metadata: authors: 0: author: Beghin,John C.
1: author: Mevel,Simon
2: author: Timilsina,Govinda R.
3: author: Van Der Mensbrugghe,Dominique
count: World
docna: 0: docna: The impacts of biofuel targets on land-use
            change and food supply : a global CGE assessment
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Rural and Renewable Energy,International Trade and Trade Rules,Food Security,Energy Demand,Energy and Environment,Energy and Mining,Renewable Energy
teratopic: Agriculture,Industry,Water Resources,Energy
abstracts: cdata!: This study analyzes the long-term
            impacts of large-scale expansion of biofuels on land-use
            change, food supply and prices, and the overall economy in
            various countries or regions using a global computable
            general equilibrium model, augmented by a land-use module
            and detailed representation of biofuel sectors. The study
            finds that an expansion of global biofuel production to meet
            currently articulated or even higher national targets in
            various countries for biofuel use would reduce gross
            domestic product at the global level; however, the gross
            domestic product impacts are mixed across countries or
            regions. The expansion of biofuels would cause significant
            land re-allocation with notable decreases in forest and
            pasture lands in a few countries. The results also suggest
            that the expansion of biofuels would cause a reduction in
            food supply. Although the magnitude of the impact on food
            supply at the global level is not as large as perceived
            earlier, it would be significant in developing countries
            like India and those in Sub-Saharan Africa. Agricultural
            commodities such as sugar, corn, and oil seeds, which serve
            as the main biofuel feedstocks, would experience significant
            increases in their prices in 2020 compared with the prices
            at baseline due to the expansion of biofuels to meet the
            existing targets.
repnme: repnme: The impacts of biofuel targets on land-use
            change and food supply : a global CGE assessment
display_title: The impacts of biofuel targets on
            land-use change and food supply : a global CGE assessment
listing_relative_url: /research/2010/12/13305015/impacts-biofuel-targets-land-use-change-food-supply-global-cge-assessment
topicv3: Agriculture and Food Security m1326205 435,Energy and Extractives m1326595 507,Biofuels Energy m1326600 532,Crop Production m1326627 174,Trade m1326924 698
publishtoextweb_dt: 2010-12-01T00:00:00Z
historic_topic: Agriculture,Industry,Water Resources,Energy
no_of_pages: 71
author_keys: 0: author_key: 538947
1: author_key: 1008889
2: author_key: 940446
3: author_key: 428604
model_extractions: WPS5513 Policy Research Working Paper 5513 The Impacts of Biofuel Targets on Land-Use Change and Food Supply A Global CGE Assessment _Govinda R. Timilsina John C. Beghin Dominique van der Mensbrugghe Simon Mevel_ The World Bank Development Research Group Environment and Energy Team December 2010
Policy Research Working Paper 5513

# **Abstract**

This study analyzes the long-term impacts of large-scale expansion of biofuels on land-use change, food supply and prices, and the overall economy in various countries or regions using a global computable general equilibrium model, augmented by a land-use module and detailed representation of biofuel sectors. The study finds that an expansion of global biofuel production to meet currently articulated or even higher national targets in various countries for biofuel use would reduce gross domestic product at the global level; however, the gross domestic product impacts are mixed across countries or regions. The expansion of biofuels would cause significant land re-allocation with notable decreases in forest and pasture lands in a few countries. The results also suggest that the expansion of biofuels would cause a reduction in food supply. Although the magnitude of the impact on food supply at the global level is not as large as perceived earlier, it would be significant in developing countries like India and those in Sub-Saharan Africa. Agricultural commodities such as sugar, corn, and oil seeds, which serve as the main biofuel feedstocks, would experience significant increases in their prices in 2020 compared with the prices at baseline due to the expansion of biofuels to meet the existing targets.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Impacts of Biofuel Targets on Land-Use Change and Food Supply:**

**A Global CGE Assessment**

Govinda R. Timilsina John C. Beghin Dominique van der Mensbrugghe Simon Mevel

**Keywords:** Biofuels, ethanol, biodiesel, land use, food supply, CGE model, impacts

**JEL Code:** Q42, Q48, Q54, Q17, D58

*Affiliations: Beghin is Marlin Cole Professor of international agricultural economics at Iowa State University, Timilsina, van der Mensbrugghe, and Mevel are respectively senior economist, lead economist, and research analyst at the World Bank. Corresponding author: Govinda Timilsina: Development Research Group, The World Bank. 1818 H Street, NW. Washington, DC 20433, USA. Tel: 1 202 473 2767. Fax: 1 202 522 1151. E-mail: <u>gtimilsina@worldbank.org. The views and findings presented here should not be attributed to the World Bank. We</u> thank Masami Kojima, Peter Berck, Mark Rosegrant, Stephen Mink and Mike Toman for their valuable comments and David Zilberman, Harry de Gorter, Will Martin and John Nash for discussions. We acknowledge the Knowledge for Change Program (KCP) Trust Fund for the financial support.
# **1. Introduction**

The last four years have been characterized by extreme price movements in commodity markets. From 2006 to summer 2008, commodity prices increased to historically record-high levels. Since, this commodity bubble subsided; nevertheless agricultural commodity prices have remained high, at roughly twice their levels of early years in the decade. These recent sharp rises in agricultural and primary food prices have made real the issue of food-biofuels trade-offs. Since many countries have announced more ambitious future targets for domestic biofuel use, implying the need for further expansion in global biofuel production going forward, a key question is how much future global biofuel expansion could lead to longer-term increases in food scarcity and hunger, especially among regions and populations at risk. A second concern has emerged regarding the environmental impact of biofuels, and in particular, their local and global impact on land-use and carbon release. Many stakeholders now question the urgency of further developing biofuels because of this triple global interface between food, bioenergy and the environment (Searchinger et al. (2008); and Runge and Senauer (2007)). We investigate some of these complex questions, with a focus on the impact of biofuel expansion on land allocation and the resulting impact on food supply and affordability.

In the midst of the price surge of spring 2008, analysts suggested that the production of biofuels was one of the key reasons for the spike in global food prices (Mitchell (2008); and Rosegrant et al. (2008), among others). However, there is no established consensus on the causes of the recent food crisis and its potential determinants, which include the surge in biofuels, costpush factors such as the increase in energy and fertilizer prices, high food consumption growth in large emerging economies, the role of market restrictions, investment funds’ hoarding, and the devaluation of the US dollar on dollar-denominated commodity prices (Timmer (2008); and Banse et al. (2008b)).

Similarly, there are many questions relating to the long-term impacts of increased biofuels production—how does the expansion affect food availability and prices in the long run? Who would gain and lose from potentially higher commodity and food prices, especially among the heterogeneous poor depending on their net buying or selling status (Winters et al. (2004))? Will biofuel production generate income and reduce poverty despite higher food prices? What are the land supply responses?

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Our paper examines some of these long-term issues using a global dynamic computable general equilibrium (GDCGE) model augmented with an explicit land allocation module and detailed biofuel production sectors. The GDCGE model is developed in ways to account for the competition between biofuel and food industries for agricultural commodities. The major biofuel feedstock is composed of corn, sugarcane, soybean oil, palm oil, and other vegetable oils and their backward linkages to oilseeds. We do not include second generation or cellulosic biofuels in the study due to limitations in data and their unknown profitability. The approach pays particular attention to productivity gains through increases in yield and to changes in land allocation between various uses between forest land and agriculture and within agricultural uses. Yield assumptions have been contentious in the biofuel literature because of their implications for land expansion (Searchinger et al., 2008; and Keeney and Hertel, 2008). An expansion of biofuels would result in diversion of land used for other agricultural commodities towards production of biofuel feedstock. Grassland and forest land could be converted to agricultural land to produce biofuel feedstock. Yield responses to higher prices mitigate these land diversions and reallocations although the exact magnitude of these responses remains uncertain. This uncertainty is an important caveat qualifying our results. We emphasize as well that our results represent estimates of long-term impacts and do not shed light on the potential for and causes of short-term food price crises, such as the one that occurred in 2008.

Our analysis aims first to estimate land-use mix at national, regional, and global levels in the baseline scenario. This is followed by the estimation of land-use mix under two main scenarios of biofuels penetration in the global energy supply mix for road transportation. All types of land such as crop land, grass land, and forest land are included. The study assesses, at national, regional, and global levels, the impacts of the increased production of biofuels on land allocation, agricultural and food production, and their prices.

Finally, we also investigate the role of increased demand for biofuel feedstock as a promoter of monoculture of some feedstock. How does the biofuel expansion impact rainforest, wetlands, and other biodiversity resources? Existing studies (e.g., Sielhorst et al., 2008) argue that the expansion of biofuels seriously impacts wetlands. In Indonesia, deforestation is linked with an increased production of palm oil, a feedstock for bio-diesel. Thus, we examine impacts of biofuels on various types of land and natural resources at national, regional, and global levels.

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We find that the impact of a considerable global expansion of biofuel production on agricultural prices is moderate (less than 5%), except for sugar crops, which increase by about 7 to 10%. Food prices and supplies are moderately impacted by these increases in biofuels. However, the land use impact is more significant with large expansion of crop land offset by reduction in forest and pasture. Within the land used for crops, a sizeable reshuffling of land takes place to accommodate the expansion of feedstock crops for biofuels. Decreases in forest areas are notable in Brazil and Canada.

# **2. Related Literature on Biofuel, Land Use and Food Prices**

There are a growing number of studies on ethanol and other biofuel markets and their impact on other sectors, as the biofuel industry has experienced a boom in recent years and raised a set of issues. Partial equilibrium analyses are first reviewed and then followed by general-equilibrium ones. Rajagopal and Zilberman (2007), and more recently, Kretschmer and Peterson (2008) summarize the recent biofuel literature on the various economic, environmental, policy and modeling aspects of biofuels. The latter source focuses on general equilibrium analyses. Here, we focus on land and price effects related in the literature.

# **_2.1 Partial equilibrium studies_**

Few analyses fully endogenize the prices of feedstock used in ethanol production, that is, sugar and corn. Recent investigations (Elobeid and Tokgoz, 2008; Tokgoz and Elobeid, 2006; Fabiosa et al. (in press); and Searchinger et al. (2008)) have endogenized these prices using large partial equilibrium multi-market models, including explicit international crop markets solved for a world equilibrium. Elobeid and Tokgoz (2008) incorporate linkages between an international ethanol model, an international sugar model, and a U.S. crops model. The U.S. crops model incorporates reduced-form equations for U.S. crop exports that capture the responses of international crop markets to changes in U.S. crop prices. Elobeid and Tokgoz (2008) analyze the impact of trade liberalization and removal of the federal tax credit in the United States on U.S. and Brazilian ethanol markets using a multi-market international ethanol model. They find that the removal of trade distortions induces an increase in the price of world ethanol but has relatively small effects on commodity prices. The U.S. domestic ethanol price decreases, which 4
results in a decline in production and an increase in consumption. U.S. net ethanol imports increase. Brazil responds to the higher world ethanol price by increasing its production. Total ethanol consumption in Brazil decreases and net exports increase. The removal of trade distortions and the tax credit to refiners blending ethanol induces a more limited increase in the world ethanol price. In these partial equilibrium investigations, yields are assumed constant and most of the production changes occur through land allocation changes, overstating the land effects. Land allocation is country specific and depends on local crop mix, suitability of land and relative crop prices. Acreage price elasticities are country specific and reflect a combination of econometric and consensus estimates. Elobeid et al. (2007) estimate the break-even price for corn implied by long-term equilibrium in ethanol production and conditioned by oil and gasoline prices. The latter prices provide a long-term floor to ethanol given its gasoline value equivalent. They estimate that corn prices would roughly double from $2 per bushel in pre-biofuel days to $4 per bushel to reach a long-term equilibrium in U.S. ethanol production and with oil prices hovering around $60/barrel.

Fabiosa et al. (in press) and Searchinger et al. (2008) extend the framework developed by Elobeid and Tokgoz to analyze the effects on global land allocation of the biofuel emergence in the United States and elsewhere in the world. Land allocation within countries is affected by the biofuel expansion, but global land and price effects are limited. The major drawback of these studies is the exogeneity of yield growth --the absence of yield response to prices. Supply adjustment in that model is through land allocation and is rather sluggish with lagged acreage response. Inventory demand adjusts to higher prices and offsets some of the supply sluggishness. These land market and production features are common to all analyses relying on the FAPRI modeling approach (Elobeid and Tokgoz; Elobeid et al.; Fabiosa et al., Searchinger et al.; Tokgoz and Elobeid; and Tokgoz et al.).

English et al. (2006) look at the impact of achieving the goal of “25x25,” that is, 25 percent of the U.S. projected energy needed in 2025 coming from renewable energy sources, on the U.S. agricultural sector. In renewable energy sources, these authors include ethanol from traditional crops and from cellulosic sources, biodiesel, and wind. They use POLYSYS (a partial equilibrium model of U.S. agriculture), which provides annual estimates of changes in land use resulting from the demand generated by bioenergy industries. The biomass feedstock crops are 5
corn, soybeans, switch grass, corn stover, wheat straw, wood residues, animal manure, and yellow grease. Their study does not incorporate impacts of the changes in the U.S. agricultural sector on international agricultural markets and the resulting adjustments on world prices and land allocation. The more recent work by Chen et al. (2010) endogenizes both yields and gasoline prices using a multi-market equilibrium model of US agriculture, biofuel and gasoline markets. Chen et al. look at the impact of meeting the 36 billion gallons renewable fuel standard (RFS) and associated subsidies with conventional and second-generation biofuels. They compare the welfare and GHG impacts of the RFS to a carbon tax of $30/mt.

Following the extreme price hikes of spring 2008, Collins (2008), Mitchell (2008), and Timmer (2008) provide ad-hoc insightful background analysis of key factors affecting food prices including the role of the emergence of biofuels and the resulting increase in agricultural feedstock demand. These papers do not formally address the global multi-crop land allocation problem.

Rosegrant (2008) uses the IMPACT model to analyze several scenarios departing from the recent (2007) biofuel demand.<sup>1</sup> The IMPACT model is a partial equilibrium (PE) global agricultural model incorporating major agricultural activities for 115 countries. The model incorporates land area and yield responses to prices, an improvement over the FAPRI-based biofuel models. Recent extensions of the IMPACT model account for demand for biofuel and feedstock. Rosegrant suggests that biofuel demand contributed about 30 percent of the price increases during the period covering the early 2000s until 2007. The latter figure refers to the effect on the weighted average grain prices, with the largest effect on corn prices (39 percent) but lesser effects on rice and wheat prices (21 and 22 percent, respectively). Roberts and Schlenker (2010) use a calorie-equivalent approach to aggregate wheat, corn, rice, and soybean production. They estimate aggregate supply and demand for calories and quantify the impact of a biofuel shock equivalent to the current U.S. biofuel mandate on the price of calories from these four crops. They find that the calorie price increases by about 20 percent when accounting for calories saved through dried distillers grains with solubles (DDGS), and 30 percent when abstracting from these by-product calories savings.<sup>2</sup>

> 1 Note there are other references to the same IFPRI analysis which are not cited here.

> 2 For an in-depth review of literature on food supply effects of biofuels, please see Timilsina and Shrestha (2010).

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# **_2.2 General equilibrium biofuel analyses_**

General equilibrium analyses provide a more encompassing assessment of the biofuel emergence on the whole economy and food prices because these multiple linkages are explicit in the model. The disadvantages of many of these analyses are the aggregation of crops in a few sectors, the lack of realistic biofuel policy parameterization, and trade elasticities, which tend to dampen trade responses to changing relative agricultural prices. Taheripour et al. (2007) describe extensions of the GTAP 6 database to account for biofuel production and their linkages, upstream and downstream, calibrated to 2001 data. The impact from biofuel expansion on land is through reallocation of land into corn, sugar cane, and oilseeds sectors. Biodiesel is treated as crushing oilseeds and producing meal as a byproduct (as DDGS are in the case of ethanol), <!-- Start of picture text --> Li Lj Ln<br>σ1<br>L _<br><!-- End of picture text --> Figure 1. Land allocation in the standard GTAP model a drastic assumption. Land supply is not explicitly described but presumably it follows the standard one level CET assumption incorporated in the GTAP model structure as shown in Figure 1. Land adjusts sluggishly according to relative rents obtained in respective activities. All countries have a common cost structure. The trade matrix is rather limited. It analyzes several policy shocks such as the increase in crude oil prices, the phasing out of MTBE, and biofuel subsidies. Keeney and Hertel (2009) propose a model incorporating land, yield, and trade responses to the biofuel expansion. Land demand is derived from a constant-return-to-scale <!-- Start of picture text --> L1 Lj Lk Ln<br>σ2 = -0.5<br>L L L<br>forest & other pasture crops<br>σ1 = -0.11<br>L _<br><!-- End of picture text --> Figure 2. Land allocation in Keeney and Hertel (2009).

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farming sector as explained below, characterized by perfect competition and endogenous input prices. Aggregate land supply is fixed but land can move across uses according to relative returns. The yield response to prices is a much needed addition. Keeney and Hertel make the yield response explicit. It depends on the substitution between land, labor, capital, and other factors. Land supply follows a CET structure as described in Figure 2. However, the differentiated bilateral trade response in Keeney and Hertel is a potential drawback. Bilateral export demand assumes differentiation for commodities that are essentially homogeneous across countries (e.g., corn, soybean). The assumed trade substitution elasticities for grains are low (- 2.6). These elasticity estimates are based on econometric work by Hertel et al. (2007). Keeney and Hertel assume that U.S. imports of ethanol from Brazil increase proportionally to the total U.S. ethanol demand, irrespective of price levels, a questionable assumption as explained later in the modeling section and which has important consequences for land allocation.

Birur et al. (2008) use the GTAP database developed by Lee et al. (2005) and (2008) to analyze the impact of biofuel production on global agricultural markets. The latter authors decompose land into 18 agro-ecological zones (AEZs). Birur et al. consider 3 biofuels, ethanol from grains, ethanol from sugar-crops, and biodiesel from vegetable oils. They treat the 2 ethanol productions as imperfect substitutes. The crops considered are coarse grains, oilseeds, sugarcane, other grains, and other agriculture. Crops, grazing, and forestry use the total land endowment. Composite land supply is made of land in the 18 AEZs, which are treated as highly substitutable (CES with an elasticity of substitution of 20). Within any AEZ, land shifts between forest, pasture, and crops with some CET value of -0.11. Within crops, land shifts moving with a CET elasticity of -0.5. Land owners maximize returns on land by choosing an optimum allocation across uses according to relative returns. Figure 3 describes the nested CET tree structure of land supply. This approach essentially parallels Keeney and Hertel within each AEZ.

Banse et al. (2008a) use a modified GTAP-E model which incorporates a 3-level nested approach to land allocation first proposed by Huang et al. (2004), shown in Figures 4 and 5. Banse et al. analyze the impact of the EU biofuel directive on agricultural markets. In addition to the nested CET for land allocation, the authors endogenize aggregate land supply to account for the possibility of new land entering or re-entering agriculture. The reclaimed land comes from non-agricultural uses such as urban areas or forestry.

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<!-- Start of picture text --> Lcrop1 Lcrop i Lcrop n<br>σCET 2 = -0.5<br>L L L<br>forest pasture crops<br>σCET 1 = -0.11<br>AEZ1 AEZi AEZ18<br>σvalue added = 20<br>L _ in value added<br><!-- End of picture text --> Figure 3. Land allocation in Birur et al. (2008) <!-- Start of picture text --> Lwheat Lcoarse grains Loilseeds<br>σCET 3<br>L L L<br>pasture sugar grains & oilseeds & protein<br>σCET 2<br>σCET 3 ≥ σCET 2 ≥ σCET 1<br>Lhorticulture Lother crops Lfield crops &pasture<br>σCET 1<br>L _<br><!-- End of picture text --> Figure 4. Land allocation in Banse et al. (2008a) Gohin (2007) and Bouët et al. (2008) analyze the impact of the EU Biofuel directive. Gohin uses a custom EU-15 CGE model. Gohin provides an extensive disaggregation of the farm sector with 32 agricultural activities and a detailed policy representation of the EU Common Agricultural Policy (CAP). He finds that the expansion of biodiesel production induces a change 9
for the EU-15 from being a net exporter to being a net importer of rapeseed oil and a substantial expansion of EU-15 rapeseed production. All world vegetable oil prices increase considerably. The expansion of bio-ethanol induces a dramatic reduction of EU soft wheat exports. The analysis also accounts for set-a-side land coming back into agricultural production and for the impact on the livestock sector. Gohin’s land supply is similar to that of Keeney and Hertel (2009), with land being allocated in two steps, first between forest, pasture, and crops, and then among crops. Gohin does not explicitly mention the values of his CET parameters.

<!-- Start of picture text --> Lwheat Lcoarse grains Loilseeds<br>σCET 3<br>L L L<br>pasture other field crops grains & oilseeds & protein<br>σCET 2<br>Lmisc Lrice Lfield crops &pasture<br>σ3 ≥ σ2 ≥ σ1<br>σCET 1<br>L _<br><!-- End of picture text --> Figure 5. Land allocation in Huang et al. (2004) Bouët et al. use the MIRAGE model of the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) to analyze the same policy and use a land decomposition also based on AEZ data. They show that EU mandates would increase EU ethanol production by 373 percent and EU sugar beet production by more than 15 percent by 2025. EU biodiesel production would increase under a mandate by 34 percent in 2025. To reach EU consumption mandated targets, the full removal of tariffs on EU imports of ethanol and biodiesel would have to be complemented by a domestic consumption subsidy. Only the trade liberalization scenario stimulates foreign production of sugar cane with Brazilian production increasing by 7.6 percent in 2025. These EU biofuel policies imply a significant reallocation of land in favor of sugar cane/sugar beet and oilseeds for biodiesel sectors, in Europe and/or in South America. EU land allocated to sugar beet would be increased by 11 percent under an EU mandate.

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Gurgel et al. (2007) use the MIT Emissions Prediction and Policy Analysis (EPPA) model to analyze an expansion of second-generation biofuel based on biomass production. The EPPA model is calibrated on 1997 data from GTAP and other sources and is rather aggregated. There is only one aggregate agricultural crop sector and many large countries are aggregated in regions. For example, Brazil and Argentina are part of the Central and South America region. Land is categorized in 5 types: crop land, pasture land, harvested forest land, natural grass land, and natural forest land. The crops sector and two biomass sectors compete for cropland. Pasture land is specific to the livestock sector, and so is harvested forest land for the forest sector. The EPPA model collapses the biomass production and biofuel transformation into a single sector. Two specifications are used for land allocation. Land moves across activities according to a CET transformation -land supply is price responsive- in a first specification, and under an alternative specification, non-agricultural land enters crop land according to an explicit cost of conversion into agricultural use. EPPA’s approach is not well suited to analyzing the food-bioenergy tradeoff because of its lack or explicit representation of agriculture, its focus on second- generation cellulosic biofuel, and the high level of aggregation.

# **_2.3 Land Module for Biofuel Modeling_**

# **_2.3.1 Biofuels, feedstock, and major biofuel producers_**

Ethanol and biodiesel are the two major commercial biofuel considered in our model. The major feedstocks used for ethanol are grains, sugar beet, sugarcane, and molasses. For biodiesel, the major feedstocks are vegetable oil from rapeseed, soybean, and palm oil. The major feedstock for ethanol in warmer countries is sugarcane, whereas grains are used in more temperate climates (corn, but also wheat, and barley), and to a much lesser extent sugar from sugar beet (Koizumi and Ohga (2007); Elobeid and Tokgoz (2006), FAPRI (2009)). Other feedstock types are not yet used in commercial operations and are not expected to be for the foreseeable future. The latter include switch grass and other forms of cellulosic feedstock, which are far from being profitable (See Carriquiry and Du (2009)). Biodiesel relies on vegetable oil and not on oilseeds directly as sometimes modeled. Vegetable oil comes from crushing oilseed crops, with oilseed meal or cake as a by-product feed to livestock. The major oilseed crops are soybean, rapeseed, and palm oil. Biodiesel production exists because of policy mandates and subsidies but it is not currently 11
profitable without subsidies. All countries growing grains, oilseeds, or sugar crops are potentially affected by the biofuel expansion through the transmission of higher world prices for these commodities as well as the changes in their relative magnitude, even though they may not produce biofuel.

Next, we briefly list countries, which are major biofuel producers along with the biofuel type and feedstock used. Argentina produces biodiesel from soybean oil. In Argentina, corn and oilseeds (soybean, and others) compete for land with pasture/forest. Argentina is a large corn producer and accordingly is modeled as a separate country. Brazil produces ethanol from sugarcane and biodiesel from soybean oil. Depending on the region, sugar cane competes with soybean and corn as a second crop or with pasture/forest or savannah land. Canada produces ethanol from corn and wheat. These crops compete for land with other coarse grains (oats, barley) and oilseeds (soybeans and rapeseeds). All Canadian crops compete with pasture land. China produces ethanol from corn. Corn is in competition for land with several oilseeds and grains depending on the region.

The EU produces ethanol from corn, wheat, barley, and sugar beet, and also produces biodiesel based on soybean, rapeseed, and imported oils. Most grains, oilseeds, and sugar crops compete for the same land. India produces ethanol using molasses a by-product of sugarcane processing which has no direct crop effect. Indonesia produces biodiesel using palm oil. The US produces ethanol from corn and biodiesel from soybean oil. Corn-based ethanol produces dried distillers grains with solubles (DDGS), which are a good substitute for feed grains in livestock production. The quality of DDGS is evolving rapidly and its price has closely tracked corn prices in recent times. It is perishable and its price is sensitive to short-term excess supply conditions.

# **_2.3.2 Modeling approach_**

The overall structure of the CGE model is briefly presented in Appendix A. Here we direct our discussions on the land-use component of the model as the paper mainly deals with impacts on land-use change and food supply. We refer the interested reader to van der Mensbrugghe (2008) for the detailed description of the model as well as to Appendix B for a description of the modifications associated with the biofuel module. The following sections describe the approach to land allocation adopted in the analysis.

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Our analysis uses an acreage response model paralleling Birur et al. (2008), Keeney and Hertel (2009) and Lubowski et al. (2006), but with significant departures regarding the CET assumptions impacting land allocation decisions. For the latter we follow a nested approach to land allocation reminiscent of Huang et al. (2004) and as applied by Banse et al. (2008a).

Huang et al. (2004) and Banse et al. (2008a) provide a more realistic land allocation tree and nesting than Keeney and Hertel do. The nested structure allows for different CET elasticities for subsets of agricultural activities and crops. For each country, a specific CET tree is designed to capture various and distinct subsets of crops competing for the same agricultural land with a higher elasticity of transformation within nests and lower ones between crop nests. As noted by Huang et al., land devoted to rice in most countries does not directly compete with other crops. Similarly, we note that sugarcane production competes with a limited number of crops. By contrast, oilseeds and grain productions are in direct and strong competition with each other in most countries where they are produced. Hence, consistent with this fact, we lump them within the same nest and to allow for greater land mobility between them.

As in Birur et al., we assume that on the top nest, forest, pasture, and crops compete for the same land. Then, we depart from the simple nesting of crops of Birur et al. We assume that crops are divided into land going to four crop categories (rice, sugar-crops, an aggregate grains and oilseeds, and an aggregate other crops). These four crop categories form a second nest. Finally, grains and oilseeds constitute a last nest with wheat, corn, other coarse grains, and oilseeds as competing for the use of the same land. This structure accounts for the relative isolation of rice and sugar cane areas, and the geographical proximity and competition between coarse grains and food grains, and oilseeds for the same land. Figure 6 illustrates the nesting.

Next, we derive the price responses of land to unit revenue for the nested land allocation explained previously. We first introduce a simple land allocation within a single nest, and then extend the intuitive structure to the 3-nest actual structure. The simple structure mimics what happens within each nest holding constant the land allocated to the nest. The simple structure also describes what happens in the top nest, for which the land constraint is fixed. For the two subsequent nests, the total land allocated to the respective nest is price dependent and will respond to price changes. This additional price response is then explained for the two nests.

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<!-- Start of picture text --> Lwheat Lcorn Lother coarse grains Loilseeds<br>σ CET 3<br>Lother crops Lrice Lsugar crops Lgrains & oilseeds<br>σ CET 2<br>L L L<br>forest & other pasture crops<br>σ CET 1<br>L _ or AEZs<br><!-- End of picture text --> Figure 6. Land allocation in this analysis

# **_2.3.3 Derivation of the land supply specification_**

We start by assuming first a single nest with a total land constraint _A._ Under such setup, equation (1) shows the first optimality condition for land allocation to activity _i_ , _Li_ , that maximizes land revenues under an aggregate land constraint _A._ The land constraint is shown in equation (2). The land constraint accounts for land quality differences. The CET transformation elasticity given by <sup>_CET_</sup> is negative. The composite price _PP_ reflects the composite revenue per unit of land in aggregate land _A_ .

The responses of land activities with respect to changes in unit revenues, _εij_ = _dlnLl_ / _dlnRj_ , are 14
derived from the log-differentiation of equation (1) holding the land constraint _A_ constant. The responses are shown in equation (3): (3)  _ij_  _CET_ (  _ij_  _j_ ), where δij indicates the kronecker delta, and _αj_ indicates the share of revenue from activity _j_ in total land revenues.

Next, we derive the land supply price elasticities for the 3-nest structure. The optimization problem assumes a sequence of separable decisions given prices for the respective choice within the nest and following a set of constraints for each respective nest ( _A_ , _An2_ , and _An3_ ). We start from the top nest allocating land _A_ between forest, pasture and aggregate crops ( _An2_ ) given returns _Ri_ ( _i_ = _f, p, n2_ ). The second nest allocates land _An2_ between rice, sugar crops, an aggregate other crops, and grains and oilseeds ( _An3_ ) given prices _Ri_ ( _i_ = _r, s, oc, n3_ ) . Last in the third nest, the aggregate land devoted to grains and oilseeds _An3_ gets allocated between wheat, corn, other coarse grains, and oilseeds according to returns _Ri_ ( _i_ = w _, c, ocg, os_ ). For each nest a structure similar to equations (1) and (2) can be derived by substituting the respective crop choice and returns and aggregate land constraint for the corresponding nest.

In the top nest, land responses to any price _j_ are given by (4)  _ij_ <sup>_CET_1</sup> (  _ij_  _j_ ), _i_  _f_ , _p_ , _n_ 2, and _j_  _f_ , _p_ , _r_ , _s_ , _oc_ , _w_ , _c_ , _ocg_ , _os_ , where <sup>_CET_1</sup> is the CET elasticity for the top nest and  _ij_ and  _j_ are defined as previously. The land allocation responses in the lower nests have two components. A first component derived as in equation (3), or as in the top nest, shows the price effect with the land allocated to the nest assumed fixed, and a second component shows the price response on the land allocated to the nest. It is best to separate the price response by nest. We first derive the price response for land allocated in the second nest to prices in the middle level, lower level, and then in the top level, respectively. This is shown in equations (5): 15
where <sup>_CET_2</sup> is the CET elasticity for the second nest.

Finally, the response for land use in the bottom nest is derived in equation (6) following a similar decomposition of the price response and noting that the land adjustment goes through the three nests for returns of activities in the bottom nest, two nests for prices of activities in the middle nest, and one nest for prices of activities located in the top nest: 16
# **_2.3.4 Yield elasticity specification_**

We follow the derivation of Mullen, Wohlgenant and Farris (1988) as recently applied by Keeney and Hertel to estimate yield responses to prices. The approach derives the comparative statics of changes in input prices on market equilibrium in a competitive agricultural output sector characterized by perfect competition. Variable _Xi_ is input _i_ ( _i_ =1,..., _k_ ) used in a given crop production _Q_ characterized by constant return to scale. In log-differential form, the supply of the input is expressed as (8) _d_ ln _X is_  _id_ ln _Wi_ , Where _μi_ denotes the price elasticity of supply and _Wi_ denote the input price.

Under cost minimization in the crop sector, the derived demand for _Xi_ is given by equation (9) in log-differential form: where _σij_ denotes the Allen-Uzawa elasticity of substitution between inputs _i_ and _j_ in the production of the agricultural output _Q_ ; cost shares are denoted by _sj_ .

The zero profit equilibrium condition in the output market equates the cost per unit and the price of the agricultural good _P_ . This condition is expressed in log-differential form in equation (10): _S D_ Market equilibrium in input markets implies that _X i_  _X i_ .

Combining equations (8) and (9) and the input market equilibrium conditions leads to equation (11): with _δij_ defined again as the kronecker delta. The system of _k_ equations (11) can be solved for the share-weighted changes in input prices ( _sjd_ ln _W_ j) as a function of output change _d_ ln _Q._ The latter 17
result can then be substituted back in equation (10) to eliminate variable _W_ . This transformation yields equation (12) which expresses the total response of output _Q_ to changes in output price _P_ induced by changes in input markets: where N indicates a diagonal matrix with diagonal elements ( _μj_ / _sj_ ), Ω is the matrix of Allen Uzawa elasticities _σij_ , and _ι_ indicates a unitary column vector. Assuming that the land supply response is zero ( _μland_ = 0), then the response _d_ ln _Q_ / _d_ ln _P_ provides the yield response to the change in _P_ . different time horizons can be considered for the yield response by varying the supply response of all inputs. Large supply responses _μj_ indicate a longer run. At the limit an infinite supply of these inputs other than land indicate a very long run and provide an upper bound on the yield response. Keeney and Hertel assume that the yield response elasticity is 0.25 for coarse grains, although they recognize that higher values could be used and have been estimated in older literature.

# **_2.3.5 Trade assumptions impacting land allocation decisions_**

The Armington structure of differentiating products is preserved but it assumes higher substitution elasticities than those chosen in the Keeney and Hertel approach, since corn, sugarcane, and soybean products are largely homogenous commodities. Although the grain and oilseed aggregates in GTAP are composite commodities, the shocks emanating from the biofuel expansion really involves a small subset of the commodities in these aggregates. Trade response in the real world will be large. Corn from Argentina and Brazil will easily substitute for U.S. corn, and Brazilian soybean products will substitute perfectly for U.S. soybeans. Similarly, ethanol trade substitution should reflect nearly perfect substitution between Brazilian ethanol, and domestic ethanol in the demand structure of any importing country, including the U.S.

A second pivotal trade policy assumption is to correctly model U.S. biofuel trade policy. In particular, the U.S. biofuel trade policy includes an ethanol ad valorem tariff of 2.5 percent and a specific tariff or 54 cents/gallon. As long as the U.S. equilibrium ethanol price is within the band of prices established by the Brazilian ethanol price and the same Brazilian price inclusive of the two U.S. tariffs, the U.S. price is endogenously determined by the U.S. market equilibrium 18
and no imports from Brazil will take place. U.S. ethanol is essentially a nontradable good within the price band. If demand expansion pushes the U.S. price to the Brazilian price inclusive of the tariffs, then Brazilian imports take place and the U.S. price moves in tandem with the Brazilian ethanol price. Note that small ethanol imports also occur under a tariff rate quota (TRQ) allowed by the Caribbean Initiative. These imports are quite limited and the TRQ mostly goes under filled. In sum, a key policy element to correctly model the land allocation under a U.S. biofuel surge, is the U.S. tariff regime blocking Brazilian ethanol imports over a large price band (Elobeid and Tokgoz (2008)). This key policy element is incorporated in our analysis.

# **3. Definitions of Baseline and Scenarios**

The baseline<sup>3</sup> represents continuation of business as usual for economic development, population growth and biofuel development. Existing biofuel policies (e.g. already implemented mandates, subsidies and import duties) are part of the baseline<sup>4</sup> . Future mandates for increased biofuel use compared to existing mandates are included in the scenarios. Like in most dynamic CGE models, three variables -- population growth, savings and investment and productivity -- are the key drivers to generate a baseline. Population growth, depreciation rate of capital stock and productivity growth are exogenous to the model. Growth in aggregate labor supply tracks the working age population (defined as those between 15 and 65 years). Savings and investment determines the overall level of the capital stock along with the rate of depreciation. Sectoral productivity growths are consistent with recent trends (World Bank, 2009). Another exogenous variable in the model baseline is the growth of energy prices, which are calibrated with projections made by the Energy Information Administration of the U.S. Department of Energy<sup>5</sup> .

For the purpose of the study we define two scenarios. The first scenario considers the implementation of biofuel use targets consistent with what countries already have announced (hereafter “AT” scenario). Please see Column 3 in Table 1 for shares of biofuels under the AT scenario in 2020. The second scenario generally considers a doubling of the announced targets

> 3 The base year for calibrating the model is 2004, the year for which the social accounting matrices, the main database for the model, were constructed. Although our base year is 2004, the model is calibrated to replicate major historical statistics until year 2009.

> 4 Impacts of removal of subsidies and import duties have been analyzed in detailed in a separate paper.

> 5 A module that can represent both conventional and un-conventional oil and gas reserves and production would be ideal; however, the model used here does not have that capacity. Hence, we used energy price forecasts from other sources instead of generating them endogenously in the baseline.

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keeping the timing of the implementation of the targets unchanged. We refer to this scenario as the enhanced scenario (hereafter the ‘ET’ scenario). In India, however, we retain the AT target level, because it is already extremely high (25% in 2020). Column 4 of Table 1 shows the shares of biofuel use in 2020 under the ET scenario. The shares of biofuels are calculated in terms of energy content.

To implement the mandates, we follow standard practice with CGE modeling by calculating the level of per-unit domestic consumption subsidies needed to achieve the desired biofuel demands. The subsidies required to implement these targets for biofuels use in 2020 are shown in the columns 5 and 6 of Table 1 for AT and ET scenarios, respectively. The model then computes where biofuels supply would grow to most efficiently meet the increased global demand – that is, increased domestic demand if there is a mandate relative to baseline, plus increased demand for biofuels imports by other countries.

Since not all announced targets will be met in the same year, we assume that all target to be met in 2020 or later are implemented linearly from 2009 (the last year for which we have actual data) out to 2020. Countries that have target dates prior to 2020 are assumed to meet those targets and then maintain them. Once percentage targets are reached, the shares remain constant but the physical volumes change as total transportation energy consumption changes over time.

Some countries, such as U.S., Brazil, Malaysia and South Africa, meet their announced targets before 2020 due to existing policies (current mandates and subsidies). For these countries we assumed that the AT scenario follows the baseline starting from the year they first become binding. For example, Brazil meets the announced targets before 2009; therefore we do not impose an additional policy requirement prior to 2020 in Brazil. In the United States, the baseline would be equal to AT scenario by 2015 and we assumed that they be the same thereafter until 2020.

We also assume government revenue neutrality, in other word, governments need to find additional revenue to finance the subsidies required to realize the targets. Taxation of gasoline and diesel would be the best source as it not only provides needed revenue, but also helps lower the level of subsidies on biofuels because it induces lower total demand for fuels. The revenue neutral tax rates turn out to be very small due to the large tax base. The rates of taxes on 20
and agricultural commodities, land use mix and food supply. There are a number of recent studies analyzing impacts of biofuels (e.g., Hertel et al. 2010; Al-Riffai et al. 2010; Kretschmer et al. 2009). We tried to compare our results to that from existing studies. However, the comparability is limited for several reasons. First, the scenarios developed for this study and other studies are significantly different. Second, some studies such as Hertel et al. 2010, Banse et al. 2008 measure the impacts in a given year as percentage change from a base year (e.g., 2001, 2006), i.e. they are comparative static exercises. Our study measures impacts as percentage change from the baseline instead of a base year. Third, the structures of the land-use and biofuel modules developed for this study are different from that of existing studies. Fourth, many model parameters used in our study are different from the existing studies. Fifth, the time horizon considered in this study (2009-2020) is different from other studies.

# **_4.1 Biofuel Penetration and Level of Economic Instruments_**

As mentioned earlier, we implement two biofuel scenarios in deviation from the model baseline, which are implemented from 2009 to 2020. Table 1 presents penetration of biofuels in the baseline and the AT and ET scenarios<sup>6</sup> . While the baseline penetration is assumed to be realized due to already existing policy instruments, the incremental penetration (i.e., penetrations in the scenarios minus those in the baseline) would require additional incentives (i.e., subsidies). For example, in the case of United States, biofuel penetration in the baseline would reach 3.9% in 2020 if existing policies are continued; about 30% of new subsidy in biofuel consumption would be required to increase this penetration to 8.14% (see Column 6 in Table 1).

At the global level, the announced targets, in aggregate, represent around 9% of the total liquid fuel consumption for road transportation in 2020. Note that the share was already 5.4% in the baseline (or in the absence of these targets). The penetration is significant without being extreme as these targets being put in place have been partially or sometime fully implemented as of 2008 as reflected in the baseline path. A significant penetration of biofuels occurs in the baseline itself, and it has significant price effects. Several factors explain the increase in some food commodity prices in the baseline. The biofuel penetration and the relatively large increase in food demand explained by population and income growth together outpace productivity

> 6 Year by year results for baseline and the scenarios are available upon the request to authors.

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growth in agriculture, with resulting large increases in land prices. In addition oil prices increase by 160% in the baseline. This combination of large increase in land prices and fossil energy leads sugar prices to increase by 46% in the baseline, corn prices by 31%, other grains prices by 21%, and oilseeds prices by 42%. Forestry and livestock prices also increase in the baseline because of the significant land price increases. Processed food prices change by little as productivity gains offset the higher price of primary products in the cost of production. Farm inputs represent a small share of the retail cost of processed food. In some countries, such as Brazil, the penetration of biofuels in the baseline would actually exceed their announced targets.

The ET scenario is more radical as it roughly considers a doubling of the announced targets. At the global level, the ET scenario leads to a 13.1 % share of biofuels in the total consumption of liquid fuels in the transport sector. Table 1 also presents the subsidy level or shadow price required if the mandates or targets are to be met through an economic instrument<sup>7</sup> . The subsidy rate varies across countries depending upon the biofuels penetration gaps between the scenarios and the baseline. Most European countries exhibit a higher subsidy level because their 10% target by 2020 is much higher than their biofuel penetration levels in the baseline. On the other hand, the level of subsidy in the United States is small as its biofuels target is small and would nearly be met by 2020 due to the existing subsidy.

# **_4.2 The Impacts on Biofuels and Agriculture_**

We present results in deviation from the baseline trajectory both in percent deviation from the model baseline, and in billions of dollars in 2004 prices when relevant<sup>8</sup> . We focus the discussion on the ET scenario which represents a larger shock on the world economy than the AT scenario does. We begin by explaining what each shock represents in biofuel expansion in deviation from the baseline followed by discussions on land-use and food supply impacts as well as food price consequences. We present key results in this section; detailed results from the main as well as sensitivity analysis are available from the authors upon request.

> 7 There could be other economic instruments, such as taxes on gasoline and diesel, taxes on ethanol –gasoline and biodiesel-diesel blends. However, we considered subsidy as it would be the most direct economic instrument to meet the targets. Several existing studies have also adopted the same approach. It would be interesting to compare different economic instruments to meet the biofuel targets. However, this is beyond the scope of this study. 8 The change in absolute values (i.e., billion dollars here) is relevant in cases where the percentage change numbers, appear to be extremely high due to small base.

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# **_4.2.1 Impacts on biofuels production and trade_**

The AT scenario induces significant changes in biofuel production although some of these are moderate in several countries with sizeable biofuel programs. The latter countries have been implementing their biofuel targets for several years. Global biofuel production increases by 64.5% in 2020 compared to what it would have been in the baseline with the largest changes in the EU, China, Thailand, and India. By contrast, the ET scenario has much larger effects in most countries, than the AT scenario does, except India. This is by design since the penetration targets are doubled or more. The targets in the ET scenarios translate into a vast increase in world biofuel production and use (+153.2% or $92.1 billion at 2004 prices) with the largest increases in the EU and EFTA countries ($65.8 billion), Brazil ($7.9 billion), and China ($8.7 billion) as shown in Table 2.

Countries like Brazil, China, France, and India would realize relatively higher production of biofuels. While the production increase in Brazil is mainly driven by international trade, the increases in other countries are driven by domestic targets. Some countries which do not have biofuel targets could experience decrease in production (of biofuels) due to increased export demand for their biofuel feedstock, but the reductions are negligible. Table 3 presents scenario impacts on international trade of biofuels. The expansion of biofuels would induce an expansion of biofuel trade of 259% under the AT scenario. The expansion of biofuel trade volume under the ET scenario is twice that of the AT scenario. Countries that would experience the highest level of imports of biofuels include India and some EU countries. Countries that would experience the highest level of exports of biofuel include Brazil and some EU countries, namely France and Germany. These countries can expand their biofuel feedstock production.

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the ET scenario. All crops, except rice<sup>9</sup> , increase in the ET scenario, and notably so for sugar crops (13.5%), other coarse grains (11.2%), oilseed (7.6%), corn (4.1%), and wheat (3.7%). Rice production falls notably in Brazil and Thailand because competing crops such as sugar are more profitable. The strong expansion of biofuel in the ET scenario explains this agricultural expansion of feedstock crops. The sugar expansion takes place principally in the EU, Thailand, Brazil, and India. The global increase in grains takes place in the EU with wheat in France, other coarse grains in Spain, and corn in the United States. Very limited expansion takes place in grains in Middle and Low-Income countries except for corn production in China. This limited expansion in grains occurs because other crops such as sugar cane are more profitable in many of these countries. The Armington structure is also partially responsible for these localized expansions. Oilseed expansion is strong in EU countries (France, Germany and Italy) and Brazil. Oilseed production expands because vegetable oil is the feedstock for biodiesel. Malaysia would have expansion of palm oil production as reflected in the increase in oilseed production and food processing related to oilseeds. Finally, livestock production contracts in most countries as the cost of feed goes up. Protein meal is slightly cheaper than in the baseline but it does not offset the more expensive feed grains.

Our results on agricultural impact are close to those of Bouët et al. (2008). They obtain large increases in EU sugar crops and relatively moderate increases in grains. They also show a vast expansion of oilseeds as we do, but with a stronger expansion in Brazil, reflecting the high global integration of oilseeds markets across countries and oil types. Gohin (2009) also obtains sizeable expansion of oilseed production in the EU induced by EU biodiesel targets and an expansion of livestock production from the cheaper protein in feed. Our results on agriculture output are also close to that of Kretschmer et al. (2009).

> 9 Rice production decreases as land is reallocated towards oilseeds, sugar, and grains. This will be discussed in detail later.

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Existing general equilibrium studies, such as Al-Riffai et al. (2010) also find similar impacts on agricultural output. For example, expansion of biofuels to meet EU mandates, AlRiffai et al. (2010) finds, at the EU level, approximately a 9% increase in sugar crops; less than 2% increase of oil seeds and less than 0.5% increase of corn and wheat. Like our study, they also find reduction in rice and vegetables and fruits (represented by other crops in our study). Fischer et al. (2009) finds that expansion of biofuels to meet the existing targets could increase production of cereals from 2.7% to 5.4% depending on their scenarios in 2020.

Changes in agricultural trade are notable and illustrate the important role of global trade to mitigate domestic shocks in biofuel and feedstock markets. These are shown in Tables 5 and 6. All countries with biofuel targets would experience increases in total trade of agricultural commodities, mostly commodities used for biofuel feedstock and which experience the largest price changes. The increase in agriculture imports would be relatively higher in EU countries and India. The percentage increase in sugar and oilseeds imports would be relatively higher as compared to imports of other agriculture commodities. The expansion of biofuels would cause substantial decrease in exports of agriculture commodities in countries such as India, Thailand and the United Kingdom. On the other hand, export of agriculture commodities, mainly biofuel feedstock would increase in countries like Brazil, Argentina, Russia and many regions, such as Sub-Saharan Africa, MENA, and South Asia (except India). These significant two-way trade effects can be attributed to various factors. For example, the heterogeneity of the basket of commodities, such as oilseeds and grains of different quality, distance, and location of production and net surpluses and deficits areas prevailing in many countries contribute to the trade patterns. Within a large country, surplus and deficits areas can export and import more cheaply than they can access the domestic market. In the EU, intra-EU trade in oilseed is quite large with EFTA countries and Germany importing from other EU countries like France. Brazil and the U.S. extend their exports of oilseeds as they are the world’s largest soybean producers and exporters.

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# **_4.2.3 Impacts on prices of agriculture and other commodities_**

Changes in prices are summarized in Figure 7 and detailed in Table 7. The changes in world prices (weighted average across countries) of commodities vary from -1.7% (crude oil) to 9.7% (sugar cane) under the ET scenario. These large increases for sugar cane prices are followed by higher prices for ethanol (4.1%), corn (3.6%), biodiesel (3.5%), oilseeds (2.9%), wheat (2.3%), and other coarse grains (2.2%). Processed food prices change moderately (0.5%) in the ET scenario.

The prices of gasoline and diesel products fall by 1.4%. These lower energy prices mitigate the higher cost of farm ingredients in processed food. We also compute price multipliers (percent change in price/percent change in world biofuel production), to gauge the price impact of the two scenarios. The sugar price multipliers are the largest at 22.3% (AT scenario) and 12.3% (ET scenario). The value of 22.3% could be interpreted as the percent increase in price if world biofuel doubled. The next multiplier is for the ethanol price with a multiplier of 6.9% for the AT scenario. The multipliers for the aggregate agricultural price index are 5.5% and 4%, respectively for the AT and ET scenarios. The price multipliers for other commodities are relatively moderate. For example, the processed food price multiplier is 0.5%, again clearly showing the moderate impact biofuels have on processed food prices. These small changes do not preclude some higher localized price changes, but the typical effect is clearly illustrated by Figure 10. Land prices (not shown in the figure) do change a lot in the EU (France especially, and also the UK and Spain) where land devoted to sugar crops and oilseeds sometimes more than double in value.

A number of existing general equilibrium studies reports impacts on commodity prices of the expansion of biofuels. Kretschmer et al. (2009) finds that a policy to meet EU’s 10% biofuel target by 2020 would cause world prices of corn, sugar crops, wheat and other grains by 0.2% to 1.7% higher relative to the reference scenario in 2020. Note that these impacts are much smaller as compared to our price impacts because they simulate only EU targets, whereas we simulate existing targets of all countries. Another general equilibrium study, Al-Riffai et al. (2010), finds very small effects on food prices of EU biofuel policies.

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# **Figure 7. Change in world prices (%) relative to the baseline in 2020**

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# **4.4 Impacts on Food Supply**

Food supply includes direct consumption of crops, fruits and vegetables, and livestock, and processed food. The composition of the food supply changes however, since the shares of livestock products, sugar, and some grains decrease the most. This global effect does not exclude stronger localized effects when feedstock and food use directly compete locally and with costly transportation. Nevertheless regional trade within a country should help dampen these potential effects. If biofuels are produced locally, trade costs must have been reduced for all commodities and arbitrage in food markets would take place if local commodity prices rise significantly.

**Figure 11. Change in world food supply (US$ Billion) relative to the baseline in 2020**

<!-- Start of picture text --> High‐income Middle & Low‐income<br>0.0<br>‐2.0<br>‐4.0<br>‐6.0<br>‐8.0<br>‐10.0<br>‐12.0<br>‐14.0<br>‐16.0<br>AT ET<br><!-- End of picture text --> In 2020, under the ET scenario, world food supply decreases by $14.1 billion or 0.2% from the corresponding food supply in the baseline. Under the AT scenario, the reduction in food supply is about half as large (see figure 11). Note that the percentage reduction in food supply is estimated to be negligible, particularly in a dynamic context. This is due to a large base as we include the entire food sector, including cereals, processed food and livestock. Considering only cereals, Fischer et al. (2009) show that expansion of biofuels to meet the existing targets would 39
income countries. About two-third of the world food supply decrease is imputable to developing countries. Lower income economies generally rely largely on agriculture and therefore are significantly dependent on food products. China, Rest of Sub-Saharan Africa, MENA, and India would suffer the most if biofuel targets are actually implemented. Food availability would be reduced by $1.4 billion in India alone under AT scenario. The United States would carry most of the burden among developed economies (see Table 9).

# **4.5 Aggregate Economic Impacts**

Finally beyond, effects on land, food production and prices, it is also interesting to gauge the overall effect of a large biofuel expansion on the overall economy of various countries/regions. Figure 12 presents the impacts of biofuel expansion on countries’ GDP for both scenarios. At the global level, the expansion of biofuels is found to decrease GDP (0.02% and 0.06% reductions under AT and ET scenarios, respectively in year 2020 as compared to the baseline). However, at the country/regional level, results are mixed. Brazil, Argentina, Thailand, and Indonesia experience increases in GDP, whereas countries like the United States, China, and India experience reductions in their GDP. The reasons can be attributed to several factors. Countries, which have very small penetration in the baseline but have set ambitious targets, would face GDP decreases because the targets significant alter the allocation of resources in production and change trade patterns, resulting in a worsening of the terms of trade. India is a good example, where the government has set a target of 20% (or 17% in terms of energy content) for biofuels by 2020, but its current level of biofuels use is very small. Achieving this target would cause India’s imports of agricultural commodities, to increase, in aggregate, by 3.9 to 4.6% depending on the scenarios (see Table 5).<sup>11</sup> Its export would drop by 1.7 to 6%. The terms of trade deteriorates thereby causing significant GDP loss (0.13% to 0.23%). Countries like Brazil, Argentina and Indonesia would experience increases in their GDP due to increased exports of biofuels or feedstock required for biofuels. These countries experience improvements of their terms of trade. Energy exporting countries/regions, such as MENA and Russia will face reductions in GDP due to decreases in their oil exports thereby causing negative terms of trade effects.

> 11 Note that India has different impacts on different scenarios although the targets are the same for both. This is because the effects of international trade would be different between these two scenarios.

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**Figure 12. Change in GDP (%) relative to the baseline in 2020 – by region**

<!-- Start of picture text --> Rest of SSA<br>South Africa<br>MENA<br>Rest of ECA<br>Russia<br>Rest of LAC<br>Brazil<br>Argentina<br>Rest of SA<br>India<br>Rest of EAP<br>Thailand<br>Malaysia<br>Indonesia<br>China<br>Rest of EU & EFTA<br>UK<br>Spain<br>Italy<br>Germany<br>France<br>United States<br>Canada<br>Japan<br>Aus‐NZ<br>‐0.4 ‐0.3 ‐0.3 ‐0.2 ‐0.2 ‐0.1 ‐0.1 0.0 0.1 0.1 0.2<br>AT ET<br><!-- End of picture text --> <!-- Start of picture text --> AT<br><!-- End of picture text -->

# **5. Conclusions**

In this study we analyze the impacts of a large scale expansion of biofuels –reflected in the announced targets of numerous countries, both developed and developing- on agriculture, landuse and food supply. The first core finding is the moderate long-term impact on commodity prices induced by a large expansion of biofuels under both scenarios (Announced Targets, and Enhanced Targets more or less doubling the former). This finding is conditioned on yield gains taking place as a response to increases in commodity and land prices. In addition, and for both 42
scenarios we also compute food price effects as found in recent CGE and PE analyzes which fully modeled biofuel and feedstock markets. Note that PE analyses do not account for all sources of productivity gains and yield effects and tend to generate somewhat higher land and food price effects.

Our results suggest that planned biofuel targets would not cause large impacts on food supply at the global level although the impacts would be significant in developing countries like India and Sub-Saharan Africa. Agricultural commodities such as sugar, corn and oil seeds (as oil), that serve as the main biofuel feedstocks, would experience 1% to 8% price increases in 2020 as compared to that in the baseline due to the expansion of biofuels to meet the existing targets.

Our study shows that land use allocation between forest, pasture and crops, would be significantly altered, and leads to considerable forest and pasture destruction in several countries, especially under enhanced targets. Vast expansion of biofuel does lead to global forest losses of about 26.3 million hectares in 2020 under enhanced targets. Once targets are reached, technical progress eventually would reduce the reliance on land to expand agricultural output and forest land could be regained in the longer run. Within the expanded crop land uses, we obtain large effects in several countries implementing large biofuel targets. The general tendency is to expand land devoted to feedstock crops (sugar crops, coarse grains and, low quality grains for ethanol; oilseeds for vegetable oil for biodiesel). Again, these effects are large in 2020, because they correspond with the expansion phase of the targets. In the longer run, one would expect that the land expansion would recede and productivity gains would reduce the long term use of land induced in the medium term and the short term constraint on food supply would be reduced. Sugar crops and oilseeds expand the most.

We have conducted extensive sensitivity analysis. Various configurations of key parameters have been tried (elasticities of transformation in land, elasticities of substitution in production and fuel use, productivity assumptions, and trade elasticities). The qualitative results for the two scenarios are invariant to major changes in these parameters. The robustness of the results has to do first with the relative small share of farm values in food processing, hence the moderate food price effects. Second, the increase in demand for feedstock can be accommodated given the various mechanisms at play via trade in agricultural commodities, land allocation, and 43
biofuels production, even under limited land substitution across uses. Small relative land changes in absolute value can accommodate a lot of new feedstock demand. Putting land into production takes resources and time; hence these results imply that adjustments can take place over time. Again the long-term nature of our analysis cannot be over stated. In addition, we have considered dry distiller grains (DDGS) as a feed crop substitute. DDGS are a byproduct of ethanol and provide revenues and reduce feed grain demand. DDGS are good substitutes for coarse grains, especially corn, almost on a one for one basis. Accordingly the implied indirect land use effects linked to feed grains are dampened once DDGS are accounted for and food price effects from biofuel expansion are even more moderate.

Considering the size and complexity of the model, we could not exactly incorporate all policy parameters which might affect the quantitative results, particularly in the enhanced scenario. For example, the United States will not subsidize ethanol production after 2015 although several bills have been introduced to extend these subsidies. The current policy in South Africa is to discourage ethanol from corn. In our model the subsidies are provided to biofuels and are not differentiated across feedstocks. Incorporating this policy would reduce the use of corn for ethanol and therefore could lower corn production and land-use for corn. Moreover, we have not accounted for existing distortions in sugar and energy markets. For example, the removal of current subsidies to fossil fuels might provide a level playing to biofuels. Even if we were able to precisely reflect these realities, the key message of the study would not have changed. There are many other similar issues which are beyond the scope of this study but could be interesting topic for further research.

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