EPA Project Number: 0210826.014 

## Land Use Implications of Biofuels: GTAP Approach Final Report January 2010 Prepared for U.S. Environmental Protection Agency Office of Transportation and Air Quality 1200 Pennsylvania Avenue, NW Washington, DC 20460 . Prepared by Dileep K. Birur Robert H. Beach Environmental, Technology and Energy

Economics Program Global Climate Change and Environmental Sciences Unit

RTI International 3040 Cornwallis Road, P.O. Box 12194 Research Triangle Park, NC 27709 -219 4.i Table of Contents Page Table of Contents ................................ ................................ ................................ ................. i List of Tables ................................ ................................ ................................ ...................... ii List of Figures ................................ ................................ ................................ .................... iii 1. Overview ................................ ................................ ................................ ..................... 1 2. Model Background ................................ ................................ ................................ ...... 1 2.1. Structure of GTAP Model ................................ ................................ ....................... 2 2.2. Aggregation of Sectors and Regions ................................ ................................ ....... 4 2.3. Land Endowment in the GTAP -BIO Model ................................ ........................... 6 2.4. Land Supply in the GTAP -BIO Model ................................ ................................ ... 8 3. Key Parameter Assumptions and Qualifications ................................ ........................ 8 3.1 Sensitivity

of Key Parameters ................................ ................................ ................. 9 3.2 Qualifications ................................ ................................ ................................ ........ 10 4. Experimental Design ................................ ................................ ................................ . 10 5. Results from Corn-Ethanol Experiment ................................ ................................ .... 12 6. Results from Soy

bean-Biodiesel Experiment ................................ ........................... 15 7. Conclusions ................................ ................................ ................................ ............... 18 References ................................ ................................ ................................ ......................... 19 Appendix A: Aggregation Tables ................................ ................................ ..................... 45 Appendix B: Land Supply ................................ ................................ ................................ 48ii List of Tables Table Page Table 1. Variation in key parameters driving the general equilibrium results on biofuels. .... 22 Table 2. The U.S. biofuel scenarios implemented in the GTAP model. ................................ . 23 Table 3. Change in agricultural output due to an increase in corn- ethanol production in the U.S. ................................ ................................ ................................ .......................... 24 Table 4. Change in commodity price due to an increase in corn- ethanol production in the U.S. ................................ ................................ ................................ .......................... 25 Table 5. Impact of an increase in U.S. corn- ethanol production on bilateral trade (Change in import volume) ................................ ................................ ................................ ......... 26 Table 6. Change in land use and land cover due to an increase in corn- ethanol production in the U.S. ................................ ................................ ................................ ..................... 27 Table 7. SSA estimates of change in harvested area of crops due to an increase in cornethanol production in the U. S. ................................ ................................ ................. 28 Table 8. SSA estimates of change in land cover due to an increase in corn- ethanol production in the U.S. ................................ ................................ ................................ ................ 29 Table 9. Change in agricultural output due to an increase in biodiesel production in the U.S.

................................ ................................ ................................ ................................ .. 30 Table 10. Change in commodity price due to an increase in biodiesel production in the U.S.

................................ ................................ ................................ ................................ .. 31 Table 11. Impact of an increase in U.S. biodiesel production on bilateral trade (Ch. in import volume) ................................ ................................ ................................ .................... 32 Table 12. Change in land use and land cover due to an increase in biodiesel production in the U.S. (million acres). ................................ ................................ ................................ . 33 Table 13. SSA estimates of change in harvested area of crops due to an increase in biodiesel production in the US (million acres). ................................ ................................ ....... 34 Table 14. SSA estimates of change in land cover due to an increase in biodiesel production in the US (million acres). ................................ ................................ ............................. 36 Table A1. Aggregation of sectors in the GTAP -BIO - 1 Model ................................ ............... 45 Table A2. Aggregation of Regions in the GTAP -BIO - 1 model. ................................ ............ 47iii List of Figures Figure Page Figure 1. An illustrative overview of the GTAP model ................................ ........................... 3 Figure 2a. Crop cover change due to an increase in U.S. corn- ethanol production (million

acres) ................................ ................................ ................................ ...................... 37 Figure 2b. Pasture cover change due to an increase in U.S. corn- ethanol production (million

acres) ................................ ................................ ................................ ...................... 38 Figure 2c. Forest cover change due to an in crease in U.S. corn- ethanol production (million

acres) ................................ ................................ ................................ ...................... 39 Figure 3. Crop cover change due to an increase in U.S. corn- ethanol production (Ha/billion

BTU) ................................ ................................ ................................ ....................... 40 Figure 4a. Crop cover change due to an increase in U.S. biodiesel production (million acres)

................................ ................................ ................................ ................................ 41 Figure 4b. Pasture cover change due to an increase in U.S. biodiesel production (million

acres) ................................ ................................ ................................ ...................... 42 Figure 4c. Forest cover change due to an increase in U.S. biodiesel production (million acres)

................................ ................................ ................................ ................................ 43 Figure 5. Crop cover change due to an increase in U.S. biodiesel production (Ha/billion

BTU) ................................ ................................ ................................ ....................... 44 F igure B1. Land supply in the GTAP -BIO models. ................................ ............................... 481 Land Use Implications of Biofuels: GTAP Approach 1. Overview The U.S. Environmental Protection Agency (EPA) has undertaken a life cycle assessment (LCA) focused on greenhouse gas (GHG) emissions associated with different types of renewable fuels as required by

the Energy

Independence and Security Act of 2007 (EI SA). One important component of this LCA is a careful accounting of GHG emissions resulting from land use change. The purpose of this study i s to examine the global land use implications of biofuels production in a general equilibrium framework. The Global Trade Analysis Project (GTAP) model (Hertel ed. 1997) , a multi -region multi - sector computable general equilibrium model, is widely used for global - scale assessment of economic policies. The use of the GTAP data base and model has been increasing with the growing research interests in international trade policies, energy p olicies, climate change, etc. The GTAP framework ensures complete accounting of the impacts of these topical policy i ssues. With the ease of access to the standard GTAP model, which is publicly

available, there are numerous GTAP -based models in use around the world. However, the GTAP Center has a mechanism to control the quality o f these applications through a peer-review process. Inrecent ye ars, a version of the model was developed to explicitly account for substitution between energy c ommodities (Burniaux and Truong, 2002). Lee et al. (2005) developed another version of the model which explicitly accounts for global competition for land across alternative uses in agro - ecological zones (AEZs). More recently B irur et al. (2008) developed the GTAP -BIO model by c ombining the above two frameworks and introducing biofuels as substitutes for petroleum products. This study c overs biofuels that are produced from agricultural feedstocks such as corn ethanol, sugarcane ethanol, and oilseed-based biodiesel. More recently, the California Air Resource Board (CARB) has used the GTAP - BIO model for its rulemaking on a Low Carbon Fuel Standard for biofuels (CARB, 2009). The modeling approach used for this analys is is discussed in detail in the next section. 2. Model Background With the growing research interest in biofuels, numerous recent studies on the economics of biofuels have employed cost- accounting procedures and/or partial equilibrium (PE) frameworks (Walsh et al. 2003; Msangi et al. 2006; Fargione et al. 2008; Searchinger et al.2

2008). However, these approaches do not capture the economy-wide implications of a policy. Also , PE approaches fail to address the linkages between factor income and expenditure s resulting from a policy. In contrast, the general equilibrium (GE) models can be used to check theoretical and accounting consistency (e.g., market clearing conditions are satisfied in input and output markets, consumers are on their budget constraints, macro - economic accounting balances as indicated by

b alance of pay

ments conditions). With the strengthening linkage between agriculture and energy

markets, biofuels production could impact food security, international trade, the environment, and natural resources. The emerging linkage is a demand-pull from the energy s ector to agriculture, which will potentially increase agricultural commodity demand due to the biofuel policies and high oil prices. S uch a linkage can be fully captured in a GE model such as GTAP. Literature on economic modeling of biofuels suggest that for analyzing the longter m consequences of biofuels , the GTAP model is a suitable economic tool to link energy and crop demand (Kløverpris et al. 2008 ; Rajagopal and Zilberman, 2007 ). S everal studies have adopted the GTAP data base and/or GTAP model -based approaches for studying the economywide implications of biofuel policies in the major biofuel producing regions such as the EU and the U.S. (Banse et al. 2007; Eickhout et al. 2008; Birur et al. 2009; Hertel et al. 2010) . The findings of these studies offer varied perspectives , with differences in results due primarily to differences in behavioral parameter assumptions and methodological approach . We will revisit these issues in Section 3 . 2.1. Structure of GTAP Model The GTAP model is a multi - commodity, multi -regional computable general equilibrium model developed by

Hertel (ed., 1997). The model has been documented in the ‘ GTAP Book ’ (Hertel ed., 1997) with a detailed discussion on the underlying theory and derivation of the behavioral equations involved in the model. The standard GTAP model basically assumes perfect competition in all markets with Walrasian adjustment 1

. As represented in Figure 1 (Hertel, Ty

ner, and Birur, 2010) , the regional household collects all the income in its region and spends it over three expenditure ty pes – private household (consumer), government, and savings, over a Cobb -Douglas utility f unction. A representative firm maximizes profits in nested Constant Elasticity o f Substitution (CES) functions in a perfectly c ompetitive market for each

1 Walras ’ Law states that if (n- 1) markets are in equilibrium, then the nth market has to be in equilibrium.3

industry /sector in each region and pays income to the regional household for utilizing the endowment commodities (land, labor, cap ital, and natural resources). Furthermore, f

irms sell the final goods consequently produced by

c ombining the endowments with the intermedi ate inputs to the private households and the government, and the investment goods to the regional household. In an open economy, firms also export the tradable commodities and import the intermediate inputs from other regions (rest of the world), but the model follows an Armington assumption , which accounts for product heterogeneity. The l and endowment is imperfectly mobile, while labor and capital are perfectly mobile within a region but imperfectly

mobile across regions. Government spending is modeled by using a Cobb Douglas sub -utility

function, which maintains constant expenditure shares across all the sectors.

Rest of World Savings

Net investments Global Savings Regional Household

Producer Govern ment Private Household

Exports

Imports (

intermediates) Govt. Expenditure

Import

Tax Pvt. Expenditure

Export Tax Exp. on Imports Exp. on Imports Dom. purchases Dom. purchases Taxes Taxes

Taxes Endowments: (Land - AEZs, Labor, Capital)

Purchases (commodities including biofuels)

Figure 1. An illustrative overview of the GTAP model Source: Hertel , Ty

ner, and Birur (2010).4

Private household consumption is modeled by adopting a non-homothetic Constant Difference of Elasticity

(CDE) implicit expenditure function, which allows for differences in income elasticities across commodities. T axes (and subsidies) go as net tax revenues (subsidy expenditures) to the regional household from private household, government, and the firms. As shown in Figure 1, the rest of the world gets revenues by exporting to private households, firms and government. These revenues are spent on export taxes and import tariffs, which eventually go to the regional household. The standard closure of the GTAP model allows for equilibrium in all the markets, all firms earn zero economic profits, the regional household is on its budget constraint, global investment equals global savings, and the sum of global exports and imports is zero. The global trade balance condition determines the world price of a given commodity. The Cobb -Douglas utility function of the regional household allows for maintaining constant budget shares . 2.2. Aggregation of Sectors and Regions The data base used in the GTAP - BIO model versions is based on the GTAP version 6 data base (GTAP v6) (Dimaranan, 2006 Ed.) , which pertains to the global economy f or 2001. The original data base has 57 sectors and 87 regions of the globe , but does not include any sectors explicitly

breaking out biofuels . To create the data base used for the GTAP - BIO model, Taheripour et al. (2007) split out three biofuels sectors from existing sectors in the standard GTAP v6 data base : the grain based ‘ ethanol - 1 ’ sector from the food products sector (ofd) receiving inputs from the cereal grains sector (gro) ; the sugar-based ‘ ethanol -2 ’ sector out of the chemicals sector (crp) with inputs from sugar- cane -beet (c_b) sector; and the biodiesel sector was created from the vegetable oils and fats (vol) sector which gets input from the oil - seeds (osd) sector. The sales of biofuels are channeled through household as well as intermediate demand. Taheripour et al. (200 9 ) further modify the data base to incorporate by products of biofuels such as dried distillers grains with solubles (DDGS ) and vegetable oil cake (soybean meal in the U.S. and rapeseed cake in the EU) , and then augment feed demand in the livestock sector by allowing for substitution b etween biofuel by products and other animal feed.5

For the corn ethanol production scenario , we utilize the version of the model used for the California LC FS (referred to in this report as GTAP -BIO - 1 ) 2

. However, due to limitations on how the oilseed by

pro ducts were treated in this version of the model, we have used a different version of the model for the biodiesel scenario . Additional details on this version of the model are documented in Taheripour et al. (2009 , in press ) (referred to in this report as GTAP - BIO - 2 ). This version of the model was not available for the corn ethanol scenario , but offers advantages for the analys is of biodiesel expansion. In the GTAP -BIO - 1 model , biodiesel is produced from oilseeds along with the production of by-products. Since biodiesel can also be produced from processed vegetable oil, Taheripour et al. (2009) split the vegetable oil sector into refined and crude -vegetable oil sectors and allow for biodiesel to be produced from crude -vegetable oil rather than directly

f rom oilseeds. Unlike GTAP -BIO - 1 where by products (oil -meal/cake) are produced along with the production of biodiesel, biodiesel is produced from crude -vegetable oil as the main input in the GTAP -BIO -2 version . The crude -vegetable oil is produced as a joint product along with oil -meal/cake from the oilseeds sector. This modification is expected to result in a better assessment of the impacts of expanded U.S. renewable fuels production, particularly the production of biodiesel from soy bean oil. B ecause the oil content in soy beans is only a bout 18% and much of the remainder is soybean-meal, which is used in animal feed, large production of biodiesel using soy bean oil as the feedstock is expected to significantly impact feed markets. Therefore, we utilize the GTAP - BIO -2 version of the model to analyz e potential impacts of increasing annual U.S. biodiesel production by 1 billion gallons. After incorporating the biofuel and by products sectors, the data base is aggregated to permit focus on the sectors and regi ons of particular interest. In the GTAP -BIO - 1 version of the model used in this report for analys es of ethanol expansion , the data base is aggregated into 20 economic sectors and 18 regions (Table A1 and Table A2 , respectively in Appendix -A). The sector aggregation was chosen such that we could focus on the linkages among feedstocks , biofuels, energy

c ommodities, and other important sectors. The regions are aggregated such that each continent is broadly divided into three categories: major energy c onsuming countries, major energy exporting countries, and all remaining countries in the continent . However, in the GTAP BIO -2 model , the ‘other grains’ sector has been kept disaggregated as ‘paddy-rice’ and ‘wheat’.

2 This analysis relies on the version of the model CARB used for the corn ethanol assessment conducted in February 2009.6

The ‘livestock’ sector is also kept disaggregated as ‘ruminants’, ‘non-ruminants’ , and ‘dairyfarms’ . This helps in keeping track of substitution of by products for traditional feed across alternative livestock uses. Regional aggregation is similar to that of the GTAP -BIO - 1 version but has 19 regi ons in aggregate . The next section focuses on incorporating disaggregated land endowment across agro - ecological zones . 2.3. Land Endowment in the GTAP-BIO Model In the standard GTAP model, land is regarded as a sluggish endowment which can be re allocated, bas ed on relative land rents. However, not all crops are taken up in all parts of a country d ue to constraints on their adaptability. In the GTAP -BIO model versions , land is treated as the ty

pical non-tradable endowment but is also further classified based on agro ecological zones (AEZs) in each region. As defined by FAO and IIASA (2000), AEZs are the classification of a parcel of land based on soil and climate characteristics such as moisture, temperature, soil ty

pe, etc. Lee et al. (2005) first classify the AEZs based on “length of growing period” (LGP) data provided by FAO and IIASA (2000). The LGP refers to the duration when soil moisture and temperature are conducive to plant growth in a given ye ar. Land is classified into six categories of LGP, each comprising 60 days of sufficient temperature and moisture to grow crops. Following Ramankutty and Foley

( 1999), Lee et al. (2005) overlay six categories of LGP on three climate zones of the world, classified as tropical, temperate, and boreal zones based on absolute minimum temperature and growing degree days. The resulting 18 AEZ categories , which account for heterogeneity of land in each region, offer better characterization of the competition for land across alternative uses. The global land use data base used in the GTAP model is compiled using three land use data sets : (i) the land cover data from Ramankutty et al. (200 5 ) which distinguishes forest, pastureland, and cropland cover ty pes, (ii) data on harvested land acreage and production from Monfreda et al. (2008), and (iii) a data base that maps forestry

activity into the 18 AEZs as documented in Sohngen et al. (200 9). Le e et al. (200 9 ) utilizes these land use components and disaggregates land rents in the GTAP data base on the basis o f prices and yi elds. D etailed discussion on this aspect is given in the volume edited by Hertel, Rose, and Tol (200 9). Since the GTAP data base corresponds to economic flows, the land endowment is observed as rents in the land-using sectors in a given region. Lee et al. (2009) share out land7

rents for cropland, pasture - cover and forest- cover based on the ye arly economic activity in a given AEZ. They

determine cropland rents based on value of crops produced in a given AEZ using 0.5 o grid level data on harvested crop area and per hectare yi eld data offered by Monfreda et al. (2008). Due to this approach, the land rents can be treated as indicators of productivity in each AEZ . The temperate AEZs with a longer length of growing period (LGP) are found to have the highest land rents worldwide, followed by the tropical AEZs, and then boreal zones. Also, the land rents are largest in those AEZs where high value crops such as vegetables, fruits and nuts, and irrigated crops such as paddy-rice and sugarcane are grown . For determining land rents for the livestock sector, which includes the economic activities of ruminants (cattle, sheep and goats), dairy production, wool, and non-ruminants (pigs and poultry), Lee et al. (2009) draw on the direct competition between these sectors with the grazing land. F or this purpose, they utilize land cover (pasture cover) data provided by Ramankutty et al. (200 5 ) which explicitly shows the availability

o f pasture/grazing land in each AEZ for all the regions in the world. F or computing livestock sectors’ land rent, ideally it is computed based on revenues resulting from growing forage crops. But the major constraint to this approach is there is no explicit ‘forage crop’ sector in the GTAP data base that would indicate the crop yi eld and hence the revenues. Therefore, Lee et al. (2009) utilized the data on average coarse grain yi eld in each AEZ and multiplied it by the pasture land cover hectares. Due to this approach, the aggregated land rent of livestock sectors is smaller than the aggregated land rents of agricultural crops. F or example, in the U.S. the average cropland cash rent is $70/acre and that of pastureland is only $9/acre, clearly indicating the marginality of pasture land. Similarly, Lee et al. (2009) compute the AEZ level forest cover land rent by using information on timberland land rent and timberland area offered by S ohngen et al. (2009). The land rents for the forestry sector in the GTAP data base are distinguished as ‘natural resource’ rents. Lee et al. split this ‘natural resource’ sector rents based on the AEZ level forest rent shares. F or computing the AEZ level forest rent shares, those authors exclude the inaccessible forest area as these forests do not generate any rents in principle. The resulting per hectare land rents indicate that forest land rents are slightly smaller than that of aggregated pasture sector rents indicating their marginality.8

2.4. Land Supply in the GTAP-BIO Model Since the land endowment is split into AEZs, Lee et al. (2005) made an assumpti on that the land is mobile across uses within an AEZ, but immobile across the 18 AEZs as the crops grown are climate and soil specific. In line with Hertel et al. (2009), in the GTAP -BIO model versions , the land mobility is effectively restricted across alternative uses within a given AEZ, by using a constant elasticity o f transformation (CET) function. In principle, the land- owners maximize total returns to land by optimal mix among alternative uses, in two tiers (Figure B1 in Appendix -B ). In the first stage, the land- owner makes the optimal allocation of a given parcel of land under crops, pasture or commercial forest, while the choice of crops is made in the second stage (five categories of crops: cereal grains, oilseeds, sugar- crops, other grains (paddy-rice and wheat) , and other agriculture). The ease with which the land-AEZi is transformed across different crops is governed by the elasticity

o f transformation, ETRAE - 2 (assumed to have a base value of - 0.5) and across different land covers by ETRAE - 1 ( assumed to have a base value of - 0.2) . As the value of these parameters rise in absolute terms , the degree of sluggishness diminishes, possibly

driving the land rents across alternative uses together . In other words, ETRAE -2 influences the extent of change in cropping pattern and ETRAE - 1 influences the extent of land conversion. For instance, if we boost corn- ethanol production in the U.S., the resulting rise in corn price is shared among all the factors of production. As a result, the land rents attract more land into cereal grain production, which is taken from alternative uses. The land supply f unction is nested in the value added nest of the firms’ production structure which follows constant elasticity of substitution (CES) form. The land supply structure is discussed in detail in Birur et al. (2008) and the role land transformation elasticities in a biofuel policy experiment is examined in Hertel et al. (2010) . 3. Key Pa rameter Assumptions and Qualifications In addition to land transformation elasticities discussed in the previous section, there are other key p arameters that drive the land use change results due to biofuels production (Table 1). Yield elasticity (YDE_Target) is a parameter which determines the change in yi eld of a crop in response to change in price of that crop. With the increment in biofuels production, the supply of feedstock has to come either from the diversion of land under other uses or increased yi elds9

and/or expansion of land area under the feedstock crop. Keeney and Hertel (2008) examine the issue of crop yi eld response in greater detail. The GTAP - BIO model versions use a long-run elasticity of yi eld response to price of 0. 25 as recommended by those authors and is calibrated to reach this targeted yi eld response by adjusting the elasticity

o f substitution in crop production. International trade in commodities also determines the land use change in a given region. The trade elasticities (ESUBD) in the GTAP model determine the substitutability o f imports among all the exporting regions. In other words, as the domestic price of a commodity changes in a given region, ESUBD determines the extent to which an importer will substitute to an alternate exporter who can supply at a lower price . In addition to key

p arameters listed in Table 1, CARB (2009) assumes there is an effective cropland adjustment (ETA). As a result, crop yi elds in the newly c onverted land are lower than the yi elds obtained on regular cropland already in production. In the CARB analys is, it is assumed that f

or every one acre of additional cropland, two acres of other land covers are to be converted (ETA=1/2 = 0.5). More recently, Taheripour et al. (2009) assumed the value of ETA as 0.66 implying that for every two acres of cropland, three acres of other land are to be converted to grow crops. We follow the latter to keep our comparisons consistent . 3.1 Sensitivity of Key Parameters Since the parameters discussed above are the key inputs that drive the general equilibrium results, there are uncertainties associated with these parameter values. For exploring this uncertainty, we undertake systematic sensitivity analys is (SSA) in which the model is re solved for different draws from the underlying parameter distributions presented in Table 1. Monte Carlo analysis is the standard approach to this problem. However, it is impractical for large scale models. Therefore , Arndt (1996) recommends a systemic sensitivity analys is (SSA) with Gaussian Quadrature (GQ) numerical integration technique, which gives robust results with only few draws from the distribution of random variables. Pearson and Arndt (2000) employ this technique in the GTAP framework using Stroud Quadrature, which requires the model to solve only 2N times where N is the number of varying parameters/varia bles . Hertel et al. (2010) follow the approach given by Pearson and Arndt (2000) and use sy mmetric triangular distributions to approximate the underlying distribution of the key parameters, which permits us to characterize the parameter distribution through the mean and10

lower end point. The mean parameter values and end points obtained from the literature are as given in Table 1. The last column of the table shows the amount of variation which is the amount that the parameter is displaced from its central point to its endpoint. In the triangular distribution, the amount of variation is equivalent to the standard deviation multiplied by the square root of six. In our biofuel policy s cenarios we simultaneously vary all the parameters listed in Table 1 and the model solves 84 times holding the solution at each point to calculate mean and standard deviation of the model variables. The results reported in this study are the mean values and confidence intervals computed based on SSA . 3.2 Qualifications  Both the versions of the GTAP -BIO model include only f irst- generation biofuels. Due to the absence of nascent feedstock sectors in the GTAP data base, second generation biofuels are not included in this study.  Since this study

i s based on the static GTAP model, we cannot capture dy namic market interactions.  Conversion of land from pasture or forest covers involve accessibility cost which we have not captured in this model.  The current structure of the GTAP -BIO model versions classif ies shrubland, savanna grassland, built-up land, and other land as “unmanaged” and treats it as exogenously fixed in the model. The “managed” categories refer to cropland, pasture, and forest. This would rule out any possibility of conversion of “unmanaged” land categories.  The scenarios included in this analys is are only a rough approximation of the renewable fuels standard required by EI SA, because we have only analy zed the impact of increasing one renewable fuel volume, rather than analyzing the impact of the entire RFS2 and then backing out the impact of one fuel at a time. Furthermore, the corn ethanol scenarios do not get close to the projected volume of 15 billion gallons. 4. Experimental Design Before implementing the biofuel scenarios in the GTAP - BIO model versions , we first perform hi storical analys is as offered by B irur et al. (2008) which validates the model as well as projects the biofuel economy f or 2006. F or implementing the historical analysis those authors11

consider three key

f actors that were responsible for the biofuel boom in the U.S. and EU during the 2001 -2006 period. 1. A major factor is the rise in annual average real price of crude oil from $25.29/barrel in 2001 to $59.69/barrel in 2006. The 136% rise in crude oil price escalated gasoline prices, which further influences the price of biofuels. 2. Another factor that was responsible for the ethanol boom , particularly in the U.S. , was the phase out of methyl tertiary-butylether (MTBE), a petroleum derived additive used as octane enhancer in the oil industry. As MTBE was phased out, it was replaced by ethanol, the other recognized additive at a rate of about 49.24% during the five year period. 3. Other factors that encouraged the biofuels industry to expand rapidly were the subsidies and tax credits offered by governments. The U. S. has subsidized biofuels for three decades. S everal member states in the European Union also announced tax credits to the biofuel industry

d uring the 2001 -2006 period. The ad valorem equivalent of subsidies and tax credits to biofuels industry in these two regions were also considered for the historical validation experiment. After implementing the historical experiment involving the key b iofuel drivers listed above, we start from the ex- post 2006 biofuel economy and implement the corn- ethanol and bio diesel scenarios separately. As mentioned earlier, for the corn ethanol analys is we use the GTAP -BIO - 1 model and for biodiesel, we use the GTAP - BIO - 2 version . In both versions of the model the resulting simulated 2006 biofuel economy baseline has 4.252 b illion gallons of cornethanol, 0.140 bg of biodiesel, and 0.303 bg of imported sugar- ethanol in the U.S . As summarized in T able 2, we conducted two analyses based on increases in biofuels volumes specified by

EPA. In the corn- ethanol scenario , we increase the quantity o f corn- ethanol production by

2 bg (47% increase) in the U.S. while keeping production of all other biofuels at 2006 level. S imilarly, we implement the biodiesel scenario by increasing quantity of biodiesel production in the U.S. by 1 bg (714% increase) while all other biofuels production remain constant.12

5. Results from Corn-Ethanol Experiment Impact on Production: In this section, we discuss the impact of increasing U.S. corn ethanol production by

2b illion gallons per ye ar on some of the important variables related to biofuel economy. As ethanol production is exogenously increased in the U.S., as expected, production of cereal grains in the U.S. rises by 7million tonnes so as to meet the new demand for feedstock from the ethanol industry (Table 3). Most of this rise is attributed to corn as 8 7 % of cereal grains area and 9 4 % of cereal grains revenue sales constitute corn in the U.S. during 200 6. Model simulations for the baseline experiment indicate that about 18% of the corn produced in the U.S. would be used in the U.S. ethanol industry in 2006 , with the share increasing to 25.3% of U.S. corn production in the post corn- ethanol scenario. As seen from Table 3, due to competition for land, production slightly declined in all other crop sectors such as oilseeds ( - 0.76 mt), other grains ( - 0.96 mt), sugar- crops ( - 0.13 mt), and other agriculture ( - 1.83 mt) due to the rise in corn production. As the domestic use of corn increases in the U.S., export sales decline slightly. The model predicted U.S. export sales of cereal grains decline from 26.3% of U.S. corn production in 2006 to 25.1% in the ex- post scenario. The rest of the world responds to this smaller decline in export supply as it leads to a slight rise in commodity price s in the international market. Except for sugar- crops, all the other agricultural sectors in the rest of the world indicate a gain in production. Production of sugar- crops in Brazil decline by 0 .66 mt mainly b ecause Brazil ends up exporting more oilseeds to partially offset the decrease in U.S. oilseeds exports to the rest of the world. In all , the 2 bg corn- ethanol production in the U.S. leads to a permanent rise in cereal grains production by 8 mt globally. Impact on Commodity Prices : Table 4 reports the simulated percent change s in market price in important regions and for the world price in agricultural and energy s ectors . It is important to note that, since we have included only c orn- ethanol shock in this exercise and not all the exogenous variables that affe ct the global economy, our model predicted prices apply only to the post-2006 biofuel economy. The market price for cereal grains in the U.S. rose by 1 .95% over the baseline where the sectoral prices are denoted as numeraire and equal to 1. The change in price of all other agricultural sectors was positive but relatively small.13

Impact on Bilateral Trade: The corn- ethanol impact in the domestic market that we discussed above could have repercussions around the world through trade linkages. Table 5 depi cts bilateral trade in some of the key c ommodities such as cereal grains, oilseeds and crude oil. Interestingly, only

the U.S. shows a modest decline in exports of cereal grains by about $143 million. The main regions that registered a decline in imports were Japan, Latin-America, and Middle -Eastern countries (these regions are folded into Rest of the World to keep the table concise). The o ilseeds sector is an important competitor for cereal grains particularly in the U.S. Even the modest decline in oilseeds production in the U.S. resulted in a decline in exports by $113 million, mainly

to the European Union, China, and other Asian regions. This trend is partially o ffset by an increase in Brazilian oilseeds exports by $ 21 million. Other agricultural c ommodities did not register any significant change in trade patterns . Impact on Land

- Use and Land

- Cover : As we discussed above, the corn- ethanol experiment boosts demand for ethanol feedstocks and due to competition for limited land resource s , the price of other crops also rise. These higher crop prices transmit across borders, implying higher profitability and hence a shift in cropping pattern towards the crop in greater demand. The corn- ethanol driven change in land use and land cover across all the regions are presented in Table 6. As seen from the table, harvested area under cereal grains in the U.S. expanded by 2.22 million acres, part of it came at the cost of other major land using sectors such as other grains (paddy-rice and wheat: - 0.98 mill. acres) and o ilseeds ( - 0.64 mill. acres) . The demand for corn spilled over to the rest of the world, resulting in a small positive change in acreage under cereal grains in most of the regions, adding up to 2.66 million acres globally. Some of the decline in oilseeds acreage in the U.S. was offset by C anada, Sub Saharan Africa, and Brazil due to which oilseeds acreage declined only by - 0.09 million acres , globally. However, not much response was reported from the rest of the world to mitigate the reduced acreage under other grains in the U.S. which lead to global reduction of - 0.92 million acres under paddy-rice and wheat. After accounting for all the cropping pattern adjustments, about 1.39 million acres of additional cropland were required to meet the demand. As discussed earlier, in the GTAP -BIO model , expansion in crop cover is allowed to come from pastureland and accessible forest covers. The model predicted expansion in cropland in the U.S. (0.58 mill. acres) comes from decline in accessible forests ( - 0.22 mill. acres) and pasture lands ( - 0.36 mill. acres). The only other regions which showed significant expansion in14

cropland were Sub Saharan Africa (0.20 mill. acres), Canada (0.15 mill. acres), and the EU (0.12 mill. acres). While much of the cropland expansion in Africa came from a decline in pasture cover ( - 0.24 mill. acres) , in Canada and EU it was due to a decline in forest cover by 0 .10 and 0.07 million acres, respectively. The forest is mainly s old to the lumber sector (for example, in the GTAP data base 90% of forest sector sales are made to the lumber sector in the U.S. and the corresponding percentage for Canada is 70%) , therefore most of the other regions did not report much change in forest cover . Instead, most of the cropland expansion came from a decline in pasture cover (Table 6). On the global scale, the 1.39 million acres of additional cropland came from a decline in pasture and forest cover by 1.16 and 0.23 million acres, respectively. Sensitivity of Impact on Land: As discuss ed previously, since the key p arameters listed in Table 1 drive the general equilibrium results on impacts of biofuel production, we also report the results from a systematic sensitivity analys is on change in harvested acreage (Table 7) and land cover (Tab le 8). As depicted in Table 7, the mean value of change in cereal grains acreage was 2.17 million acres which ranged between 1.66 and 2.69 million acres in the U.S., when all the key p arameters are varied simultaneously. The model reported no significant variation in the range of cereal grains acreage in the rest of the regions and globally the cereals grains acreage ranged between 2.25 and 3.0 million acres . Global oilseeds acreage indicated a positive upper bound, suggesting that the decline in U.S. oil seeds acreage ( - 0.46 mill. acres) due to corn- ethanol production may be completely o ffset by other regions leading to a marginal increase in global oilseeds acreage (0.05 mill. acres) . However, the other land using sectors did not indicate significant variation in their harvested acreage. Sensitivity

on expansion of cropland to other land cover is presented in Table 8. The SSA results on crop cover indicate that U.S. corn- ethanol production may require as low as 0.98 million acres of additional cropland and the same could be 1.78 million acres on the upper scale subjected to variability

in parameter values. It is clear from the table that although most of the additional cropland came from pasture cover ( - 1.15 mill. acres), the SSA results indicate a range of pasture cover change between - 1.50 and - 0.83 million acres. However, all the significant variation in the change in forest cover was observed in Canada and EU regions. For a better understanding of these results, change s in cropland, pasture cover, and forest cover with error bars are depicted in Figures 2 a, 2b, and 2 c, respectively. The SSA exercise suggests that much of the variation in the land cover was reported in the U.S. and not in the rest of the regions.15

Hence the theory

that variation in land transformation elasticities drive the impact on land use though the role of trade elasticities is not undermined. These sensitivity results prove useful especially when accounting for land conversion due to biofuels production. Apart from the sensitivity of the key parameters, the magnitude of the shock and the ty pe of biofuel also drives the general equilibrium results. F or relative comparison purpose, we have reported land required for the production of corn- ethanol normalized on an energy b asi s 3

. A s seen from Figure 3 , production of one billion BTUs of corn- ethanol in the U.S. requires about 3.70 hectares of cropland globally, which ranges from 1.93 to 5.42 hectares subjected to sensitivity

of the key

parameters. 6. Results from Soybean-Biodiesel Experiment Impact on Production: As mentioned in the experimental design section, we implemented a 1bg biodiesel production scenario starting from the 2006 biofuel economy. The results on change s in agricultural output across the regions due to biodiesel production are presented in Table 9. The demand for oilseeds feedstock resulted in the production of 3.61 million tonnes in the U.S. which came at the expense of a reduction in all other agricultural sectors such as cereal grains ( - 1.82 mt), wheat ( - 0.55 mt), and other agriculture ( -2.60 mt). Unlike the corn- ethanol case, a strong spillover effect on the rest of the world was observed in the biodiesel scenario. S oy beans are the major feedstock used for production of biodiesel in the U.S. , but they contain only

a bout 18% oil . Thus, expanded biodiesel production in this experiment results in a very l arge increase in demand for the soy bean feedstock. Note that, as USDA reports, nearly 57% of the global oilseed production basket constitutes soy bean, followed by rapeseed (12%), cottonseed (11%), peanuts (10%) and the remaining 10% include palm-kernel, copra, sunflower, etc. This clearly indicates the extent of the impact that could result due to soy bean derived biodiesel production in the U.S. The impact of biodiesel production on the rest of the world is such that all of the regions indicate a positive change in the production of oilseeds resulting in 9.46 million tonnes of gl obal production. M alaysia and Indonesia end up producing 2. 26 mt of oilseeds basically to meet the demand for refined vegetable oil from EU and other Asian regions. Global production of cereal grains and wheat drop by 2 .20 mt and 1.38 mt, respectively. However, production of paddy-rice , the other staple crop , does not get affected much.

3 Considered low-heating value of ethanol = 76,000 Btu/gallon.16

Impact on Commodity Prices : Change s in regional market price s and world price s in the agricultural and energy

sectors due to biodiesel production are presented in Table 10. Keeping in mind that our simulation experiment included only a biodiesel sho ck and not any attempt to project the global economy in time, the model predicted a wide range of implications on sectoral prices, particularly

on the feed industry. As seen from the table, although the market price of oilseeds in the U.S. increased by only 3.58%, the price of crude -vegetable oil , which is the major component of biodiesel in the model, rose by 1 11% in the U.S. market. S ince a large portion of soy bean constitutes oil -meal/cake (VOBP sector), the production of which is complementary to biodiesel, expanded biodiesel production resulted in a 51% drop in its market price. S ince oil meal is mainly

used by the livestock industry

as animal feed, it substitutes for processed- feed leading to a drop in the latter’s market price by 10% in the U.S. Though there is a mixed spillover of market price to other regions, the world price of oilseeds rises by 1 .44% and that of crude vegetable oil b y 17%. Impact on Bilateral Trade: The change s in market prices due to biodiesel production discussed above have implications on bilateral trade. T able 11 depicts change in bilateral trade volume in selected commodities after implementing the biodiesel scenario. As seen from the middle panel of the table, exports of oilseeds from the U.S. fall b y $208 million and this deficit is adequately met in the international market by B razil ($163 million) and Canada ($101 million) . Overall, the world trade in oilseeds rises by $ 269 million. Though U.S. cereal grains production decreases due to biodiesel production, exports of cereal grains fall only $17 million. Impact on Land

- Use and Land

- Cover : As the U.S. biodiesel production escalates demand for oilseeds, the changing crop prices drive land use changes in all the regions. Table 12 depicts change in harvested area across the six crop categories and change in land cover across the three cover ty

pes. In these simulations, 1 bg of increased biodiesel production required a permanent shift in oilseed acreage by 4 .18 mill . acres in the U.S. and 11 mill . acres globally. The cropping pattern changes in the U.S. were larger than that of any other regions. The acreage under coarse grains, wheat, and other agriculture fell by 1 .73, 1.09, and 1.29 million acres, respectively in the U.S. As reported by USDA acreage data, nearly 80% of the oilseeds area planted in the U.S. constitutes soy beans acreage, followed by c ottonseed which forms about 15%. Therefore, any change in the oilseeds sector in the U.S. may b e treated as soy

beans. Due to this model predicted pattern essentially no additional cropland was required in the U.S. to17

meet the demand for oilseed. Oilseed acreage increased mainly in the EU (1.27 mill. acres), followed by

C anada, Brazil and other South American regions. Globally, harvested acreage under cereal grains, wheat, other agriculture, and paddy-rice, fell by 2 .70, 2.23, 2.32, and 0.56 million acres, respectively. After adjusting cropping pattern across all the regions, the model predicted an additional 3.01 million acres of cropland to meet the U.S. oilseeds demand. Globally, this additional cropland mainly came from pasture cover ( -2.24 mill. acres) and accessible forests ( - 0.77 mill. acres). However, due to the complementary nature of feed and pasture in the livestock industry, the model predicted an incre ase in pasture - cover by 0 .72 million acres which mainly c ame from accessible forests ( - 0.69 mill. acres) in the U.S . In contrast, most other regions indicated an expansion in cropland that mainly came from the decline in pasture cover. Sensitivity of Im

pact on Land: As depicted in Table 13, the SSA results on harvested acreage due to the biodiesel experiment indicates a range of land use change particularly in the oilseeds and wheat sectors in the U.S . While the mean estimate of oilseeds acreage in the U.S. is 4.10 mill. acres, it ranged from 3.06 to 5.14 mill. acres subjected to variation in the key parameters listed in Table 1. Though the change in paddy-rice acreage was only - 0.54 mill. acres on the global scale, much of this change came from Malays ia and Indonesia with a range of - 0.29 to - 0.18 mill. acres. These countries also reported an increase in oilseeds acreage with a range of 0.34 to 0.56 mill. acres. This increase may be due to the fact that some of the reduction in U.S. oilseeds exports is offset by M alaysia and Indonesia. The major competing crop for soy bean acreage in the U.S. is corn. The decline in area under cereal grains ranged between - 2.05 and - 1.34 mill. acres in the U.S.. Since we kept the sugar- ethanol constant at the 2006 level, no significant change in sugar- crops acreage was predicted by the model. The Africa region also reported a range in the increase in oilseeds acreage (0.72 to 1.12 mill. acres) though it did not come at the cost of a large decline other crop acres. Given the range of harvested acreage adjustments, the mean cropland requirement in the U.S. was - 0.01 mill. acres with a range of - 0.14 to 0.13 million acres (Table 14). This indicates that though U.S. cropland acreage actually went down in the biodiesel experiment, subjected to sensitivity

o f the key

p arameters, it could even demand additional cropland to meet the demand for oilseeds. It is clear from Table 14 that pasture - cover change in all regions was sensitive to18

variation in the key

p arameters. A large increase in forest cover change was predicted only in the U.S., EU, and Canada. The model predicted oilseeds acreage in Brazil ranged from 0.61 to 1.01 mill. acres, most of this came from the decline in pasture - cover without any large impact on fore st cover. It is interesting to note that China, Eastern Europe, and some of the South American regions indicated positive change in forest cover . However, these regions reported a decline in pasture cover due to U.S. biodiesel production. These variations are depicted as error bars in Figure 4 a, 4b, and 4 c, corresponding to land cover change in crops, pasture, and forest, respectively. The SSA exercise on the biodiesel experiment suggests that subjected to variation in the parameters there is a range in the requirement of additional cropland across the regions including the U.S. F igure 5 depicts the hectares required to produce one billion BTUs of biodiesel normalized on an energy b asis 4

. As shown in the figure , about 11 hectares of cropland is required to produce 1 billion BTU of biodiesel . Most of this land comes from regions other than the U.S. However the error bars indicate that that results are sensitive to the parameters, so that the total hectares required could even range between 3.6 to 18.4 hectares per billion BTU of biodiesel. 7. Conclusions This study

illustrates the global impact of expanded production of corn- ethanol and biodiesel in the U.S. in a general equilibrium framework, which simulates the changes in markets that would result from expanded demand while ensuring that all factor and commodity markets in all the regions are in equilibrium. Keeping the relative treatment of biodiesel by products in mind, we used two different versions of the GTAP -BIO model to predict the implications due to the producti on of 2 billion gallons of corn ethanol and 1 billion gallon of biodiesel. We found that the corn- ethanol experiment requires 1.39 million acres of additional cropland or 3.70 ha per billion BTU of ethanol, whereas the biodiesel experiment revealed that about 3.21 million acres of additional cropland is required to meet the oilseeds demand or 11 ha per billion BTU of biodiesel. The systematic sensitivity analys is showed that the model results are sensitive to variation in the key

parameter values.

4 Considered low-heating value of biodiesel = 118,296 Btu/gallon.19

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Table 1 . Variation in key parameters driving the general equilibrium results on biofuels. Parameters Lower

bound Me an Upper

bound Standard Deviation Amount of

Variation: SD*(6^0.5) 1 . Yield elasticity (YDE_Target) 0 0.25 0.5 0.102 0.25 2 . Elasticity

of transformation across land cover ty pes (ETRAE - 1) - 0.04 -0.2 - 0.36 0.065 0.16

3 . Elasticity. of transformation acro ss cropland (ETRAE -2) - 0.1 -0.5 - 1 0.163 0.40 4 . Armington CES elasticity of substitution for domestic and imported (ESUBD): a. Coarse Grains 0.75 1.30 1.85 0.225 0.55 b. Other Grains 2.66 4.75 6.84 0.853 2.09 c. Oilseeds 2.05 2.45 2.85 0.163 0.4 0 d. Sugarcane 1.7 0 2.70 3.7 0 0.408 1 .00 e. Other Agri 1.57 1.89 2.21 0.129 0.32 b 1 . Paddy

Rice 3.05 5.05 7.05 0.816 2.00 b2 . Wheat 2.35 4.45 6.55 0.857 2.10 Source: Adapted from Hertel, Tyner, and Birur (2010) .23 Table 2 . The U.S. biofuel scenarios implemented in the GTAP model. Billion gallons Corn-Ethanol Biodiesel Sugar-Ethanol 2006 baseline 4.252 0.140 0.303 Corn- ethanol Experiment: +2.00 (47.04%) 0.140 0.303 Biodiesel Experiment: 4.252 +1.00 ( 714.29% ) 0.30324

Table 3 . Change in agricultural output due to an increase in corn- ethanol production in the U.S. (million tonnes) Regions C ereal grains Oilseeds Other

grains Sugar

crops Other

Agri U.S.A. 7.24 - 0.76 - 0.96 - 0.13 - 1.8 3 Canada 0.08 0.06 0.00 0.00 0.02 EU-27 0.09 0.03 0.07 0.02 0.18 Brazil 0.09 0.12 0.00 - 0.66 0.01 Japan 0.00 0.00 0.01 0.00 0.00 China-Hong Kong 0.18 0.07 0.07 0.01 0.02 India 0.00 0.01 0.04 0.02 0.02 Latin American EEx. 0.15 0.11 0.00 - 0.04 - 0.12 Rest of Latin Am. 0.02 0.02 0.01 - 0.04 - 0.01 EE & FSU EEx. 0.03 0.01 0.02 0.01 0.06 Rest of Europe 0.04 0.01 0.04 0.00 0.02 Middle Eastern N Africa EEx. 0.04 0.00 0.05 0.00 - 0.06 Sub Saharan EEx. - 0.01 0.06 0.02 0.00 0.07 Rest of North Africa & SSA 0.03 0.00 0.00 0.01 0.00 South Asian EEx. 0.01 0.18 0.02 0.00 0.00 Rest of High Income Asia 0.00 0.00 0.00 0.00 0.00 Rest of Southeast & South Asia 0.01 0.03 0.03 0.01 0.00 Oceania countries 0.04 0.01 0.00 0.01 0.02 Global Total 8.05 - 0.02 - 0.57 - 0.79 - 1.5825

Table 4 . Change in commodity price due to an increase in corn- ethanol production in the U.S. ( percent change) Sectors Market Price World Price

USA Canada EU-27 Brazil C ereal Grains 1.95 0.21 0.08 0.17 0.48 Other Grains 0 .43 0.17 0.08 0.13 0.07 Oilseeds 0.80 0.25 0.12 0.16 0.25 Sugarcane 1.08 0.24 0.06 0.07 0.12 Other Agri 0.35 0.12 0.04 0.07 0.08 Proc Livestock 0.11 0.05 0.00 0.01 0.03 Ethanol1 0.98 - 3.42 - 0.04 0.01 0.80 DDGS - 0.22 12.95 0.41 0.17 0.09 Biodiesel 0. 53 0.11 0.10 0.06 0.21 Oil_Cake 0.06 0.05 0.01 0.03 0.03 Ethanol2 - 0.03 - 0.09 - 0.08 0.00 - 1.18 Coal 0.06 0.04 0.05 0.04 0.03 Oil - 0.87 - 0.75 - 0.66 - 0.64 - 0.70 Gas - 0.04 - 0.06 - 0.04 - 0.08 - 0.05 Oil_Pcts - 0.64 - 0.61 - 0.58 - 0.53 - 0.58 Electricity - 0.02 - 0.02 - 0.02 - 0.05 - 0.0626

Table 5. Impact of an increase in U.S. corn- ethanol production on b ilateral trade (Change in import volume) ($ millions) Exporters: (post-2006 ) Coarse Grains Oilseeds US EU RoW Total

Exports US EU RoW Total Exports 1 US 0 -4 - 139 - 143 0 - 34 - 79 - 113 2 Canada - 3 0 2 - 1 1 0 9 10 3 EU-27 - 1 0 12 11 0 0 2 2 4 Brazil 0 0 7 7 0 8 12 21 5 Japan 0 0 0 0 0 0 0 0 6 China-Hong Kong 0 0 9 9 0 1 5 6 7 India 0 0 0 0 1 1 3 4 8 Latin American Energy Exporters 0 - 1 10 9 0 0 9 9 9 Rest of Latin America & Caribbean - 1 0 2 1 0 0 3 4 10 EE & FSU Energy Exp 0 0 4 5 0 3 1 4 11 Rest of Europe 0 0 1 1 0 0 0 1 12 Middle Eastern N Africa energy exporters 0 0 1 1 0 1 0 1 13 Sub Saharan Energy exporters 0 0 1 1 0 1 3 4 14 Rest of North Africa & SSA 0 0 4 4 0 0 0 0 15 South Asian Energy exporters 0 0 0 0 0 0 1 1 16 Rest of High Income Asia 0 0 0 0 0 0 0 0 17 Rest of Southeast & South Asia 0 0 1 0 0 0 1 1 18 Oceania countries 0 0 5 5 0 0 2 2 Total -5 -5 -81 -90 3 - 19 -29 -44 Note: Change in volume of exports of coarse grains, and oilseeds from all the 18 regions to the US, EU , and Rest of the World were evaluated at initial market prices.27 Table 6 . Change in land use and land cover due to an increase in corn- ethanol production in the U . S. (million acres) Regions Change in harvested area Change in land cover CrGrains Oilseeds Sugar crops OthGrains OthAgri Cropland Forest Pasture U .S.A. 2.22 - 0.64 - 0.01 - 0.98 - 0.02 0.58 - 0.22 - 0.36 Canada 0.05 0.10 0.00 0.00 0.00 0.15 - 0.10 - 0.05 EU-27 0.04 0.03 0.00 0.03 0.02 0.12 - 0.07 - 0.04 Brazil 0.05 0.08 - 0.04 0.00 - 0.02 0.06 0.00 - 0.07 Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 China-Hong Kong 0.06 0.06 0.00 0.00 - 0.09 0.02 0.01 - 0.03 India 0.00 0.01 0.00 0.00 - 0.01 0.00 0.00 0.00 Latin American EEx. 0.07 0.06 - 0.01 - 0.02 - 0.05 0.06 0.00 - 0.06 Rest of Latin Am. 0.02 0.01 - 0.01 0.00 - 0.01 0.02 0.02 - 0.04 EE & FSU EEx. 0.03 0.03 0.00 0.01 0.01 0.09 0.09 - 0.17 Rest of Europe 0.03 0.01 0.00 0.01 0.00 0.03 - 0.02 - 0.02 Middle Eastern N Africa EEx. 0.03 0.01 0.00 0.01 - 0.03 0.02 0.00 - 0.02 Sub Saharan EEx. - 0.01 0.11 0.00 0.04 0.06 0.20 0.04 - 0.24 Rest of North Africa & SSA 0.03 0.01 0.00 0.00 - 0.01 0.03 0.00 - 0.02 South Asian EEx. 0.00 0.03 0.00 0.00 - 0.03 - 0.01 0.01 - 0.01 Rest of High Income Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rest of Southeast & South Asia 0.00 0.01 0.00 0.00 - 0.01 0.00 0.00 0.00 Oceania countries 0.04 0.01 0.00 - 0.02 0.00 0.03 0.00 - 0.03 Global 2.66 - 0.0 9 - 0.06 - 0.92 - 0.19 1.39 - 0.23 - 1.1628

Table 7 . SSA estimates of change in harvested area of crops due to an increase in corn- ethanol production in the U.S . (million acres) Regions CrGrains Oilseeds Sugar crops OthGrains Other Agri Mean Confidence Interval (95%) Mean Confidence Interval (95%) Mean Confidence Interval (95%) Mean Confidence Interval (95%) Mean Confidence Interval (95%) Lo wer Upper Lo wer Upper Lo wer Upper Lo wer Upper Lo wer Upper U.S.A. 2.17 1.66 2.69 - 0.62 - 0.79 - 0.46 - 0.01 - 0.01 - 0 .01 - 0.95 - 1.37 - 0.53 - 0.03 - 0.08 0.02 Canada 0.05 0.01 0.09 0.09 0.05 0.14 0.00 0.00 0.00 0.00 - 0.04 0.04 0.00 - 0.02 0.02 EU-27 0.04 - 0.01 0.09 0.03 0.00 0.05 0.00 0.00 0.00 0.02 - 0.01 0.05 0.02 0.01 0.03 Brazil 0.05 0.02 0.08 0.08 0.04 0.12 - 0.03 - 0.0 4 - 0.03 0.00 - 0.01 0.00 - 0.02 - 0.04 - 0.01 Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 China-Hong Kong 0.06 0.03 0.10 0.05 0.02 0.08 0.00 0.00 0.00 0.00 0.00 0.00 - 0.09 - 0.12 - 0.06 India 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 - 0.01 - 0.01 0.00 Latin American EEx. 0.07 0.02 0.12 0.06 0.03 0.10 - 0.01 - 0.01 0.00 - 0.02 - 0.04 0.00 - 0.04 - 0.06 - 0.03 Rest of Latin Am. 0.02 0.01 0.04 0.01 0.00 0.01 - 0.01 - 0.01 0.00 0.00 - 0.01 0.01 - 0.01 - 0.02 0.00 EE & FSU EEx. 0.03 0.02 0.05 0.03 0.02 0.04 0.00 0.00 0.00 0.01 - 0.01 0.03 0.01 0.00 0.02 Rest of Europe 0.03 0.02 0.04 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 - 0.01 0.01 Middle Eastern N Africa EEx. 0.04 0.02 0.05 0.01 0.00 0.01 0.00 0.00 0.00 0.0 1 0.00 0.02 - 0.03 - 0.05 - 0.02 Sub Saharan EEx. - 0.01 - 0.03 0.01 0.11 0.07 0.14 0.00 0.00 0.00 0.04 0.02 0.05 0.06 0.03 0.09 Rest of North Africa & SSA 0.03 0.01 0.04 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 - 0.01 - 0.01 0.00 South Asian EEx. 0.00 0.0 0 0.01 0.02 0.01 0.04 0.00 0.00 0.00 0.00 0.00 0.00 - 0.03 - 0.04 - 0.02 Rest of High Income Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rest of Southeast & South Asia 0.00 0.00 0.01 0.01 0.01 0.02 0.00 0.00 0.00 0.00 0.0 0 0.00 - 0.01 - 0.02 - 0.01 Oceania countries 0.04 0.02 0.06 0.01 0.01 0.02 0.00 0.00 0.00 - 0.02 - 0.05 0.01 0.00 - 0.01 0.00 Global 2.63 2.25 3.00 - 0.09 - 0.23 0.05 - 0.06 - 0.07 - 0.05 - 0.90 - 1.19 - 0.61 - 0.19 - 0.29 - 0.1029 Table 8 . SSA estimates of change in land cover due to an increase in corn- ethanol production in the U.S . (million acres) Regions Crop Cover Forest Cover Pasture Cover Mean Confidence

Interval (95%) Mean Confidence Interval (95%) Mean Confidence Interval (95%) Lo wer Upp er Lo wer Upper Lo wer Upper U . S .A. 0.57 0.35 0.78 - 0.21 - 0.28 - 0.14 - 0.36 - 0.51 - 0.20 Canada 0.15 0.06 0.23 - 0.10 - 0.15 - 0.04 - 0.05 - 0.08 - 0.02 EU-27 0.12 0.05 0.18 - 0.08 - 0.12 - 0.03 - 0.04 - 0.06 - 0.02 Brazil 0.06 0.03 0.10 0.00 - 0.01 0.01 - 0.06 - 0.09 - 0.04 Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 China-Hong Kong 0.02 0.01 0.04 0.01 0.00 0.02 - 0.03 - 0.06 - 0.01 India 0.00 - 0.01 0.01 0.00 - 0.01 0.01 0.00 0.00 0.00 Latin American EEx. 0.06 0.03 0.09 0.00 0.00 0.01 - 0.06 - 0.10 - 0.03 Rest of Latin Am. 0.02 0.01 0.03 0.02 0.01 0.03 - 0.04 - 0.06 - 0.02 EE & FSU EEx. 0.09 0.04 0.13 0.08 0.03 0.13 - 0.17 - 0.24 - 0.10 Rest of Europe 0.03 0.02 0.05 - 0.02 - 0.02 - 0.01 - 0.02 - 0.03 - 0.01 Middle Eastern N Africa EEx. 0.02 0.01 0.03 0.00 0.00 0.00 - 0.02 - 0.03 - 0.01 Sub Saharan EEx. 0.20 0.12 0.27 0.04 0.01 0.06 - 0.24 - 0.32 - 0.16 Rest of North Africa & SSA 0.03 0.01 0.04 0.00 - 0.01 0.00 - 0.02 - 0.04 - 0.01 South Asian EEx. - 0.01 - 0.01 0.00 0.01 0.00 0.02 - 0.01 - 0.01 0.00 Rest of High Income Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rest of Southeast & South Asia 0.00 0.00 0.01 0.00 0.00 0.00 0.00 - 0.01 0.00 Oceania countries 0.03 0.01 0.05 0.00 0.00 0.01 - 0.03 - 0.05 - 0.01 Global 1.38 0.98 1.78 - 0.24 - 0. 36 - 0. 11 - 1.15 - 1.50 - 0.8330

Table 9 . Change in agricultural output due to an increase in biodiesel production in the U.S. (million tonnes) Cereal Grains Oilseeds Paddy

Rice Wheat Sugar

crops Other

Agri U.S.A. - 1.82 3.61 - 0.08 - 0.55 - 0.05 - 2.60 EU-27 0.31 0.75 0.00 - 0.52 0.04 - 2. 17 Brazil - 0.12 0.59 - 0.01 - 0.01 - 0.38 - 0.30 Canada - 0.18 0.35 0.00 - 0.13 0.00 - 0.64 Japan 0.00 0.00 0.00 0.00 0.00 0.01 China, Hong Kong - 0.18 0.24 - 0.07 - 0.01 - 0.01 0.04 India - 0.02 0.11 - 0.03 - 0.04 - 0.06 0.11 Caribbean and Central America - 0.05 0. 13 0.01 0.00 0.07 0.01 South and Rest of America - 0.06 0.70 0.02 - 0.05 0.14 - 0.23 East Asia 0.00 0.00 0.00 0.00 0.00 0.01 Malaysia, Indonesia - 0.04 2.26 - 0.03 0.00 - 0.02 - 0.02 Rest of Southeast Asia - 0.05 0.16 0.02 0.00 0.00 0.06 Rest of South Asia 0. 00 0.01 0.00 - 0.01 - 0.01 0.02 Russia - 0.01 0.04 0.00 - 0.01 0.00 0.01 Other Eastern Europe & FSU 0.00 0.04 0.00 0.00 0.00 0.10 Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.03 Middle East & North Africa 0.00 0.05 - 0.01 - 0.02 0.02 0.23 SSA & Rest of Africa 0.01 0.36 - 0.01 0.00 0.17 0.29 Oceania countries 0.01 0.07 0.00 - 0.02 - 0.01 0.25 Global Total -2.20 9.46 - 0.15 - 1.38 - 0.10 -4.8031

Table 10 . Change in commodity price due to an increase in biodiesel production in the U.S. ( percent change) Sectors Market Price World Price

USA EU-27 Brazil Canada Paddy _Rice 0.35 0.20 0.58 - 0.02 0.14 Wheat - 0.09 0.31 0.33 0.10 0.15 CrGrains 0.27 0.51 0.44 0.11 0.22 Oilseeds 3.58 1.34 1.02 1.23 1.44 Sugar_Crop 0.60 0.41 0.48 0.34 0.28 OthAgri 0.43 0 .36 0.47 0.21 0.23 Proc_Dairy - 0.88 0.00 0.09 - 0.23 - 0.25 Proc_Rum - 1.18 - 0.05 0.11 - 0.33 - 0.44 proc_NonRum - 1.29 - 0.13 0.07 - 0.29 - 0.36 Proc_Feed - 10.44 - 0.70 0.15 - 1.91 -2.71 Ethanol1 - 0.94 - 0.04 0.08 0.00 - 0.88 DDGS 0.21 0.79 0.79 0.15 0.28 Cveg_Oil1 110.89 6.98 2.28 16.42 16.53 VOBP - 50.89 - 12.02 - 0.24 - 15.11 -22.07 Biodiesel 56.44 5.44 0.40 2.77 28.57 Ethanol2 0.02 - 0.02 0.13 - 0.01 - 0.69 Coal - 0.13 - 0.02 - 0.03 - 0.04 - 0.05 Oil - 0.35 - 0.20 - 0.18 - 0.27 - 0.23 Gas - 0.03 - 0.02 - 0.02 - 0.05 - 0.02 Oil_Pcts - 0.24 - 0.16 - 0.14 - 0.19 - 0.17 Electricity - 0.04 - 0.02 - 0.01 - 0.03 - 0.0332

Table 11. Impact of an increase in U.S. biodiesel production on b ilateral trade (Ch . in import volume) ($ millions) Exporters: (post-2006 ) Cereal Grains Oilseeds US EU RoW Total

Exports US EU RoW Total Exports 1 U.S.A. 0 1 - 18 - 17 0 - 74 - 134 -208 2 EU-27 - 1 0 - 7 - 8 0 0 4 4 3 Brazil 0 1 - 3 -2 0 101 62 163 4 Canada - 1 0 0 0 21 7 73 101 5 Japan 0 0 0 0 0 0 0 1 6 China, Hong Kong 0 0 2 3 1 12 30 43 7 India 0 0 0 0 5 4 13 22 8 Caribbean and Central America 0 0 0 0 4 0 8 13 9 South and Rest of America - 1 2 - 3 -2 3 10 36 49 10 East Asia 0 0 0 0 0 0 2 2 11 Malaysia, Indonesia 0 0 0 0 0 0 0 - 1 12 Rest of Southeast Asia 0 0 1 1 1 0 5 6 13 Rest of South Asia 0 0 0 0 0 0 2 2 14 Russia 0 1 0 1 0 4 0 4 15 Oth Eastern Europe & FSU 0 1 1 2 2 18 2 22 16 Rest of Europe 0 0 0 0 0 0 0 0 17 Middle East & North Africa 0 0 0 0 1 3 1 5 18 SSA & Rest of Africa 0 0 0 0 2 7 12 21 19 Oceania countries 0 0 0 0 7 3 10 20 Total -3 7 -25 -21 47 96 126 269 Note: Change in volume of exports of coarse grains and oilseeds from all the 18 regions to the US, EU , and Rest of the World were evaluated at initial market prices.33

Table 12. Change in land use and land cover due to an increase in biodiesel production in the U.S. (million acres). Change in harvested area Change in land cover Paddy Rice Wheat CrGrains Oilseeds Sugar crops OthAgri Cropland Forest Pasture U.S.A. - 0.07 - 1.09 - 1.73 4 .18 - 0.03 - 1.29 - 0.03 - 0.69 0.72 EU-27 0.00 - 0.48 0.11 1.27 0.00 - 0.33 0.57 - 0.35 - 0.22 Brazil - 0.02 - 0.03 - 0.16 0.80 - 0.03 - 0.14 0.41 - 0.08 - 0.34 Canada 0.00 - 0.16 - 0.12 0.92 0.00 - 0.09 0.55 - 0.08 - 0.47 Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 China, Hong Kong - 0.10 - 0.05 - 0.16 0.42 0.00 - 0.08 0.03 0.12 - 0.15 India - 0.04 - 0.05 - 0.06 0.30 0.00 0.02 0.16 - 0.09 - 0.07 Caribbean and Central America 0.01 0.00 - 0.04 0.09 0.01 0.01 0.08 0.17 - 0.25 South and Rest of America - 0.01 - 0.19 - 0.15 0.80 - 0.01 - 0.27 0.18 0.12 - 0.29 East Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 - 0.01 Malaysia, Indonesia - 0.24 0.00 - 0.08 0.46 - 0.01 - 0.11 0.02 - 0.01 0.00 Rest of Southeast Asia - 0.06 0.00 - 0.08 0.16 0.00 - 0.01 0.00 0.01 - 0.01 Rest of South Asia 0.00 - 0 .01 0.00 0.02 0.00 0.01 0.02 - 0.01 - 0.02 Russia 0.00 - 0.05 - 0.05 0.15 0.00 - 0.06 - 0.01 0.16 - 0.15 Oth Eastern Europe & FSU 0.00 - 0.01 0.00 0.13 0.00 0.01 0.12 0.01 - 0.12 Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.01 Middle East & North Africa 0.00 - 0.05 - 0.02 0.16 0.00 0.02 0.11 0.00 - 0.11 SSA & Rest of Africa - 0.04 - 0.01 - 0.18 0.91 0.01 - 0.06 0.63 - 0.09 - 0.53 Oceania countries 0.00 - 0.03 0.02 0.11 0.00 0.05 0.16 0.04 - 0.19 Global - 0.56 -2.23 -2.70 10.88 - 0.07 -2.32 3.01 - 0.77 -2.2434

Table 13 . SSA estimates of change in harvested area of crops due to an increase in biodiesel production in the US (million acres).

Regions Paddy Rice Wheat CrGrains Mean Confidence

Interval (95%) Mean Confidence Interval (95%) Mean Confidence Interval (95%) Lo wer Upper Lo wer Upper Lo wer Upper U.S.A. - 0.07 - 0.08 - 0.05 - 1.07 - 1.42 - 0.73 - 1.69 -2.05 - 1.34 EU-27 0.00 0.00 0.00 - 0.47 - 0.56 - 0.39 0.11 0.02 0.21 Brazil - 0.02 - 0.03 - 0.01 - 0.03 - 0.04 - 0.03 - 0.16 - 0.20 - 0.12 Canada 0.00 0.00 0.00 - 0.15 - 0.23 - 0.07 - 0.11 - 0.15 - 0.08 Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 China, Hong Kong - 0.10 - 0.12 - 0.07 - 0.05 - 0.07 - 0.02 - 0.16 - 0.19 - 0.12 India - 0.03 - 0.07 0.00 - 0.05 - 0.06 - 0.04 - 0.06 - 0.07 - 0.04 Caribbean and Central America 0.01 0 .01 0.01 0.00 0.00 0.00 - 0.04 - 0.05 - 0.03 South and Rest of America - 0.01 - 0.01 0.00 - 0.18 - 0.21 - 0.16 - 0.15 - 0.17 - 0.12 East Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Malaysia, Indonesia - 0.24 - 0.29 - 0.18 0.00 0.00 0.00 - 0.08 - 0.10 - 0.06 Rest of Southeast Asia - 0.06 - 0.10 - 0.02 0.00 0.00 0.00 - 0.08 - 0.10 - 0.06 Rest of South Asia 0.00 - 0.01 0.01 - 0.01 - 0.01 0.00 0.00 0.00 0.00 Russia 0.00 0.00 0.00 - 0.05 - 0.06 - 0.03 - 0.05 - 0.06 - 0.05 Oth Eastern Europe & FSU 0.00 0.00 0.00 - 0.01 - 0.06 0.04 0. 00 - 0.02 0.02 Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Middle East & North Africa 0.00 0.00 0.00 - 0.04 - 0.08 - 0.01 - 0.02 - 0.03 - 0.01 SSA & Rest of Africa - 0.03 - 0.04 - 0.03 - 0.01 - 0.02 0.00 - 0.17 - 0.26 - 0.07 Oceania countries 0.00 0.0 0 0.00 - 0.03 - 0.05 - 0.01 0.02 0.01 0.03 Global

- 0.54 -2.16 -2.6335

Table 13 . SSA estimates of change in harvested area of crops due to an increase in biodiesel production in the US (million acres) ( Continued

) Regions Oilseeds Sugar crops Other Agri Mean Confidence

Interval (95%) Mean Confidence Interval (95%) Mean Confidence Interval (95%) Lo wer Upper Lo wer Upper Lo wer Upper U.S.A. 4.10 3.06 5.14 - 0.03 - 0.03 - 0.02 - 1.25 - 1.55 - 0.96 EU-27 1.27 1.13 1.41 0.00 - 0.01 0.01 - 0.32 - 0 .41 - 0.22 Brazil 0.81 0.61 1.01 - 0.02 - 0.04 - 0.01 - 0.14 - 0.18 - 0.09 Canada 0.92 0.77 1.07 0.00 0.00 0.00 - 0.08 - 0.15 - 0.01 Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 China, Hong Kong 0.42 0.32 0.53 0.00 0.00 0.00 - 0.08 - 0.12 - 0.05 India 0.30 0 .22 0.37 0.00 - 0.01 0.00 0.02 - 0.01 0.05 Caribbean and Central America 0.09 0.08 0.11 0.01 0.00 0.01 0.01 0.00 0.03 South and Rest of America 0.79 0.67 0.91 - 0.01 - 0.01 0.00 - 0.26 - 0.32 - 0.21 East Asia 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 Malaysia, Indonesia 0.45 0.34 0.56 - 0.01 - 0.01 - 0.01 - 0.11 - 0.13 - 0.08 Rest of Southeast Asia 0.16 0.10 0.21 0.00 - 0.01 0.00 - 0.01 - 0.03 0.00 Rest of South Asia 0.02 0.01 0.03 0.00 0.00 0.00 0.01 0.01 0.02 Russia 0.15 0.13 0.18 0.00 0.00 0.00 - 0.06 - 0.07 - 0. 04 Oth Eastern Europe & FSU 0.14 0.09 0.18 0.00 0.00 0.00 0.01 - 0.02 0.05 Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Middle East & North Africa 0.16 0.13 0.18 0.00 0.00 0.00 0.02 0.00 0.04 SSA & Rest of Africa 0.92 0.72 1.12 0.01 0.00 0.01 - 0.04 - 0.26 0.18 Oceania countries 0.11 0.10 0.13 0.00 0.00 0.00 0.06 0.01 0.10 Global 10.82 - 0.07 -2.2136

Table 14 . SSA estimates of change in land cover due to an increase in biodiesel production in the US (million acres).

Regions Crop Cover Forest Cover Pasture Cover Mean Confidence

Interval (95%) Mean Confidence Interval (95%) Mean Confidence Interval (95%) Lo wer Upper Lo wer Upper Lo wer Upper U.S.A. - 0.01 - 0.14 0.13 - 0.66 - 0.83 - 0.49 0.67 0.45 0.89 EU-27 0.59 0.28 0 .91 - 0.37 - 0.58 - 0.15 - 0.23 - 0.33 - 0.13 Brazil 0.43 0.20 0.67 - 0.09 - 0.20 0.01 - 0.34 - 0.49 - 0.19 Canada 0.57 0.28 0.86 - 0.10 - 0.24 0.04 - 0.47 - 0.68 - 0.27 Japan 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 China, Hong Kong 0.04 - 0.01 0.09 0.12 0.05 0.19 - 0.16 - 0.26 - 0.06 India 0.18 0.02 0.34 - 0.10 - 0.19 - 0.01 - 0.08 - 0.15 - 0.01 Caribbean and Central America 0.08 0.04 0.12 0.17 0.10 0.23 - 0.25 - 0.34 - 0.15 South and Rest of America 0.18 0.08 0.29 0.12 0.04 0.20 - 0.30 - 0.48 - 0.13 East Asia 0.00 0.00 0.00 0.01 0.00 0.01 - 0.01 - 0.01 - 0.01 Malaysia, Indonesia 0.02 0.01 0.02 - 0.01 - 0.02 - 0.01 0.00 - 0.01 0.00 Rest of Southeast Asia 0.00 - 0.01 0.00 0.01 0.00 0.02 - 0.01 - 0.02 0.00 Rest of South Asia 0.03 0.00 0.05 - 0.01 - 0.02 0.00 - 0.02 - 0.04 0.00 Russia 0.0 0 - 0.04 0.03 0.16 0.03 0.30 - 0.16 - 0.28 - 0.04 Oth Eastern Europe & FSU 0.14 0.01 0.26 0.00 - 0.03 0.03 - 0.14 - 0.25 - 0.03 Rest of Europe 0.00 0.00 0.01 0.00 0.00 0.00 - 0.01 - 0.01 0.00 Middle East & North Africa 0.12 0.04 0.19 0.00 0.00 0.00 - 0.12 - 0.19 - 0 .05 SSA & Rest of Africa 0.67 0.20 1.15 - 0.11 - 0.23 0.02 - 0.57 - 0.93 - 0.21 Oceania countries 0.16 0.08 0.24 0.03 0.02 0.05 - 0.20 - 0.29 - 0.10 Global 3.21 - 0.83 -2.3937 1.382

- 0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00

Ch in Crop Cover

Figure 2 a. Crop cover change due to an increase in U.S. corn- ethanol production (million acres ) Note: This figure is based on the data reported in Table 5.38 - 1.146

- 1.60 - 1.40 - 1.20 - 1.00 - 0.80 - 0.60 - 0.40 - 0.20 0.00 0.20

Ch in Pasture Cover

Figure 2b. Pasture cover change due to an increase in U.S. corn- ethanol production (million acres ) Note : This figure is based on the data reported in Table 5.39 - 0.236

- 0.40 - 0.30 - 0.20 - 0.10 0.00 0.10 0.20

Ch in Forest Cover

Figure 2 c. Forest cover change due to an in crease in U.S. corn- ethanol production (million acres ) Note: This figure is based on the data reported in Table 5.40 3.707

- 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Cropcover change (Ha/bBTU)

Figure 3 . Crop cover change due to an increase in U.S. corn- ethanol production (Ha/ billion BTU) Note: This figure is based on the data shown below. Cropcover change (Ha/billion BTU) Corn- ethanol scenario: Me an Lo wer Upper USA 1.532 0.928 2.085 CAN 0.393 0.170 0.606 EU27 0.306 0.141 0.476 BRAZIL 0.171 0.077 0.261 JAPAN 0.007 0.003 0.012 CHIHKG 0.059 0.019 0.102 INDIA - 0.003 - 0.038 0.038 LAEEX 0.165 0.084 0.245 RoLAC 0.042 0.017 0.067 EEFSUEX 0.229 0.114 0.350 RoE 0.087 0.046 0.128 MEASTNAEX 0.043 0.016 0.069 SSAEX 0.538 0.332 0.729 RoAFR 0.071 0.035 0.107 SASIAEEX - 0.017 - 0.031 - 0.001 RoHIA 0.000 0.000 0.000 RoASIA 0.004 - 0.009 0.019 Oceania 0.081 0.028 0.133 GLOBAL 3.707 1.933 5.42541 3.214

- 0.50 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

Ch in Crop Cover

Figure 4 a. Crop cover change due to an increase in U.S. biodiesel production (million acres) Note: This figure is based on the data reported in Table 8.42 -2.388

- 3.00 -2.50 -2.00 - 1.50 - 1.00 - 0.50 0.00 0.50 1.00 1.50

Ch in Pasture Cover

Figure 4b. Pasture cover change due to an increase in U.S. biodiesel production (million acres ) Note: This figure is based on the data reported in Table 8.43 - 0.80 - 0.60 - 0.40 - 0.20 0.00 0.20 0.40

Ch in Forest Cover

Figure 4 c. Forest cover change due to an increase in U.S. biodiesel production (million acres ) Note: This figure is based on the data reported in Table 8.44 10.997

- 2.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0

Cropcover change (Ha/bBTU)

Figure 5 . Crop cover change due to an increase in U.S. biodiesel production (Ha/ billion BTU) Note: This figure is based on the data shown below. Crop-cover change (Ha/billion BTU) Biodiesel scenario: Me an Lo wer Upper USA - 0.031 - 0.490 0.428 EU27 2.035 0.967 3.103 Brazil 1.484 0.686 2.282 Canada 1.955 0.958 2.952 Japan 0.008 - 0.006 0.023 China-hkg 0.146 - 0.030 0.322 India 0.614 0.079 1.149 C_C_Amer 0.273 0.137 0.408 S_o_Amer 0.632 0.278 0. 986 E_Asia 0.003 0.001 0.005 Mala_Indo 0.061 0.037 0.084 R_SE_Asia - 0.001 - 0.019 0.017 R_S_Asia 0.095 0.007 0.183 Russia - 0.011 - 0.140 0.117 Oth_CEE_CIS 0.464 0.041 0.887 Oth_Europe 0.013 0.004 0.022 MEAS_NAfr 0.398 0.151 0.646 S_S_AFR 2.309 0.680 3.937 Oceania 0.550 0.278 0.823 GLOBAL 10.99745 Appendix A : Aggregation Tables Table A1. Aggregation of sectors in the GTAP -BIO - 1 Model No. Aggregated Sectors GTAP sectors Description 1 CrGrains gro Other Grains : maize (corn), barley, rye, oats, other cereals 2 OthGrains pdr wht Paddy Rice : rice, husked and unhusked; Wheat : wheat and meslin 3 Oilseeds osd Oil Seeds : oil seeds and oleaginous fruit; soy beans, copra 4 Sugarcane c_b Cane & Beet : sugar cane and sugar beet 5 Livestock ctl oap rmk wo l Cattle : cattle, sheep, goats, horses, asses, mules, and hinnies; and semen thereof; Other Ani mal Products : swine, poultry and other live animals; eggs, in shell (fresh or cooked), natural honey, snails (fresh or preserved) except sea snails; frogs’ legs, edible products of animal origin n.e.c., hides, skins and furskins, raw , insect waxes and spermaceti, whether or not refined or coloured; Rawmilk; Wool : wool, silk, and other raw animal materials used in textile 6 Forestry frs Forestry : forestry, logging and related service activities 7a Ethanol1 eth1 Ethanol1 (Grain based) 7b DDGS By product of eth1 Distillers dried grains solubles 8 Ethanol2 eth2 Ethanol2 (sugarcane based) 9a Biodiesel biod Biodiesel 9b BDBP By product of biod Oil -me al/cake

10 OthFoodPdts voln ofdn Vegetable Oils : crude and refined oils of soya-bean, maize (corn),olive, sesame, ground-nut, olive, sunflower- seed, safflower, cotton- seed, rape, colza and canola, mustard, coconut palm, palm kernel, castor, tung jojoba, babassu and linseed, perhaps partly or wholly hydrogenated,inter- esterified, re - esterified or elaidinised. Also margarine and similar preparations, animal or vegetable waxes, fats and oils and their fractions, cotton linters, oil - cake and other solid residues resulting from the extraction of vegetable fats or oils; flours and meals of oil seeds or oleaginous fruits, except those of mustard; degras and other residues resulting from the treatment of fatty substances or animal or vegetable waxes; Other Food : prepared and preserved fish or vegetables, fruit juices and vegetable juices, prepared and preserved fruit and nuts, all cereal flours, groats, meal and pellets of wheat, cereal groats, meal and pellets n.e.c., other cereal grain products (including corn flakes), other vegetable flours and meals, mixes and doughs for the preparation of bakers’ wares, starches and starch products; sugars and sugar syrups n.e.c., preparations used in animal feeding, bakery products, cocoa, chocolate and sugar confectionery, macaroni, noodles, couscous and similar farinaceous products, food products n.e.c. 11 ProcLivestoc cm t om t mil Cattle Meat : fresh or chilled meat and edible offal of cattle, sheep, goats, horses, asses, mules, and hinnies. raw fats or grease

from any animal or bird.; Other Meat : pig meat and offal. preserves and preparations of meat, meat offal or blood, flours, meals and pellets of meat or inedible meat offal; greaves; Milk: dairy products.46 Table A1. Aggregation of sectors in the GTAP -BIO - 1 Model (continued )

[[ 12 OthAgri v_f pfb ocr

pcr sgr b_t Veg & Fruit : vegetables, fruitvegetables, fruit and nuts, potatoes, cassava, truffles,; Plant Fibres : cotton, flax, hemp, sisal and

other raw vegetable materials used in textiles; Other Crops : live plants; cut flowers and flower buds; flower seeds and fruit seeds; vegetable seeds, beverage and spice crops, unmanufactured tobacco, cereal straw and husks, unprepared, whether or not chopped, ground, pressed or in the form of pellets; swedes, mangolds, fodder roots, hay, lucerne (alfalfa), clover, sainfoin, forage kale, lupines, vetches and similar forage products, whether or not in the form of pellets, plants and parts of plants used primarily in perfumery, in pharmacy, or for insecticidal, fungicidal or similar purposes, sugar beet seed and seeds of forage plants, other raw vegetable materials; Processed Rice : rice, semi - or wholly milled; Sugar ; Beverages and Tobacco products . 13 OthPrimSect fsh omn Fishing : hunting, trapping and game propagation including related service activities, fishing, fish farms; service activities incidental to fishing; Other Mining : mining of metal ores, uranium, gems. other mining and quarrying 14 Coal coa Coal : mining and agglomeration of hard coal, lignite and peat 15 Oil oil Oil : extraction of crude petroleum and natural gas (part), service activities incidental to oil and gas extraction excluding surveying 16 Gas gas gdt Gas : extraction of crude petroleum and natural gas (part), service activities incidental to oil and gas extraction excluding surveying (part); Gas Distribution : distribution of gaseous fuels through mains; steam and hot water supply 17 oil_pcts p_c Petroleum & Coke : coke oven products, refined petroleum products, processing of nuclear fuel 18 electricity ely Electricity : production, collection and distribution 19 En_Int_Ind crpn i_s nfm Che mi cal Rubber Products : basic chemicals, other chemical products, rubber and plastics products; Iron & Steel : basic production and casting; Non-Ferrous Metals : production and casting of copper, aluminium, zinc, lead, gold, and silver

20 Oth_Ind_Se tex wap lea

lum ppp nmm fmp mvh otn ele ome omf wtr cns trd otp wtp atp cmn ofi isr obs ros osg dwe Textiles : textiles and man-made fibres; Wearing Apparel : Clothing, dressing and dyeing of fur; Leather : tanning and dressing of

leather; luggage, handbags, saddlery, harness and footwear; Lumber : wood and products of wood and cork, except furniture; articles of straw and plaiting materials; Paper & Paper Products : includes publishing, printing and reproduction of recorded media; Petroleum & Coke : coke oven products, refined petroleum products, processing of nuclear fuel; Non-Metallic Minerals : cement, plaster, lime, gravel, concrete; Fabricated Metal Products : Sheet metal products, but not machinery and equipment; Motor Vehicles : cars, lorries, trailers and semi -trailers; Other Transport Equipment : Manufacture of other transport equipment; Electronic Equipment : office, accounting and computing machinery, radio, television and communication equipment and apparatus; Other Machinery & Equipment : electrical machinery and apparatus n.e.c., medical, precision and optical instruments, watches and clocks; Other Manufacturing : includes recycling; Water : collection, purification and distribution; Con struction : building houses factories offices and roads; Trade : all retail sales; wholesale trade and commission trade; hotels and restaurants; repairs of motor vehicles and personal and household goods; retail sale of automotive fuel; Other Transport : road , rail ; pipelines, auxiliary transport activities; travel agencies; Water transport; Air transport; Communications : post and telecommunications; Other Financial Intermediation : includes auxiliary activities but not insurance and pension funding (see next) ; Insurance : includes pension funding, except compulsory social security; Other Business Services : real estate, renting and business activities; Recreation & Other Services : recreational, cultural and sporting activities, other service activities; private households with employed persons (servants); Other Services (Government) : public administration and defense; compulsory social security, education, health and social work, sewage and refuse disposal, sanitation and similar activities, activities of membership organizations n.e.c., extra-territorial organizations and bodies; Dw ellings : ownership of dwellings (imputed rents of houses occupied by owners).47 Table A2. Aggregation of Regions in the GTAP -BIO - 1 model. No. New Code New Description GTAP regions D escription of GTAP Regions 1 USA United States usa United States of America 2 CAN Canada can Canada 3 EU27 European Union 27 aut bel dnk fin fra deu gbr grc irl ita lux nld prt esp swe bgr cy

p cze hun mlt pol rom svk svn est lva ltu Austria, Belgium, Denmark, Finland , France , Germany, United Kingdom, Greece , Ireland, Italy, Luxembourg , Netherlands , Portugal, Spain , Sweden, Bulgaria, Cy prus , Czech Republic , Hungary, Malta, Poland , Romania, Slovakia, Slovenia, Estonia, Latvia, Lithuania 4 BRAZIL Brazil bra Brazil 5 JAPAN Japan jpn Japan 6 CHIHKG China, Hong Kong chn hkg China, Hong Kong 7 INDIA India ind India 8 LAEEX Latin American Energy Exporters mex col ven arg Mexico , Colombia, Venezuela, Argentina 9 RoLAC Rest of Latin America + Caribb ean xna per xap chl ury xsm xca xfa xcb Rest of North America, Peru , Rest of Andean Pact, Chile , Uruguay, Rest of South America, Central America, Rest of Free Trade Area of the Americas , Rest of the Caribbean . 10 EEFSUEX EE & FSU Energy Exp xef rus xsu Rest of EFTA , Russian Federation , Rest of Former Soviet Union 11 RoE Rest of Europe che xer alb hrv tur Switzerland, Rest of Europe , Albania, Croatia, Turkey 12 MEASTNAEX Middle Eastern N Africa E Exp xme tun xnf bwa Rest of Middle East, Tunisia, Rest of North Africa, Botswana 13 SSAEX Sub Saharan Energy Exporters xsc mwi moz tza zwe xsd mdg uga xss Rest of South African Customs Union , Malawi , Mozambique , Tanzania, Zimbabwe , Rest of Southern African Development Community, Madagascar, Uganda, Rest of Sub - Saharan Africa . 14 RoAFR Rest of North Africa & SSA mar zaf zmb Morocco , South Africa, Zambia 15 SASIAEEX South Asian Energy Exporters idn mys vnm xse Indonesia, Malaysia, Vietnam, Rest of Southeast Asia 16 RoHIA Rest of High Inc Asia kor twn Kore a, Taiwan 17 RoASIA Rest of Southeast & South Asia xea phl sgp tha bgd lka xsa Rest of East Asia, Philippines , Singapore , Thailand, Bangladesh , Sri Lanka, Rest of South Asia 18 Oceania Oceania countries aus nzl xoc Australia, New Zealand, Rest of Oceania48 Appendix B: Land Supply

Figure B 1. Land supply in the GTAP -BIO models .

Land-AEZi Cropland Pasture Forest ETRAE 2 = - 0.5 Coarse grains Oilseeds Sugar- crops Other grains Other agri culture Land Supply in Value-Added N est of Production Structure

ETRAE 1 = - 0.20