changes in SOC and global GHG emissions [Lal, 2004a; Lambin and Meyfroidt, 2011]. Understanding the role between bioenergy crops and land use changes on SOC will be critical to assess overall sustainability of these emerging systems. The objective of this chapter is to look at the existing knowledge on the relationship between SOC changes and bioenergy cropping systems. Specifically, we will address the importance of SOC, review literature on land use and land management experiments from bioenergy cropping systems, and synthesize current knowledge gaps related to SOC changes and bioenergy crops. For this chapter, we define land use changes as the conversion of a natural ecosystem (e.g., forest and grassland) or managed agro­ ecosystem (i.e., pasture) to a bioenergy crop, while land management changes relate to increased intensification of existing cropping systems (e.g., agricultural residue removal for bioenergy) or cellulosic biofuel feedstocks (fertilizer management and harvest frequency) on SOC. 7.2. SOIL ORGANIC C 7.2.1. Definition and Importance Croplands represent around 8% of SOC stocks worldwide, turning C over rapidly (6 years, 0–20 cm, [Paul, 2016]). Agroecosystems not only have a major impact on C cycling under intensive management but also have the potential to sequester SOC and mitigate climate change impacts. SOC comprises on average only 1%–9% by mass of mineral soils, yet it affects water holding capacity and infiltration, soil fertility and quality, and physical structure and erosion potential. With increased population growth, long‐term soil resource management is critical to minimize environmental impacts while meeting food, energy, and climate change abatement demands [FAO, 2015; O’Rourke et al., 2015]. Soil C stocks at equilibrium are a balance of C inputs through plant residues, roots, and microbial products and CO2 loss by decomposition and respiration [Paustian et al., 2016]. Atmospheric CO2 is fixed through photosynthesis into plant biomass (roots and shoots). Soil microbes use plant residues and root exudates as an energy source, producing CO2 as they respire. 7.2.2. Forming Factors SOC is determined by climate, landscape position, vegetation, geological parent material, and time [Jenny, 1941].

Climatic factors, including temperature and precipitation, determine vegetation type, C input, and decomposition rates. SOC generally increases with increasing precipitation and decreases with hotter temperatures [Follett et al., 2012a]. SOC turnover is greater in warm, wet climates compared to cold, dry ones. Climate variables weather the geological parent material over time to produce a wide array of clay minerals and oxides that have variable capacity to store SOC. Plant C inputs regulate the amount, placement, and chemistry of organic C into the soil. Annual crops have been bred to produce more aboveground biomass with a  high proportion of C in reproductive structures that consequently requires N fertilization. Perennial crops, such as forage grasses and tree species, have a greater proportion of their biomass in roots compared to shoots [Agostini et al., 2015; Chimento et al., 2016]. Root C inputs can contribute up to 50%–70% of SOC in some systems. Although root inputs are important to determining SOC stocks, their relative contribution is uncertain due to fine‐root turnover and root exudates. Plant species also determines plant chemistry, which strongly controls plant decomposition rates [Parton et al., 2015]. Generally, plant material with greater lignin content and a higher C:N ratio decomposes more slowly compared to residues with higher cellulose or sugar content and lower C:N ratios. Many annual crops have a lower C:N ratio compared to perennials and decompose faster. Recent work suggests that because of more efficient microbial C use efficiency, crops with a lower C:N ratio potentially contribute more to SOC stocks [Cotrufo et al., 2013; Stewart et al., 2015]. SOC is comprised of partially decomposed plant material and root exudates and microbes and faunal bodies amalgamated together with soil minerals to form a complex, heterogeneous environment. Although SOC is actually a continuum of organic materials in various stages of decay, it can be separated into labile and recalcitrant fractions. Soil C has historically comprised of four pools: nonprotected, aggregate‐protected, mineral‐ protected (chemically protected), and biochemically protected C, which can be separated by physical, density, or chemical fractionations to provide more detailed information on C stability and turnover [Six et al., 2002]. Nonprotected C is comprised of partially decomposed plant materials, fungal hyphae, and spores and can be isolated by density floatation (light fraction) or physically as particulate organic matter (POM; >250 µm) [Cambardella and Elliott, 1992; Gregorich and Ellert, 1993]. POM is considered a labile C pool with a short turnover time of months to years and is frequently used to assess land use and agricultural management effects owing to this responsiveness [Cambardella and Elliott, 1992]. Agricultural management practices that increase C input such as

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N  fertilization or higher yielding crop selection will increase nonprotected C. Aggregate‐protected C is physically occluded by soil minerals and protected from microbial degradation due to inaccessibility [Six et al., 2004]. Soil aggregates are isolated with either a wet or dry sieving technique over several sieve sizes [Denef et al., 2004; Six et al., 2004; USDA‐NRCS, 2004]. Soil aggregates generally have a quicker turnover compared to silt and clay‐protected C, on the order of months to centuries. Turnover generally decreases with aggregate size, with larger aggregates turning over months and microaggregates turning over on the scale of centuries. Soil tillage breaks soil aggregate structure and promotes decomposition. Substantial loss of aggregate‐protected C occurs with tillage and an increase in aggregate‐protected C under no‐tillage (NT) practices. Important exceptions should be noted in cool wet climates in clay soils where plow tillage can store as much and sometimes more C than NT soils due to substantial decreases in corn stover (nongrain aboveground biomass) under NT [Angers and Eriksen‐Hamel, 2008]. Mineral‐associated C is microbially processed organic C (low C:N ratio) associated with silt and clay particles. Silt and clay‐protected C has the longest turnover, from centuries to millennia. Mineral‐associated C is strongly related to soil texture and mineralogy [Six et al., 2002] but not all cases [Plante et al., 2006]. Minerals with 2:1 clay have stronger interactions with organic matter compared to soils with 1:1 clay minerals and Fe and Al oxides. Since mineral surface area is limited, silt and clay C has a saturation capacity [Hassink et al., 1997; Stewart et al., 2009], most evident in sandy soils with a low clay content [Stewart et al., 2012]. Mineral protection is particularly important in subsoils, where the majority of stabilized C is microbially processed and quite old [Rumpel and Kögel‐ Knabner, 2011]. Biochemically protected C is composed of molecules that are difficult for soil microbes to decompose and have been previously defined as humin‐, lignin‐, lipid‐, and aromatic‐containing compounds [Six et al., 2002; Schmidt et al., 2011; Dungait et al., 2012; Paul, 2016]. Traditionally, these have been identified after involved chemical extractions (humic, fulvic, acid hydrolysis, or cupric oxide extraction). Recent studies and reviews suggest that few chemical compounds are truly recalcitrant [Schmidt et al., 2011; Dungait et al., 2012]. Some of the oldest C associated with mineral fractions is highly microbially processed and subject to loss with the addition of fresh organic C [Fontaine et al., 2007]. Researchers suggest that C accessibility is more important than biochemical composition in determining C turnover [Dungait et al., 2012] and emphasize the importance of microbial processing in the creation of stable organic C [Schmidt et al., 2011; Cotrufo et al., 2015].

7.2.3. Temporal Changes SOC is sensitive to agricultural management practices that alter C inputs (i.e., crop selection, rotation, aboveground/allocation pattern, and N fertilization) and decomposition rates (tillage and irrigation). SOC change is highly dependent on previous land use, with native systems brought into cultivation, losing 50%–70% of their SOC stocks [Gollany et al., 2015]. Degraded soils with low SOC stocks have the potential to store more soil C compared to those with a greater C content when converted into a management that either lowers SOC decomposition rate or increases soil C input [Stewart et  al., 2007; Ogle et al., 2014]. After a management change, the system will take 10–100 years to reach a new steady state, and the IPCC assumes a 20 year equilibrium in their calculations of C stock changes [IPCC, 2006]. During this time, SOC accrual or loss will be greatest immediately after the change and decrease in magnitude over time [Paustian et al., 2000]. 7.3. SAMPLING CONSIDERATIONS Measuring SOC change over time is challenging, since the change is very small relative to pool size. Soil C sequestration estimates are sensitive to the use of baseline data and measurement depth reported [Powlson et al., 2014]. Many tillage comparisons are made on studies after a certain time period without accounting for initial SOC content, assuming that the soil system is at steady state and that the relative treatment increase over time represents SOC storage. However, treatment differences at a given time point may overestimate SOC change. Using space‐for‐time substitution, many studies evaluate SOC stock at a single time point and assume the agroecosystem is at steady state. However, this method only enables the comparison of SOC stocks at a single time point, not inference over time. To accurately document SOC changes over time, treatments must be compared to a baseline sampling. This is done infrequently, as long‐ term studies require substantial investment [Peterson et  al., 2012] and 5–10 years are needed to document changes in SOC stocks. 7.3.1. Sample Depth Measuring SOC throughout the soil profile is important for accurate SOC change evaluation [Rumpel and Kögel‐Knabner, 2011]. Many management effects are observed only in the top 30 cm, with C stocks deeper in the profile reported more infrequently and with greater variability [VandenBygaart et al., 2007]. When the entire profile is considered, SOC sequestration with NT is much lower and frequently not significant [Denef et al., 2004;

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Angers and Eriksen‐Hamel, 2008; Schmer et al., 2014]. A meta‐analysis has found that the majority of studies show a redistribution of C through the profile with NT, with a SOC stock accumulation at the surface and a decrease at depth, resulting in a small C gain across the whole profile [Angers and Eriksen‐Hamel, 2008].

Urban 2 Mg C ha−1 year−1 [Davidson and Ackerman, 1993; Ogle et al., 2005; Wei et  al., 2014]. In one review, land use changes between cropland, forest, and grassland have resulted in measured SOC losses of 9% in the top 1 m of soil, with the greatest changes occurring in the top 20–30 cm soil depth [Guo and Gifford, 2002] (Figure 7.2). Whether soils gain or lose C will be determined by the quantity, quality, and timing of organic matter inputs to soils, which themselves will be affected by plant species composition associated with land use [Stockmann et al., 2013]. In general, high SOC systems (i.e., forest and grassland) are expected to be more susceptible to SOC losses following

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land use conversion compared to low SOC systems (i.e., croplands) [Poeplau et al., 2011]. Conversion to cropland generally results in SOC losses, while restoration of cropland to perennial systems (i.e., forest and grassland) results in SOC gains. These increases in SOC, however, are typically slower than SOC loss rates when land was originally converted to cropland [Conant et al., 2001; Guo and Gifford, 2002]. Further, rates of SOC gain as a result of restoration to perennial systems or improved manage­ ment in existing cropland soil are expected to decrease over time and reach a new SOC steady state [Stewart et  al., 2007]. In a meta‐analysis of  biofuel system soils,  SOC changes were most rapid during the first 10  years following conversion to bioenergy crops and then declined to near zero within 20 years postconversion [Qin et al., 2016]. 7.4.1.1. Conversion to Cropland From 1700 to the 1990s, approximately 1168 Mha of forested lands and 669 Mha of grasslands were converted to agricultural land uses [Foley et al., 2005]. Conversion to agriculture has resulted in major SOC losses in cultivated soils compared to undisturbed or native soils because of soil degradation from land use conversion, drainage, tillage, burning, and other agricultural practices [Lal, 2004a]. Soil‐management practices and soil erosion over the last century have resulted in 40–78 Pg of SOC losses overall [Paustian et al., 2000; Lal, 2004b; Doetterl et al., 2012], although up to 10% of erosional C losses may be offset by the deposition and burial of sediment [Berhe et al., 2007]. Nonetheless, measured losses in SOC stocks in the top 1 m of soil were −59% and −42% when pasture or native forest was converted to cropland, respectively [Guo and Gifford, 2002]. A global meta‐analysis of 453 paired or chronosequential sites found that a 45% depletion in SOC stocks at 98% of all sites following conversion from forest to agricultural land and that forests greater than 50 years old showed the greatest losses following conversion [Wei et al., 2014]. In a recent meta‐analysis of bioenergy production systems, conversion of forest or grassland to corn (with residue removal) decreased SOC by up to 35% [Qin et al., 2016]. 7.4.1.2. Conversion to Grassland Sequestration of SOC in grasslands is estimated as 0.54 Mg C ha−1 year−1 (with a range of 0.11–3.04 Mg C ha−1 year−1), though the rate is affected by prior land use [Conant et al., 2001]. Guo and Gifford [2002] found that measured SOC stocks increased +19% and +8% when cropland or native forest was converted to pasture, respec­ tively. Forest to pasture conversion showed SOC stock increases for studies confined within annual precipitation regions between 2000 and 3000 mm [Guo and Gifford, 2002]. These trends are consistent with expectations for

C gains in soils that have lower initial SOC (i.e., cropland soils) compared to higher initial SOC (i.e., forest soils). Increases in SOC, however, are also dependent on time after land use conversion. In a recent review of cropland soils converted to grassland, shrubland, or forest in China, Deng et al. [2014] found that SOC in the top 1 m of soil initially decreased during the first 5 years follow­ ing restoration but then increased in systems older than 5 years. In biofuel production systems, SOC stocks did not change after conversion of grassland to Miscanthus (Miscanthus × giganteus) [Qin et al., 2016]. Conversion of agricultural land to perennial bioenergy grasses, however, increased SOC by 26%, but converting grassland to perennial bioenergy grass decreased SOC by 11% [Harris et al., 2015]. 7.4.1.3. Conversion to Forest The current potential for SOC storage in forest soils is estimated to be 160–170 Pg C [Lal, 2010]. Afforestation (i.e., tree establishment on historically treeless land) and reforestation (i.e., tree establishment on historically for­ ested land that was cleared) tend to increase SOC, though the magnitude of changes depends on land use history, forest system type, time, and climate. A meta‐analysis in tropical zones indicated that 15 years following afforestation, climate (mean annual temperature and precipitation) had a greater impact on SOC to 1 m depth than former land use, forest type, or forest age [Marín‐Spiotta and Sharma, 2013]. In more temperate zones, afforestation of cropland to forest or shrubland increased SOC stocks in the top 60 cm depth [Shi et al., 2013], though conversion of cropland to short‐rotation coppice (SRC) with bioenergy willow or poplar (Populus spp.) resulted in no SOC change [Harris et al., 2015; Walter et al., 2015]. Conversion of cropland to tree plantation resulted in an 18% increase in SOC, and cropland conversion to secondary broadleaf forest increased SOC by 53%, but conversion of native forest to tree plantation decreased SOC stock by −13% [Guo and Gifford, 2002]. Li et al. [2012] found that SOC initially decreased after afforestation of cropland and then increased in systems older than 30 year, and that SOC gains occurred only after afforestation with hardwoods but not softwoods (i.e., pine; Pinus spp.). In contrast, grassland afforestation caused no change [Li et al., 2012] or tended to decrease SOC stocks [Guo and Gifford, 2002; Shi et al., 2013]. 7.4.2. Bioenergy The development of bio‐based feedstocks has focused on plant residues from both annual row‐crop species (corn, sugarcane, wheat, and sorghum), perennial bioenergy grasses (Miscanthus and switchgrass), and various woody plant species [Mitchell et al., 2008; van der Weijde et al., 2013].

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Annual crops and the forestry sector currently provide the majority of the global biofuel production, with North America, South America, and Europe the primary bioen­ ergy producers. Brazil, Finland, Denmark, Sweden, and Austria have the highest percentage of bioenergy used within their total primary energy supply, each exceeding 15% [Bacovsky et al., 2016]. While current feedstock candi­ dates for immediate utilization tend to be those associated with residues of annual row crops, converting row‐crop land to dedicated herbaceous or woody perennial bioenergy crops is expected to provide positive environmental impacts by reducing soil erosion, increasing SOC storage, providing wildlife habitat, improving disease suppression, and reducing agrochemical inputs [Mitchell et al., 2008; Werling et al., 2014; Harris et al., 2016]. In the United States, approximately 113 million Mg of corn grain, 5.3 million Mg of vegetable oils, and 155 million Mg of forestry/wood are harvested and annually used for the bioenergy sector [U.S. Department of Energy, 2016]. In the near term, the use of agricultural residues is expected to play a major role in meeting global renewable energy goals [Scarlat et al., 2010; Ji, 2015; USDOE, 2016]. Agricultural residues suitable for bioenergy include corn stover, wheat (Triticum aestivum L.) straw, rice (Oryza sativa) straw, and sugarcane bagasse. Land allocated to dedicated energy crops is expected to increase in the long term on the basis of  future bioenergy demands. Dedicated energy crop examples include herbaceous crops (e.g., switchgrass and Miscanthus), short‐rotation woody crops (SRWC), and SRC. Temperate woody species include the fast‐growing SRC species poplar and willow (Salix spp.), single‐stem tree plantations of eucalyptus (Eucaplytus spp.) or pine (Pinus spp.), and in more tropical climates, oil‐producing species

such as jatropha (Jatropha curcas L.) and oil palm. Short‐ rotation woody crops in the United States, typically as fast‐ growing multistem hardwood coppice species (Populus spp., Salix spp.) and single‐stem tree species (Pinus spp.), rely on short cutting cycles of 2–4 years for a rotation period of 18–22 years [Dickmann, 2006; McKenney et al., 2014]. Further, SRWC can be grown on sites not suitable for other annual or perennial bioenergy crops [Vance et al., 2014]. 7.4.2.1. Land Availability and Conversion to Bioenergy Crops In addition to annual crops, herbaceous perennials such as switchgrass (Panicum virgatum L.), alfalfa (Medicago sativa L.), bermudagrass (Cynodon dactylon L. (Pers.)), Miscanthus, napiergrass (Pennisetum purpureum Schumach.), and reed canarygrass (Phalaris arundinacea L.) have bioenergy feedstock potential in different geographic regions, depending on climate and land availability [Mitchell et al., 2016]. Owing to economic and environmental concerns about the competition of prime farmland for food or biofuel production, marginally productive lands (e.g., low long‐term yield potential or negative economic returns) have been targeted for bioenergy production. Globally, abandoned and degraded cropland is estimated at 320–702 Mha [Campbell et al., 2008; Cai et al., 2011]. Land use conversion of cropland to perennial, herbaceous bioenergy crops (~485 Mha), followed by SRWC (~26.3 Mha) and afforestation (~185 Mha) was determined to provide the best global carbon mitigation potential [Albanito et al., 2016]. The inclusion of marginally productive grasslands, savannahs, and shrublands further increases global estimates of marginally productive land to greater than 2000 Mha (Figure 7.3) [Cai et al., 2011].

2500 Marginally productive land area (Mha)

Cropland Crop/mixed veg 2000

Forest/shrubland Grassland/savannah

1500

1000

500

0 Africa

China

Europe

India

South America

United States

Worldwide

Figure 7.3 Estimated marginally productive land area in millions of hectares (Mha); redrawn from Cai et al. [2011]. (See insert for color representation of the figure.)

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Producing dedicated bioenergy feedstocks on margin­ ally productive lands avoids multiple negative impacts, including higher food commodity costs expected when  using prime farmland for biofuel production and  expected SOC losses associated with forestland conversion to either herbaceous or woody perennial biofuel species. Because marginally productive lands are also among the most environmentally sensitive lands, perennial feedstock production on these higher risk areas is also likely to result in improved ecosystem services, including reduced soil erosion losses, improved water quality, pest suppression, decreased greenhouse gas emissions and/or SOC storage, and the provision of critical wildlife and pollinator habitats [Mitchell et al., 2008; Fargione et al., 2009; Valentine et al., 2011; Werling et al., 2014]. 7.4.2.2. Temporal Changes in SOC Perennial bioenergy crops will tend to increase SOC through minimal soil disturbance coupled with continued high levels of root‐derived C inputs such as herbaceous rhizodeposition and coarse woody roots and stumps [Schmidt et al., 2011; Anderson‐Teixeira et al., 2013; Agostini et al., 2015]. Root density and biomass comparisons between poplar, switchgrass, corn, and soybeans indicated that growing season fine root biomass was 7–10 Mg ha−1 for switchgrass and 6–8 Mg ha−1 for poplar and that root density was greater for poplar and switch­ grass than for corn or soybeans [Tufekcioglu et al., 1999]. For example, belowground C allocation in extensive root and rhizome structures in perennial bioenergy grasses was 400%–750% greater than that in corn in a corn‐ soybean rotation system [Anderson‐Teixeira et al., 2013]. As a result of these belowground inputs and limited soil disturbance, measured SOC accrual rates range from 0.4 to >3.0 Mg C ha−1 year−1 under herbaceous and woody biofuel species, depending on soil depth evaluated [Anderson‐Teixeira et al., 2009; Don et al., 2012; Agostini et al., 2015], with SOC storage in subsoils as much as 12 Mg ha−1 greater compared to cropland soils [Liebig et  al., 2005]. Similar to perennial bioenergy grass, the conversion of row‐crop land to SRC can result in measured SOC sequestration rates of 0.44 Mg C ha−1 year−1 (poplar and willow) [Don et al., 2012], with higher rates of SOC accrual occurring in low SOC soils converted to SRC [Rowe et al., 2016]. Some management choices result in no SOC changes or localized SOC losses. For example, SOC decreased by 21% when switchgrass was inter­ cropped with loblolly pine (Pinus taeda L.) in the south­ eastern United States owing to soil priming caused by grass litter inputs [Strickland et al., 2015]. Long‐term oil palm plantations resulted in greater SOC stocks than adjacent grasslands [Goodrick et al., 2015], and surface soil stocks of SOC under oil palm were similar to native

forest SOC in northern Brazil [Frazão et al., 2013] However, conversion of tropical peatlands to oil palm results in significant SOC loss [Fargione et al., 2008] with an estimated oxidation of 4.6 million Mg of belowground C in Southeast Asia and biodiversity loss [Koh et al., 2011]. For sugarcane production in Brazil, soil C stocks decline following conversion of native forests and pastures while increase on cropland [Mello et al., 2014]. However, degraded pastures converted to sugarcane production in south‐central Brazil showed increased SOC stocks with significant increases occurring at subsurface soil depths [Oliveira et al., 2016]. 7.4.2.3. Landscape Intensification and Placement Both spatial and temporal incorporation of dedicated perennial bioenergy crops into agricultural systems diversifies both farming enterprises as well as landscape function [Asbjornsen et al., 2013]. Because landscape context can be as important as crop choice in maintaining or improving ecosystem services [Werling et al., 2014], landscape placement and timing of bioenergy crops can play a critical role in sustainably intensifying these multifunctional production systems. In a review of the U.S. Corn Belt, Asbjornsen et al. [2013] summarized five strategies for perennializing agricultural landscapes: (i)  using perennial vegetation strips in critical zones of lateral and vertical water movement to  improve nutrient, water, and sediment retention; (ii)  planting perennial species at more poorly drained landscape positions or more erodible slope positions to enhance SOC storage and control erosion; (iii) within‐ and around‐field perennial plantings to increase pest suppression and improve the abundance and diversity of pollinators; (iv) adding perennial species to crop rotation sequences as a temporal intensification method to improve soil health indicators and decrease nutrient losses; and (v) inclusion of perennial species in  integrated crop‐livestock systems to boost both economic and environmental benefits. A review of grass, SRWC, and short‐rotation forestry buffer strips in the European Union similarly showed improved nutrient and moisture retention as well as increased recreational benefits when buffers were placed along water courses [Christen and Dalgaard, 2012]. Individual case studies also have demonstrated that strategic placement of perennials in annual row‐crop landscapes can maximize overall biomass yields. For example, landscape zones correlating with highest biomass productivity varied across annual and perennial biomass crops such that annual crop production could be concentrated at well‐drained summit positions, SRC willow at poorly drained depositional positions, and alfalfa and SRC poplar on steep, highly erodible slopes [Thelemann et al., 2010].

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7.5. LAND MANAGEMENT EFFECTS ON SOIL ORGANIC CARBON 7.5.1. Agricultural Residues Corn stover is expected to play an important role in the United States and China for second‐generation biofuels on the basis of current availability, producer familiarity, and projected costs [Ji, 2015; Mitchell et al., 2016]. Determining harvestable corn stover amounts without causing negative impacts on SOC, soil fertility, and grain yield has been a primary research focus for agricultural scientists. Corn stover preserves important soil functions by protecting against wind and water erosion while adding carbon back to the soil. Stover also contains a high proportion of nutrients, which are essential for soil fertility and may need to be replaced. Corn stover retention levels for SOC maintenance are higher than retention levels needed to control water or wind erosion [Wilhelm et al., 2007]. Determining stover collection thresholds to maintain SOC and minimize erosion will differ by soil types, baseline SOC levels, cropping systems, tillage practices, and field topography [Wilhelm et al., 2010; Jin et al., 2015]. Soil property response to stover removal is most sensitive to near‐surface soil layers [Blanco‐Canqui et al., 2014; Jin et al., 2015], but subsurface SOC cycling processes can play a role in cumulative SOC storage [Follett et al., 2012b; Schmer et al., 2014, 2015]. On the basis of corn stover removal experiments in the Corn Belt Region, USA, it was estimated that minimum stover retention rates of 6.4 ± 2.2 Mg ha−1 year−1 were required to maintain SOC, but significant variation across sites were apparent [Johnson et al., 2014]. This estimate is higher than a global meta‐analysis estimate, which indicated that crop residue retention rates of greater than 2.0 Mg ha−1 year−1 were needed to maintain SOC in 85% of the case studies under medium soil fertility levels [Han et al., 2016]. A universal, stover harvestable amount recommendation to minimize SOC change is not possible since manage­ ment, landscape, and soil type influence stover retention requirements at the field level. Stover harvest frequency, rotation complexity, and tillage practices impact how much stover can be sustainably removed within a growing season [Wilhelm et al., 2007]. On the basis of current research, partial corn stover removal may occur when grain yields exceed 11 Mg ha−1 in temperate climates, with conservation tillage being practiced, and on landscapes with minimal potential for wind or water erosion [Mitchell et al., 2016]. Amelioration practices (e.g., manure and cover crops) may still be required to maintain SOC stocks depending on region and soil type. There is limited information on long‐term SOC trends from wheat straw and sorghum residue removal compared

with corn stover removal [Johnson et al., 2006; Blanco‐ Canqui, 2010]. For wheat systems, the minimum source C inputs required to maintain SOC stocks ranged from 0.3 to 4.0 Mg C ha−1 year−1 [Johnson et al., 2006]. Evidence suggests that C inputs from wheat roots are more impor­ tant for maintaining SOC stocks than C derived from straw [Powlson et al., 2011]. In Canada, long‐term (>10 years) baling of barley (Hordeum vulgare L.) or wheat straw resulted in similar SOC stocks as straw retention treatments [Nyborg et al., 1995; Solberg et al., 1997; Lafond et al., 2009], while repeated straw removal in Europe has shown SOC stock declines [Singh et al., 1997; Thomsen and Christensen, 2004]. Similar to corn stover, the amount of residues needed to maintain SOC stocks for rice‐, wheat‐, and sorghum‐based cropping systems is dependent on rotation, climate, tillage, and baseline SOC levels. 7.5.2. Dedicated Energy Crops Soil organic carbon typically increases when annual cropland is converted to perennial energy crops [Anderson‐ Teixeira et al., 2009; Agostini et al., 2015; Qin et al., 2016]. The amount of SOC stored by perennial species depends on the climate, soil type, baseline SOC content, time, and soil C placement in the soil profile [Conant et al., 2001; Kämpf et al., 2016]. Belowground C allocation is the primary driver for SOC changes in perennial systems, especially C allocation at soil depth. While invasion potential and higher water use by some SRWC species such as Eucalyptus are of potential concern, SRWC production generally is viewed to have more favorable environmental impacts relative to row‐crop agriculture [Vance et al., 2014]. Research on SRWC in the northeastern and midwest United States have resulted in improvements in both biomass yield as well as the efficiency of harvest in the last 30 years, though high economic costs remain a challenge to more widespread adoption [Geyer, 2006; Volk et al., 2006; McKenney et al., 2014]. Benefits to SRWC include greater soil quality and SOC storage, decreased erosion, and lower nutrient losses [McKenney et al., 2014; Vance et al., 2014]. In the northeastern United States, SOC stocks were unchanged in a SRC willow 19 years chronosequence [Pacaldo et al., 2013], but significant SOC storage occurred in SRC poplar stands following conversion from row‐crop land in the north central United States [Hansen, 1993]. In the European Union, both SRWC willow and poplar generally have been found to be net greenhouse gas sinks compared to grassland or cropland [Rytter et al., 2015; Harris et al., 2016; Sabbatini et al., 2016], with greater SOC storage potential for SRC establishment on sites with low initial SOC levels [Rowe et al., 2016]. In a Mediterranean climate, woody and herbaceous bioenergy

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crops increased SOC stocks at the soil surface after transi­ tioning from a cropland field with woody species having greater SOC accumulation rates largely from increased C input from leaf litter [Chimento et al., 2016]. Conversely, SOC accumulation rates were similar for woody and perennial crops in Northern Italy [Ceotto and Di Candilo, 2011]. Agricultural soils with higher clay content were found to have greater capacity to accumulate SOC in an afforestation meta‐analysis [Laganière et al., 2010]. No significant SOC changes were observed in land converted from cropland, grassland, or forests to short‐ rotation woody species [Harris et al., 2015; Qin et al., 2016]. Overall changes in SOC from short‐rotation woody species are also dependent on the study duration. Conversion to short‐rotation woody species results in an initial SOC loss, but over time SOC results in an overall gain a decade after conversion from cropland [Qin et al., 2016]. Soil organic C sequestration rates were estimated to be 0.56 Mg C ha−1 year−1 for willow and 0.63 Mg C ha−1 year−1 for poplar [Agostini et al., 2015]. Perennial energy grasses (switchgrass and Miscanthus) tend to have higher median SOC sequestration rates than SRWC [Agostini et al., 2015; Qin et al., 2016]. Switchgrass contains a fibrous root system that extends 2.7–3.3 m below the soil surface [Weaver and Darland, 1949]. For switchgrass, SOC storage changes are partially related to root structure, and this will vary by cultivar and ecotype [Adkins et al., 2016; Roosendaal et al., 2016]. In general, cropland conversion to switchgrass or Miscanthus increases SOC stocks, but conversion from grassland to perennial bioenergy crops resulted in no changes to SOC [Qin et al., 2016]. Soil carbon stocks on low‐input switchgrass fields were higher than adjacent cropland fields in  the Northern Plains, USA [Liebig et al., 2005]. In a paired site comparison study in Iowa, a similar result was found between switchgrass and a corn‐soybean‐ alfalfa (Medicago sativa L.) rotational system [Al‐Kaisi et al., 2005]. Processes that affect C turnover and SOC storage at subsurface soil profiles are important for proper C accounting [Schmer et al., 2015]. Switchgrass on marginally productive cropland sites in the Central Plains, USA, resulted in a 2.9 Mg C ha−1 year−1 increase in the top 1.2 m of the soil profile over a 5 year period [Liebig et al., 2008]. Over a 9 year period in eastern Nebraska, increases in SOC exceeded 2 Mg C ha−1 year−1 in the top 1.5 m of soil for switchgrass managed as a bioenergy crop, with over 50% of the SOC increase occurring below 30 cm [Follett et al., 2012b]. Bioenergy cropping systems in southwestern Germany resulted in subsurface SOC making up between 44% and 55% of the total stocks [Gauder et al., 2016]. Soil organic C increased after establishment of switchgrass throughout North America at rates ranging from 0.17 to 10.1 Mg C ha−1 year−1 [Garten and Wullschleger, 2000;

Zan et al., 2001; Frank et al., 2004; Lee et al., 2007]. Median soil carbon sequestration rates (0–100 cm soil depth) were 1.09 and 1.28 Mg ha−1 year−1 for Miscanthus and switchgrass when grown on historical cropland land use [Qin et al., 2016]. 7.5.3. Management Practices Management practices can influence SOC change in both agriculture (e.g., tillage, fertilization, and crop type) and forestry (e.g., species, site preparation, fertilization, and harvest method). Management will also be a critical factor for perennial bioenergy crops as land classified as marginal or idle will be primarily used to reduce conflicts with food production. However, conversion of marginal or degraded land to perennial bioenergy cropping systems also increases risk for land managers. Site disturbance is a major factor in SOC loss, and methods to minimize soil disturbance can lessen soil C loss. In continuous cropping systems, changing conven­ tional tillage practices to NT, SOC equilibrates at a higher pool size in 15–20 years [West and Post, 2002]. As stated earlier, the use of NT tends to store greater amounts of SOC than full‐inversion tillage, especially near the soil surface, but smaller SOC accumulation occurs lower in  the soil profile [Angers and Eriksen‐Hamel, 2008]. No‐tillage practices are also expected to reduce SOC stock declines from conversion from perennial to annual cropping systems [Gelfand et al., 2011] but still can result in root biomass declines and reductions in the active soil carbon fraction [DuPont et al., 2010]. Minimizing site disturbance in establishing woody plantations resulted in a 15% SOC stock increase compared with high intensity site preparation (e.g., plowing, mounding, and trenching) [Laganière et al., 2010]. Mechanical harvesting sugarcane in Brazil showed an increase in SOC stocks (0–30 cm) of 0.73 ± 0.69 and 2.04 ± 0.28 Mg ha−1 year−1 for sandy and clayey soils, respectively, compared with burned sugar­ cane [Galdos et al., 2010]. Meta‐analysis results related to corn stover removal and SOC changes have been mixed with results from one study indicating significant SOC loss [Anderson‐Teixeira et al., 2009] and from another showing SOC increases when residue removal was less than 70% [Qin et al., 2016]. Differences between meta‐analysis results can be partially attributed to regional differences and the number of overall studies used. For example, crop residue removal in tropical climates has resulted in 18% lower SOC levels than when crop residues were retained [Raffa et al., 2015], a result of low biomass production and excessive residue removal. Amelioration practices such as biochar application, manure amendments, cover crops have been proposed and studied on SOC change with and without crop residue removal.

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Biomass harvest can influence nutrient availability by impacting biomass removal output, decomposition input, and indirect impacts on soil water and temperature [Gollany et al., 2015]. Nitrogen addition to annual crop­ ping systems in North America has either maintained or increased SOC as a result of increased crop biomass [Christopher and Lal, 2007]. Fertilizer additions to grass­ land systems increase root C storage and SOC, but overall change will vary by soil type and climate [Conant et al., 2001; Lee et al., 2007; Heggenstaller et al., 2009; Blanco‐Canqui, 2010; Monti and Zegada‐Lizarazu, 2016]. Fertilizer effects on woody crops have been mixed, depending on baseline SOC levels and former land use [Sartori et al., 2006]. Fertilized poplar stands showed increased SOC levels compared with unfertilized poplar and annual crop systems on a loamy, sand soil in northeast Germany [Hellebrand et al., 2010]. Manure applications have resulted in higher SOC accumulation rates than

inorganic fertilizer in herbaceous and woody species [Lee et al., 2007; Matos et al., 2012]. Positive increases in SOC occurred over nine growing seasons for switchgrass (0–30 cm soil depth) with N fertilizer [Follett et al., 2012b], but moderate N fertilizer rates (60 kg N ha−1) maximized belowground root biomass C [Stewart et al., 2016]. However, N fertilizer additions to increase above‐ and belowground biomass production need to be balanced with potential increased nitrous oxide (N2O) emissions during crop production. Irrigation has shown neutral to negative effects on SOC changes from corn stover harvest. Irrigated corn, which results in stable temporal yields, represents approximately 13% of total corn production in the United States [Grassini and Cassman, 2012]. However, SOC increases under irrigation are expected to be low because high biomass production is offset by increased residue decomposition and soil microbial activity. For example, in a

Rotation effects on SOC storage over time Continuous energy crops Year 1–10

Year 11–20

Year 21–30

Energy crop-annual crop-energy crop Year 1–10 Year 11–20 Year 21–30

ΔSOC

ΔSOC

No-tillage

Time

Till

age Time

Figure 7.4 Perennial energy crops may be grown as a rotational crop with existing annual crops or continuously within environmentally sensitive areas (e.g., near streambanks and highly erodible areas). Management factors (i.e., conversion practices, crop rotation, and rotation duration) will impact soil organic carbon (SOC) storage more for perennial energy crops grown in a rotation, while soil C storage under continuous perennial crops will be largely dependent on initial soil C.

EMpIRICAL EVIDENCE Of SOIL CARBON CHANGES IN BIOENERGY CROppING SYSTEMS 109

semiarid irrigated site, corn yields were higher for partial stover removal than no stover removal, but SOC stocks declined in the 0–30 cm soil depth, while no residue removal showed an increase in SOC stocks over time [Halvorson and Stewart, 2015]. A similar result was found in a sub­ humid climate where SOC declines occurred in the 0–30 cm soil depth under all management practices (i.e., tillage and stover removal), but SOC stocks in the top 150 cm of the soil profile were unchanged over 10 growing seasons [Schmer et al., 2014]. Stover removal in other irrigated studies has resulted in surface SOC loss [Biau et al., 2013; Kenney et al., 2013]. For irrigated wheat systems, straw removal did not result in SOC stock changes, a likely result of increased belowground biomass [Tarkalson et al., 2011]. Fertilized poplar plantations that were irrigated increased SOC by 0.68 Mg ha−1 year−1 in the western United States [Sartori et al., 2006]. Irrigation studies with perennial energy crops are limited and are unlikely to become widespread. However, switchgrass under irriga­ tion resulted in SOC sequestration rates of 1.0 Mg ha−1 year−1 in the Pacific Northwest [Collins et al., 2010]. The potential for SOC loss following perennial energy crops will be dependent on the subsequent management practices, SOC stabilization, and SOC placement depth. Placement of perennial energy crops within a landscape, duration of perennial energy crops within the landscape, form of land use, and future land use will also impact long‐ term SOC stocks (Figure 7.4). For example, placement of perennial energy crops strips within erodible cropland could provide multiple ecosystem services, including minimizing soil C loss by wind and water erosion [Heaton et al., 2013]. 7.6. SUMMARY Soil organic C is an important indicator of overall soil health that affects soil’s chemical, physical, and biological properties that are sensitive to human influence. Plant C inputs regulate the quantity, quality, and turnover of organic C in soils. Bioenergy cropping systems can contribute to climate change mitigation when best management prac­ tices are followed and land conversion of existing native ecosystems is minimized. High‐yielding perennial energy crops grown on degraded or idle cropland have the greatest potential to increase SOC storage. Sustainable landscape designs to incorporate bioenergy crops within existing cropland will be required to improve cropland utilization and improve marginal land. Bioenergy from agricultural residues may avoid SOC loss when properly managed and when limited to certain regions or soil types. Conversely, deforestation or conversion of native grasslands to first‐generation bioenergy crops or perennial energy crops will likely cause SOC loss. Research on the relative contribution to rhizodeposits, soil microbial

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B. J. Stokes, and L. M. Eaton (Leads), ORNL/TM‐2016/160. Oak Ridge National Laboratory, Oak Ridge, TN, 448p. Unger, N., T. C. Bond, J. S. Wang, D. M. Koch, S. Menon, D. T. Shindella, and S. Bauer (2010), Attribution of climate forcing to economic sectors, Proc. Natl. Acad. Sci. U. S. A., 107(8), 3382–3387. USDA‐NRCS (2004), Soil survey laboratory methods manual Soil Survey Investigations, Report No. 42. Version 4.0, USDA, Washington, DC. Valentine, J., J. Clifton‐Brown, A. Hastings, P. Robson, G. Allison, and P. Smith (2011), Food vs. fuel: The use of land for lignocellulosic “next generation” energy crops that minimize competition with primary food production, Glob. Change Biol. Bioenergy, 4, 1–19. Vance, E. D., C. Loehle, T. B. Wigley, and P. Weatherford (2014), Scientific basis for sustainable management of Eucalyptys and Populus as short‐rotation woody crops in the U.S, Forests, 5, 901–918. VandenBygaart, A. J., E. G. Gregorich, D. A. Angers, and B. G. McConkey (2007), Assessment of the lateral and vertical variability of soil organic carbon, Can. J. Soil Sci., 87, 433–444. Volk, T. A., L. P. Abrahamson, C. A. Nowak, L. B. Smart, P. J. Tharakan, and E. H. White (2006), The development of short‐rotation willow in the Northeastern United States for bioenergy and bioproducts, agroforestry and phytoremediation, Biomass Bioenergy, 30, 715–727. Walter, K., A. Don, and H. Flessa (2015), No general soil carbon sequestration under Central European short rotation coppices, Glob. Change Biol. Bioenergy, 7, 727–740.

Weaver, J. E., and R. W. Darland (1949), Soil‐root relationships of certain native grasses in various soil types, Ecol. Monogr., 19, 303–338. Wei, X., M. Shao, W. Gale, and L. Li (2014), Global pattern of soil carbon losses due to the conversion of forests to agricultural land, Sci Rep, 4, 4062, doi:10.1038/srep04062. van der Weijde, T., C. L. Alvim Kamei, A. F. Torres, W. Vermerris, O. Dolstra, R. G. F. Visser, and L. M. Trindade (2013), The potential of C4 grasses for cellulosic biofuel production, Front. Plant Sci., 3, doi:10.3389/fpls.2013.00107. Werling, B. P., T. L. Dickson, R. Isaacs, H. Gaines, C. Gratton, et al. (2014), Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes, Proc. Natl. Acad. Sci., 111, 1652–1657. West, T. O., and W. M. Post (2002), Soil organic carbon seques­ tration rates by tillage and crop rotation: A global data analysis, Soil Sci. Soc. Am. J., 66, 1930–1946. Wilhelm, W. W., J. M. F. Johnson, D. L. Karlen, and D. T. Lightle (2007), Corn Stover to sustain soil organic carbon  further constrains biomass supply, Agron. J., 99, 1665–1667. Wilhelm, W. W., J. Hess, D. L. Karlen, J. M. F. Johnson, D. Muth, J. Baker, H. Gollany, J. Novak, D. Stott, and G. E. Varvel (2010), Review: Balancing limiting factors and economic drivers for sustainable Midwestern U.S. agricultural residue feedstock supplies, Ind. Biotechnol., 6, 271–287. Zan, C. S., J. W. Fyles, P. Girouard, and R. A. Samson (2001), Carbon sequestration in perennial bioenergy, annual corn and uncultivated systems in southern Quebec, Agric. Ecosyst. Environ., 86, 135–144.

8 The Importance of Crop Residues in Maintaining Soil Organic Carbon in Agroecosystems David E. Clay1 and Umakant Mishra2

ABSTRACT In agroecosystems, about 50% of the surface crop residues are generally returned to the soil. This nonharvested carbon (C) helps maintain the soil health, increase resilience, and reduce soil erosion. Globally, scientists and policymakers are interested in knowing how much crop residue can be sustainably removed for cellulosic ethanol production, livestock feed, or for processing food. Several field experiments and regional modeling exercises have attempted to answer this question. Research shows that the sustainable crop residue harvest percentage in annual cropping systems is influenced by many factors, including the length of time that the field was cultivated, crop rotation, soil texture, climatic conditions, initial carbon content of the soil, and crop yields. However, the ability to conduct meta‐analysis and modeling studies across diverse agroecosystems is complicated by inconsistencies in the soil carbon data sets. This study presents how inconsistencies in soil and plant root sampling and processing are reported in research papers and discusses how the regional predictions of soil C change can be impacted by available data sets. In addition, the use of 13C natural abundance and importance of populating simulation models with accurate soil C benchmarks are discussed. We believe that future assessments should provide information about the uncertainty of the predictions. 8.1. INTRODUCTION Since the beginning of the agriculture, crop residues have been harvested from croplands for a multitude of reasons ranging from, but not limited to, being used as a source of livestock feed to energy for heating homes or cooking food [Garcia and Kalscheur, 2006; Carlson et al., 2010; Clay et al., 2016]. However, the removal of these materials from soil surface can result in increased soil erosion and reduced crop productivity [West and Post, 2002; Wilhelm et al., 2004; Qin et al., 2016]. For example, when the U.S. Great Plains was homestead, prairies and 1 Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, South Dakota, USA 2 Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA

forests were plowed, and the grains and associated crop residues were harvested [Clay et al., 2017]. The net result was extensive erosion and a 40%–50% reduction in the soil organic matter (SOM) during the twentieth century [Clay et al., 2012, 2015, 2017]. Associated with this loss was the mining of soil nutrients and the increased use of inorganic fertilizer [Lee et al., 2014]. Crop residues are used for a variety of purposes in the United States and are being mixed with ethanol distillers grain to produce livestock feed and being converted into ethanol through the cellulosic process [Janssen et al., 2008; Mamani‐Pati et al., 2010a]. The removal of plant biomass for these purposes can adversely impact the functioning of soil ecosystems [Larson et al., 1972; Morachan et al., 1972; Klepper, 1991; McCarthy et al., 1993; Ellert and Bettany, 1995; Ortega et al., 2002; Campbell et al., 2005; Causarano et al., 2006; Varvel, 2006; Carlson et al., 2010].

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 115

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However, mass balance calculations suggest that impact of residue removal can be partially mitigated by returning the manure to the field [Carlson et al., 2010] or growing cover crops [Kuo et al., 1995]. In addition, West and Post [2002] suggested that carbon loss could be partially miti­ gated by increasing crop diversity. Because soil organic carbon (SOC) degradation is a biological process, the degradation process is influenced by complex interactions between temperature, moisture, quality of the material added to the soil, management, and texture [Baldock and Nelson, 2000]. Mathematically the decomposition process has been defined using first‐ order kinetics. In first‐order kinetics, the amount of SOC that is mineralized is dependent on the amount of SOC in the soil [Mamani‐Pati et al., 2010a]. In cropped systems, simulation models generally separate fresh residues and soil organic carbon into different pools, with each pool having a unique rate constant. Of the numerous studies that have investigated the impact of residue harvesting on soil resilience and adapt­ ability, few have provided the information needed to calculate soil organic C and nonharvested C turnover rates [West and Post, 2002; Clay et al., 2010; Qin et al., 2016]. Deficits in these data sets include the following: (i) respired and erosional C losses were combined; (ii) bulk density was not measured or used to adjust the sampling depths, (iii) initial SOC was not measured; (iv) the SOC analysis approach changed or conversion factors for converting Walkley‐Black [1934] to total organic C were not provided, (v) simplifying assumptions were not stated, (vi) the entire soil profile was not assessed, (vii) roots were not measured [Clay et al., 2010]; (viii) complete carbon budgets were not developed [Mamani‐Pati et al., 2010a]; and (ix) the experiments were not long enough. Clay et al. [2010] reviewed the research papers associated with stover removal across central United States, and they reported that the SOC first‐order rate constant increased with tillage intensity. This finding suggests that soil organic C maintenance requirement, the amount of nonharvested carbon (biomass) that must be returned to maintain the SOC at the current level, is higher in tilled than in no‐tillage system. Other studies have reported similar findings [West and Post, 2002]. An accurate carbon budget is needed for assessing the sustainability of corn stover removal for ethanol production. 8.2. DEVELOPING CARBON BUDGETS The terrestrial carbon cycle can be summarized by the difference between the C uptake through photosynthesis and release by plant and soil respiration. Creating a carbon budget starts with accurate accounting of carbon gains and losses. In soil system, carbon losses are the carbon dioxide (CO2) released to the atmosphere, whereas

gains are the amount of shoots and roots that are not harvested from the soil. In many annual cropped and rangeland systems, 50% of the aboveground biomass is harvested, and 50% is returned to the soil. The belowground biomass is generally measured using soil cores, followed by washing the soil from the root biomass. Root biomass estimates generated through this process are underestimates as this method does not measure root exudates, fine root hairs [Asady and Smucker, 1989; Clay et al., 2015], and fungal mycelium. On the basis of the amount of biomass carbon returned to the soil and changes in the soil organic matter with time, the SOC maintenance requirement can be determined using the method proposed by Clay et al. [2006]. 8.2.1. Preparing Soil Samples for Carbon Budgets Soil carbon budgeting starts with collecting soil samples for SOC or soil organic matter (SOM) at the beginning and end of the experiment. Measuring the change in SOC over time is an important component in determining the impact of a rotational sequence on soil C budgets. However, the ability to accurately measure the change in SOC (δSOC/δt) is influenced by changes in bulk density and the methodology adopted in soil sample processing. In many experiments bulk densities change during the experiment period, and this change can complicate the ability to detect small changes in SOC [Clay et al., 2015]. Mishra et al. [2010] reported that changes in soil C could be better quantified by using equal mass of soil rather than the equal depth interval. After the soil samples are collected, they are dried, ground, and sieved [Balesdent et al., 1988; Barber, 1979; Huggins et al., 1998; Collins et al., 1999; Accoe et al., 2002]. However, the process of sieving the sample can preferentially remove roots and other organic materials from the sample. There are a few studies that use an alternative procedure to process the samples. For example, Clapp et al. [2000] returned the removed roots to the soil, whereas Clay et al. [2015] did not use a sieve and used a mortar and pestle to grind the samples. Depending on the analysis approach, root removal during processing can bias the resulting calculations. For example, the removal of 2034 kg C (ha × 15 cm)−1 of the crown and roots in the  surface 15 cm [Clay et al., 2015] can reduce the soil organic carbon by 0.1%. If the soil contains 2% SOC, this loss represents 5% the total SOC in that zone. 8.2.2. Nonisotopic Techniques to Create C Budgets The rate at which SOC is mineralized can also be deter­ mined using a relatively simple approach that was proposed by Clay et al. [2006]. This nonisotopic regression‐based approach requires measurement of SOC at the beginning

THE IMpORTANCE Of CROp RESIDUES IN MAINTAINING SOIL ORGANIC CARBON IN AGROECOSYSTEMS 117

0.04

% Corn stover that can be removed

0.05 Ksoc (g (g × year)–1

90

Larson et al. [1972] Barber [1979] years 1–6 Barber [1979], years 6–11 Allmaras et al. [2004] Wilts et al. [2004] Pikul et al. [2008] Russel et al. [2005] Huggins et al. [1998]

0.06

0.03 0.02 0.01 Ksoc = 0.0115 + 0.00631 (# annual tillage events); r = 0.823

0.00 –0.01 0

1 2 3 4 The number tillage events within a year

5

80 70

Footslope Lower backslope Backslope Upper backslope Shoulder y = 34.6 + 39.4x;r = 0.92

60 50 40 30 20 0.0

0.2

0.4

0.6

0.8

1.0

1.2

Root-to-shoot ratio

Figure 8.1 The influence of tillage intensity on the first‐order rate constants of relic carbon mineralization in seven published studies [Clay et al., 2010].

of the experiment, as well as measurement of estimates of above‐ and belowground biomass returned to the soil. The advantages with Clay et al.’s [2006] approach were as follows: (i) modeling was not required to calculate carbon turnover, and (ii) it was suitable for conducting a meta‐ analysis of published data sets. Clay et al. [2010] used this nonisotopic approach to assess the impact of tillage intensity on C turnover in historic studies (Figure 8.1). They showed that C turnover was directly related to tillage intensity and that the calcu­ lated amount of surface residue that could be sustainably harvested was dependent on the estimated amount of roots produced by the growing plant. Increasing the root‐ to‐shoot ratio reduced the calculated amount of aboveground C required to maintain SOC, and it suggests that accurate measures of belowground biomass are needed to predict accurate SOC maintenance recommendations. However, obtaining accurate root measurement is expensive and may be biased because root exudates typically are not measured [Laboski et al., 1998; Kuzyakov and Domansk, 2000; Kuzyakov, 2001; Kuzyakov and Larionova, 2005, 2006].

Figure  8.2 Relationship between the root‐to‐shoot ratio and the amount of aboveground biomass that can be harvested while still maintaining the SOC at the current level [Clay et al., 2010].

[Amos and Walters, 2006]. Allmaras et al. [2004] modified Barber [1979] equation by including the 13C natural abundance mixing equation, and they concluded that harvesting 100% of the aboveground corn residue over a 13 year period reduced SOC derived from corn by 35%. In addition, different cultivars may have different root growth characteristics [Hérbert et al., 2001; Bradford et al., 2005; Amos and Walters, 2006; Johnson et al., 2006]. As a result, a wide range of root‐to‐shoot ratios, each having a unique definition, have been reported in the literature [Allmaras et al., 2004; Johnson et al., 2006]. Differences between the values are important because they impact the calculated amount of surface residue that can be sustainably removed. For example, Figure  8.2 shows that if a root‐to‐shoot value of 0.2 is used in the calculation, then 40% of the aboveground stover can be harvested if the goal is to maintain the soil organic matter, whereas if a root‐to‐shoot value of 0.5 is used, then 50% of the stover can be harvested. Qin et al. [2016] suggested that, to avoid this problem, C budgets over the entire soil profile must be determined.

8.2.3. Belowground Biomass Plant roots make an important contribution to soil system, and in many studies, it is often reported as a root‐to‐shoot ratio, which has been defined differently in different studies [Barber, 1971; Follett et al., 1974; Crozier and King, 1993; Johnson et al., 2006; Bolinder et al., 2007]. For example, Johnson et al. [2006] reported that the ratio was between the roots and the total aboveground biomass (leaves, stalks, and grain), whereas Amos and Walters [2006] did not include the grain in the aboveground biomass estimates. In addition, some studies consider the root crown or exudates as roots [Clay et al., 2015], whereas others do not

8.2.4. Use of the 13C Natural Abundance Approach to Develop C Budgets To overcome experimental difficulties with measuring and quantifying root degradation rates, many experiments used the 13C natural abundance approach [Balesdent et al., 1988; Balesdent and Mariotti, 1996; Mamani‐Pati et al., 2010a, 2010b]. The 13C natural abundance approach is based on plants with different photosynthetic pathways having different C isotopic signatures. For example, plants with a C4 pathway (corn, Zea mays) typically have δ13C values ranging from −12‰ to −14‰, whereas plants with

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a C3 pathway (soybeans, Glycine max) have δ13C values ranging from −20‰ to −26‰ [Accoe et al., 2002; Mamani‐ Pati et al., 2010b]. On the basis of these differences, the mixing equation 13

FC3

CC4 13

CC4

13

C soil sample 13

C C3

,

where δ13Csoil sample = δ13C value of the collected soil sample, 13 C C 4 = δ13C value of the C4 plant material, 13 C C3 = δ13C value of the C3 plant material, was developed and widely used to assess the contribution of C4 and C3 plants to the soil organic matter [Balesdent et al., 1988; Huggins et al., 1998; Collins et al., 1999; Clapp et al., 2000; Allmaras et al., 2004; Clay et al., 2005; Zach et al., 2006; Mishra et al., 2010]. However, this calculation ignores 13C fractionation [Accoe et al., 2002; Clay et al., 2006, 2007, 2010, 2015]. The accuracy of the 13C natural abundance approach was tested by Clay et al. [2007], and they showed that not considering 13C fractionation resulted in underestimating the contribution of relic carbon from C3 plants and over­ estimating the importance of biomass from the C4 plant to the SOC. In Northern Great Plains grassland systems,

where C3 perennial grasses are replaced with C4 annual plants such as corn, this bias results in overestimating the mineralization of the relic soil organic C and underesti­ mating the mineralization of fresh crop residues. Clay et al. [2015] developed C budgets in an experiment where 13C fractionation was measured and integrated into the calculations. In addition, this experiment accounted for changes in soil bulk density, measured roots, and developed C budgets for the entire soil profile. This study showed that (i) the relic SOC in the 0–15 cm and 15–30 cm soil depth mineralized at different rates (Table  8.1), (ii) harvesting 60% of the surface residue increased the mineralization of the remaining nonharvested carbon, and (iii) the rate that root biomass mineralized decreased with increasing soil depth. In the surface soil, only 20% of the nonharvested carbon remained after 1 year, whereas at the 15–30 cm soil depth, between 50% and 90% of the nonharvested C remained after 1 year. Tillage accelerated the conversion of nonharvested C in the 15–30 cm depth to CO2. These findings are conceptually in agreement with findings of Lemke et al. [2010], where it has been reported that harvesting crop residue is only feasible with appro­ priate fertilizer management. Clay et al. [2015] suggested that C sequestration could be increased when no‐tillage was adopted. No‐tillage reduced relic SOC mineralization in both depths, which in turn reduced the amount of

Table 8.1 The Influence of Residue Harvesting, Yield Zone, and Tillage on the Amount of Relic C Lost, Ksoc, Knhc, and the Maintenance Requirement 0–15 cm Residue (%) SOC lost Ksoc Knhc Maintenance

SOC lost Ksoc Knhc Maintenance SOC lost Ksoc Knhc Maintenance

Mg C/ha g/(g × year) g/(g × year) MgC/ha

Mg C/ha

Mg C/ha Mg C/ha

Mg C/ha

15–30 cm P

Residue (%)

p

0 1.83 0.0101 0.202 1.72

60 1.85 0.0099 0.151 2.364

Ns Ns 0.011 0.07

0 0.822 0.007 0.778 0.258

60 1.38 0.103 0.623 0.469

ns ns ns 0.03

Yield zone Medium 2.18 0.0119 0.178 2.4

High 1.49 0.008 0.174 1.87

P 0.048 0.424 ns ns

Yield zone Medium 1.02 0.0097 0.705 0.394

High 1.2 0.0076 0.696 0.333

p ns ns ns ns

No‐tillage 1.49 0.008 0.169 1.92

Chisel plow 2.19 0.0118 0.184 2.44

p 0.03 0.0894 ns ns

No‐tillage 0.86 0.0088 0.88 0.266

Chisel plow 1.34 0.0085 0.515 0.465

p 0.052 ns 0.002 0.38

Source: Modified from Clay et al. [2015]. Note: The Ksoc and Knhc are first‐order rate constants, which represent amount SOC lost after 1 year and the amount of nonharvested C (NHC) remaining after 1 year. The maintenance requirement is the amount of NHC that must be applied to maintain the SOC. The yield zones represent the amount of corn that is produced in nonirrigated and irrigated plots. Average yields in the moderate zones were 10.8 ± 458 Mg ha−1, whereas average yields in the high yield zone were 12.2 ± 265 kg Mg−1.

THE IMpORTANCE Of CROp RESIDUES IN MAINTAINING SOIL ORGANIC CARBON IN AGROECOSYSTEMS 119

nonharvested C required to maintain the SOC. These findings were also conceptually in agreement with the following: (i) Torbert et al. [2000], who reported that root exudations could reduce the mineralization of other materials; (ii) Clapp et al. [2000], who reported that returning corn stover and adding N fertilizer slowed relic SOC decomposition; (iii) Green et al. [1995], who reported that increasing NHC when no N fertilizer was added reduced the SOC mineralization rate; (iv) Gale and Cambardella [2000], who reported that when no‐till management was used, 75% of the new C incorporated into SOC was root derived, while a larger percentage of surface residue was released as CO2; and (v) Barber and Martin [1976], who reported that roots mineralized much more slowly than shoots. Research reported by Wilhelm et al. [2004]; Wortmann et al. [2016], and Lal [2005] also supports this interpretation. These findings have clear implications on residue harvesting activities, and they suggest that SOC and NHC mineralization must be considered simultaneously. If fresh nonharvested C is not available, as in the case of residue harvesting, the microbial community may switch to other C sources. 8.2.5. Modeling Carbon Turnover Simulation models are used to expand the research results beyond the borders of the experiment, and they can be used to study the impacts of different land use change scenarios at larger scales. These models can range from simple [Clay et al., 2006] to complex [Coleman and Jenkinson, 1996]. An important difference between the models is the number of pools that soil organic matter and fresh biomass are separated into. For example, Clay et al. [2006, 2010] separated fresh biomass and soil into two pool, nonharvested C (NHC) and soil organic C, whereas Coleman and Jenkinson [1996] separated SOC into five pools called the decomposable plant material, resistant plant material, microbial biomass, humified organic matter, and inert. Findings from the simulation can be used for a variety of purposes, which includes conducting local and regional assessments, testing hypothesize, and conducting historical assessments of management and climate change [Liska et al., 2014]. However, the value of such assessments is only as good as the data used to populate the model [Reitsma et al., 2016]. 8.3. DATA USED TO POPULATE SIMULATION MODELS Two widely used data sources for obtaining SOC or soil organic matter (SOM) benchmarks in regional SOC modeling exercises are the USDA‐NRCS STATSGO2 and USDA‐NRCS SSURGO databases. In many situations, SOC and SOM have been used interchangeable by

the equation SOM × 0.58 = SOC. STATSGO2 is a broad‐ based soil inventory at a mapping scale of 1:250,000 in the continental United States, Hawaii, Puerto Rico, and the Virgin Islands and 1:1,000,000 in Alaska. This database is designed for planning and management at state, regional, and national levels. SSURGO information was collected by scouting and analyzing soil samples collected from targeted location. SSURGO provides information at scales ranging from 1:12,000 to 1:63,360, and this information source is intended for use during natural resource planning. A disadvantage of using the SOC information within the SSURGO data set is that the exact sampling location, date, protocols, and analysis approach are not provided. To validate the SOM information within the STATSGO2 and SSURGO databases for modeling purposes, the SOM values for the soil mapping units were compared with the South Dakota USGS benchmarks [Smith et al., 2013]. This analysis only considered information from the surface A horizon. In South Dakota, the USGS database consisted of 119 samples collected between November 2008 and November 2009. Soil organic C was determined by subtracting total inorganic C from the total carbon. At the USGS sampling points, soil organic matter values in the STATSGO2 and SSURGO data sets were determined. The three databases showed different C distributions. Across South Dakota, the skewness value was higher in the SSURGO (2.49) than the USGS‐benchmark (1.24) or STATSGO2 (0.69) data sets. The higher skewness value in SSURGO suggests that data were not symmetric and were skewed toward large values. In addition, the kurtosis values were higher in the SSURGO (13.3) than in the USGS‐benchmark (1.98) or STATSGO2 (−0.33). Normal distributions have a kurtosis value of 0, whereas population distributions with a positive value are peaked. The STATSGO2 (p = 0.02) had a lower SOC (18.4 g/kg) than the USGS (20.7 g/kg), whereas the SSURGO (18.9 g/kg) and USGS were different at the 10% level (p = 0.06). These results are important because they show that the USGS benchmark information is statistically different from STATSGO2 and SSURGO. The three data sets were correlated. For example, the r value between USGS and SSURGO was 0.33 (p < 0.05), whereas the correlation between SSURGO and STATSGO2 was 0.41 (p < 0.01). Others have reported inconsistent results between STATGO and SSURGO [Mednick et al., 2008]. The lack of consistency between the data sets complicates the interpretation of the simulation results. Differences between measured and SSURGO values have been reported by several investigators [Grunwald and Vasques, 2010; Reinsch and West, 2010]. Reinsch and West [2010] reported that SSURGO overestimated clay con­ tents and underestimated SOC contents in the Ap horizon of a Miami Soil Series.

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The SSURGO or STATSGO2 databases have been used as soil benchmarks in a number of regional modeling efforts that were designed to assess the ramifications of changing the soil and crop management practices on sustainability. For example, Liska et al. [2014] predicted that the annual removal of 6 Mg ha of surface residues across the Midwestern United States would reduce SOC levels. In a similar study, Causarano et al. [2008] used the EPIC model and NRCS‐SSURGO and STATSGO databases to assess C sequestration in Iowa. The dominant crops in the study area were maize and soybean (Glycine max). They assumed that conventional tillage decreased, while conservation tillage increased. On the basis of this analysis, they reported that from 1980 to 2019 soil C stocks in the surface of 20 cm would decrease from 72 to  57 Mg ha−1 from 1971 to 2019 in conventional management, whereas no‐tillage would result in a modest increase in SOC (78 Mg ha−1). When they considered the increased adoption in reduced tillage systems, they esti­ mated that SOC sequestration increased by 5.9% in the surface of 20 cm from 1971 to 2019. These findings were in contrast to millions of soil samples collected from Iowa farm fields between 1997 and 2013 that showed that, over this time period, SOM levels increased by 26%; SOC = SOM × 0.58 (Figure 8.3). This comparison brings us back to the classic analysis, which is correct the simulation or the measured values. Differences between the farmer values and the simulated results may be related to the digital data sources used as soil benchmarks. Causarano et al. [2008] and Liska et al. [2014] both used SSURGO or STATSGO2 databases for SOC benchmarks. However, the time periods for both studies were different. Causarano et al. [2008] evaluated the time period between 1971 and 2019, whereas Liska et al. [2014]

evaluated the time period between 2001 and 2010. An alternative to using the SSURGO and STATSGO2 bench­ marks is to accept the uncertainty in the databases and conduct the analysis on the basis of the range of values indicated by the data sets. For example, SSURGO provides a range of values (i.e., SOM ranges from 3% to 4%), and on the basis of these ranges, the simulation analysis could provide three values: the mean and both extremes. The extremes could represent the variation in the prediction. A second component that may contribute to the differences between the measured (Figure  8.3) and simulated results [Causarano et al., 2008] and predicted data sets may be related to the different approaches used  to calculate the root‐to‐shoot ratio and that most physical measurements underestimate total roots. In addition, many simulation results fail to validate root estimates with measured values that include root exu­ dates [Qin et al., 2016]. The root‐to‐shoot ratio is critical because belowground biomass mineralizes much slower than aboveground biomass and the removal of biomass can influence the mineralization of carbon in the 15–30 cm soil depth [Clay et al., 2015]. A third component that may contribute to differences between the measured and simulated results may result from a failure to account landscape impacts on the first‐order SOC mineralization rate constants. Clay et al. [2005] reported that soil located in summite/shoulder areas was more resistant to mineralization than soil located in footslope areas. For example, the percent of SOC that was annually mineralized in the lower elevational zones ranged from 1.86% to 2.08%, whereas in higher elevation zones, the percent of SOC mineralized annually ranged from 0.61% to 0.95%. 8.4. RESIDUE HARVESTING IMPACT ON NUTRIENT REMOVAL AND CROP YIELDS

% Soil organic matter

4.0 3.5 3.0 2.5 2.0 y = 2.59 + 0.041(year), r2 = 0.272** 1.5 0

2

4

6

8

10

12

14

16

18

Year

Figure 8.3 Soil‐test results for Iowa from Midwest Laboratories from 1997 to 2013. In this chart, year 0 is 1997. Data were provided by Midwest Laboratories. These samples were collected from the production fields similar to Clay et al. [2012]. **p < 0.01.

Removing the residues has the potential to reduce both the soil organic matter and the ability of the soil to provide nutrients to the plant. In agronomy, this risk is assessed by determining the nutrient budget, which represents the difference between the nutrients removed by plant growth and the fertilizer application rate [Murrell, 2007; Sawyer and Mallarino, 2008; Arora et al., 2014; Thompson and Tyner, 2014]. The amount of nutrients contained in crop residues is significant. For example, a 12.5 Mg ha−1 (200 bu acre−1) maize crop produces about 9.8 Mg ha−1 of crop residue (4.75 tons of stover per acre [Clay et al., 2011; Arora et al., 2014]). The amount of N, P2O5, and K2O contained in the grain of a 12.5 Mg ha−1 maize crop is approximately 200, 85, and 60 kg ha−1, respectively. In contrast, the surface residue contains 18, 6.5, and 45 kg N, P2O5, and K2O ha−1, respectively. Over the short term, the removal of 218, 91.5, and 101.5 kg ha−1

THE IMpORTANCE Of CROp RESIDUES IN MAINTAINING SOIL ORGANIC CARBON IN AGROECOSYSTEMS 121 Table 8.2 The Impact on Corn Yields of Removing 60% of Corn Residue Annually Year 1

Year 2

Year 3

Year 4

Year 5

Residue removed

2008

2009

2010

2011

2012

60% 0% P

13.0 12.3 Ns

11.0 11.5 0

Figure 11.1 The figure provides a graphical intuition for emissions due to price effects in the short run for biofuels. Panels (a) and (b) depict the impact of an ethanol shock on land (an input) and gasoline (the substitute to ethanol) markets, respectively. The x and y axes depict, respectively, quantity and price. Upward sloping lines represent the supply function of a commodity, while downward sloping lines denote demand. P, price; L, quantity of land; G, quantity of gasoline; B, quantity of biofuel; D, demand; S, supply. Superscripts 0 and 1 denote pre and post biofuel mandate, respectively. Subscripts F, B, and T denote food, biofuel, and total land, respectively. An ethanol consumption mandate shifts out the demand for land.

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104 grams of carbon dioxide equivalent per megajoule (g CO2e/MJ) [Searchinger et  al., 2008]. When this is added to a conventional LCA estimate of GHG emission intensity of ethanol, it is more than twice as GHG intensive as gasoline from conventional crude oil. Subsequent predictions by Hertel et al. [2010] and Tyner et al. [2010] place the average ILUC emissions at 27 and 12 g CO2e/MJ, respectively, which are substantially lower than those by Searchinger et al. [2008]. Sugarcane ethanol production in Brazil is also predicted to entail positive ILUC emissions [Lapola et al., 2010]. In contrast, second‐ generation biofuels from lignocellulosic feedstock are predicted to be substantially lower in term of ILUC emissions than first‐generation biofuels from edible feedstock [Dwivedi et al., 2015; Hudiburg et al., 2016]. An important assumption in all estimates of ILUC is that they amortize ILUC emissions over a period of 20–30 years [Qin et al., 2016]. Amortization is a financial concept that involves discounting monetary flows at different points to derive a present value. However, since discounting physical flows of such emissions is less meaningful, amortization of emissions involves simply computing the mean emissions per unit output or time. It is the ratio of the total land use change emissions across the total quantity of biofuel that is likely to be derived from a given land parcel during a given time. When land use change caused by biofuels involves felling of trees or release of large amounts of soil carbon, biofuels will carry a carbon debt. In other words, biofuels will deliver net emission reduction only after certain number of years, called the carbon payback period [Fargione et al., 2008]. Depending on the nature of land use change, the carbon payback period could be several years or decades. This, of course, then raises the question of how one ought to weigh the damage of one additional unit of emission today due to ILUC against the benefits of reducing emissions by one unit in the future by avoiding the use of fossil fuels [O’Hare et al., 2009]. Following ILUC, studies on IFUE (or IFUC) began to appear [de Gorter and Drabik, 2011; Hochman et al., 2011; Rajagopal et al., 2011]. This was followed by studies analyzing the combined effect of ILUC and IFUE [Chen and Khanna, 2012; Bento et  al., 2013; Huang et  al., 2013; Rajagopal and Plevin, 2013]. These studies suggest that IFUE effect by itself could lead either to additional emissions, in which they add on the ILUC effect, or under some conditions, to additional emission reduction. In the latter case, they offset the ILUC effect. However, as with ILUC, the estimates of IFUC are wide ranging for it hinges on important assumptions (including the modeling framework, functional forms, and behavioral parameters) that need empirical validation [Rajagopal, 2013]. However, the range of estimates provide reason for greater attention from researchers and policymakers to

the fuel market effects of growth in biofuel consumption. For one, even with a shift to second‐generation biofuels that have smaller footprint and smaller ILUC effects, one still has to contend with fuel market effects. To summarize, the biofuel literature on the environmental consequences of price effects focuses largely on ILUC and to a lesser extent on IFUE. It appears that the benefits of biofuels could be lower relative to what a comparison of a standard LCA of biofuel and gasoline would show. It is worth pointing out that the RFS and the California Low Carbon Fuel Standard (LCFS) (https:// www.arb.ca.gov/fuels/lcfs/lcfs.htm.) have both adopted estimates of ILUC that lie in the range of the estimates produced by the studies of Tyner et al. [2010] and Hertel et  al. [2010]. However, both these regulations directly ignore IFUE. These regulations are discussed in more detail in the next section. Moreover, there has been little attention, if any, on other types of indirect effects such on the market for water, fertilizers, other farm chemicals, electricity, natural gas, and coal, which are associated with biofuel and gasoline production. Let us now turn our attention to the challenges in mitigating indirect emissions. 11.3. CHALLENGES IN MANAGING INDIRECT EMISSIONS 11.3.1. Modeling and Verification Challenges In an ideal world, with a global cap onto GHG emissions, the changes in emissions caused by price effects would not have any effect on total global emissions, and indirect emissions of biofuels would not be salient. But in the absence of such a cap, pollution spillovers such as indirect emissions cannot be ignored unless there is reasonable evidence that these are small relative to direct emissions reduction. In theory, within an interconnected system of markets for different commodities, some of which might be global in nature, the indirect effects could ripple through each and every commodity market. And, therefore, the indirect emissions might manifest across the globe, which increases both the complexity and uncertainty associated with quantifying them. There exist two broad economic modeling frameworks (partial equilibrium (PE) and computable general equilibrium (CGE)) that could be used to compute the changes in prices, consumption, and pollution on such a scale. PE models are used to simulate the effect of a policy or technology shock on one or few commodity markets on a global or subglobal scale. These models assume that the conditions in other economic sectors, and wages and interest rates, are unchanged due to the shock. These models might miss unintended consequences on sectors that are outside the model. On the other hand, CGE

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models can capture the effects of a technology or policy shock on all commodities including the effect on wages and interest rates simultaneously. They are conceptu­ ally equipped to model the unintended consequences economy‐wide. Examples of PE models applied to biofuel policies include Food and Agricultural Policy Research Institute (FAPRI‐CARD) model, the Forest and Agriculture Sector Optimization Model (FASOM), the International Model of Policy Analysis and Agricultural Commodity Trade (IMPACT), and the Agribusiness Linkage Program‐ Commodity Simulation Model (AGLINK‐COSIMO), Global Biomass Optimization Model (GLOBIOM), and Biofuel and Environmental Policy Analysis Model (BEPAM). While PE models can be set up to analyze price effects in any one or more markets, in the case of biofuels, they have been mainly exploited for ILUC and to a lesser extent for IFUE. Global Trade Analysis Project (GTAP) model, the LEI Trade Analysis Project (LEITAP) model, the Integrated Global System Model (IGSM), and the Modeling International Relationships in Applied General Equilibrium (MIRAGE) model are four CGE models that have been used to analyze the price effects of biofuels. In addition to the difference due to the partial or general equilibrium nature of a model, these models differ in whether they are static or dynamic, the number of countries or regions into which the global market is disaggregated, the number of distinct economic sectors in the model, and the specific data sets and databases they exploit, to name a few. This makes it hard to reconcile the differences in the estimates of indirect effects produced by different studies. For ILUC, empirical evidence of global land use change caused by biofuels is lacking and likely to remain so. However, there is some indirect support to suggest that the current estimates might be biased. For instance, Barr et al. [2011] find that the correlation between increase in profitability of crop production and increase in crop planted area in the midwestern U.S. states is small. Swinton et  al. [2011] find that, between 2006 and 2009 when profitability of the typical farm increased by 64%, crop planted area increased only by 2%. Another challenge in modeling and managing ILUC is that while crop prices and farm profitability are important drivers, government policies (such as settlement policies or weak property rights, industrialization of rural areas, and construction of highways and shipping ports) play a major role in driving land use change [Zilberman, 2016]. A historical analysis of the development of agricultural sector within the industrialized economies suggests that, during early stages of economic development, when land is less scarce, government policies encourage deforestation and agricultural expansion. However, with rising land scarcity to development and growth, intensification driven

by technical change takes over as the primary means by which agricultural output grows. For instance, the peak in U.S. agricultural acreage occurred during early 1900s and was the result of land settlement policies after which agricultural intensification began to dominate. Ever since, the total agricultural land base in the United States, Canada, and other industrialized nations of Europe and Asia has declined and until recently remained stable [Cochrane, 1993]. In these countries, government policies tend to affect marginal lands, which enter agriculture when returns to farming exceed government incentives to retire idle farmland and exit when the opposite is true. Prominent estimates of land use change due to biofuels do not accord with such longer term trends that have seen agricultural output increase severalfold without a net increase in cropland area [Zilberman, 2016]. However, this is not a reason to disregard estimates of ILUC. Firstly, it would be unwise to simply extrapolate from long‐term trends to conclude that biofuels will, in the long run, not expand the agricultural land base. A careful analysis of recent trends is necessary to establish that the rate of growth in demand is not outpacing trends in productivity. A historical analysis shows that sustained high food prices have also spurred innovation whose long‐run impact was a surfeit of supply, which then led to farm subsidies for reducing food production and land retirement through programs such as CRP [Gardner, 1992]. Therefore, some contend that the higher prices caused by biofuels will spur innovation that raises productivity and lead to land sparing in the longer run. Resolving this requires predicting how much induced innovation would occur because of biofuels, which is itself an area of debate [Keeney and Hertel, 2009; Berry, 2011; Nassar et al., 2011]. Finally, even when technological progress outpaces demand, such that the agricultural land base shrinks, this implies that, absent biofuels, the land base would have shrunk further. In any case, such dynamic processes are not accounted for in the current estimates of ILUC. The literature on IFUE shows that the fuel market effects also depend on the policy regime. Biofuel mandates (e.g., U.S. Federal RFS), biofuel subsidies (e.g., the volumetric ethanol excise tax credit, which was in effect from 2005 to 2012), and fuel GHG intensity standards (e.g., the California LCFS regulation) each lead to different fuel market effects per unit increase in biofuel consumption caused by these policies. Naturally, stacking these policies together or coupling them with a carbon tax or a fuel tax will result in different fuel market effects per unit increase in biofuel consumption. Finally, not just with respect to IFUE but relevant to price effects in general is the fact that, when one region adopts a biofuel policy, the policy conditions in other regions will determine the nature and extent of price effects by simultaneously increasing the competition for certain types of

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resources while decreasing it for others. Therefore, any particular study’s assumptions about the policy regime in each of the distinct regions (provinces or nations) within the computational model will affect the study’s estimate of price effects. 11.3.2. Policy Challenges The uncertainty in the estimates of ILUC, notwith­ standing, the need to mitigate environmentally undesir­ able land use changes due to biofuel is widely appreciated in the policy arena. Beyond mere statements of policy intent, two broad approaches are being pursued to regulate GHG emissions due to ILUC. One approach is to limit the volumetric targets for the more land‐intensive biofuels. This is the approach taken by the RFS, under which the target for advanced biofuels is 21 billion gallons, while that for corn ethanol is only 15 billion gallons [EPA, 2005]. The success of this strategy hinges on the assumption that the next generation of biofuels will be derived from crops grown on marginal land that is uneconomical for production of food crops. This remains to be seen. Furthermore, the 15 billion gallon mandate for first‐ generation biofuels is only a lower bound or a floor on their consumption. If such biofuels can compete with oil products, then additional volumes might be consumed regardless of their land use impacts. An extreme version of this approach is to altogether ban biofuels from edible feedstock, which has been adopted in China, India, and other developing countries. A second approach is to regulate ILUC emissions directly. An example of this approach is the California Low Carbon Fuel Standard as part of which regulators have “assigned” a rating for the ILUC emissions intensity for different types of biofuels [CARB, 2015]. Under this approach, the GHG rating of any given batch of fuel is comprised of two parts. One is the life cycle GHG emission intensity of the fuel that is supplied by that facility, which is facility specific, and the second is the “assigned” GHG emission intensity rating for ILUC. For compliance, a regulated fuel supplier needs to ensure that his or her average GHG rating weighted across all fuels sold in a year is below the prescribed limit and then use this rating to verify that the life cycle GHG emissions intensity of a given batch of fuel, taking into account it’s rating for indirect emissions, is below a chosen emissions intensity standard. Once such a rating is established, it could be used to levy a tax or provide a subsidy to fuel producers or used in conjunction with a trade policy to levy a tariff only to imports (or subsidize exports). However, actual examples of these latter types of applications of life cycle emissions or indirect emissions are not available today. The two strategies mentioned above exhibit similarities as well as differences. In the former case, estimates of the

life cycle GHG emission intensity along with estimates of indirect GHG emissions are used only to select the volumetric targets for different broad categories of biofuels but are not used to “actively” regulate each unit of biofuel as is the case with the second approach. Neither strategy bans the consumption of biofuels that might have higher GHG footprint than fossil fuels. Instead they rely on providing incentives that encourage the consumption of dirtier biofuels. Both approaches rely on the availability of good estimates of the indirect effects, although this burden is higher for the second strategy that actively regulates indirect effects [Gawel and Ludwig, 2011]. The uncertainty in estimates of indirect emissions, while problematic, is not an intractable problem per se [Rajagopal, 2016]. A policymaker could ex ante use the best available estimate of emission leakage to implement any given policy, and once empirical evidence for indirect emis­ sions becomes available, ex ante estimates could be adjusted to match their ex post true values. For instance, if a particular type of fuel was assigned an excessively high rating ex ante, then the suppliers of such fuel would have overcomplied. That is, their actions would have resulted in them reducing their emission intensity by more than the required amount. In such cases, regulators could compensate the overcompliant entities in a number of ways. They could relax their compliance targets for the subsequent year, refund the excess emission credits they might have turned in, or refund any excess taxes or fines paid. Likewise, if a particular type of fuel was assigned an excessively low rating ex ante, then the sup­ plier (or buyers) of such fuel would have undercomplied. That is, their actions would have resulted in them reducing their emission intensity by less than the required amount. In such cases, regulators could increase their compliance targets for the subsequent year or require them to submit additional emission credits or levy addi­ tional taxes. However, such true up‐based approaches are feasible only if reasonably accurate estimates could be obtained ex post. The unique and intractable problem with the emissions due to price effects is that this is unlikely the case. The inability to empirically estimate indirect emissions implies that it will be hard to ascertain the effectiveness of any approach to mitigate indirect emissions. This suggests that policymakers should, in the first place, seek approaches that do not rely on precise estimation of indirect emissions. This will help minimize the “transaction costs” of reducing indirect emissions. The transaction costs are the costs associated with designing, enacting, administering, monitoring, enforcing a policy, and last but not least, impact assessment. In this regard, both the RFS and LCFS regulations seem to entail high transaction costs. In particular, both the methodology for

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estimation and the chosen point estimate of GHG rating for ILUC emissions of the different major first‐generation biofuels have been a source of controversy (http://www. ucsusa.org/assets/documents/clean_vehicles/call_to_ action_biofuels_and_land_use_change.pdf; http://www. economist.com/node/14205727/). It is worth reiterating that the indirect emissions that are under consideration so far, ILUC emissions, are but one among many potential different types of indirect effects. The cost of extending the regulation on indirect emissions to include the effects in other sectors is not hard to imagine. An overview of a few potential alternatives to RFS and LCFS approaches to the regulation of emissions due to price effects is provided here. For a more detailed discussion, see Rajagopal [2016]. It is worth recalling that ILUC is the only type of emissions due to price effect of biofuels that is being regulated today. In choosing the proper approach to indirect effects, four factors merit consideration: (i) the accuracy of the estimates of emission leakage; (ii) whether or not the magnitude of indirect effects is large enough to render the technology counterproductive to the environment; (iii) whether or not pollution reduction is the primary benefit of the new technology or it is one among multiple important other benefits (such as diversification of energy supply, balance of trade benefits, rural development, which is the case with biofuels); (iv) the potential for learning‐by‐doing and technical changes that might reduce the importance of indirect effects. If accurate estimates exist and they suggest a substantial risk of backfire, and if emissions reduction is the primary objective of the policy and there is not much potential for learning‐by‐doing improvements, then the policy to pursue the technology in question merits a complete reconsideration rather than seeking to regulate the indirect effects. If, however, any one of the above conditions is not true, then imposing a de facto ban on a technology by adopting a high estimate of indirect emission intensity of a technology requires further justification. One approach to limit the adoption of a potentially risky technology is to cap the total quantity or its market share. This means that, in the case of RFS, the 15 billion target for first‐generation ethanol becomes both a floor and cap on the quantity of biofuels. Even in this case, estimates of indirect emissions are needed, but they are not used to “actively” regulate each gallon of biofuel; instead it is used to select the proper volumetric target, which might imply a reduction of mandate from 15 to, say, 12 or 13 billion gallons. There is sound theoretical reasoning to support such an approach to ILUC emissions. The larger the biofuel mandate, the larger the effect on land prices, which is the source of ILUC emissions, and therefore, scaling down or slowing the policy ramp allows more time for

adjustment through improvements in productivity that reduce ILUC. Another modification to the RFS is to make the GHG threshold for direct life cycle emissions to decrease over time. For instance, the GHG threshold for first‐generation biofuel is 80% of gasoline’s GHG emission intensity. This target is held fixed for the entire  policy horizon. The approach suggested here is to reduce the upper limit to, say, 60% over a 5 or 10 year horizon. There is a need for greater research and policy discussions into these and other alternatives to current approaches to regulating indirect emissions, which cannot simply be ignored. Notwithstanding the above, in reality, a number of other factors appear to help limit the growth of the currently commercial biofuels beyond their mandated levels. One such factor is public concern about food price inflation and food security, which have led to additional safeguards to either discourage or cap altogether the quantity of biofuels from edible sources. A second development is the steep decline in oil prices (http://www.eia.gov/dnav/pet/hist/LeafHandler. ashx?n=pet&s=rbrte&f=m) driven by technical inno­ vations in shale oil extraction that have increased oil supply [Baffes et  al., 2015]. This means that biofuels are more costly than gasoline today compared to the period prior to the oil shock. This reduces the compet­ itiveness of biofuels beyond mandated levels. Of course, the decline in oil price also means that the second‐generation biofuels, which were costlier than the first‐generation biofuels, are even costlier today. That said, the current levels of commercial production of second‐generation biofuels are far below mandated levels on account of technical constraints, which mitigates this negative effect. A third development, one that is specific to the situation in the United States, is the so‐called blend‐wall constraint. On account of factors such as the slow rate of growth in the adoption of flex‐fuel vehicles, slow recovery from the greater recession of 2007–2008, and rapid improvements in fuel economy, the demand for gasoline has risen at a rate slower than expected a decade ago. This has meant that targets for annual biofuel consumption exceed the ability of the current vehicle fleet to consume ethanol in the form E10 blend (10% ethanol and 90% gasoline), which is referred to as the blend wall. 11.4. CONCLUSION Public policies in large oil‐importing countries have spurred a massive expansion of the biofuel industry over the past decade. These policies were adopted during a time of rising oil scarcity and rising trade deficits caused by oil imports. The environmental concerns associated with oil use provided, at best, a partial

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motivation for promoting biofuels. As a result, the mix of biofuels being produced today is not ideal from an environmental perspective. Policymakers have taken cognizance of this shortcoming, which is being addressed through the kinds of safeguards against ILUC emissions adopted under regulations such as the RFS and LCFS. Today, however, the oil market is vastly different and contrary to that a decade ago. To be pre­ cise, the increase in supply, primarily driven by innova­ tion in shale oil extraction, is outstripping growth in demand, which is slower than anticipated [Baffes et al., 2015]. As a consequence, the justification for biofuels on account of oil scarcity and energy security is weaker today compared to a decade ago. In contrast, after a decade of growth in global GHG emissions and given limited progress toward a “binding” global agreement on capping global GHG emissions, the environmental reasons to pursue oil substitution policies are much stronger today. Biofuel expansion, and large‐scale expansion of any technology for that matter, will entail unintended consequences because of price effects. Managing this mechanism of unintended consequences poses two main challenges. One set of problems is due to the global scope of GHG emissions and impacts, the global nature of major commodity markets (such as crops and fuels), and the heterogeneous and diffuse nature of land use emissions, which all combine to make estimation of indirect emissions, and in particular land use change emissions, complex. Without doubt the application of sophisticated computational tools has helped shed light on the potential size of the indirect land and fuel market effects, and there is a need to continue this line of work. However, it must also be recognized that estimates of indirect emissions will, in spite of our best efforts, remain uncertain. The second problem is that not all the sources of indirect emissions can be regulated. For instance, pollution arising outside the region or sector being regulated cannot be directly managed. In light of the computational, empirical, economic, and legal challenges to current approaches to regulating indirect emissions, there is a need for research into a broader suite of approaches to manage emissions that arise outside of the supply chain of a new technology. It is key for these policies to strike a balance between supporting infant industries and avoiding price effects that could have negative unintended consequences. ACKNOWLEDGMENTS While the views expressed here are my own, this chapter has benefitted from my earlier collaborative work with Prof. David Zilberman and Gal Hochman, for which I am grateful.

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Rajagopal, D. (2013), The fuel market effects of biofuel policies and implications for regulations based on lifecycle emissions, Environmental Research Letters, 8(2), 024013. Rajagopal, D. (2016), On mitigating emissions leakage under biofuel policies, Global Change Biology. Bioenergy, 8, 471–480. Rajagopal, D., and R. J. Plevin (2013), Implications of market‐ mediated emissions and uncertainty for biofuel policies, Energy Policy, 56(C), 75–82. Rajagopal, D., and D. Zilberman (2013), On market‐mediated emissions and regulations on life cycle emissions, Ecological Economics, 90, 77–84. Rajagopal, D., G. Hochman, and D. Zilberman (2011), Indirect fuel use change and the environmental impact of biofuel policies, Energy Policy, 39(1), 228–233. Rajagopal, D., R. Plevin, G. Hochman, and D. Zilberman (2015), Multi‐objective regulations on transportation fuels: Comparing renewable fuel mandates and emission standards, Energy Economics, 49, 359–369. REN21 (2010), Renewables 2010 global status report, Technical report, Renewable Energy Policy Network for the 21st Century. www.ren21.net/renewables-2010-global-status-report/ (accessed 19 September 2017). REN21 (2014), Renewables 2014 global status report, Technical report, Renewable Energy Policy Network for the 21st Century. www.ren21.net/renewables-2014-global-status-report-full (accessed 19 September 2017). Rocha, M. H., R. S. Capaz, E. E. S. Lora, L. A. H. Nogueira, M. M. V. Leme, M. L. G. Renó, and O. Almazán del Olmo (2014), Life cycle assessment (LCA) for biofuels in Brazilian conditions: A meta‐analysis, Renewable and Sustainable Energy Reviews, 37, 435–459. Searchinger, T., R. Heimlich, R. A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, and T.‐H. Yu (2008), Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land use change, Science, 319(5867), 1238–1240. Spatari, S., D. M. Bagley, and H. L. MacLean (2010), Life cycle evaluation of emerging lignocellulosic ethanol conversion technologies, Bioresource Technology, 101(2), 654–667. Swinton, S. M., B. A. Babcock, L. K. James, and V. Bandaru (2011), Higher us crop prices trigger little area expansion so marginal land for biofuel crops is limited, Energy Policy, 39(9), 5254–5258. Taheripour, F., and W. E. Tyner (2013), Biofuels and land use change: Applying recent evidence to model estimates, Applied Sciences, 3(1), 14–38. Tokgoz, S., and D. Laborde (2014), Indirect land use change debate: What did we learn? Current Sustainable and Renewable Energy Reports, 1(3), 104–110. Tyner, W. E., F. Taheripour, Q. Zhuang, D. Birur, and U. Baldos (2010), Land use changes and consequent CO2 emissions due to us corn ethanol production: A comprehensive analysis. Technical report, Department of Agricultural Economics, Purdue University. Yang, Y., and S. Suh (2015), Marginal yield, technological advances, and emissions timing in corn ethanols carbon payback time, International Journal of Life Cycle Assessment, 20(2), 226–232. Zilberman, D. (2016), Indirect land use change: Much ado about (almost) nothing, GCB Bioenergy, 9, 485–488.

12 Biofuels, Land Use Change, and the Limits of Life Cycle Analysis Richard J. Plevin

ABSTRACT The question of whether the use of biofuels mitigates or exacerbates climate change has eluded a definitive answer despite decades of analysis. Indeed, years of study has increased, rather than decreased, the uncer­ tainty, as previously excluded climate‐related factors have been uncovered. This is explained partly by the temporal and spatial heterogeneity and complexity of the affected natural systems and partly by disagreement about the most appropriate method to answer this question. The commonly used method (life cycle assessment, or LCA) includes two approaches that differ in their goals and system boundaries, and both methods depend strongly on subjective decisions required of the analyst. The recent inclusion of emissions from bioenergy‐ induced land use changes in the analysis of bioenergy has complicated matters further by requiring projections of the response of the global economy to bioenergy‐related perturbations. By exploring the interactions between bioenergy and climate and the methods used to estimate the land use change emissions, we show that LCA is unable to assign a definitive climate effects property to bioenergy systems. This conclusion stems primarily from the challenges of predicting the future state of complex, nonstationary systems such as the global economy and climate. 12.1. INTRODUCTION The question of whether the use of biofuels mitigates or exacerbates climate change has eluded a definitive answer despite decades of analysis. Early studies limited their analytic boundaries to product systems that were defined by direct inputs and outputs along a supply chain. More recent analyses have attempted to incorporate greenhouse gas (GHG) emissions from market‐mediated effects such as changes in global fuel consumption and land use, which have presented numerous challenges. To estimate emissions from biofuel‐induced land use changes (LUC), analysts have applied a variety of methods, most frequently involving economic models representing markets for agricultural and forestry products to identify the location and magnitude of LUC, and models of

Transportation Sustainability Research Center, University of California, Berkeley, California, USA

changes in terrestrial carbon stocks to calculate the resulting GHG emissions. Unfortunately, both economic and emission models are rife with uncertainty, and the common approach of summing these results with those of conventional supply chain analyses produces an incoherent result. This chapter reviews the climate effects of bioenergy, the methods and models used to quantify these effects, and explores the strengths and limitations of these methods. 12.2. BIOENERGY AND CLIMATE The production and use of bioenergy affect the climate system in several ways, and the production itself is affected by climate change. Many of the effects of bioenergy on climate are a function of LUC, and LUC affects the conditions of feedstock production in ways that affect bioenergy’s net carbon balance. Although measuring these effects where land is converted directly to bioenergy

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production can be challenging, estimating the magnitude and location of LUC induced by biofuel production and consequent market effects adds considerable epistemic uncertainty to the overall calculation [Plevin et al., 2015]. We begin by examining the primary interactions between bioenergy and climate. 12.2.1. Effects of Bioenergy Production on Climate The production and use of bioenergy affect climate in several ways [Creutzig et  al., 2015], which include the following: 1. Storage of atmospheric CO2 as carbon in plant matter and its release upon combustion or decay 2. Storage and release of soil carbon 3. N2O emissions resulting from N fertilization 4. GHG emissions (primarily from energy use) during the product’s life cycle 5. Albedo change associated with land use changes 6. Emission of volatile organic compounds (VOC) from forests, which affect cloud formation and thereby albedo 7. Reduced GHG emissions to the extent that bioen­ ergy avoids fossil fuel consumption Of these effects, only the storage and release of biogenic carbon is readily measurable. Most of the other phenomena (e.g., N2O and VOC emissions, soil carbon changes, and albedo change) vary substantially over small spatial and temporal scales, owing to the heterogeneity and complexity of natural systems, variations in precipitation and tempera­ ture, and differences in land use history [Snyder et al., 2009; Weligepolage et al., 2013; Zhu et al., 2013; Rafique et al., 2016]. Estimation of changes in soil carbon is further com­ plicated by the challenge of measuring small changes in large stocks. As a result of these challenges, measurements of these phenomena can vary greatly across studies, making generalization (and parameterization of models) challeng­ ing [Luo et al., 2016]. As we shall see in section 12.3.2, these uncertainties are compounded by those in projections of the magnitude and location of land use change. We discuss life cycle assessment further in section 12.3. 12.2.2. Effects of Climate Change on Bioenergy Production Climatic change affects the production of bioenergy (and terrestrial primary productivity, generally) in several ways [Field et al., 2012], which include the following: 1. Changes in precipitation, including increased drying in some areas and flooding in others, can damage crops [Iizumi and Ramankutty, 2016], as do changes in the geo­ graphic range of insects [Kurz et al., 2008]. 2. Changes in precipitation and temperature also affect soil carbon turnover time [Carvalhais et al., 2014].

3. Plant productivity increases with CO2 concentration [Lashof et al., 1997; Cox et al., 2013] and decreases owing to increased temperature [Lobell et  al., 2013; Challinor et al., 2014] and tropospheric ozone concentration [Sitch et al., 2007]. These effects are also complex, often involving poorly characterized feedback mechanisms and lagged responses, and are therefore difficult to predict [Reichstein et  al., 2013]. For example, the vulnerability of crops to extreme temperatures is a function of water availability [Anderson et  al., 2015], which itself is a function of temperature, though the effects of higher temperature include local drying and potential increases or decreases in regional precipitation. Thus, when projecting the effects of bioen­ ergy on climate, we must acknowledge that, as the climate changes, historical data (and models calibrated on these data) will have decreasing predictive value. The foregoing describes the challenges faced in esti­ mating climate effects of land use change for a given loca­ tion. Given the globally well‐mixed nature of the primary GHGs, estimates of the net climate effects of bioenergy also require that we consider how LUC is propagated globally through markets. Notably, major models used to estimate ILUC emissions disagree on both the magnitude of ILUC and even on which continents it will occur. Before we delve into market‐mediated effects, we consider the conceptual basis for life cycle assessment, into which these estimates of market‐mediated effects have been incorporated. 12.3. LIFE CYCLE ASSESSMENT The method commonly used to assess the climate effects of bioenergy is life cycle assessment (LCA), which attempts to describe the full environmental effects associated with a product system [ISO, 2006]. As it is commonly understood, life cycle assessment (LCA) is a method for evaluating the so‐called “complete environmental effects” of a product system by tracing direct inputs and outputs of all relevant processes, from raw materials extraction to product disposal [Hunt et  al., 1996; Neitzel, 1996; Fava, 1997]. This method assesses the flow of pollutants, energy, and materials through a set of processes over a given time period [Curran et al., 2005]. Confusingly, the same term (life cycle assessment) is used to describe a very different type of analysis that assesses the broader environmental effects resulting from an action, such as a change in production of some product system induced by, say, a bioenergy policy. In the LCA literature, the first method is referred to as attributional LCA (ALCA), and the second form is called consequen­ tial LCA (CLCA). In ALCA, a set of environmental effects are attributed to a product system using a protocol based on the chain of direct input‐output relationships.

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In CLCA, the consequences of a decision or action are projected, including processes not connected by direct input‐output relationships. Simply put, while an attributional life cycle inventory (LCI) traces material and energy flows, a consequential LCI follows causal chains [Ekvall and Weidema, 2004]. Even within the more narrowly defined ALCA, it is common for different life cycle analysts to reach divergent conclusions when examining the same ostensible product. This divergence is generally attributable to different assumptions about location, time frame, system boundaries, proxy data used to fill data gaps and how to isolate the effects of a single product in a multiproduct system [Farrell et al., 2006; Cherubini et al., 2009; Plevin, 2009; Hoefnagels et  al., 2010]. The consequential approach is less well defined and more controversial [Zamagni et al., 2012; Dale and Kim, 2014; Hertwich, 2014; Plevin et al., 2014; Suh and Yang, 2014]. 12.3.1. The Semantics of LCA ALCA identifies the sum of average energy use and emissions across a set of processes defined by input‐ output relationships within a product system. There is, in general, no reason to expect an ALCA result to predict the marginal effect of a change in output of that product system [Plevin et al., 2014]. In contrast, the purpose of a CLCA is to estimate the change induced by these changes in output. The two methods rely on different life cycle inventories and should be expected to produce different results [Weidema et al., 1999]. The underappreciated distinction between consequential and attributional LCA has resulted in confusion in the use and interpretation of LCAs. Part of the confusion stems from the common practice in ALCA studies of not identifying themselves as attributional. This is understandable given the historical predominance of ALCA, but it can lead the casual observer to believe there is only one approach to LCA. Another source of confusion is the common practice of incorporating consequential elements into otherwise attributional analyses [Brander and Wylie, 2011]. For example, a common method of isolating the effects of one product in a multiproduct system is to assume that coproducts of the product of interest displace other products in the market and thereby avoid the emissions associated with the displaced products. This so‐called displacement method attempts to identify a change induced by the analyzed product system, but this change‐based approach is not applied to the rest of the analysis. If it were, the result would be a consequential analysis, by definition. Perhaps as a result of this confusion, biofuel analyses (and some regulatory protocols) combine the results of attributional and consequential analyses. Since the

ALCA and CLCA elements are presented in the same units of grams CO2‐equivalent per megajoule of fuel, it would appear that the values produced by these two methods are commensurable and even additive, but on closer analysis, it is clearly not the case. Consider the case of adding a biorefinery in a corn‐ producing region where corn is diverted from a prior use (e.g., animal feed) to be used as an ethanol feedstock. By tracing the input‐output chain, an ALCA would consider the emissions from the production of the corn used by the biorefinery, recursively following the chain of inputs back to raw materials extraction. A consequential analysis, in contrast, would recognize that the corn used by the biorefinery would be produced in the counterfactual case as well, and therefore, this corn production process and its antecedents drop out of the change analysis. Instead, the CLCA would recognize that the effect of the new biorefinery might be to induce production of additional corn or another feed crop elsewhere, perhaps resulting in emissions from land use change. These market‐ mediated changes in emissions are excluded from ALCA. As this example illustrates, adding the attributional analysis of corn ethanol to the consequential analysis of market‐mediated effects produces an incoherent result [Plevin et al., 2014]. 12.3.2. Biofuel‐Induced Land Use Change Despite disagreements about how to best characterize the effects of biofuels, several regulations have been promulgated since 2007 that rely on LCA to estimate or regulate these effects [CARB, 2009; European Parliament, 2009; UK RFA, 2009; USEPA, 2010]. In 2007, the United States enacted the Energy Independence and Security Act, which included revisions to the Renewable Fuel Standard (RFS2) requiring the EPA to estimate the life cycle GHG emissions from biofuels used to comply with the regulation. The regulation included the requirement that the agency consider “direct emissions and significant indirect emissions such as significant emissions from land use changes,” thereby requiring inclusion of effects beyond the processes directly linked by input‐output relationships that are considered in standard ALCA. The concept of indirect land use change (ILUC) is straightforward: the laws of supply and demand dictate that diverting a resource from an existing use to a new one will result in some combination of increased production and decreased consumption [Searchinger et  al., 2008]. In the case of agricultural commodities, a farmer can increase output on existing land (intensification) or bring additional land under cultivation (extensification). In addition, overall consumption may decline as a result of price increases [Searchinger et al., 2015]. When extensification involves clearing grass, shrubs, and trees, or

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plowing soil, substantial quantities of CO2 may be emitted [Fargione et al., 2008]. These land use changes are sometimes termed market‐mediated: they are a consequence of the reequilibration of supply and demand in global commodity markets around a new price. 12.3.2.1. Carbon Pools Affected by LUC The IPCC guidelines for national GHG inventories [IPCC, 2006] recognize the following factors affecting GHG emissions from LUC: 1. Aboveground live biomass (AGLB): trunks, branches, foliage, and understory 2. Belowground live biomass (BGLB): coarse and fine roots 3. Dead organic matter (DOM): deadwood and litter 4. Soil organic matter 5. Biomass diverted into harvested wood products 6. Non‐CO2 climate‐active emissions (e.g., CH4 and N2O) affected by changes in carbon 7. Foregone sequestration (i.e., sequestration that would have occurred if not for the LUC) Accounting for changes in these pools is challenging. Estimates of the size of these pools typically involve substantial uncertainty owing to challenges of large‐ scale sampling of highly heterogeneous natural systems. Estimating changes in these pools is likewise difficult given dependence on environmental factors, management systems, and the projected alternative fate of the carbon pool. Forest biomass carbon estimates typically include only live tree trunks, branches, and foliage. In addition to live biomass, forests often contain a substantial quantity of dead organic matter (DOM). For example, according to the U.S. Forest Inventory, 35% of the total forest carbon pool is in live vegetation, 52% in soil, and 14% in dead organic matter, excluding fine woody debris [Woodall et al., 2008]. Elsewhere, these ratios vary across climatic zones. DOM consists of litter and deadwood. Deadwood includes all nonliving tree biomass not included in litter, including standing dead trees, down dead trees, dead roots, and stumps larger than a specific diameter, often 10 cm [Woodall et al., 2008]. Owing to a paucity of data, some of these categories (e.g., DOM and understory) are frequently omitted from LUC carbon accounting frameworks. The quantity of deadwood in a forest depends on several factors, including the density of live trees, forest age, temperature, humidity, harvest frequency, self‐thinning mortality, time elapsed since the last disturbance, and whether this was fire, which removes deadwood, or an event that introduces deadwood, such as blowdowns, diseases, or pests. Because of these diverse influences, there is no predictive relationship between the stocks of live tree biomass carbon and deadwood carbon [Woodall

and Westfall, 2009]. Ratio methods fail spectacularly in cases of low live and high dead biomass. Large‐scale disturbances are location specific, so it is difficult to generalize from these results. To complicate matters further, deadwood is infrequently measured. Existing empirical data are based on diameter measurements, from which volume and carbon are estimated [Woodall et al., 2008]. The carbon density of deadwood also varies as the wood decays, adding further uncertainty to the magnitude of this carbon pool. Forest AGLB is usually derived from various remote‐ sensing and ground‐based sources, which have a limited ability to discern among some land cover types. Remote‐ sensing data are combined with measurements of tree heights and diameters to estimate tree mass, of which approximately 50% is usually assumed to be carbon. Belowground biomass is generally computed from AGLB using root‐to‐shoot ratios [Saatchi et  al., 2011]. Forest carbon data estimates are understandably uncertain, given this sequence of approximations. Soil carbon data can be found in the Harmonized World Soil Database (HWSD), which offers values for carbon for 0–30 and 30–100 cm of depth [FAO/IIASSA/ISRIC/ ISS‐CAS/JRC, 2012]. Unfortunately, the HWSD documentation is silent about uncertainty. Carbon accounting models differ in whether they include soil carbon (and thus changes) below 30 cm. High‐carbon‐density peatland is sometimes treated as a special case, though estimates of the carbon stocks and losses from conversion (e.g., to palm plantations) depend on factors such as peat thickness and water table depth, and postconversion use of fertilizer, which are uncertain or difficult to predict [Hooijer et al., 2011; Draper et al., 2014]. After we have estimates of all carbon pools, we must estimate the changes in these pools resulting from LUC. In the case of foregone sequestration, we must estimate the rate of carbon storage that would have occurred in the forests had they not been converted, including both land converted to forest (which tends to accumulate carbon quickly) and forest left standing, which can continue to accumulate carbon for centuries [Luyssaert et al., 2008]. The carbon presumed stored in the counterfactual case depends, in turn, on estimates of disturbances such as pest outbreaks, storm‐related losses, and wildfires that would have released some of the carbon [Anderson‐ Teixeira et al., 2012]. Harvested forest carbon can remain sequestered in wood products for decades and thus should be subtracted from estimates of tree carbon losses. To estimate the carbon remaining after a given period requires estimates of the volume of wood harvested, the fraction that is converted to long‐lived products, and the fate of those products over time, as well as the fractions added to landfills and the fractions of the landfill biomass sequestered for a

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long term, emitted as CH4, or combusted for energy generation either as biomass or CH4 [Earles et al., 2012]. 12.3.2.2. Biomass Emissions from Land Clearing One of the primary sources of LUC emissions is the use of fire to clear land of biomass prior to cultivation [Fargione et al., 2008]. To illustrate the difficulty of esti­ mating LUC emissions, consider first the relatively simple case of estimating the CO2‐equivalent GHG emissions resulting from the combustion of 1 kg of forest biomass. We can reach the first‐order approximation by multiplying the assumed carbon fraction of the biomass (~50%) by the ratio of the mass of CO2 to that of carbon, or 0.5 × 44/12 = 1.83 kg CO2 kg−1 of biomass combusted (the leftmost bar in Figure 12.1). However, this is not the end of the story. Combusting biomass, particular in open fires, produces a wide variety of emissions: Andreae and Merlet [2001] identify nearly 100 distinct substances released by burning grassland and forest. Although the quantity and species of emissions resulting from this combustion are measurable or can be approximated given expected emission ratios, several subjective decisions are required to aggregate these

into a single value representing the effect of this burning on climate. First, we must identify a metric that allows us to combine the effects of substances with different atmospheric lifetimes. By convention, CO2‐equivalence is computed using the 100 year global warming potential (GWP‐100) values from the latest IPCC assessment report: the mass of each GHG is multiplied by its GWP‐100 value, and these values are summed. However, as the IPCC makes clear, the GWP approach is one of many, and there is no perfect or even “best” method of combining the effects of disparate climate forcings [Myhre et  al., 2013]. Several approaches exist, and each has its limitations and involves subjective elements [Fuglestvedt et  al., 2010; Borken‐ Kleefeld et al., 2013]. For example, we must decide which climate forcing emissions to include: bioenergy LCAs typically consider only CO2, CH4, and N2O, yet combustion of biomass can produce CO, SO2, NOX, black carbon, organic carbon, and nonmethane hydrocarbons in quantities that vary with the type of biomass and whether the combustion involves flaming, smoldering, or both [Andreae and Merlet, 2001]. The IPCC does not define 100 year GWP values for all these species, and for those

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Figure 12.1 CO2‐equivalent emissions from burning tropical forest for different subsets of emission species and different analytic time frames. In the three‐gases cases, only CO2, CH4, and N2O are included. (See insert for color representation of the figure.)

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reported in the literature, values are highly uncertain. In addition, time horizons other than 100 years are equally legitimate, and for processes that produce significant quantities of substances with atmospheric lifetimes much less than 100 years, such as methane and aerosols, using a shorter time horizon materially affects the result. Figure  12.1 illustrates the effect of different choices of GWP time horizon and emission species included. As shown, compared to the de facto standard approach (three gases and GWP‐100), which produces a value of 1810 g CO2 kg−1 dry biomass combusted, including more emission species and a 20 year time horizon produce a result of 3354 g CO2 kg−1, nearly double the standard value. Note that the GWP values for some of the emissions, especially aerosols such as black and organic carbon, are highly uncertain. The “three gases” case produces a lower value than the “CO2‐only” case, which presumes that all carbon in the biomass is combusted to CO2. In the three‐gases case, the carbon emitted in species other than CO2 and CH4 is ignored. See Appendix 12.A for further details. 12.3.2.3.  Emissions From  Soil After Land Clearing and Management Changes Changes in land use or land cover can result in gains or losses of soil organic carbon (SOC) as the new regime settles into a new equilibrium. For example, conversion of native forest to cropland may release 40% of the SOC in the uppermost 20 cm of soil, whereas converting cropland to pasture or tree plantations may increase SOC by 20% [Guo and Gifford, 2002]. Reversing these land use changes can eventually reverse the changes in SOC [Post and Kwon, 2000]. Some models of LUC emissions include sequestration of carbon associated with a reduction in tillage [Beach and McCarl, 2010; USEPA, 2010]. The conventional wisdom has long held that conservation tillage increases soil C sequestration compared to conventional tillage [Dick et al., 1998; Six et al., 2002; West and Post, 2002]. However, Baker, et  al. [2007] reported that the appearance of increased SOC under no‐till is a result of insufficiently deep soil sampling. In earlier studies, samples were generally taken at depths of less than 30 cm, which appears to be above the level at which conventional tillage deposits carbon, whereas most of the increase in soil carbon under no‐till occurs at shallower depths. Baker et al. [2007] conclude that “it is premature to predict the C sequestration potential of agricultural systems on the basis of projected changes in tillage practices, or to stimulate such changes with policies or market instruments designed to sequester C.” Additional studies have confirmed these findings [Gál et al., 2007; Yang et al., 2008; Blanco‐Canqui and Lal, 2009]. For example, Blanco‐Canqui and Lal [2009] report that no‐till increases SOC concentrations in the upper

layers of some soils, but it doesn’t store SOC more than plow tillage for the whole soil profile. Estimates of biofuel‐induced LUC typically use a 100 year analytical horizon by virtue of using GWP‐100 values to compute CO2‐equivalence. Sequestration owing to reduced tillage is equivalent to avoided emissions only to the extent that the carbon remains sequestered for this same period [Moura Costa and Wilson, 2000]. Estimates of soil carbon sequestration should therefore be discounted by the risk that the carbon will be prematurely reemitted. This risk, though unpredictable, is clearly nonzero. Finally, we note that the loss of SOC results in mineralization of soil nitrogen, which then becomes available for emission as N2O [IPCC, 2006, section 11.2.1.3]. 12.3.2.4. Inclusion of LUC in Biofuel Regulations Estimates of LUC emissions (and their inclusion in regulatory systems) have proved highly controversial [Zilberman and Rajagopal, 2010; Warner et  al., 2013; Finkbeiner, 2014; Tokgoz and Laborde, 2014; Muñoz et al., 2015]. Biofuel‐induced LUC occurs in response to an increase in production capacity: the increased demand for feedstock (and thus land) results in some amount of land clearing, which generally involves removal of biomass and cultivation of land. CO2 emissions from biomass clearing occur in the first year or two following land clearing, while CO2 emissions from soil cultivation can continue for several decades [O’Hare et al., 2009]. This irregular time profile does not fit well with LCA, which, by generally lacking a time component, treats all emissions as occurring at once [O’Hare et al., 2009; Levasseur et al., 2010]. The emission intensity metric required by biofuel regulations (e.g., g CO2e MJ−1) requires dividing a quantity of emission by a quantity of fuel. Existing regulatory regimes use simple linear amortization of emissions over 20 (in the European Union) or 30 (in the United States and Canada) years. Conceptually, this can be thought of as either flattening ILUC emissions over a chosen time period or dividing the total emissions by the total fuel production associated with the ILUC over a chosen time period. Either way, consideration of this multidecadal time horizon raises several methodological issues. The first challenge is to estimate the quantity of fuel associated with the initial ILUC and ongoing emissions. Since most emissions from ILUC occur in the first few years, changes in the time horizon have relatively little effect on the quantity of emissions (the numerator) while exerting a much larger effect on the quantity of fuel produced (the denominator), which is typically treated as constant over time. ILUC emissions estimated with any given model will thus produce an ILUC intensity that is approximately 50% greater using a time horizon of 20 years rather than 30 years [Plevin et al., 2010].

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For the metric to be meaningful, the chosen time horizon should represent the actual longevity of fuel production from this facility, which is unpredictable. Over‐ or underestimating the value will result in ILUC intensity measures that are too low or high, respectively. The second challenge is to estimate the emissions attributable to the increased biofuel production capacity. This requires a comparison between scenarios with and without the added biofuel production, over the same chosen multidecadal time horizon. Of course, many factors can affect these estimates, including assumptions about the existence and stringency of land, food, fuel, transit, and climate policies; the state of global economy and relations among trading partners; the pace and direction of technological development (e.g., existence of other renewable technologies); human behavioral change (e.g., changes in distances traveled and transport modes); and the effects of climate change on agricultural production [Plevin, 2016]. Changes to any of these factors would produce a different result in a model that includes these dynamics. Since prediction of the evolving state of the world over decades is beyond human capabilities, there is no single, definitive answer to the question of a biofuel’s ILUC emission intensity. 12.3.3. Other Market‐Mediated Effects Although LUC is perhaps the most challenging aspect of understanding biofuels’ role in climate change mitigation, it is far from the only challenge. Here we briefly examine related sources of GHG emissions. 12.3.3.1. Agricultural Production Large‐scale biofuel production results in changes in commodity prices relative to the counterfactual “without biofuel” world. These price changes perturb markets for, inter alia, livestock and rice, resulting in nonnegligible changes in emissions of CH4 and N2O [USEPA, 2010]. In addition, the overall increase in agricultural production results in greater use of nitrogen fertilizer and resulting N2O emissions [Melillo et al., 2009]. Changes in the location of production can also result in changes in energy use, fertilization, and irrigation, each with effects on total GHG emissions. 12.3.3.2. Food Consumption Diversion of land to biofuel production also affects food prices: price increases reduce consumption, somewhat reducing the total land area required to replace displaced crop production [Searchinger et  al., 2015]. Hertel et  al. [2010] estimated that market‐mediated reduced nutrition would make a substantial contribution to a lower ILUC emission intensity estimate: holding food consumption fixed increased estimated emissions

from biofuel expansion in their model by 41%. Searchinger et  al. [2015] examined four models of LUC emissions used in fuel regulations, showing that 25%–50% of the calories from corn or wheat diverted to ethanol production (net of coproducts) are not replaced in the market; rather overall demand is reduced owing to price effects. Thus, regulatory estimates of LUC emissions are lower than they would be had food and feed demand not declined. 12.3.3.3. Fossil Fuel Displacement Although not associated directly with LUC, a key benefit of bioenergy is the supposed avoidance of the use of fossil energy. The degree to which this occurs is dependent on market conditions and is susceptible to the same type of modeling used to estimate biofuel‐induced LUC. We discuss this here to help assess the limitations of LCA. Many LCAs determine whether bioenergy systems result in net reductions by comparing these to their fossil fuel equivalents. For example, the carbon intensity (CI) of a biofuel will be compared to that of a petroleum‐based fuel, and if the biofuel CI is lower than that of the petroleum fuel, the biofuel is said to reduce emissions by the percentage difference in the two CI values. Implicit in the comparison of CI values is the assumption of 100% substitution of an incumbent product by an alternative [de Gorter, 2010; Rajagopal et  al., 2011]. For example, if alternative fuel A is rated at 80 g CO2e MJ−1, and the incumbent fuel B is rated at 100 g CO2e MJ−1, the expectation that using fuel A reduces GHG emissions is based on the assumption that using fuel A avoids the 100 g CO2e MJ−1 associated with fuel B. The nominal GHG change is estimated to be [80 − 100 g CO2e MJ−1] = −20 g CO2e MJ−1, a 20% reduction. However, if less than 100% substitution occurs, producing more of a product with a lower rating can result in greater emissions than the status quo [Drabik and de Gorter, 2011]. If, owing to price effects in the fuel market, 1 MJ of alternative fuel A replaces only, say, 0.7 MJ of fuel B, then the net GHG effect of producing the alternative is [80 g CO2e MJ−1 − (0.7 × 100 g CO2e MJ−1)] = +10 g CO2e MJ−1, a 10% increase in GHG emis­ sions, rather than a 20% decrease. Estimates from the recent literature suggest that biofu­ els have a substitution rate in the 10%–70% range [de Gorter, 2010; Hochman et al., 2010; Ros et al., 2010; Stoft, 2010; de Gorter and Drabik, 2011; Rajagopal et al., 2011; Thompson et al., 2011; Chen and Khanna, 2012; Rajagopal and Plevin, 2013], which implies that the 100% substitution assumption overstates the GHG reduction benefits of biofuels by approximately 30–90 g CO2e MJ−1, which is a similar range (and additional to) ILUC emissions. The actual fuel substitution rate depends on numerous factors, including relative fuel prices, rates of depletion

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of conventional oil and new discoveries, the rate of deployment of unconventional petroleum, OPEC response to demand changes, the existence and stringency of pol­ icies regulating GHG emissions, changes in fuel demand from improved vehicle efficiency, and the growth of auto­ mobile use in developing countries. Importantly, both LUC emissions and the magnitude of fossil fuel displacement resulting from biofuel use are very uncertain and subject to changing economic, tech­ nological, social, and political conditions. The net effect of increased biofuel use is difficult to ascertain. 12.4. ESTIMATING THE CLIMATE EFFECTS OF BIOENERGY USE 12.4.1. Defining “Mitigation” Climate change mitigation is commonly defined as a reduction in radiative forcing (though generally consid­ ering only GHG emissions), relative to a counterfactual baseline trajectory. The mitigation effects of bioenergy can therefore be defined as the net global changes in radiative forcing resulting from an increase or decrease in the use of a specified quantity bioenergy. This, in turn, requires that we identify a baseline or “business‐as‐usual” case against which change can be estimated. As we will see, estimates of climate change mitigation benefits are highly dependent on the assumed counterfactual scenario. Several authors have identified a common “baseline error” in which the alternative fate of biomass and of the land on which biomass is produced is not considered when estimating bioenergy benefits [Searchinger, 2010; Hudiburg et al., 2011; McKechnie et al., 2011; Bird et al., 2012; Haberl et  al., 2012; Schulze et  al., 2012; Zanchi et al., 2012; Helin et al., 2013]. Some studies use a static baseline, or a “reference point”: the removal of biomass over time is compared to the state of the forest at some prior point in time [Johnson and Tschudi, 2012]. If there is no change, the biomass removal is treated as having no climate effect. Forests, however, continue to sequester carbon in the absence of biomass harvesting. Combusting biomass and releasing CO2 that would have otherwise remained sequestered in the biomass (and out of the atmosphere) is equivalent to combusting fossil carbon [Searchinger et al., 2009; Bright et al., 2011]. Regardless of the baseline net CO2 flows for a parcel of land, any activity that decreases the net biome productivity from this baseline will result in a net increase in atmospheric CO2 relative to that baseline [Hudiburg et  al., 2011; McKechnie et al., 2011; Schulze et al., 2012]. For example, the removal of residues that would otherwise decompose slowly reduces the amount of soil carbon accumulation at a site and can result in net emissions from some residue‐ based bioenergy systems [Repo et al., 2011]. Several studies have concluded that the use of relatively slow growing

(e.g., forest) biomass unavoidably results in a loss of carbon stocks for decades or even permanently compared to a reference scenario without bioenergy harvesting [Melin et al., 2010; Delucchi, 2011; Hudiburg et al., 2011; Malmsheimer et al., 2011; McKechnie et al., 2011; Repo et al., 2011, 2012; Böttcher et al., 2012; Holtsmark, 2012, 2013; Pingoud et al., 2012; Zanchi et al., 2012; Haberl et al., 2013]. In contrast, the use of easily decomposable residues and wastes for bioenergy can produce GHG benefits even in the near term [Zanchi et al., 2012; Lamers and Junginger, 2013]. The climate change mitigation benefits of harvesting wood for bioenergy can be increased by intensifying wood growth beyond the baseline level using forest management options such as fertilization [Jassal et  al., 2010; Sathre et al., 2010; Albaugh et al., 2012; Sathre and Gustavsson, 2012] or by harvesting wood from forests damaged by insect infestations such as mountain pine beetle, which turned some forests (e.g., in British Columbia) into net carbon sources [Lamers et al., 2014]. Production of bioenergy on land that would otherwise be abandoned should be compared to changes in carbon stocks from natural succession [Milà i Canals et  al., 2007] or to active management for maximum climate benefit [Johnson and Tschudi, 2012]. If the land in question would otherwise be used to produce plants for food, feed, fiber, or other ecosystem services, a comprehensive accounting of market‐ mediated effects is required to correctly assess changes from the baseline [Delucchi, 2011; Haberl et al., 2012]. In forests subjected to fire suppression, thinning (select removal of biomass) and/or burning treatments can help reduce the buildup of fuel and the severity of fires, resulting in net increases in forest carbon over time [North and Hurteau, 2011; Sorensen et  al., 2011; Fulé et  al., 2012]. However, when thinning is coupled with prescribed burning, the benefit may be lost [Campbell et al., 2012]. These findings suggest that some amount of biomass harvesting for bioenergy may have dual benefits of reducing loss of forest carbon to fire, while displacing fossil fuel. 12.4.2. Modeling Market‐Mediated Effects We have seen that, even in the simplest case of direct combustion, we are forced to make subjective choices about emission species, time frames, and CO2‐equivalence metrics and to construct a baseline scenario projecting what would have occurred in the absence of combusting biomass. A further complication is that land clearing, and any resulting biomass combustion, is a market‐mediated process: determining what is burned, and where, requires modeling global markets for food, feed, land, and energy. Global economic models used to estimate LUC emissions attempt to estimate how agricultural and forestry markets (among other) are affected by diverting existing land or production to use for bioenergy, and where and to what degree the diverted production is replaced, and

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whether this is achieved through intensification or exten­ sification [Golub and Hertel, 2012]. 12.4.2.1. Economic Equilibrium Models The models most commonly used to estimate market‐ mediated effects are economic equilibrium models, which assume an economic system starting in equilibrium (i.e., all markets clear) is “shocked” by changing some parameter and allowed to reequilibrate around this change according to laws of supply and demand. Equilibrium models are typically subdivided into partial equilibrium (PE) and general equilibrium (GE) variants. A PE model represents one or a few sectors of the global economy (e.g., just agriculture and forestry); the term “partial” refers to achieving equilibrium with this subset of the overall economy. In contrast, a GE model represents all economic sectors. This increased breadth, however, usually comes at the expense of depth: a practicable model requires a reduction in the level of detail in each sector and region [Kretschmer and Peterson, 2010]. The global economy may be represented in a few dozen industrial sectors, each of which is less detailed than it would be in a PE model. These aggregated sectors effectively represent a single product. For example, the version of GTAP commonly used in ILUC analyses uses a single petroleum sector that includes all varieties of crude oil, gasoline, diesel, jet fuel, lubricants, asphalt, petroleum coke, and all other coproducts of petroleum refining. These are treated as a single commodity despite the differing supply and demand elasticities that might be estimated for these coproducts. Disaggregating energy‐ and emissions‐intensive sectors and assigning distinct Armington elasticities (which affect international trade) and the nesting structure (which affects which commodities compete with one another) strongly affected estimates of border adjustment (CO2) taxes in one study [Alexeeva‐Talebi et al., 2012]. In practice, regional data to support the required level of specificity are not available in all regions, in which case proxy values are taken from regions where the required data are available. Similarly, the world’s regions are typically aggregated to a few dozen entities, with major economic regions such as the United States, Europe, Russia, Brazil, China, India, and Japan represented as individual nations, and nations deemed less relevant to the analysis grouped together in regions that might capture, for example, all remaining Asian or South American countries. Similar to sectoral aggregation, regional aggregates are treated as a single entity when parameter values are assigned. 12.4.2.2. Representation of Land A wide range of models have been applied to estimate the LUC emissions from biofuels. Among the computable general equilibrium (CGE) models used are versions of  Purdue University’s Global Trade Analysis Program (GTAP) model [Hertel et  al., 2010; Banse et  al., 2011;

Taheripour et  al., 2013], the International Food Policy Research Institute’s MIRAGE‐BioF model [Laborde and Valin, 2012], and MIT’s Emissions Prediction and Policy Analysis (EPPA) model [Melillo et al., 2009]. Several PE models have been used as well, including Iowa State University’s FAPRI‐CARD model [Elobeid et al., 2012], Texas A&M University’s Forestry and Agricultural Sector Optimization Model with Greenhouse Gases (FASOM‐ GHG) [Beach et  al., 2012], IIASA’s Global Biosphere Management Model (GLOBIOM) [Havlík et  al., 2011; Valin et al., 2015], the European Commission’s Common Agricultural Policy Regionalised Impact (CAPRI) model [Britz and Hertel, 2011], the Global Change Assessment Model (GCAM) [Wise et al., 2015], and others. To estimate emissions from biofuel‐induced LUC requires assumptions about the types of land uses affected. Models represent land use and land cover with different levels of detail, and the choice of land uses to represent obviously limits the possibilities for land conversion. A comparison of a few of these models (GTAP‐BIO‐ADV, MIRAGE‐BioF, and GCAM) shows the range of different representations implemented. Although these models differ in many respects, here we compare only their choices of land classes. The GTAP‐BIO‐ADV model, which has been used to estimate LUC emissions for the California Low‐Carbon Fuel Standard, represents only commercial forestry, livestock grazing, about 20 crops, and an intermediate land use referred to as “cropland‐pasture” whose status is, by definition, anywhere between cropland and livestock pasture [Plevin et al., 2015]. The MIRAGE‐BioF model, which has been used to inform European biofuel policy, represents primary forest, commercial forest and pasture, natural grassland and savannah, 11 crops, and “other” arable land. The Global Change Assessment Model (GCAM), a PE model of the agriculture, forestry, and energy sectors, which represents commercial and noncommercial forest and pasture, grassland, shrubland, a dozen food crops, bioenergy crops, and “other” arable land. Consider, for example, that GTAP‐BIO‐ADV does not represent noncommercial forest, pasture, shrubland, or grassland. All land conversions must come from commercial forestry, livestock, or cropland (the only land types available). Notably, in GCAM the noncommercial land types are the primary source of land conversion in response to increased biofuel production [Wise et al., 2015]. In addition to the types of land uses represented, models differ in how they represent competition among land uses. In GTAP‐BIO‐ADV, for example, forestry, pasture, and cropland compete directly with one another, whereas in GCAM, grassland/shrubland, forest, and cropland compete directly, and the result of this competition competes with pasture. In both models, all crops compete directly against one another (constrained by suitable growing locations). In MIRAGE‐BioF, some

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crops are directly substitutable with one another but less substitutable with rice. Agricultural land competes first with pasture, and this combination of managed land types competes with managed forest. Another important determinant of where LUC occurs is the model’s representation of trade. Models such as GCAM, which assume an integrated world market for all commodities, tend to distribute LUC more broadly than do models such as GTAP‐BIO‐ADV and MIRAGE‐BioF, which differentiate imports from domestic production. Naturally, these different representations of land use types, land competition, and trade produce different outcomes in terms of the magnitude and location of LUC, and in the emissions therefrom. Importantly, all of these models are essentially cartoon representations of the global economy. They represent a world with perhaps a few dozen products, a few dozen regions, a small number of distinct land classes, and dynamic behavior represented by historical statistical relationships drawn from a small number of studies of a few regions of the world. 12.4.2.3. Limitations of LUC Models It is even difficult to know when changes to a global economic model should be treated as improvements. One can always add more detail to a model, but to know whether this improves model veracity requires an ability to compare the model with and without the change with some benchmark of “truth,” but no such reliable benchmark exists. The quantity of interest (the net global change in CO2‐equivalent GHG emissions induced by an increase in biofuel production) is unobservable, so we don’t know which model result is closest to the “truth.” Nor is it possible to know if adjusting one aspect of a complex model moves results closer to the truth or if it eliminates a counterbalancing error, moving the results further from the truth. Ultimately, the problem is not that the models of LUC emissions lack detail but that models of open systems cannot be verified [Oreskes et al., 1994]. Moreover, economic relationships are nonstationary [Scher and Koomey, 2011], rendering (at least some) past estimates of supply and demand elasticities increasingly unrepresentative of the evolving reality. In his book on general equilibrium, Truman Bewley [2007] suggests that “it would surely be unwise to elaborate the model in order to simulate an entire economy in detail with the hope of making accurate predictions. Such simulations would require radical revision of the standard general equilibrium model since it excludes many important aspects of reality, such as externalities, imperfect markets, absence of certain markets, expectation formation, increasing returns to scale, inflexible prices, and lack of market clearance.” He concludes that “[s]uccessful simulations use reasonably simple models to give rough estimates.” Considering that estimates of LUC emissions require projections of the quantity, location, and type of

LUC (about which we can produce only rough estimates), there is little value in coupling global economic models with highly detailed emissions accounting models, as these can produce only specious precision, not accuracy. 12.5. CONCLUSION The climate effects of a bioenergy program are unobservable and difficult to estimate reliably [Delucchi, 2010; Plevin et al., 2010; van der Voet et al., 2010; McKone et al., 2011]. In principle, the climate effects of a program should be calculated as the difference in the estimated value of some climate metric in worlds with and without the program. These effects include many phenomena that are difficult to quantify owing to, inter alia, the complexity and heterogeneity of natural systems; the interactions of bioenergy programs with global agriculture, land, and energy markets; and the global nature of the primary greenhouse gases. Indeed, years of study of the effects of bioenergy has increased, rather than decreased, the uncertainty, as previously excluded climate‐related factors have been uncovered. As George Box famously said, “[A]ll models are wrong, but some are useful” [Box and Draper, 1987]. All models are simplifications of the real world, involving generalizations, distortions, and deletions. It is neither possible nor desirable to build a “complete” model of the world, so it is always possible to correct “one more” feature of any model. Suggestions by some observers that models of LUC have neglected one feature or another are generally arguing not for “better science” but for changes that move model results in a preferred direction. Unfortunately, because estimating LUC emissions requires comparison to a hypothetical counterfactual, we cannot be certain that any “improvement” to a model moves the overall results closer to those that would occur in the real world. Despite the challenges, attempts to estimate the climate effects of expanding biofuel production have generated important information. First, many observers now recognize that the attributional LCA method fails to account for important environmental effects induced outside the supply chain. Fuel ratings based exclusively on ALCA, such as in the EU and BC fuel standards, do not represent the effect of using the fuel. ALCA is not a change‐based model: it counts emissions from all processes within the supply chain, including those that don’t change, and excludes processes outside the supply chain that do change [Tillman, 2000]. Second, we have learned that even consequential LCA is no panacea: although it is the right approach conceptually, it produces estimates that are inescapably scenario dependent, subjective, and of untestable accuracy [Plevin et  al., 2014; Plevin, 2016]. The range of results from different models and the presence of many forms of uncertainty and model simplification suggest that models of LUC emission intensity (and therefore of the climate

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effects of land‐based biofuels, generally) should be treated as illustrative rather than accurate. Most importantly, we have learned that the climate effects of land‐competitive bioenergy are more difficult to predict than those for feedstocks that do not require additional land, such as waste and residues. However, even wastes and residues must be subjected to change analysis to compare projected outcomes with alternative fates. For example, “mining” paper from a dry landfill is equivalent to mining fossil carbon: in both cases, carbon that would otherwise have remained sequestered is released to the atmosphere as CO2. Differentiating the risks associated with different bioenergy systems provides useful information to policymakers. Even without an accurate prediction of the effects of a bioenergy system, a policymaker may prefer low‐risk mitigation strategies that might achieve lower reductions over a high‐risk strategy offering potentially larger reductions but with a risk of backfiring, that is, increasing emissions. A potentially fruitful direction for future research would be to develop tools and techniques to assist decision makers in coping with the type of ambiguity and risk inherent in bioenergy analyses. Analyses should focus on characterizing the range of possible outcomes, the main drivers of these outcomes, and the potential for policy to help avoid the most undesirable outcomes. Attempting to put an ever‐finer point on deterministic estimates from bioenergy models would be to misunderstand the nature of the uncertainties involved. In summary, our ability to estimate the climate change mitigation effects of many forms of bioenergy remains quite limited, and there is little reason to expect great improvement given the challenges of modeling complex, nonstationary, global environmental and human behavioral systems.

APPENDIX 12.A. ESTIMATING THE CLIMATE EFFECTS OF BURNING BIOMASS In this section, we provide the data used to produce Figure  12.1, showing the climate effects of burning bio­ mass as it might occur during land clearing. Table 12.A.1 lists values for 20 and 100 year time horizons for several GHGs and aerosols emitted during biomass combustion. Note that the effects of aerosols are more localized than are the effects of well‐mixed GHGs: conversion to CO2‐ equivalent masks these regional differences. The GWPs of aerosols are also comparatively uncertain. Table  12.A.2 shows the emissions of these gases and aerosols resulting from burning savanna and grassland versus tropical forest, converted to CO2‐equivalents and summed. Figure  12.1 presents these results graphically for tropical forest.

Table 12.A.1 CO2‐Equivalent Global Warming Potentials for 20 and 100 Year Time Horizons Species GWP‐100 GWP‐20 Source CO2 CO CH4 NMHC NOX N2O

1 2.7 25 8 −1.2 298

SO2 BC OC

−40 680 −69

1 10 72 8 −1.2 289 −140 2200 −240

IPCC AR4 [Forster et al., 2007] Brakkee et al. [2008] IPCC AR4 [Forster et al., 2007] Brakkee et al. [2008] Brakkee et al. [2008] IPCC AR4 [Forster et al., 2007] Fuglestvedt et al. [2010] Bond and Sun [2005] Fuglestvedt et al. [2010]

BC = black carbon; GWP = global warming potential; NMHC = nonmethane hydrocarbons; OC = organic carbon.

Table 12.A.2 Emissions Factors for Trace Gases and Aerosols for Savanna and Tropical FOREST fires, in Mass of Dry Matter (g kg−1) [Andreae and Merlet, 2001] and as CO2‐Equivalents (g CO2e kg−1) for 20 and 100 Year Time Horizons Using IPCC AR4 Global Warming Potentials [Forster et al., 2007] Savanna and grassland g kg−1 dm Species CO2 CO CH4 NMHC NOX N 2O SO2 BC OC Total

EF [Andreae and Merlet, 2001] 1613 65 2.3 3.4 3.9 0.21 0.35 0.48 3.4

Tropical forest

g CO2e kg−1

g kg−1 dm

GWP‐100

GWP‐20

1613 176 58 27 −5 63 −14 326 −235 2009

1613 650 166 27 −5 61 −49 1056 −816 2703

EF [Andreae and Merlet, 2001] 1580 104 6.8 8.1 1.6 0.2 0.57 0.66 5.2

g CO2e kg−1 GWP‐100

GWP‐20

1580 281 170 65 −2 60 −23 449 −359 2220

1580 1040 490 65 −2 58 −80 1452 −1248 3354

BC = black carbon; dm = dry matter; GWP = global warming potential; NMHC = nonmethane hydrocarbons; OC = organic carbon.

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Sitch, S., P. M. Cox, W. J. Collins, and C. Huntingford (2007), Indirect radiative forcing of climate change through ozone effects on the land‐carbon sink, Nature, 448(7155), 791–794. Six, J., C. Feller, K. Denef, S. M. Ogle, J. C. de Moraes Sa, and A. Albrecht (2002), Soil organic matter, biota and aggregation in temperate and tropical soils  –  Effects of no‐tillage, Agronomie, 22(7–8), 755–776. Snyder, C. S., T. W. Bruulsema, T. L. Jensen, and P. E. Fixen (2009), Review of greenhouse gas emissions from crop production systems and fertilizer management effects, Agriculture, Ecosystems and Environment, 133(3–4), 247–266. Sorensen, C. D., A. J. Finkral, T. E. Kolb, and C. H. Huang (2011), Short‐ and long‐term effects of thinning and prescribed fire on carbon stocks in ponderosa pine stands in northern Arizona, Forest Ecology and Management, 261(3), 460–472. Stoft, S. (2010), Renewable fuel and the global rebound effect, Global Energy Policy Center, Berkeley, p. 19. http://papers. ssrn.com/sol3/papers.cfm?abstract_id=1636911 (accessed 22 July 2017). Suh, S., and Y. Yang (2014), On the uncanny capabilities of consequential LCA, International Journal of Life Cycle Assessment, 19, 1179–1184. Taheripour, F., T. W. Hertel, and J. Liu (2013), The role of irrigation in determining the global land use impacts of biofuels, Energy, Sustainability and Society, 3(1), 4. Thompson, W., J. Whistance, and S. Meyer (2011), Effects of U.S. biofuel policies on U.S. and world petroleum product markets with consequences for greenhouse gas emissions, Energy Policy, 39(9), 5509–5518. Tillman, A.‐M. (2000), Significance of decision‐making for LCA methodology, Environmental Impact Assessment Review, 20(1), 113–123. Tokgoz, S., and D. Laborde (2014), Indirect land use change debate: What did we learn? Current Sustainable/Renewable Energy Reports, 1(3), 104–110. UK RFA (2009), The renewable transport fuel obligations order 2007 (as amended). http://webarchive.nationalarchives. gov.uk/20101218044624/http://www.legislation.gov.uk/ uksi/2007/3072/contents/made (accessed 22 July 2017). USEPA (2010), Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis, U.S. Environmental Protection Agency, Washington, DC, p. 1120. http://www.epa.gov/otaq/ renewablefuels/420r10006.pdf (accessed 22 July 2017). Valin, H., D. Peters, M. van den Berg, S. Frank, P. Havlik, N. Forsell, C. Hamelinck, J. Pirker, A. Mosnier, J. Balkovic, E. Schmid, M. Dürauer and F. di Fulvio (2015), The land use change impact of biofuels consumed in the EU: Quantification of area and greenhouse gas impacts, IIASA, E4Tech, and Ecofys. https://ec.europa.eu/energy/sites/ener/files/documents/

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13 Lost Momentum of Biofuels: What Went Wrong? Govinda Timilsina

ABSTRACT The momentum of biofuel expansion seen during the decade of 2000 has not continued since 2010 due to several factors. The concerns surrounded with food vs. fuel debate and indirect land­use change were the main factors which discouraged policy makers not to introduce further policies to support biofuels. In some cases, existing policies were also discontinued. Global financial crises and lower oil prices have also contributed. Since support policies cannot be continued forever, the only way for further expanding biofuels is technological breakthrough in second generation biofuel technologies that substantially lowers their costs making them competitive with their petroleum counterparts. This chapter presents an overview of global biofuel markets and factors that adversely influences the market in the recent past. 13.1. INTRODUCTION During the decade of 2000, production of biofuels surged. Production of ethanol increased by almost three­ folds over the period of 6 year between 2004 and 2010 from 31 billion liters in 2004 to 86 billion liters in 2010. Production of biodiesel during the same period increased even at a higher rate, more than eight times, from 2.3 bil­ lion liters in 2004 to 19 billion liters in 2010 [REN21, 2016]. However, contrary to many projections made before 2010, the production of biofuels got stagnated and even decreased since 2010 (see Figure  13.1). While total biofuel production in 2011 remained stagnant at the level of 2010, it decreased in 2012 by about 2% from the 2011 production level. While the production bounced back in 2013 and has been increasing gradually, the recent rates at which production increases are much smaller compared to those observed a decade ago (see Figure 13.2). Before 2010, government policies (especially targets and mandates) were on rise, and the investments, Development Research Group, World Bank, Washington, District of Columbia, USA

particularly from the private sector, were pouring in the biofuel industry in response to the policies. Thus, the biofuel industry gained a momentum, and all stake­ holders, including governments, industry, and research community, were expecting continuation of the situation with the high growth rates of biofuel production. Contrary to this belief, biofuel production growth substantially dropped. Why has the momentum gained by the biofuels in the decade of 2000 lost? Were the policies aimed to support biofuels not continued? Has the fall in oil prices also led  to fall of biofuel market? Has the food‐security concerns raised during the time of global food crisis (2007–2008) braked the expansion of biofuels? Has the environmental concerns, particularly the indirect land use change (ILUC) debate, contributed to the loss of momentum of biofuel industry? This chapter presents qualitative discussions that might help explain the slow growth of biofuel production observed in recent years, mainly after 2010. The objective of the chapter is to highlight possible factors that have contributed to slow down the growth of global biofuel expansion since 2010. It will briefly discuss various factors instead of going deeply into a particular

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 181

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BIOENERGY AND LAND USE CHANGE 140 Biodiesel 120

Ethanol

Billion liters

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Figure 13.1 Production trends of biofuels at the global level.

60%

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Figure 13.2 Growth rates of biofuel production at the global level.

factor. There exists much literature dealing with each factor in depth. Interested readers may want to refer to several existing studies, including Timilsina [2012] and various chapters in Timilsina and Zilberman [2014]. This chapter does not aim to compare positive and negative impacts of biofuels; instead, it attempts to highlight the potential factors that might have constrained the expan­ sion of biofuels since 2010. This chapter is organized as follows. Section 13.2 briefly discusses the biofuel markets before and after the year 2010, followed by a discussion on biofuel policy change after the global food crisis in 2007–2008. Section  13.4 highlights the potential role of ILUC debate in biofuel policy landscape, followed by the implication of oil price collapse in the biofuel market. Section  13.6 highlights some other factors, such as the blending wall and lower demand for petroleum products globally, followed by a discussion on the failure of delivery of second‐generation

biofuels despite policy refocus on them. Finally, key con­ clusions are drawn in section 13.8. 13.2. INVESTMENT ON BIOFUEL PRODUCTION CAPACITY BEFORE AND AFTER 2010 Figure 13.2 indicates that the growth of production of biofuels was much higher before 2010 compared to the growth after 2010, with exception of biodiesel in 2013 when the production increase of biodiesel bounced back. A good indicator of change in biofuel production overtime would be the change in investment on biofuel production capacity. Since consistent database on biofuel production capacity is not available, we can use cumulative investment on biofuel industry as a proxy of production capacity. The cumulative investment at the very beginning (2004 and 2005 here) may not represent the production capacity as there might be some capacity already available due to

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140 Cumulative investment

Production

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Figure 13.3 Indices (value for year 2010 = 100) of total biofuel production capacity (represented by cumulative investments) and annual biofuel production.

Billion US$ (nominal price) 30 25 20 15 10 5 0 2003

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Figure  13.4 Indices of total biofuel production capacity (represented by cumulative investments) and annual biofuel production.

investments prior to year 2004 for which we do not have data. Figure 13.3 presents indices of cumulative investment on biofuels (used as proxy for biofuel production capacity) and production of biofuels for the 2004–2015 period. The graph illustrates that production capacity increased rapidly during the 2004–2008 period (just before the global food crisis). The annual average growth rate of biofuel production capacity for the 2004–2010 period was 76%. This number is overestimated as the cumulative investment of year 2004 accounts for annual investment of that year owing to lack of data on annual investment prior to 2004. The annual average growth rate of produc­ tion for the same period is 21%. The corresponding annual average growth rate numbers for the 2010–2015 period are

manyfold smaller than those for the 2004–2010 period. Figure 13.4 better explains how the annual investments on biofuel industry are declining after 2006. One might wonder whether it is only the biofuels whose investments dropped or this is the trend for all renewable energy sources. Figure 13.5, which plots annual investment indices (value for year 2010 = 100) for all renewable energy technologies, helps answer this question. As illustrated in Figure 13.5, annual investments on solar and wind energy sources continuously increase except in 2011 and 2012, whereas annual investments on the biofuel industry decline continuously since 2006. Annual investments in other bio­ mass industries and other renewable energy technologies, particularly small hydro, decline continuously since 2010.

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BIOENERGY AND LAND USE CHANGE 300

250

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Wind

Biofuels

Others*

Other biomass

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50

0 2004

2005

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Figure 13.5 Indices representing trends of annual investments in various renewable energy technologies (value for year 2010 = 100). Source: The author’s calculation is based on data from REN21 [2016]. (See insert for color representation of the figure.)

13.3. IMPACTS OF GLOBAL FOOD CRISIS ON BIOFUEL POLICIES One of the key factors that contributed to the lost momentum of biofuels was the 2007–2008 global food crisis, resulting from the price spikes of food commodities. Prices of major food commodities started to rise in 2001 after 25 years’ stagnant or declining trends since 1975 [Trostle et al., 2011]. Many experts and institutions blamed biofuels for the crisis, and a fierce food versus fuel debate ignited [Timilsina, 2012]. Although the blame could not be substantiated through rigorous quantitative analysis (A large number of studies such as Zilberman et al. [2013] and Timilsina et al. [2012] carried out rigorous research and found that the biofuels did not play a big role in causing the global food crisis.), the wider coverage of the issue by media and the opinion expressed by prominent experts and leaders of international organizations (Timilsina [2012] accounts for some of the arguments expressed for and against biofu­ els during the food crisis.) certainly influenced the policy­ makers. As a result, policies that had earlier provided incentives to crop‐based biofuels were either withdrawn, reduced, or no such policies introduced thereafter. For example, China banned use of crops, particularly corn, for biofuel production although the country has set an ambi­ tious plan to supply 15% of the total transportation energy through biofuels by 2020 [Wang and Tian, 2011]. The same was followed by many sub‐Saharan African countries, such as Angola, Zambia, and Senegal. Brazil introduced a regu­ lation to limit use of crops for biofuel production through an agroecological zoning, which puts a ceiling that the total land with sugarcane plantation should not exceed 7.5% of

the total cultivated land in the country [Timilsina, 2012]. The United States has capped the use of corn to produce ethanol at 15 billion U.S. gallons annually starting from 2015 although the national biofuel mandate is expanded to 36 billion U.S. gallons per year in 2022. In India, the biofuel programs and policies are intended for nonfood by‐prod­ ucts such as molasses and nonedible feedstock, such as Jatropha. Through these policy changes, production of bio­ fuels using crops was discouraged in many countries.

13.4. ILUC DEBATE AND CHANGE IN BIOFUEL POLICIES Another factor that discouraged investment in biofuels, particularly crop‐based biofuels, was the indirect land use change (ILUC) debate. This debate started when biofuel production was rapidly expanding in response to government policies to support biofuels. Many govern­ ments at the national and subnational levels in both indus­ trialized and developing countries introduced financial instruments (e.g., various types of subsidies) and regulatory instruments (biofuel mandate and targets) to support large‐ scale expansion of biofuels. One of the main arguments these governments used to defend the supporting policies was biofuels’ potential contribution to climate change miti­ gation through substitution of petroleum products with ethanol and biodiesel. However, some studies [e.g., Searchinger et al., 2008; Danielsen et al., 2009] alerted that biofuels’ potential role in mitigating climate change could be illusive because the GHG mitigation it caused through fossil fuel substitution would be smaller than the GHG

LOST MOMENTUM Of BIOfUELS

emissions released during the production of feedstocks; particularly more and more natural forest and pasturelands would be cleared for the plantation of biofuel feedstocks to meet the expanded demand of biofuels. In fact, it is claimed that demand for biodiesel in EU countries resulted from EU biofuel directories caused massive deforestation in Indonesia and Malaysia because of clearing of rain forests for the plantation of palm, the main feedstock of biodiesel in Southeast Asia. The Union of Concerned Scientists have produced fact sheets explaining how the expansion of palm plantation has caused deforestation and GHG emission. Please see UCS [2013] for an example of the fact sheet. Similarly, there exists a threat of forest and pastureland clearance in Brazil to meet biofuel demand in other coun­ tries, and ultimately large‐scale expansion of biofuel demand at the global level could cause more GHG than it reduces through fossil fuel substitution [see e.g., Searchinger et al., 2008; Danielsen et al., 2009; Timilsina and Mevel, 2012]. These indirect land use impacts of biofuels, also pop­ ularly known as ILUC, created a big concern. Several gov­ ernments started further scrutinizing biofuels, especially their claim for climate change mitigation. For example, the European Union changed its 2009 Renewable Energy Directive, which targeted to meet 20% of the total final energy demand in the European Union as a whole through renewable sources by 2020 and required renewable sources (mainly biofuels) to meet 10% of the total transport‐sector fuel consumption in each member state by 2020. The 2009 directive was the modified version of the earlier similar directive introduced in 2003, which required 10% of the total fuel consumed in  road transportation in each of its member states to be biofuels by 2010. Although the 2009 directive addressed potential leakage of GHG emissions from biofuel supply chains through the two “sustainability criteria,” it was still  criticized for not addressing the ILUC impacts that could increase GHG emissions through land use changes in some parts of the world while meeting biofuel mandates of other parts of the world [Timilsina, 2013a]. First, a biofuel, whether domestically produced or imported, must have 35% GHG saving potential from bio­ fuel plants starting operation until 2016, 50% for the those starting operation in 2017, and 60% for those starting oper­ ation since 2018, and secondly, production of biofuels must meet a long list of land use criteria so that emission leak­ ages through direct land use change could be minimized [Timilsina, 2013a]. As a result the European Union changed its biofuel provisions under its 2009 renewable energy direc­ tive in 2013, suggesting that biofuels produced from crop‐ based feedstock should be limited to meet only a half (i.e., 5%) of  the biofuel targets set under the 2009 renewable energy directive; the remaining half of the target should be met through biofuels produced using noncrop feedstocks, particularly second‐generation biofuels such as agricultural and forest residues, municipal solid wastes, waste fats, and

185

microalgae [Timilsina, 2013a]. The latest EU directive on biofuels [EU, 2015], however, allows the member states to meet up to 7% (out of 10%) of their biofuel mandates through crop‐based or first‐generation biofuels. As Timilsina [2013a] argues, the European Union’s revision of its 2009 renewable energy directives certainly put a brake on the momentum of global expansion of biofuels. 13.5. OIL PRICE AND BIOFUEL MARKET Literature is divided on the potential impacts of world oil price on the expansion of biofuels. Several studies that simulate the long‐term markets of biofuels [e.g., Fischer et al., 2009; Timilsina et al., 2012] consider that, in the long run, biofuels will substitute petroleum products for two reasons. First, existing biofuel mandates and targets suggest that a certain fraction of petroleum products would be substituted with biofuels. For example, the 10% ethanol blending mandates in several states in the United States mean substitution of 10% gasoline demand (volu­ metric terms) with ethanol. These studies also imply that increasing oil prices also cause price‐induced substitution of petroleum products with biofuels. For example, Timilsina [2014] and Timilsina et al. [2011] show that a 25% increase in oil price from the baseline would increase global biofuel production by 20.4% in 2020. However, others argue that there exists correlation between prices of oil and prices of biofuels as the increased prices of oil get transmitted to prices of biofuel feedstocks [see e.g., Ciaian and Kancs, 2011; Rajcaniova and Pokrivcak, 2011; Busse et al., 2012; Mallory et al., 2012]; therefore, the pos­ sibility of price‐induced substitution of petroleum prod­ ucts with biofuels is unlikely or highly limited. Figure  13.6 plots petroleum price indices and biofuel production indices over the last decade. The figure illus­ trates that there is no relation between the change of oil price and the change in global biofuel demand. Although the oil prices and production of biofuels are not corre­ lated, the recent drops in oil price would certainly dis­ courage the potential biofuel investors to expand the production capacity. 13.6. DEMAND FOR PETROLEUM PRODUCTS AND PRICE COMPETITIVENESS OF BIOFUELS A comparison of wholesale prices of biofuels and their petroleum counterpart, gasoline and diesel are the main petroleum counterparts of ethanol and diesel, respec­ tively (see Timilsina [2013b] for the illustration), shows that biofuels cannot compete with their petroleum coun­ terparts on energy‐equivalent basis. When biofuel prices are adjusted with energy contents of their petroleum counterparts, energy content of ethanol is about 30% lower than that of gasoline. Similarly, energy content of

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BIOENERGY AND LAND USE CHANGE 180 160

Diesel price

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Figure 13.6 Indices of petroleum prices and biofuel production (value for 2010 = 100).

biodiesel is 12% lower than that of diesel. Biodiesel cannot compete with diesel even on volumetric basis. While there are some exceptions in Brazil in some years for ethanol as production of sugarcane‐based ethanol in Brazil is cheaper compared to that in the rest of world, the lack of price competitiveness of biofuels holds true in most other countries. Even in Brazil, where production cost of sugarcane‐based ethanol is low, the plant‐gate price of ethanol is higher compared to refinery‐gate price of gasoline except in some years after 2009. Although the price gap (i.e., the difference between the prices of biofu­ els and their petroleum counterparts) is decreasing over­ time, until recently when oil price started to plummet, prices of ethanol are higher than that of gasoline [Timilsina, 2013b]. The situation is  much worse in the case of biodiesel, where prices of biodiesel are much higher than that of diesel. For example, in the European Union, the average wholesale prices of biodiesel are almost twice as high as that of diesel for all years. As discussed in Timilsina [2013b], market‐driven substitution of petroleum products with biofuels is unlikely. The substitution happened so far is all policy driven (i.e., due to blending mandate), and the same would be the case in the near future. This suggests that demand for biofuels is proportional to the demand for their petroleum counterparts as the blending ratio (or so‐ called blending wall) in many countries has remained the same. The global demand for gasoline and diesel has remained stagnant or even decreasing. Rigid blending wall and slow growth of petroleum products obviously result into slow growth of biofuel production. Figure 13.7

shows that the ratio of biofuels to petroleum products has been decreasing since 2010. By 2013, it, however, started again and exceeded the level of 2010. This is due to the demand for petroleum products for transportation in countries where biofuel mandates do not exist. Although a large number of countries have introduced biofuel mandates and targets, those measures are yet to be operational. In fact, biofuel mandates are operational in the United States, the European Union, Brazil, China, and a few other countries. In most other countries, the mandates will be operational starting from year 2020. 13.7. FAILURE OF ADVANCED BIOFUELS’ DELIVERY Due to the controversial issues related to biofuels, such as food versus fuel and ILUC, many countries considered revisions in their already existing and planned biofuel pol­ icies. One of the key elements of the policy changes is pro­ motion of second‐generation or advanced biofuels. For example, the U.S. Renewable Fuel Standard (RFS) set a target of 36 billion gallons of total biofuels by 2022, of which 21 billion (almost 60% of the total target) was set for the second‐generation biofuels (16 billion gallons from cellulosic raw materials and 5 billion gallons of other advanced biofuels). The European Union revised its 2009 directive to limit the contribution of first‐generation bio­ fuels up to 7% (out of 10%) of its renewable fuel mandates for transportation energy [EU, 2015]; the remaining 3% would be expected from second‐generation biofuels and other zero‐carbon energy sources. However, progress in the commercial production of second‐generation biofuels

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2006

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Figure 13.7 Indices showing growth of petroleum products, biofuels, and ratio of biofuels to petroleum products for road transportation.

Brazil, 126, 9%

European Union, 131, 9% United States, 490, 35%

Canada, 303, 22%

China, 340, 25%

production of biofuels in 2015, which was 133 billion liters [REN21, 2016]. Despite the dedicated policies to promote second‐ generation biofuels, particularly mandates or targets assigned to the second‐generation biofuels, production of second‐generation biofuels is very low and would still remain small in the near future. This is due to the fact the production capacity of second‐generation biofuels under construction is much smaller than its demand implied by the existing mandates and targets. For example, total installed capacity of second‐generation biofuels, including the capacity planned and under construction in the United States as of year 2015, is 1.39 billion liters, whereas its target for 2022 as specified by the Renewable Fuel Standard is 21 billion liters (out of 36 billion liters of total biofuels targeted in that year). It is very unlikely that the required production capacity of second‐generation biofuels will be available to meet the demand in 2022 in the United States. 13.8. KEY CONCLUSIONS

Figure  13.8 Installed capacity of second‐generation biofuels as of 2015 (in million liters and percentage). (See insert for color representation of the figure.)

is very slow because the costs of their production are much higher than that of first‐generation biofuels and, obviously, their petroleum counterparts (i.e., gasoline and diesel). Figure 13.8 indicates that global installed capacity of second‐generation biofuels stands at 1.4 billion liters as of year 2015 [UNCTAD, 2016]. Of which 66% is located in OECD countries. The 1.4 billion liter installed capacity is much smaller (about only 1%) as compared to the total

Biofuels exhibited rapid expansion during the decade of 2000. Two factors are responsible for this high growth of biofuels. First, the expansion started at virtually zero base; growth rates look bigger when the base is small despite having small increase in the absolute value. Second, the expansion was driven by government policies, and inves­ tors responded to the financial and regulatory policy instruments introduced by governments. However, the momentum of the expansion did not continue long; expansion of biofuels substantially slowed down by the year 2010 and remained almost stagnant in the following two years. However, the recession in the growth of biofuels

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BIOENERGY AND LAND USE CHANGE

ceased, and it started to reemerge in 2013. Although the slow growth of biofuels surprises many stakeholders who earlier were expecting the biofuel momentum to continue, it might be the normal course for any emerging technol­ ogies whose growth is supported by government policies and the technology fails to compete in the market in the absence of such policies. Moreover, biofuel policies and the resulted expansion were criticized owing to the food versus fuel debate and biofuels’ controversial role in miti­ gating global climate change. Some policy supports have been either withdrawn or not further strengthen. On the other hand, the biofuel sector remained solely dependent on the policy drivers, it could not improve its competitive­ ness with petroleum products, and the competitiveness further worsened because of recent fall in oil prices. This situation did not allow the market to play a role in expand­ ing biofuels; the situation does not seem to improve in the near future. While biofuels would still continue with this slow growth as long as support policy measures are in place, their rapid expansion for a long term may not hap­ pen as elaborated by Timilsina and Shrestha [2011] because the room for biofuels to be cost competitive with petro­ leum products is limited. REFERENCES Busse, S., B. Brümmer, and R. Ihle (2012), Price formation in the German biodiesel supply chain: A Markov‐switching vector error correction modeling approach, Agricultural Economics, 43, 545–560. Ciaian, P., and A. Kancs (2011), Interdependencies in the energy–bioenergy–food price systems: A cointegration anal­ ysis, Resource and Energy Economics, 33, 326–348. Danielsen, F., H. Beukema, N. D. Burgess, F. Parish, C. A. Bruhl, P. F. Donald, D. Murdiyarso, B. Phelan, L. Reijnders, M. Struebig, and E. B. Fitzherbert (2009), Biofuel planta­ tions on forested lands: Double jeopardy for biodiversity and climate, Conservation Biology, 23(2), 348–358. European Union (EU) (2015), Directive (EU) 2015/1513 of the European Parliament and of the Council of 9 September 2015 amending Directive 98/70/EC relating to the quality of petrol and diesel fuels and amending Directive 2009/28/EC on the pro­ motion of the use of energy from renewable sources. http://eur‐ lex.europa.eu/legal‐content/EN/TXT/?qid=1453992836873 &uri=CELEX:32015L1513 (accessed 21 July 2017). Fischer, G., E. Hizsnyik, S. Prieler, M. Shah, and H. van Velthuizen (2009), Biofuels and Food Security, OPEC Fund for International Development, Vienna. International Energy Agency (IEA) (Various Annual Issues between 2006 and 2015), Energy Balances of Non‐OECD Countries. IEA, Paris. Mallory, M., S. H. Irwin, and D. J. Hayes (2012), How market efficiency and the theory of storage link corn and ethanol markets, Energy Economics, 34, 2157–2166.

Rajcaniova, M., and J. Pokrivcak (2011), The impact of biofuel policies on food prices in the European Union, Journal of Economics, 5, 459–471. Renewable Energy Network for Twenty First Century (REN21) (2016), Global Status Reports of various years between 2005 and 2016, REN21 Secretariat, Paris. Searchinger, T., R. Heimlich, R. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, and T.‐H. Yu (2008), Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land‐use change, Science, 319, 1238–1240. The Union of Concerned Scientists (UCS) (2013), Palm oil and global warming: Fact sheet. http://www.ucsusa.org/sites/ default/files/legacy/assets/documents/global_warming/palm‐ oil‐and‐global‐warming.pdf (accessed 21 July 2017). Timilsina, G. R. (2012), Biofuels: The food versus fuel debate, CAB Reviews, 7(36), 1–8. Timilsina, G. R. (2013a), How will the EU’s cap on crop‐ based biofuels impact the future of biofuels? Biofuels, 4(2), 139–141. Timilsina, G. R. (2013b), Biofuels in the long‐run global energy supply mix for transportation, Philosophical Transactions of the Royal Society A, 372–392. December. Timilsina, G. R. (2014), Oil price and biofuels, in The Impacts of Biofuels on the Economy, Environment, and Poverty: A Global Perspective, edited by G. R. Timilsina and D. Zilberman, Springer, New York. Timilsina, GR and A Shrestha (2011). How much hope should we have for biofuels? Energy, Vol 36, pp. 2055–2069. Timilsina, G. R., and S. Mevel (2012), Biofuels and climate change mitigation: A CGE analysis incorporating land‐use change, Environmental and Resource Economics, 55, 1–19. Timilsina, G. R., and D. Zilberman (2014), The Impacts of Biofuels on the Economy, Environment, and Poverty: A Global Perspective, Springer, New York. Timilsina, G. R., S. Mevel, and A. Shrestha (2011), Oil price, biofuels and food supply, Energy Policy, 39(12), 8098–8105. Timilsina, G. R., J. C. Beghin, D. van der Mensbrugghe, and S. Mevel (2012), The impacts of biofuel targets on land‐ use change and food supply: A global CGE assessment, Agricultural Economics, 43, 313–330. Trostle, R., D. Marti, S. Rosen, and P. Westcott (2011), Why Have Food Commodity Prices Risen Again? U.S. Department of Agriculture, Washington, DC. United Nations Conference on Trade and Development (UNCTAD) (2016), Second Generation Biofuel Markets: State of Play, Trade and Developing Country Perspectives, UNCTAD Secretariat, Geneva. Wang, Q., and Z. Tian (2011), Biofuels and the policy implica­ tions for China, Asian‐Pacific Economic Literature, 25(1), 161–168. Zilberman, D., G. Hochman, D. Rajogopal, G. Timilsina, and S. Sexton (2013), The impact of biofuels on commodity food prices: Assessment of findings, American Journal of Agricultural Economics, 95(2), 275–281.

INDEX Absolute global temperature change potential (AGTP), 71f Absolute global warming potential (AGWP), 71f Agro‐Ecological Zone (AEZ), 21 land cover data for, 26 Agroecosystems carbon budgets for, 116–19, 117f, 118t crop residues in maintaining SOC in, 115–21 data used to populate simulation models for, 119–20, 120f residue harvesting impact in, 120–21 AGTP. See Absolute global temperature change potential AGWP. See Absolute global warming potential Albedo change, 73–74 ALCA. See Attributional LCA Alfalfa, 104 Annual sugarcane expansion, 43 Attributional LCA (ALCA), 166

modeling market‐mediated effects, 172–73 representation of land, 173–74 evidence and modeling challenges for, 10–12, 11f failure to deliver advancement in, 186–87, 187f feedstock production with LUC, 69–70, 69f flex‐fuel engines using, 55 GHG emission as driver for expansion of, 66, 83 global food crisis impacts on policies for, 184 graphical intuition for emissions from, 157f greenhouse gas emission reduction with, 3 growth rates for global level production of, 182f human history with, 65 ILUC debate and change in policies for, 184–85 investment in production capacity for, 182–83, 183f, 184f land use analysis critical for future of, 4–5 land use change associated with, 4, 125 implications of, 3 limits of LCA, 165–75 literature review for, 19–22 modeling methodologies estimating, 7–10 research of, 5–7, 7f uncertainty in estimates of, 143–51 life cycle analysis for production of, 74–75, 75f, 155 examples, 76t schematic of processes, 85f lost momentum of, 181–88 market‐mediated effects on agricultural production, 171 food consumption, 171 fossil fuel displacement, 171–72 matrix of likelihood compared to severity for, 88f natural capital, impact of, 83–96 combustion process, 92 crop and forest management, 91 feedstock production, 90–91 method and metrics for evaluation of, 93 processing and transportation, 91–92 during stages of bioenergy value chain, 89–92, 89f oil price and market for, 185, 186f price competitiveness of, 185–86, 187f production with fossil fuel price rise, 4 quantifying and regulating indirect emissions of, 155–62, 157f schematic of life cycle assessment of, 85f SOC increase with, 4 soil carbon changes in cropping systems for, 99–109 soil organic carbon with land use effects on, 103–5, 104f as solution to problems, 53

BCAP. See Biomass Crop Assistance Program Bermudagrass, 104 Best management practices (BMPs), 127 Bioenergy advantage of renewability with, 65 agricultural runoff with uncontrolled transition to, 4 biofuel life cycle overview, 75f Brazilian Amazon deforestation with expansion of, 53 certification for, 13–14 challenges in managing indirect emissions from modeling, 158–60 policy challenges, 160–61 verification challenges, 158–60 climate impacts in, 74–79, 75f, 76t, 77f, 165–66 conservation practices for, 125–37 cropland cover, harvested area data for, 22–26, 24f, 25t, 26f demand for, 39–40, 53 economic methods for supply estimation of, 4 ecosystem services, impact of, 83–96 combustion process, 92 crop and forest management, 91 feedstock production, 90–91 method and metrics for evaluation of, 93 processing and transportation, 91–92 during stages of bioenergy value chain, 89–92, 89f effects of climate change on production of, 166 estimating climate effects for use of burning biomass, 175, 175f, 175t economic equilibrium models, 173 mitigation, 172 modeling limitations for, 174

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc. 189

190

INDEx

Bioenergy (cont’d) sources of, 3 sugarcane for, 5 surging production during decade of 2000 for, 181 trends for global level production of, 182f Biogeochemical process, 66, 68t carbon balance in terrestrial ecosystems, 72 land use change impact of, 72–73, 72f nitrous oxide emissions, 72–73, 72f other emissions, 73 Biogeophysical process, 66, 68t albedo change with, 73–74 evapotranspiration change with, 73–74 land use change impact of, 73–74 Biomass Crop Assistance Program (BCAP), 13 Biomass Research and Development Act of 2000, 12 Biomass thermal electricity, electricity generation’s impact using, 88t BMPs. See Best management practices Bra Miljoval, 13 Brazil Amazon forest and states of, 54f annual growth rate in crop yield for, 26f cattle herd, sugar cane, and soybean trend regression for, 60t cattle‐ranching expansion in, 55–57, 57f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 24f, 25t cropland cover increase in, 23 geographic distribution of cattle herd in, 57f Goiás sugarcane fields in, 40f, 41–43 harvested area increase in, 24 land use emissions for sugarcane ethanol in, 32t LUC with corn ethanol expansion in, 30t, 34t Mato Grosso do Sul sugarcane fields in, 40f, 41–43 price of pastureland for regions in, 60t second‐generation biofuels in, 187f soybean expansion in, 55–57, 56f soybean planted area, production, and geographic distribution in, 56f spatial economy conditions leading to ILUC in, 53–61 sugarcane ethanol impact of expansion to Cerrado with, 39–49 model testing results for, 31–32, 32t, 35, 35t sugarcane expansion in, 55–57, 55f sugarcane planted area, production, and geographic distribution in, 55f Brazilian Institute of Geography and Statistics (IBGE), 43 Brazilian National Institute for Space Research (INPE), 43 California Air Resources Board (CARB), 29, 31 Land Use Change Assessment report prepared by, 70 Canada annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t LUC with corn ethanol expansion in, 30t, 34t second‐generation biofuels in, 187f Capital expenditure (CAPEX), 86, 95 CAPRI model. See Common Agricultural Policy Regionalized Impact model

CARB. See California Air Resources Board Carbon budgets belowground biomass for, 117, 117f 13 C natural abundance approach to, 117–19, 118t development of, 116–19, 117f, 118t influence of tillage intensity with, 117f modeling carbon turnover for, 119 nonisotopic techniques to, 116–17, 117f preparing soil samples for, 116 residue harvesting’s influence on, 118t root‐to‐shoot ratio with, 117f tillage with, 118t yield zone’s influence on, 118t Carbon Calculator for Land Use Change from Biofuels Production. See CCLUB Carbon capture and storage (CCS), 83 Cattle‐ranching Brazilian geographic distribution of, 57f capital constraints with, 58–59, 59f ILUC with expansion of, 55–57, 57f land rents and displacement with, 57–59, 58f, 59f trend regression for Brazilian, 60t CCLUB (Carbon Calculator for Land Use Change from Biofuels Production), 76t CCS. See Carbon capture and storage CDL. See Cropland Data Layer CEAP. See Conservation Effects Assessment Project CEE. See Central and Eastern European countries Central America annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t LUC with corn ethanol expansion in, 30t, 34t Central and Eastern European countries (CEE) cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t LUC with corn ethanol expansion in, 30t, 34t CfD. See Contracts for Difference CGE models. See Computable general equilibrium models China annual growth rate in crop yield for, 26f cropland cover, harvested area data for, 22, 24f, 25t estimated marginally productive land area in, 104f GHG emissions by, 83 harvested area increase in, 24 LUC with corn ethanol expansion in, 30t, 34t second‐generation biofuels in, 187f CICES. See Common international classification of ecosystem services CIS. See Commonwealth of Independent States CLCA. See Consequential LCA Climate effects, 165 bioenergy use with estimates for burning biomass, 175, 175f, 175t economic equilibrium models, 173 mitigation, 172 modeling limitations for, 174 modeling market‐mediated effects, 172–73 representation of land, 173–74

INDEx Climate impact. See also Greenhouse gas bioenergy and, 165–66 in bioenergy life cycle analysis, 74–79, 75f, 76t, 77f land use change with, 68t, 70–74, 71f, 72f 13 C natural abundance approach, 117–19, 118t Coal electricity generation’s impact using, 88t natural capital and ecosystem services impact from combustion, 94–95 method and metrics for evaluation of, 95 mining and material extraction, 93–94 processing and transportation, 94 during stages of bioenergy value chain, 93–95, 93f COH. See Ratio of crop production over harvested area Common Agricultural Policy Regionalized Impact (CAPRI) model, 173 Common international classification of ecosystem services (CICES), 85–86 Commonwealth of Independent States (CIS) cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t LUC with corn ethanol expansion in, 30t, 34t Computable general equilibrium (CGE) models, 20, 173 Consequential LCA (CLCA), 7, 166 Conservation Effects Assessment Project (CEAP), 127 Conservation practices, 125–37 analytical methods for, 126–27, 127t average annual output in, 130, 130t comparison of dry year and wet year with, 130–32, 133f land management for, 126 land use scenarios using, 128–29, 129f limitations of, 137 results and discussion for, 129–37, 130t, 131f–36f seasonal variability of nutrients and sediment with, 132, 134f study area using, 127–29, 128f, 128t, 129f temporal variability of hydrologic responses with, 134–37, 135f, 136f uncertainty with, 137 yearly distribution with, 130, 131f, 132f Conservation Reserve Program (CRP), 12, 144 Contracts for Difference (CfD), 84 Corn ethanol cropland and harvested area changes due to expansion in, 31t, 34t land use emissions for, 32t LUC with expansion in, 30t, 34t testing results with, 30–31, 30t, 31t, 33–35, 34t Corn stover, 106 Cropland corn ethanol and harvested area changes with, 31t, 34t cover, harvested area data for bioenergy in, 22–26, 24f, 25t, 26f HOL parameter with, 29t land use effects with conversion to, 103 wedge between harvested area and, 33 Cropland Data Layer (CDL), 70 Crop residues soil erosion with removal of, 115 soil organic carbon maintained with, 115–21 use of, 115

191

Crop rotation, oil organic carbon impacted by, 108f CRP. See Conservation Reserve Program Data uncertainty aggregation of classes causing ambiguity and, 147–49, 148t, 149t in estimates of bioenergy‐induced LUC, 143–51 inconsistent classification of LUC data with, 146–47, 147f LULC data versus LULC change in, 145–46 pixel‐to‐pixel and two‐point comparison causing, 149–50, 150t Digital elevation model (DEM), 43 Direct land use change (DLUC), 66 bioenergy development examples for, 69f bioenergy feedstock production with, 69–70, 69f research on, 5–6 DRAX, 84 East Asia cropland changes with corn ethanol expansion in, 31t, 34t LUC with corn ethanol expansion in, 30t, 34t Economic model, 8–9 Economic Research Service (ERS), 146 Ecosystem services (ES) bioenergies impact on, 83–96 combustion process, 92 crop and forest management, 91 feedstock production, 90–91 method and metrics for evaluation of, 93 processing and transportation, 91–92 during stages of bioenergy value chain, 89–92, 89f coal impact on combustion, 94–95 method and metrics for evaluation of, 95 mining and material extraction, 93–94 processing and transportation, 94 during stages of bioenergy value chain, 93–95, 93f comparison and discussion of impacts to, 95–96 defined, 85 electricity generation’s impact with, 88t energy sector impacts to, 85–89, 87t method and metrics for evaluation of, 87–88, 88f, 88t metrics to compare systems for, 88–89 matrix of likelihood compared to severity for, 88f EISA. See Energy Independence and Security Act Emissions Prediction and Policy Analysis (EPPA) model, 173 Energy Independence and Security Act (EISA), 12 Energy Policy Act of 2005, 12 Energy use efficiency (EUE), 89 Environmental Choice, 13 EPPA model. See Emissions Prediction and Policy Analysis model ERS. See Economic Research Service ES. See Ecosystem services EUE. See Energy use efficiency Eugene, 13 EU‐Renewable Energy Directive (RED), 70 European Renewable Energy Directive, 13

192

INDEx

European Union annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t GHG emissions by, 83 land use emissions for rapeseed biodiesel in, 32t LUC with corn ethanol expansion in, 30t, 34t rapeseed biodiesel, model testing results for, 31–32, 32t, 35, 35t second‐generation biofuels in, 187f Evapotranspiration change, 73–74 Extensification, sugarcane expansion effects with, 39–49, 46f, 47f FAO. See Food and Agriculture Organization of United Nations FAPRI. See Food and Agricultural Policy Research Institute FAPRI‐CARD model, 173 Farm and Agricultural Policy Research Institute (FAPRI) model, 70 Farm Bill (2002), 12 FASOM. See Forest and Agriculture Sector Optimization Model FASOMGHG. See Forestry and Agricultural Sector Optimization Model with Greenhouse Gases Flex‐fuel engines, 55 Food and Agricultural Policy Research Institute (FAPRI), 21 Food and Agriculture Organization of United Nations (FAO), 21, 143 cropland cover, harvested area data from, 22 Food versus fuel debate, 6 Forest Brazilian, 54f Brazilian Amazon deforestation with expansion of bioenergy, 53 crop and forest management, 91 Global Forest Change data, 43 Global Forest Watch interactive map, 43 land use effects with conversion to, 103 simulation models of projected future deforestation, 59 Forest and Agriculture Sector Optimization Model (FASOM), 70 Forestry and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG), 173 FOREST‐SAGE model, 10 Forest Stewardship Council (FSC), 13 GCAM. See Global Change Assessment Model German Biofuels Sustainability ordinance, 13 GHG. See Greenhouse gas Global Biosphere Management Model (GLOBIOM), 173 Global Change Assessment Model (GCAM), 173 Global Forest Change data, 43 Global Forest Watch interactive map, 43 Global temperature change potential (GTP), 71 Global Trade Analysis Program (GTAP) model, 173. See also GTAP‐BIO model Global warming potential (GWP), 71, 71f GLOBIOM. See Global Biosphere Management Model

Grassland, land use effects with conversion to, 103 Green‐e, 13 Greenhouse gas (GHG) bioenergy in drive to reduce, 83 bioenergy with emission reduction of, 3 biofuel life cycle overview with emission estimate for, 75f challenges quantifying indirect emissions of bioenergy with, 155–62 as driver for expansion of bioenergy, 66, 83 LCA for estimate emissions of bioenergy, 66, 84 soil carbon changes’ impact on, 99 Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation. See GREET model Green Power, 13 GREET model (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation), 76t GTAP‐BIO model, 19 assumptions in, 21 cropland cover, harvested area data in, 22–26, 24f, 25t, 26f experiments testing, 29 2011 database based, 33–35, 34t, 35t literature review for, 20–22 modifications for, 26–29, 29t results in testing Brazilian sugarcane ethanol, 31–32, 32t, 35, 35t EU rapeseed biodiesel, 31–32, 32t, 35, 35t other biofuel pathways, 31–32, 32t, 35, 35t U.S. corn ethanol, 30–31, 30t, 31t, 33–35, 34t U.S. soybean biodiesel, 31–32, 32t, 35, 35t tuning parameter for HOL and COH ratios in, 29t process of, 28–29, 29t rules with growing cropland in, 27 wedge between cropland and harvested area in, 33 YDEL parameter in, 21, 28–31, 33–34 GTAP model. See Global Trade Analysis Program model GTP. See Global temperature change potential GWP. See Global warming potential HOL. See Ratio of total harvested area over cropland cover Hong Kong, cropland cover, harvested area data for, 22, 24f Hydrologic response units (HRUs), 127 IBGE. See Brazilian Institute of Geography and Statistics IEA. See International Energy Agency IFUC. See Indirect fuel use change effect ILUC. See Indirect land use change India annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t cropland cover in, 24 estimated marginally productive land area in, 104f harvested area increase in, 24 LUC with corn ethanol expansion in, 30t, 34t Indirect fuel use change effect (IFUC), 157 Indirect land use change (ILUC), 15, 66 bioenergy development examples for, 69f bioenergy feedstock production with, 69–70, 69f

INDEx Brazilian spatial economy conditions with, 53–61 capital constraints with, 58–59, 59f cattle herd, sugar cane, and soybean trend regression with, 60t cattle‐ranching expansion with, 55–57, 57f debate and change in policies for bioenergy, 184–85 extensive margin with, 58–59, 59f food versus fuel debate with, 6 land rents and displacement with, 57–59, 58f, 59f net increase in farmland area with, 156 price elasticity effects with, 59, 59f regression models of Brazil’s expanding soybean sector and, 59 research on, 6 simulation models of projected future deforestation, 59 soybean expansion with, 55–57, 56f sugarcane expansion with, 55–57, 55f von Thünen’s locational rent model with, 54, 57–58, 58f, 60 Indonesia annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 24f, 25t cropland cover increase in, 23 harvested area increase in, 24 LUC with corn ethanol expansion in, 30t, 34t INPE. See Brazilian National Institute for Space Research Integrated environmental economic model, 9–10 Intensification, sugarcane expansion effects with, 39, 41–49, 46f, 47f International Energy Agency (IEA) energy consumption estimates by, 85 Japan annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t harvested area decrease in, 24 LUC with corn ethanol expansion in, 30t, 34t Joint Research Centre (JRC), 76t Kyoto Protocol, 84 Land clearing biomass emissions from, 169–70, 169f emissions from soil after, 170 Land cover cropland cover, harvested area data for, 22–26, 24f, 25t, 26f diagram of global, 102f HOL parameter and, 29t LULCC, 67, 143, 145–46 NLCD, 70, 145 Land management, 126 Land use change (LUC) albedo change with, 73–74 bioenergy associated with, 4, 125 implications of, 3 literature review for, 19–22 modeling methodologies estimating, 7–10 research of, 5–7, 7f bioenergy feedstock production with, 69–70, 69f

193

biogeochemical and biogeophysical processes with, 66, 68t biogeochemical impacts on, 72–73, 72f biogeophysical impacts on, 73–74 biomass emissions from land clearing, 169–70, 169f biomass production and, 67–71, 67f, 68t, 69f carbon balance in terrestrial ecosystems with, 72 carbon pools affected by, 168–69 classifications and concept of, 68t climate impacts from bioenergy production with, 65–79 climate impacts with, 68t, 70–74, 71f, 72f CO2 emissions with, 73 conservation practices for, 125–37 corn ethanol expansion with, 30t, 34t cropland cover, harvested area data for, 22–26, 24f, 25t, 26f definition, 67 different uses with, 67f evapotranspiration change with, 73–74 evidence and modeling challenges for, 10–12, 11f five changes from biofuel demand increase for, 19–20 government policies for, 12–14 implications of, 3 inclusion in regulations of, 170–71 inconsistent data classification giving uncertainty for, 146–47, 147f intensive and extensive margins, 19–35 LCA for quantifying, 7 climate impacts in bioenergy, 74–79, 75f, 76t, 77f LCA limits with biofuels and, 165–75 life cycle analysis on, 65–79 literature review for, 19–22 market‐mediated effects on agricultural production, 171 food consumption, 171 fossil fuel displacement, 171–72 matrix of likelihood compared to severity for, 88f measurement of, 70 methane emissions with, 73 modeling methodologies estimating, 7–10 N2O emissions with, 73 nitrous oxide emissions with, 72–73, 72f policy imperatives for, 12–14 rapeseed biodiesel expansion with, 32t research on, 5–7, 7f soybean biodiesel expansion with, 32t sugarcane ethanol expansion with, 32t two processes for impact climate with, 66 uncertainty in estimates of bioenergy‐induced, 143–51 Land use control (LUC), 128 Land use efficiency (LUE), 89 Land use/land cover change (LULCC), 67, 143 data versus change for, 145–46 Land uses response (LUR) conceptual framework for, 41–42 discussion for study of, 48–49 equations representing, 41–42 extensification of sugarcane land as, 39–49, 46f, 47f imagery in study of, 42 intensification of sugarcane land as, 39, 41–49, 46f, 47f measuring, 44, 46f, 47f

194

INDEx

Land uses response (LUR) (cont’d) methods in study of, 42–44, 43t, 45f, 46f results for study of, 44–45, 46f, 47f, 47t statistical logit model of, 47t statistical model of, 43–44, 43t, 45f, 46f statistics summary of explanatory variables for, 43t study area, 42–43 sugarcane ethanol expansion with, 39–49 sugarcane’s model for, 44–45, 47t two alternative, 39 LCA, Life cycle assessment or Life cycle analysis … Life cycle assessment (LCA), 6 biofuels, land use change, and limits of, 165–75 challenges with, 77–79 climate effects of bioenergy assessed with, 165 climate impacts from bioenergy production in, 65–79 climate impacts in bioenergy, 74–79, 75f, 76t, 77f different types of, 7 estimate emissions of bioenergy in, 66 future needs for, 77–79 GHG in, 66, 84 land use changes quantified with, 7 quantifying indirect emissions of bioenergy with, 155–62, 157f schematic of bioenergy processes in, 85f semantics of, 167 LUC. See Land use change; Land use control LUE. See Land use efficiency LULCC. See Land use/land cover change LUR. See Land uses response MA. See Millennium ecosystem assessment Malaysia annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 24f, 25t cropland cover increase in, 23 harvested area increase in, 24 LUC with corn ethanol expansion in, 30t, 34t Middle East cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 25t LUC with corn ethanol expansion in, 30t, 34t Milieukeur, 13 Millennium ecosystem assessment (MA), 85–86 Miscanthus, 84, 104 Mitigation, 172 Modeling methodologies CAPRI model, 173 CGE models, 20, 173 challenges for, 10–12, 11f CLCA, 7 drivers in, 7–8 economic approaches, 8–9 economic equilibrium models, 173 EPPA model, 173 FAPRI‐CARD model, 173 FAPRI model, 70 FASOM, 70 FASOMGHG, 173 GCAM, 173

GLOBIOM, 173 GTAP model, 173 integrated environmental economic approaches, 9–10 land uses response, 43–44, 43t LCA, 6, 7 LUC model limitations, 174 market‐mediated effects, 172–73 modeling bioenergy, challenges in managing indirect emissions, 158–60 modeling carbon turnover, 119 spatially disaggregated approaches, 8 von Thünen’s locational rent theory, 54, 57–58, 58f, 60 Moderate Imaging Spectroradiometer (MODIS), 42 Multi‐Resolution Land Characteristics Consortium (MRLC), 145 Napiergrass, 104 Nash–Sutcliffe efficiency (NSE), 127 National Agricultural Statistics Service (NASS), 146 National Climate Data Center (NCDC), 127 National Land Cover Database (NLCD), 70, 145 National Oceanic and Atmospheric Administration (NOAA), 127 National Resource Council Survey (NRCS), 143 National Resources Conservation Service (NRCS), 126, 146 Natural capital (NC) bioenergies impact on, 83–96 combustion process, 92 crop and forest management, 91 feedstock production, 90–91 method and metrics for evaluation of, 93 processing and transportation, 91–92 during stages of bioenergy value chain, 89–92, 89f coal impact on combustion, 94–95 method and metrics for evaluation of, 95 mining and material extraction, 93–94 processing and transportation, 94 during stages of bioenergy value chain, 93–95, 93f comparison and discussion of impacts to, 95–96 defined, 85 energy sector impacts to, 85–89, 88f, 88t method and metrics for evaluation of, 86–87, 87t metrics to compare systems for, 88–89 Naturemade Star, 13 NC. See Natural capital NCDC. See National Climate Data Center NCE. See Net carbon exchange NDVI. See Normalized Difference Vegetation Index NELUP extension model, 9 Net carbon exchange (NCE), 71 NHC. See Nonharvested C Nitrous oxide emissions, 72–73, 72f NLCD. See National Land Cover Database NOAA. See National Oceanic and Atmospheric Administration Nonharvested C (NHC), 119 Normalized Difference Vegetation Index (NDVI), 42 North Africa annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t

INDEx cropland cover, harvested area data for, 22, 24f, 25t LUC with corn ethanol expansion in, 30t, 34t NRCS. See National Resource Council Survey; National Resources Conservation Service NSE. See Nash–Sutcliffe efficiency Oceania annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 24f, 25t LUC with corn ethanol expansion in, 30t, 34t Ok‐Power, 13 Operating expenditure (OPEX), 86, 96 Petroleum, U.S. consumption of, 3 Rapeseed biodiesel GTAP‐BIO model testing results with, 31–32, 32t, 35, 35t land use emissions for, 32t LUC with expansion in, 32t Ratio of crop production over harvested area (COH), 23 tuned parameters according to, 29t Ratio of total harvested area over cropland cover (HOL), 23, 25, 26 growth rates by region for 2003–2013 time period, 25t trends by region for 2003–2013 time period, 24f tuned parameters according to, 29t RED. See EU‐Renewable Energy Directive Reed canarygrass, 104 Renewable Fuels Standard (RFS), 12 Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis, 69–70 Renewable Obligation Certificates (ROCs), 84 Residue harvesting, 118t impact on corn yields of, 121t impact on nutrient removal and crop yields of, 120–21 RFS. See Renewable Fuels Standard RFS2. See Renewable Fuel Standard Program ROCs. See Renewable Obligation Certificates Root‐to‐shoot ratio, 117f Roundtable on Sustainable Biomaterials (RSB), 14 Roundtable on Sustainable Palm Oil (RSPO), 14 RSB. See Roundtable on Sustainable Biomaterials RSPO. See Roundtable on Sustainable Palm Oil Russia annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t harvested area decrease in, 24 LUC with corn ethanol expansion in, 30t, 34t SFIR. See South Fork of Iowa River Short‐rotation woody crops (SRWC), 104 Shuttle Radar Topography Mission (SRTM), 43 SOC. See Soil organic carbon Soil and Water Assessment Tool (SWAT), 125 analysis using, 126–27, 127t average annual output in, 130, 130t calibration and validation of, 127 comparison of dry year and wet year in, 130–32, 133f

195

input data for, 126 land use scenarios using, 128–29, 129f limitations of, 137 parameter values calibration for, 127, 127t results and discussion for, 129–37, 130t, 131f–36f seasonal variability of nutrients and sediment in, 132, 134f study area using, 127–29, 127t temporal variability of hydrologic responses in, 134–37, 135f, 136f uncertainty with, 137 yearly distribution in, 130, 131f, 132f Soil organic carbon (SOC), 4 bioenergy cropping systems with, 99–109 carbon budgets for, 116–19, 117f, 118t composition of, 100 crop residues in maintaining, 115–21 data used to populate simulation models for, 119–20, 120f definition, 100 forming factors for, 100–101 impact on GHG emissions of, 99 importance of, 100 irrigation’s negative effects on, 108 land clearing and management changes with, 170 land management effects on agricultural residues, 106 dedicated energy crops, 106–7 management practices, 107–9, 108f land use effects on bioenergy, 103–5, 104f conversion to bioenergy crops, 104–5, 104f conversion to cropland, 103 conversion to forest, 103 conversion to grassland, 103 general agriculture, 102–3, 102f land availability, 104–5, 104f landscape intensification and placement, 105 measured changes in, 102f plant species for, 100 residue harvesting impact in, 120–21 rotation effects on, 108f sampling considerations for sample depth, 101–2 sample methodology, 102 temporal changes in, 101, 105 Soil organic matter (SOM), 73, 116 Soil Survey Geographic Database (SSURGO), 119–20, 127 SOM. See Soil organic matter South America annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 24f, 25t cropland cover increase in, 23 estimated marginally productive land area in, 104f harvested area increase in, 24 LUC with corn ethanol expansion in, 30t, 34t South Asia cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t LUC with corn ethanol expansion in, 30t, 34t South Fork of Iowa River (SFIR), 126

196

INDEx

Soybean ILUC with expansion of, 55–57, 56f planted area, production, and geographic distribution in Brazil for, 56f trend regression for Brazilian, 60t Soybean biodiesel GTAP‐BIO model testing results with, 31–32, 32t, 35, 35t land use emissions for, 32t LUC with expansion in, 32t Spatially disaggregated model, 8 SRTM. See Shuttle Radar Topography Mission SRWC. See Short‐rotation woody crops SSURGO. See Soil Survey Geographic Database STATSGO2 database, 119–20 Subregional Timber Supply model, 10 Sub‐Saharan Africa annual growth rate in crop yield for, 26f cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 24f, 25t cropland cover increase in, 23–24 harvested area increase in, 24 LUC with corn ethanol expansion in, 30t, 34t Sugarcane extensification of land for, 39–49, 46f, 47f fields to mills distance for, 45f ILUC with expansion of, 55–57, 55f intensification and extensification responses with expansion of, 46f, 47f intensification of land for, 39, 41–49, 46f, 47f LUR model for, 44–45, 47t paved road location and presence of, 46f planted area, production, and geographic distribution in Brazil for, 55f popularity of, 5 trend regression for Brazilian, 60t

Sugarcane ethanol calculation for annual expansion of, 43 GTAP‐BIO model testing results with, 31–32, 32t, 35, 35t land use emissions for, 32t LUC with expansion in, 32t LUR with expansion to Cerrado of, 39–49 SWAT. See Soil and Water Assessment Tool Switchgrass, 104 United Nations Framework Convention on Climate Change (UNFCCC), 83–84 United States annual growth rate in crop yield for, 26f corn ethanol, model testing results for, 30–31, 30t, 31t, 33–35, 34t cropland changes with corn ethanol expansion in, 31t, 34t cropland cover, harvested area data for, 22, 24f, 25t cropland cover in, 24 estimated marginally productive land area in, 104f GHG emissions by, 83 land use emissions for corn ethanol in, 32t land use emissions for soybean biodiesel in, 32t, 35t LUC with corn ethanol expansion in, 30t, 34t second‐generation biofuels in, 187f soybean biodiesel, model testing results for, 31–32, 32t, 35, 35t U.S. Department of Agriculture (USDA), 126 CRP program of, 12 U.S. Geological Survey (USGS), 143, 146 Von Thünen’s locational rent theory, 54, 57–58, 58f Water consumption per unit of energy (WUE), 89 Word cloud, 7, 7f WUE. See Water consumption per unit of energy Yield‐price elasticity (YDEL), 21, 28–31, 29t, 33–34 Yield zone, 118t

(a) 200 180

Million acres

160 140

Planted pine

120

Natural pine

100

Oak pine

80

Upland hardwood

60

Lowland hardwood

40 20 0 2010

2015

2020

2025

2030

2035

2040

2045

2050

Year

(b)

Million acres

200 180 160 140 120 100 80 60 40 20 0 2010

Planted pine Natural pine Oak pine Upland hardwood Lowland hardwood

2015

2020

2025

2030 Year

2035

2040

2045

2050

Figure 1.2 Private forest acreage change for five forest types in Southern United States comprising 13 states under (a) no biomass diverted to energy scenario and (b) moderate consumption of woody biomass for energy scenario.

Bioenergy and Land Use Change, Geophysical Monograph 231, First Edition. Edited by Zhangcai Qin, Umakant Mishra, and Astley Hastings. © 2018 American Geophysical Union. Published 2018 by John Wiley & Sons, Inc.

122

1.40

160

1.30

120

1.30

140

1.20

118

1.20

116

1.10

Area

1.00 40

112

0.90

60

0.80

110

0.80

30

40

0.70

108

0.70

20

20

0.60

106

0.60

10

0

0.50

104

0.50

0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

0.90

0.70

10

0

0.50

1.40

160

1.30

140

1.20

120

1.10

100

1.00

80

0.90

60

0.80

40

0.70

20

0.60

0

0.50

Central America

1.50

40

1.40

35

1.30

1.00

Harvested area

0.90

20 15

Ratio of HOL

10

Area

Area of available land

HOL

25

1.20

1.00

40

1.00

30

0.90

0.60

10

0

0.50

0

0.90

100

0.80 0.70

50

0.60 0.50

0

Malaysia and Indonesia

60

1.50

1.10

5

1.10

150

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

50

Area of available land

0.80

Harvested area

0.70

20

0.70

1.30

Ratio of HOL

1.30 1.20

40

1.10 1.00

30

0.90 20

0.60

0.80 0.70

10

0.50

0.60

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

0.50

0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 1.40

70

1.20

Area

HOL

1.00

0.90

0.90 20

0.80

20

0.70

10

0.80

0

0.50

Other CEE and CIS

1.50

70

0.50

Mena and North Africa

40

1.00

0.90 0.80

20

0.70 10

0.60 0.50

Oceania

1.00 0.90 0.80 0.70 42

0.60 0.50

40 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

HOL

Area

1.10

44

1.00 0.90

100

0.80 0.70

50

0.60 0.50

0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Left vertical axes represent area of arable land and harveted in million hectares.

1.20

46

1.10 150

1.50

1.30

48

0.50 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

1.40

50

1.20

0.60

0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

1.30

200

0.90 0.80

Right vertical axes represent ratio of harvested area over available cropland. Legends are: Area of available land Ratio of HOL

1.50 1.40

1.10

0.70

52

Sub-Saharan Africa

250

1.20

40 30

20 0

0.50

300

Area

Area

HOL

Area

50

1.10 1.00

54

1.50

1.30

1.20

60

0.60 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

1.40

1.30

80

0.80

0

60

100

0.90

0.70

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

1.40

120

1.00

20

0.60

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

1.10

60

0.70

10

0.60 0

1.20

80

40

HOL

Area

30

1.30 100

1.10 30

1.50 1.40

120

1.20

1.10 1.00

Russia

1.30

40

50 40

140

1.40 50

1.30

60

1.50

HOL

Rest of South Asia

Harvested area

Figure 2.1 Area of cropland, harvested area, and their ratio by region in 2003–2013. Three regions including Japan, East Asia, and the Other Europe with limited cropland and harvested area are dropped from this figure.

HOL

60

1.50

Area

Rest of Southeast Asia

HOL

80

140

1.50 1.40

50

1.20

0.80

1.50 1.40

200

1.30

60

1.10

India

1.40

70

1.20

30

South America

80

0.50

250

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

45

1.50

180

Area

20

0.60

HOL

Area

30

HOL

1.10

0.70

HOL

1.30 40

0.80

Area

50

China and Hong Kong

0.90

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

HOL

200 1.50

1.20

50

0.90

Canada

1.30

1.10

HOL

1.00

1.50 1.40

70

80

60

Brazil

80

60

114

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Area

90

HOL

Area

1.00

100

Area

1.10

European Union

HOL

124

1.40

United States of America

120

Area

1.50

1.50

180

HOL

200

$/ha

$/ha

iv ns te In e

iv ns te In e

Ex

ten

Agriculture

Exte

nsiv

siv

e

e t

t

t

t

t

Distance

t

t Distance

Distance

Pasture Forest

Figure 4.5 Von Thünen’s conceptual model of locational land rents.

$/ha

$/ha

$/ha

ns ive

e

siv

ive

en

ns

te

te

In

In

Int t

t

t Distance

t

t

Figure 4.6 ILUC from capital transfers to the extensive margin.

t

t

Distance

t

t

$/ha

$/ha

$/ha

ive ns te In

e

e

iv ns te In

iv ns te In

Ex

ten

t

Ex

ten

siv

e t

Distance

t

t

siv

e t

Distance

t

Figure 4.7 ILUC from price elasticity effects.

(a)

(b)

(c)

Figure 5.1 Land with different uses. (a) Forest in fall, upper peninsula of Michigan. (b) Grassland/pastureland in spring, Indiana. (c) Cropland in fall, South Dakota. Photos courtesy of Dr. Wen Sun.

t

t

Distance

(a)

(b) Domestic land use

International land use

Before

Managed land

Managed land

Unmanaged land

Unmanaged land

(d)

ct re di In

re di

Bioenergy

In

After

ct

LU

LU

C

C

(c)

Direct LUC

Figure 5.2 Examples of direct and indirect LUC due to bioenergy development. Generally, the land consists of managed (e.g., cropland) and unmanaged land (e.g., forest and grassland) in both (a) domestic and (b) international domains. (c) After bioenergy is introduced, the managed/unmanaged land may be converted to grow crops for energy use (direct LUC). Indirect LUC may occur in a (c) neighboring region or (d) even other countries in response to market shocks.

Direct emissions

Indirect emissions

N2O emissions not necessarily associated with land use change

N2O emissions resulted from land clearing with fire where applicable N2O emissions associated with land use change N2O emissions not necessarily associated with land use change

Synthetic fertilizer

Crop residue Others (e.g., manure)

SOM loss

Biomass burning

SOM loss Synthetic Synthetic Crop residue Others (e.g., via leaching fertilizer via fertilizer via via leaching manure) and runoff volatilization leaching and and runoff runoff

Figure  5.4 Direct and indirect N2O emissions sourced from agricultural management practices and land use change. Darker colors indicate N2O emissions associated with land use change, while lighter ones indicate emissions normally treated as farming‐related emissions. The bar charts reflect relative amount of N2O emissions.

Urban