### Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis *

by

Dileep K. Birur, Thomas W. Hertel, and Wallace E. Tyner 1

Department of Agricultural Economics, Purdue University

GTAP Working Paper No. 53 2008

* The earlier version of the paper was presen ted at the Tenth Annual Conference on Global Economic Analysis, Purdue University , West Lafayette, IN, USA, June 7-9, 2007.

1 Birur is Ph.D. student and graduate research assistant in the Department of Agricultural Economics, Purdue University ( dbirur@purdue.edu ). Hertel is Distinguished Professor and Executive Director ( hertel@purdue.edu ), Tyner is Professor and Senior Policy Advisor ( wtyner@purdue.edu ), at the Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, 403 W State Street, West Lafayette, IN 47907-2056.i IMPACT OF BIOFUEL PRODUCTION ON WORLD AGRICULTURAL MARKETS: A COMPUTABLE GENE RAL EQUILIBRIUM ANALYSIS
Dileep K. Birur, Thomas W. Hertel, and Wallace E. Tyner

Abstract
This paper introduces biofuels sectors as ener gy inputs into the GTAP data base and to the production and consumption structures of the GTAP-Energy model developed by Burniaux and Truong (2002), and further modi fied by McDougall and Golub (2008). We also incorporate Agro-ecological Zones (AEZs ) for each of the land using sectors in line with Lee et al . (2005). The GTAP-E model with bi ofuels and AEZs offers a useful framework for analyzing the growing importan ce of biofuels for gl obal changes in crop production, utilization, commodity prices, factor use, trade, land use change etc. We begin by validating the model over the 2001-2006 period. We focus on six main drivers of the biofuel boom: the hike in crude oil prices, replacement of MTBE by ethanol as a gasoline additive in the US, and subsidies for ethanol and biodiesel in the US and EU. Using this historical simulation, we calibra te the key elasticities of energy substitution between biofuels and petroleum products in each region. With these parameter settings in place, the model does a reasonably good job of predicting the shar e of feedstock in biofuels and related sectors in accordance with the historical evidence between 2001 and 2006 in the three major biofuel producing region s: US, EU, and Brazil. The results from the historical simulation reveal an increased production of feedstock with the replacement of acreage under other ag ricultural crops. As expected, th e trade balance in oil sector improves for all the oil exporti ng regions, but it deteriorates at the aggregate for the agricultural sectors.
JEL Classification : C68, Q18, Q42, R14
Keywords : Biofuels, Renewable Energy, Computable General Equilibrium (CGE), Agricultural Markets, Agro Ecologi cal Zones (AEZs), Land use change.ii Table of Contents

1. Introduction ................................................................................................................. 1 1.1 An Overview of Biofuel Markets ......................................................................... 1 2. Review of CGE Modeling for Biofuels ...................................................................... 3 2.1 History of GTAP-E .............................................................................................. 5 3. Study Approach .......................................................................................................... 6 3.1 Modifications to the GTAP-E Model ................................................................... 7 3.1.1 Modification of the Consumption Structure ................................................. 8 3.1.2 Modification of the Production Structure ................................................... 10 3.2 Modeling Land Use change for Biofuels ........................................................... 13 3.2.1 Structure of AEZs ....................................................................................... 15 4. Database for Biofuels ................................................................................................ 17 5. Historical Analysis .................................................................................................... 18 5.1 Crude oil price shock .......................................................................................... 19 5.2 Phasing out of MTBE in the US......................................................................... 20 5.3 Subsidies for Biofuels ........................................................................................ 21 5.4 Calibration of Substitution Parameters .............................................................. 23 5.5 Validation of the Model ..................................................................................... 24
6. Impact of Biofuel Drivers on the Global Economy .................................................. 25 6.1 Decomposition of Change in Output and Prices ................................................ 27 6.2 Land Use and Land Cover Change across AEZs ............................................... 28 6.3 Impact on Trade ................................................................................................. 30 6.4 Impact on Terms of Trade .................................................................................. 31 7. Conclusions and Directions for Future Research ...................................................... 32 References: ................................................................................................................... ..... 34 Tables and Figures ............................................................................................................ 40 Appendix-1: Closure and Shocks for Anal yzing the Impact of Biofuel Boom ................ 59iii 

### List of Tables

Table 1. The Major Biofuel Produc ers in the World during 2006 ................................... 40 Table 2. Aggregation of Sectors a nd Regions used in the Model ..................................... 41 Table 3. Major Drivers of Ethanol Boom in the US and EU-27 ...................................... 42 Table 4. Key Elasticities of Substitution in Biofuels and Land Use Module. .................. 43
Table 5. Validation of the Mode l from Historical Evidence. .......................................... 44 Table 6. Impact of Biofuel Drivers on the Agricultural Production: 2001-2006 ............. 46 Table 7. Impact of Biofuel Drivers on Ma rket Price across Selected Regions ................ 47 Table 8. Change in Land Cover and Crop Area due to Biofuel Drivers: 2001-2006 ...... 48 Table 9. Impact of Biofuel Driver s on Bilateral Trade ($ millions) ................................ 49
Table 10. Performance of Trade Balance due to Biofuel Drivers ($ billion). ................. 50 Table 11. Decomposition of Terms of Trad e for the US and EU ($ million). ................. 51



### List of Figures

Figure 1. World Production of Ethanol and Biodiesel (million gallons) ......................... 52 Figure 2. Modification of Consumption Structure in the GTAP-E Model ...................... 52 Figure 3. Modification of Production St ructure in the GTAP-E Model ........................... 53 Figure 4. Splitting the Three Types of Bi ofuels in the GTAP Data Base ........................ 54 Figure 5. Change in Land Area across AEZs under Coarse Grains: 2001-2006. ............ 55 Figure 6. Change in Land Area acro ss AEZs under Oilseeds: 2001-2006. ..................... 55 Figure 7. Change in Land Area across AEZs under Sugar Crops: 2001-2006. ............... 56 Figure 8. Change in Land Area across AEZs under Other Grains: 2001-06. .................. 56 Figure 9. Change in Land Area across A EZs under Other Agri Goods: 2001-2006. ....... 57 Figure 10. Change in Land Area acro ss AEZs under Forestry: 2001-2006. .................... 57 Figure 11. Change in Land Area across AEZs under Pasture cover: 2001-2006. ........... 581 Impact of Biofuel Production on World Agricultural Mark ets: A Computable General Equilibrium Analysis

1. Introduction
Energy is an important factor of produc tion in the global economy, and 90% of the commercially produced energy is from fossil fuels such as crude oil, coal, and gas,
which are non-renewable in nature. Much of the energy supply in the world comes from geo-politically volatile economies. In order to enhance energy security, many countries, including the US, have been emphasizi ng production and use of renewable energy sources such as biofuels, which is emerging as a growth industry in the current economic environment. This paper develops a framewor k which can shed light on the drivers of the
current biofuel boom as well as its impacts on agricultural markets.

Biofuels have become a high priority issu e in Brazil, the US, the European Union as well as many other countries around the world, due to concerns of oil dependence and interest in reducing CO 2 emissions. All these regions ha ve had significant subsidies or mandates for renewable energy production fr om agricultural sources. The impacts of
these subsidies and mandates reach far be yond the borders of these economies. The purpose of this paper is to assess the global a nd sectoral implications of biofuel programs on agricultural markets and land use across the world. The very nature of biofuels production as a global economic activity aff ecting the pattern of energy demand and resource use motivates us to employ a global computable general equilibrium (CGE) economywide approach for this study. Since biofuel programs in various countries are mainly driven by external shocks and dome stic policies, CGE serves as an ideal framework to study potential repercussions.

1.1 An Overview of Biofuel Markets
Ethanol, a predominant biofuel, is pr oduced today from s ugarcane and cereal grains mainly corn. Biodiesel is produced from oilseeds or palm o il. Table 1 lists the major ethanol and biodiesel producing countries in the world. In 2006, the United States became the largest producer and consumer of ethanol in the world, producing about 37% (4.86 billion gallons) of the world ethanol produ ction (13.1 billion gallons). Brazil is the2 second largest producer with 36 % (4.76 billion gallons) of world ethanol production. As seen from Figure 1, the world ethanol production has grown rapidly at a compound growth rate of 10 percent per annum since 1975, and at 23 percent per annum from 2001 through 2006. This recent acceleration may be attributed to push to wards ethanol in the United States. Similarly, world biodiesel produc tion has grown at a rate of 35 percent per annum since 1991; the majority of the boom co mes from the biofuel initiative in the European Union countries. As seen from Table 1, Germany is the leading producer of biodiesel (41% of world market share) w ith the production of 799 million gallons during 2006, followed by the US (20%), France (11%), Italy (7%), and ot her countries.
In Brazil, ethanol is produced mainly from sugarcane beginning during the 1970s in order to reduce dependence on foreign o il. However, the ethanol industry had a setback in the 1990s due to cheap crude oi l (Regaldo and Fan, 2007). When oil prices began to soar again in the recent years, et hanol became a more attr active alternative to gasoline, aided by the launch of flex-fuel vehicles (FFVs) in 2003. Brazil has a comparative advantage in producing ethanol, main ly due to its availability of land and its favorable climate for sugarcane cultivation. As Martines-Filho, Burniquist, and Vian (2006) report, the tota l cost of ethanol production in Brazil was about $1.10 per gallon and that of US was between $2.01 to $3.96 pe r gallon, during 2005. With its tremendous export potential, Brazil currently exports more than 50% of its sugar production and about 15% of ethanol production.
Though international trade in biofuels is st ill in an early stage, US imports from Brazil grew dramatically since 2004. Braz il invested heavily in ethanol production during the energy crisis of 1970s and now has one of the world's most advanced production and distribution systems. One impedime nt to trade in biofuels is the US tariff of about 50%. As Valdes (2007) reports, Brazil is aiming to replace 10% of gasoline consumed worldwide by 2012, which requires it to export 20% of its current production. It is interesting to see the potential for trade in biofuels amongst the major producing countries. The US has proposed 36 billion gallons of alternative fuel by 2022 which would replace about 15% of ga soline consumption in the country. The EU is also targeting a 10% share of biofuels in the transport fuel market by 2020.3 For production of ethanol, US, China, Fr ance, Germany, Russia, and Canada mainly use corn as their main feedstock, wh ereas, Brazil and India use sugarcane, which is more energy efficient. In the US, about 90% of the ethanol is produced from corn (about 22% of total corn production in 2007) an d in China, about 80% of the ethanol is corn-based, with the remainder produced from cassava and wheat (Konishi and Koizumi, 2007). For biodiesel production, al l the remaining countries in Table 1 use rapeseed as their main feedstock, except for the US which uses soybeans.
The remainder of the paper is organized as follows: section-2 gives a review of literature on CGE analyses of biofuels a nd a brief history of the GTAP-E model. Section-3 deals with the study approach comprising modifica tions in the GTAP-E model and modeling land use change, followed by sec tion-4 which informs about the database employed for this study. Secti on-5 illustrates the hi storical analysis involving the major biofuel drivers, calibration of the key parameters, and validation of the model. Resulting impact of biofuel drivers on out put, prices, tr ade, and land-use change, are discussed in section-6, followed by conclusions in section-7.

2. Review of CGE Modeling for Biofuels
Though there is a plethora of literature on biofuel economics, most of them employ cost-accounting procedures and/or partial equilibrium frameworks. More recently, researchers have began to use a CGE framework, however, with several caveats such as lack of incorporating policy issues, absence of linkages to other energy markets, and land use changes etc. Our study makes an attempt to address all these issues. However, the studies on CGE modeling of biof uels are few, largely due to infancy of the industry and limitations on availability of data.

Sims (2003) described the benefits of di splacement of oil through biofuels, on a country’s balance of trade and domestic economic activity and recommended general equilibrium modeling in order to understand the full benefits of biofuel production. A study by McDonald, Robinson, and Th ierfelder (2006) is one of the earlier ones to utilize the GTAP data base for analyzing the effect s of substituting a biom ass (switchgrass) for crude oil in petroleum production in the US, using a CGE framework. As switchgrass is4 not recorded in the data base, the authors assume that the primary input coefficients were same as those for the US cereal crops and the intermediate input coefficients were 70% of those for cereals in the US They also a ssumed that the output is purchased as an intermediate input by the petroleum industry. The results from a di rect substitution of switchgrass for crude oil revealed an increase in world price for cereals, but decline in world price of other crops, livestock, and cr ude oil. However, the world has yet to witness commercial production of switchgrass based biofuel, and the timing is uncertain. Furthermore, the study does not take into account the prevailing ethanol and biodiesel industries in many of the regions.

Banse et al . (2007) extended the GTAP-E m odel (Burniaux and Truong, 2002) to analyze the impact of the EU biofuel directive on agricultural markets. They introduced biofuels in implicit form in the production structure as a substitution between vegetable oil, crude oil, petroleum products, and et hanol composite. The ethanol composite comprised substitution between the feedstocks such as sugar-beet-cane, wheat, grain, and forestry sectors. In order to account fo r land conversion and land abandonment, they included a land supply curve by specifying a relationship between the land supply and rental rates. They adjusted the GTAP data base to account for the input demand for the
biofuel feedstocks in the petroleum industry. Their EU biofuel mandatory scenario analysis revealed that the target of the EU biofuel directive will not be reached by 2010
and the increase in demand for biofuel feedstocks will result in a larger agricultural trade
deficit.
A disaggregated CGE approach was adopt ed by Gohin and Moschini (2007) to analyze the potential impacts of full implementation of th e European biofuel policy in EU-15 economy where the farm sector is fine ly represented in terms of product coverage and behavioral specification. Their policy simulation of an exogenous increase in
demand for ethanol and biodiesel revealed significant positive effects on the arable crop sectors with increase in pric e and production. In addition, th e demand for ethanol is fully met by domestic production due to significant import tariffs, while the demand for biodiesel is met by imported vegetable oils. They also argued that the downstream
livestock sectors are not ne gatively affected as the pr oduction cost of compound feed5 increases only slightly and in the case of dairy sector, milk production is constrained by milk quotas. Finally, they concluded that there would be a positive impact on farm income and the creation of additional farm jobs.
Rajagopal and Zilberman (2007) provide an extensive review of the literature on environmental, economic, and policy studies on biofuels. While highlighting the gaps, they emphasize the need to focus on potential biofuel producing developing countries and impact of producing biofuels on the poor. Also they caution that while measuring welfare impacts, the models should account fo r the utility derived by the consumers from
the cleaner environment due to biofuels. Seve ral studies in the rece nt past have focused on modeling production of biomass or cellulosi c ethanol in a long run, recursive-dynamic CGE framework (Reilly and Paltsev, 2007; Di xon, Osborne, and Rimmer, 2007). In this study we do not consider biofuel from cellulo sic materials since it has not been produced commercially; rather, we focus on liquid bi ofuels produced from food or feed crops.

2.1 History of GTAP-E In order to analyze the implications of biofuel production in a CGE framework,
we utilize a modified version of the GTAP-E nergy model. The GTAP-E model was first developed by Truong (1999) where the substitution between capital and fuels was allowed by modifying the standard GTAP model (Hertel, 1997). For representing energy substitution, a simple top-down 1 approach was used with allowing for capital and energy to be either substitutes or complements. The GTAP-E model introduces energy substitution in production by allowing energy and capital to be either substitutes or complements. In order to allow for different elasticities of substitution across value added and energy, and non-energy inputs, a nested CES func tion has been employed in the model. First, the energy inputs are sepa rated from the non-energy intermediate inputs in the production structure, and then the en ergy inputs are aggregated with capital in a composite, allowing for capital-energy substituti on with other factors. One of the main assumptions of the standard GTAP production st ructure is separability of primary factors from intermediate inputs, implying that the optimal mix of primary factors is invariant to

1 A top-down approach starts with a detailed description of the macro economy, and the demand for energy inputs in various sectors’ outputs are derived through highly aggregated production or cost functions (Wilson and Swisher, 1993).6 price of intermediates. Thus, the elastic ity of substitution between any primary factor and intermediates is the same. This assump tion is relaxed in the GTAP-E model such that, in the value added branch, labor-energy substitution is different from capital-energy substitution. The non-energy intermediate inputs exclude all the energy inputs, but
include fossil-fuel based feedstocks.
Since energy usage affects the environment through emission of CO 2 and other green house gases (GHGs), Burniaux and Tr uong (2002) further improved the GTAP-E model to encompass carbon emission from the combustion of fossil fuels along with the
mechanisms to trade these emissions intern ationally. In their model, reduction in CO 2 emission can be achieved either through ener gy substitution or by out put reduction. They used an aggregated database of eight sect ors and eight regions keeping in view the emission policy analyses as per the 1997 Ky oto Protocol Annex I (OECD countries except for Korea and Mexico) countries that pledged to reduce their emissions of GHGs to 5.2 percent below 1990 levels. Though the US decided to withdraw from the Protocol, the remaining Annex I countries except for Aust ralia, reiterated their commitment. Thus, the GTAP-Energy model emerged with the main purpose of climate change policy analysis, such as GHG mitigation. Howe ver, over time a large number of problems emerged through continued use of this model, and these have been recently addressed by McDougall and Golub (2007). Most importantly, they made substantial improvements in the programming of the GTAP-E model which greatly facil itate its modification by others. Therefore, we build on the McDougall/Gol ub version of GTAP-E in this paper.

3. Study Approach
This technical paper introduces biofuel linkages into the improved version of GTAP-E model in order to capture the implications of biofuels mandates for global agricultural markets. Since the primary focus of this study is to analyze the impact of biofuel production on agricu ltural markets and land use change, we ignore the CO 2 emissions module for this analysis. We c onsider both ethanol a nd biodiesel, the two prominent biofuels produced across the wo rld today. Ethanol is produced from feedstocks such as cereal grains, sugarcane, and sugar beet, and biodiesel is produced mainly from vegetable oil seeds.7 In order to distinguish the source of feedstock, and in line with the work of Taheripour et al . (2007), we name the biofuels as follows: ethanol-1 is coarse grain based, ethanol-2 is sugarcane-beet base d, and biodiesel is vegetable oil based 2 . The substitution of biofuels is represented by intermediate demand substitution as well as household substitution, which required approp riate modifications in the production and consumption structures, respectively. For an alyzing the land use changes, we use the GTAP land use data base developed by Lee et al . (2005) which disaggregates the land endowment into 18 Agro-ecological zones (A EZs), which characterize the biophysical growing conditions and land use for crops and forestry. These modifications are explained in detail as below.

3.1 Modifications to the GTAP-E Model
Given the emerging potential for trade in biofuels, we have treated biofuels as a
tradable sector. The new GT AP-E model re-coded in a prudent style by McDougall and Golub (2008) does not require us to define distinct price and quantity variables for any
addition of new sectors into the model – these are simply inherited from the set definitions, which have been expanded to include biofuels. Apart from the standard GTAP sets used in this model, listed below are some of the new sets used for convenience in representing private househol d demand, biofuel production structure, and land-use change.

New Sets Elements HHLD_COMM: TRAD_COMM + henergy + hbiooil CDE_COMM : henergy + all non-energy commodities ( NEGY_COMM) BIOOIL_COMM : oil_pcts, etha nol1, ethanol2, biodiesel HEGY_COMM : coal, oil, gas, electricity, hbiooil AEZ_COMM: the 18 Agro-Ecological Zones CROP_COMM: Coarse grains, oilseeds, sugar cane, other grains, other agri. NCROP_COMM: All other non-crop sectors AGRLAND_COMM: Land-using agri commodities (CROP + GRAZE) LAND_COMM: All land-using sector s (AGRLAND + FOREST)

2 We recognize that ethanol1 and ethanol2 should be perfect substitutes in use. This is not the case in our current formulation and needs to be addressed in future work.8 New sets introduced by McDougall and Gol ub (2007) along with biofuel components: SUBPR_COMM: vaen, land, ken, eny, nely, ncoal, biooil FIRM_COMM: DEMD_COMM + SUBPR_COMM NCOAL_COMM : oil, gas, biooil NELY_COMM: coal, ncoal ENY_COMM : electricity, nely KEN_COMM : capital, eny VAEN_COMM : Land , UnSkLab, SkLab, NatRes, ken TOP_COMM : vaen + all non-energy commodities ( NEGY_COMM )

3.1.1 Modification of the Consumption Structure

The standard GTAP model (Hertel 1997) has separate structures for household ‘private’ consumption and ‘government’ consumption 3 . Private consumption assumes constant-difference of elasticities (CDE) functional form to accommodate nonhomothetic preferences and fully flexible functional fo rm. Since biofuels are substitutable for petroleum products at the pump, we allow for substitution in the private household demand through CES nesting. Figure 2 represents the modified consumption structure of household demand for private goods.

3.1.1.1 Composite Demands:
At the top level, private household consumption demand is defined over CDE_COMM which is comprised of an aggreg ated composite energy good including biofuels ( henergy ) and all other non-energy tradeables. The following is the linearized form of the demand equation (in percentage cha nge form) as stipulated in Hertel (1997). 

# ∑

∈ − + = − COMM CDE i Y P r pop r yp r i r k pp r k i r pop r i qp

_ )] ( ) ( [ * ) , ( ) , ( * ) , , ( ) ( ) , ( σ σ (1)

Where; i, k ∈ CDE_COMM; 

### ) , , ( r k i P 

### σ and 

### ) , ( r i Y 

### σ are the uncompensated price and income elasticities of demand respectively; pp(i,r) and qp(i,r) are the private

3 For in-depth discussion, please refer to Hertel, T.W. and M.E. Tsigas “Structure of GTAP”, Chapter-2 in Hertel (1997)9 consumption price and quantities for co mmodity i in region r; the term [ yp(r)

- pop(r) ] represents percent change in per capita income. In the energy nest, we specify a CES sub-structure allowing for substitution between petroleum-biofuel composite ( hbiooil) and all other energy commodities. Furthermore, within the hbiooil composite good, we specify a CES sub-structure allowing for s ubstitution between petr oleum products and the three types of biofuels.

3.1.1.2 Composite Tradeables:
The composite tradeables at the lowest level are determined as follows. 

# ∑

∈ Ψ = COMM BIOOIL i CSHHBIOIL r k pp r hbiooil pp

_ )] , ( * [ ) , " (" (2)



### )] , " (" ) , ( [ * ) ( ) , " (" ) , ( r hbiooil pp r i pp r r hbiooil qp r i qp ELHBIOIL 

### − − = σ (3)

where i, j ∈ BIOOIL_COMM; CSHHBIOIL Ψ is the share of good i in cost to j of household biofuel-petroleum ( hbiooil ) sub-product; ELHBIOIL σ is the elasticity of substitution in

hbiooil sub-consumption which is calibrated us ing historical evidence, which will be discussed in the subsequent sections. Equati on (2) determines the price of the composite
hbiooil sub-product and (3) represents the demand for inputs into hbiooil subconsumption nest. At the energy composite sub- product level, the price of henergy and the demand for inputs of henergy sub-consumption are determined by equations (4) and (5): 

# ∑

∈ Ψ = COMM HEGY i CSHEGY r j pp r j r henergy pp

_ )] , ( * ) , ( [ ) , " (" (4)



### )] , " (" ) , ( [ * ) ( ) , " (" ) , ( r henergy pp r i pp r r henergy qp r i qp ELEGY 

### − − = σ (5) where i, j ∈ HEGY_COMM ; CSHEGY Ψ is the share of good i in cost to j of household energy sub-product; ELEGY σ is the elasticity of substitution among energy commodities and the petroleum-biofuel composite. Typi cally, the energy demands are found to be relatively price-inelastic. Cooper (2003) estimates the shor t-run and long-run elasticities
of demand for crude oil in 23 countries and c oncluded that demand for crude oil is highly insensitive to changes in pric e. The estimated short-run el asticities range from 0.001 to -10 0.109 and that of long-run elasticities ra nge from 0.005 to -0.453. Following Beckman et
al . (2008), we assume a uniform own price elasticity of 0.1 ( ELEGY σ ) across all regions 4

.

3.1.2 Modification of the Production Structure
One of the major improvements made by McDougall and Golub (2007) in the GTAP-E model (Burniaux and Truong, 2002) is th e ease with which additional levels of nesting can be added within the production and consumpti on structures. We take advantage of this feature to incorporate biof uels as well as land-use information as shown in Figure 3. This production tr ee represents how the firm co mbines its individual inputs to produce its output qo(i,s) . Truong (1999) removed energy commodities from the intermediate input nest and in troduced them into the value- added nest thereby allowing for substitution between capital and energy goods in a composite.
Two important variables in the production structure are qf(i,j,r) and pf(i,j,r) which indicate demand and firm’s price for commodity i for use by j in r . At the bottom-most level of the CES technology tree (Figure 3) we incorporate substitution between petroleum production and the three types of biofuels, with an elasticity ( ELBIOOIL σ ) of 0. That is, we treat biofuels and petroleum sect ors as complementary inputs. This permits us to separately model the use of ethanol as an oxygenator (as opposed to an energy source - the role of ethanol as an energy substitute is handled through the consumption structure). As Yacobucci and Schnepf (2007) report, nearly half of all US gasoline contains some
ethanol blended around 10% level or lower. In 2006, the United States consumed most of the ethanol as an additive in gasoline. We discuss more on the additive demand aspect
of ethanol in Section 5.2.

4 In this study, we use revised GTAP-E parameters offered by Beckman et al . (2008). They seek to validate GTAP-E model using stochastic simulation approach of Valenzuela et al . (2007) and they found that the price elasticities of demand for petroleum products used originally by Burniaux and Truong (2002) are too elastic and hence they offer revised se t of GTAP-E parameters as below.
Elasticities Burniaux and Truong (2002) Beckman et al . (2008) ELEGY 1 0.1 ELKE 0.5 0.1 ELEN 1 0.1 ELNEL 0.5 0.5 ELCOAL 1 0.2511 The price of biooil energy sub-production is dete rmined by equation (6) and the demand for inputs into biooil energy sub-production is gi ven by equation (7) below. 

### } )] , , ( ) , , ( [ * ) , , ( { ) , , " ("

_ 

# ∑

∈ 

### − Ψ = COMM BIOOIL k CSHBIOOIL 

### r j k af r j k pf r j k r j biooil pf (6)



### )] , , " (" ) , , ( ) , , ( [ * ) , ( ) , , " (" ) , , ( ) , , (

r j biooil pf r j i af r j i pf r j r j biooil qf r j i af r j i qf ELBIOOIL



### − − − +

− = σ (7)

where i, k ∈ BIOOIL_COMM and j ∈ PROD_COMM ; CSHBIOOIL Ψ is the share of k in cost

to j of biooil energy sub-product.
Moving upward in the production structure, the price and demand of non-coal energy sub-production are determined as below.


### } )] , , ( ) , , ( [ * ) , , ( { ) , , " ("

_ 

# ∑

∈ 

### − Ψ = COMM NCOAL k CSHNCOAL 

### r j k af r j k pf r j k r j ncoal pf (8)



### )] , , " (" ) , , ( ) , , ( [ * ) , ( ) , , " (" ) , , ( ) , , (

r j ncoal pf r j i af r j i pf r j r j ncoal qf r j i af r j i qf ELNCOAL



### − − − +

− = σ (9)

where i, k ∈ NCOAL_COMM and j ∈ PROD_COMM; CSHNCOAL Ψ is the share of k

in cost

to j of non-coal energy sub-product. The non-coal nest allows substitution between crude oil, natural gas, and biooil composite good, with an el asticity of substitution ( ELNCOAL σ ) of

0.25.
The non-electricity sub-production nest al lows for substitution between coal and non-coal energy composite with an elasticity of substitution ( ELNEL σ ) of 0.1. The equations (10) and (11) refer to the pri ce of non-electricity energy sub-product and demand for input into non-elec tricity energy sub-production.


### } )] , , ( ) , , ( [ * ) , , ( { ) , , " ("

_ 

# ∑

∈ 

### − Ψ = COMM NELY k CSHNELY 

### r j k af r j k pf r j k r j nely pf (10)



### )] , , " (" ) , , ( ) , , ( [ * ) , ( ) , , " (" ) , , ( ) , , (

r j nely pf r j i af r j i pf r j r j nely qf r j i af r j i qf ELNEL



### − − − +

− = σ (11)12 where i, k ∈ NELY_COMM and j ∈ PROD_COMM; CSHNELY Ψ is the share of k

in cost to j of non-electricity energy sub-product.
Further up in the production tree, the price of com posite energy good and demand for input into energy sub-produc tion are given by equations (12) and (13), respectively.


### } )] , , ( ) , , ( [ * ) , , ( { ) , , " ("

_ 

# ∑

∈ 

### − Ψ = COMM ENY k CSHENY 

### r j k af r j k pf r j k r j eny pf (12)



### )] , , " (" ) , , ( ) , , ( [ * ) , ( ) , , " (" ) , , ( ) , , (

r j eny pf r j i af r j i pf r j r j eny qf r j i af r j i qf ELEN



### − − − +

− = σ (13)

where i, k ∈ ENY_COMM and j ∈ PROD_COMM ; CSHENY Ψ is the share of k

in cost to j of energy sub-product. The elasticity of s ubstitution between electri city and non-electric composite ( ELEN σ ) used here is 0.1.
The important sub-nest is the capital-e nergy composite which determines the following variables:


### } )] , , ( ) , , ( [ * ) , , ( { ) , , " ("

_ 

# ∑

∈ 

### − Ψ = COMM KEN k CSHKEN 

### r j k af r j k pf r j k r j ken pf (14)



### )] , , " (" ) , , ( ) , , ( [ * ) , ( ) , , " (" ) , , ( ) , , (

r j ken pf r j i af r j i pf r j r j ken qf r j i af r j i qf ELKE



### − − − +

− = σ (15)

where i, k ∈ KEN_COMM and j ∈ PROD_COMM; CSHKEN Ψ is the share of i in cost to j of capital-energy sub-product, and the elasticity of substitution ( ELKE 

### σ ) employed here is 0.1. Equation (14) indicates the price of capital-energy su b-product and equation (15) denotes demand for inputs into the capital- energy sub-production. Burniaux and Truong (2002) mention that in order to ensure capit al and energy are complements in the shortrun, and substitutes in the long-run, the elasticity ELKE 

### σ must be lower than the elasticity between capital and other commodities in the value added nest. In the value-added-energy nest , the price of the sub-product vaen is determined by equation (16) and the demand for inputs in the VAE nest is implied by equation (17).13 

### } )] , , ( ) , , ( [ * ) , , ( { ) , , " ("

_ 

# ∑

∈ 

### − Ψ = COMM VAEN k CSHVAEN 

### r j k af r j k pf r j k r j vaen pf (16)



### )] , , " (" ) , , ( ) , , ( [ * ) , ( ) , , " (" ) , , ( ) , , (

r j vaen pf r j i af r j i pf r j r j vaen qf r j i af r j i qf ESUBVA



### − − − +

− = σ (17)

where i, k ∈ VAEN_COMM and j ∈ PROD_COMM ; CSHVAEN Ψ is the share of i in cost to
j of value-added-energy sub-product; the CES substitution elasticity ( ESUBVA σ ) to combine the primary factors of production. At the top-level nest, the firm combines va lue-added and intermediate inputs with an elasticity of substitution ( ESUBT σ ) equal to 0.



### )] , ( ) , ( ) , , ( ) , , ( [ * ) ( ) , ( ) , ( ) , , ( ) , , (

r j ao r j ps r j i af r j i pf j r j ao r j qo r j i af r j i qf ESUBT



### − − − − − +

− = σ (18)

where i, k ∈ TOP_COMM and j ∈ PROD_COMM; af(i,j,r) is the input augmenting technical change and ao(j,r) is the Hicks-neutral technical change. Following Keeney and Hertel (2008), we assume a medium r un crop-yield-response which is used to calibrate the elasticity of substitution for primary factors ( ESUBVA σ ) and the elasticity of intermediate input substitution ( ESUBT σ ) for the five crop sectors.

3.2 Modeling Land Use change for Biofuels
The growing importance of biofuels has cr eated a huge demand for bio-feedstock. The energy demand coupled with demand for fo od have put tremendous pressure on land which can result in intensification and change in cropping patterns as well as steer
additional land from forest and pasture lands for agricultural use. Several studies have
raised concerns on environmental and social impacts of biofuel programs. Kelly (2007) predicts that the increased biofuels production in the US could lead to a shift in cropping patterns towards corn and it could bring margin al land prone to erosion, forest, pasture
land etc. under corn 5 . Any tendency towards importing foreign-grown feedstocks could also result in massive displacement of agricu lture and rain forest in the developing

5 For example, Kelly (2007) reports that California’s st ate law stipulates to increas e the share of alternative fuels from the current 6% to 20% by 2020 and 30% by 2030 which might require cutting down distant forests to grow biofuel fe edstocks consequently exacer bating the global warming.14 countries. Leahy (2007) reports that biofuels are causing deforestation in Indonesia, Malaysia, and Thailand due to monocultures of oil palm 6 . Buckland (2005) estimated that, development of oil-palm plantations of about 16 million acres across Sumatra and Borneo during 1985-2000, was responsible for 87% of deforestation (about 25 million acres of rainforest).
The potential for displacement of fossil fuels by biofuels could result in significant land-use change, w ith possible unfavorable impacts on the environment. Therefore in order to capture these potential land use changes due to biofuel programs, we adopt the GTAP-AEZ framework (Lee et al ., 2008). In the original GTAP model, land is regarded as a sluggish endowment which can be re-allocated, based on relative land rents. However, not all cr ops are taken up in all parts of a country due to constraints on their adaptability. Owing to this limitation Lee et al . (2005) disaggregate national land endowment in GTAP into 18 Agro-Ecolo gical-Zones (AEZs) as per U.N. Food and Agricultural Organization convention. In th e GTAP land use data, the land used by the GTAP land-based sectors are distinguished by agro-ecological zones. Their 2001 crop and forest data has adopted the “length of growing period” data which is derived by combining information on moisture and temp erature regimes, soil type, topography, and knowledge on crop requirements 7 . The AEZ data are derived based on six categories of 60 day interval growth period in the world subd ivided into three clim atic zones (tropical, temperate, and boreal) using criteria base d on absolute minimum temperature and growing degree days.
The global land use database developed by Lee et al. (2008) involves three land use databases: (i) the land cover data from Ramankutty et al. (2008) which distinguishes forest, pastureland, and cropland cover types, (ii) data on ha rvested land cover and yields
from Monfreda et al . (2008a, 2008b), and (iii) database wh ich maps forestry activity in the 18 AEZs as documented in Sohngen et al . (2008). Lee et al . (2008) utilizes these

6 Palm oil is a low-cost vegetable oil highly efficient in biodiesel production. As FAPRI (2007) reports Malaysia and Indonesia are the major producers accounting for 88% of total world palm oil production and China, India, and EU-25 are the major importers. 7 For detailed discussion on construction of this data base, refer to Lee et al . (2005). The GTAP-AEZ database is also useful for assessing the mitigation potential of land-based emissions as illustrated in Hertel et al . (2008).15 land use components and disaggregates land rent s in the GTAP data base on the basis on prices and yields. The detailed discussion on this aspect is given in the volume edited by Hertel, Rose, and Tol (2008). Therefore, in corporating aforementioned rich land-use
AEZ level database into the biofuels module would yield be tter, and close to accurate presentation of sectoral competition for land due to biofuel production.

3.2.1 Structure of AEZs
In order to allow for substitutability among the AEZs, we incorporate a CES subproduct nest in the value-added-energy nest of the production structure (Figure 3). In the value-added nest, “land” is the com posite good (sub-product) which allows for substitution between AEZs for a given use. 

### } )] , , ( ) , , ( [ * ) , , ( { ) , , " ("

_ 

# ∑

∈ 

### − Ψ = COMM AEZ k CSHAEZ 

### r j k af r j k pf r j k r j land pf (19)



### )] , , " (" ) , , ( ) , , ( [ * ) , ( ) , , " (" ) , , ( ) , , (

r j land pf r j i af r j i pf r j r j land qf r j i af r j i qf ESAEZ



### − − − +

− = σ (20)

where i, k ∈ AEZ_COMM and j ∈ PROD_COMM; CSHAEZ Ψ is the share of k th AEZ in

cost to j of AEZ sub-product nest. The equations (19) and (20) determine the price of AEZ sub-product and demand for inputs in AEZ sub-production. The degree of substitution is determined by the parameter, ESAEZ σ , which we assume to be very high
( ESAEZ

σ = 20). This is dictat ed by the homogeneity of the products be ing produced on the different land types (Hertel et al ., 2008).
Since crops grown are climate and soil specific, Lee et al . (2005) made an assumption while applying AEZ classification that , the land is mobile across uses within an AEZ, but immobile across the 18 AEZs. In line with Hertel et al . (2008), the land mobility is effectively restrict ed across alternative uses within a given AEZ, by using a Constant Elasticity of Transformation (CET) frontier analogous to CES function with one proviso, the convex revenue function implying that land owners maximize total returns by optimal mix among crops. As structured in Hertel et al. (2008), we adopt a nested
CET function which allocates land in two tiers (refer to AEZ nest in Figure 3); with the assumption of homothetic separability on th e revenue function. The land-owner makes16 optimal allocation of a given pa rcel of land under crops, pastur e or commercial forest in the first stage, while the choi ce of crops is made in the se cond stage. Given that any increase in biofuel production would necessitate an increase in the supply of feedstock,
which has to come from diversion of feedstoc k from other uses, incr eased yields and/or expansion of land area under that feedstock crop. Keeney and Hertel (2008) examine the issue of crop yield response in greater detail. They r ecommend a long-run yield response to price of 0.4, which we adopt here and calibra te to reach this targeted yield response by adjusting the elasticity of substitution in crop production.
The supply of land-AEZ endowment across the sectors is determined by the following equation (in percentage change form):
)] , ( ) , ( [ * ) , ( ) , ( ) , ( 1 r i pm r i pm r i r i qo r i qo cropland land cropland − Ω +

− = ω (21) The composite price for AEZ-land endowments is given by: ) , , ( * ) , , ( ) , ( 1 r k i pm r k i r i pm es k land 

## ∑ Ψ = (22)

The market price of AEZ-land endowment allocated to different crops ) , , ( * ) , , ( ) , ( 2 r k i pm r k i r i pm es k cropland 

## ∑ Ψ = (23)

where i , k ∈ AEZ_COMM ; ) , ( r i ω is the slack variable in endowment market clearing condition; 

### ) , , ( 1 

### r k i Ψ is the revenue share of i th AEZ in k th land using sector (LAND_COMM); 

### ) , , ( 1 

### r k i Ψ is the revenue share of i th AEZ in k th crop sector using land (CROP_COMM); ) , , ( r k i pm es is the market price of AEZ-land endowment i used by producing sector j in region r. The sensitivity of land allocation across th e three cover types is determined by the elasticity, 1 

### Ω , in equation (21). For this parame ter, we rely on Ahmed, Hertel, and Lubowski (2008) who recommend a value of -0.2 (for roughly a decade-long land cover transformation) based on a study on land use elasticities by Lubow ski, Plantinga, and Stavins (2006). The solution for ) , ( r i qo cropland obtained from (21) is distributed across non-crop land (forestry and pa sture lands) in as below:
)] , , ( ) , ( [ * ) , ( ) , ( ) , , ( 1 r j i pm r i pm r i r i qo r j i qoes es land − Ω +

− = ω (24)17 where i
, ∈ AEZ_COMM ; j ∈ NCROP_COMM.
The supply of cropland is allocated ac ross crops as given by equation (25).
)] , , ( ) , ( [ * ) , ( ) , ( ) , , ( 2 r j i pm r i pm r i r i qo r j i qoes es cropland cropland − Ω +

− = ω (25)

where i ∈ AEZ_COMM ; j ∈ CROP_COMM; 2 

### Ω is the elasticity of transformation across crops, taken as -0.5 (FAPRI, 2004) which is th e maximum acreage response elasticity for corn across different regions in th e United States. The CET parameters 1 

### Ω and 2 

### Ω are non-positive and their absolute value increase s in absolute terms as the degree of sluggishness diminishes possibly driving the rental rates acro ss alternative uses together (Hertel et al. , 1997). For instance an increase in ethanol production in the US would boost demand for corn, and the resulting increase in corn prices is shared among all the factors of production. Thereby, an increase in land rents attracts more land into corn production taken out from alternative uses.

4. Database for Biofuels
Given that the liquid biofuels industry has only recently emerged onto the global economic scene in a large way (outside of Brazil), it presents a unique modeling challenge. The GTAP data base (Dimar anan, ed., 2007) does not include explicit biofuels sectors. Taheripour et al . (2007) deal with this challenge by incorporating
biofuel sectors into th e GTAP data base using the availa ble information on the patterns of sales and purchases for these sectors. As not ed previously, the biof uel industry included in this study 8 constitutes three distinct sectors: ethanol-1, ethanol-2, and biodiesel based on the type of feedstock used to produce them.

In order to break out the thr ee biofuel sectors, Taheripour et al . (2007) made use of ‘SplitCom’ software developed by Horridge (2005). As depicted in Figure 4, there are 57 sectors and 87 regions in the version 6 of GTAP data base. Thus, those authors generate: the grain based ethano l-1 sector from the food pro ducts sector (ofd) receiving inputs from the cereal grains sector (gro), the sugar based ethanol-2 sector out of

8 Since the focus of our study is implications of biofuels on agricultural and land use markets, we ignore the carbon module in the GTAP-E model and hence CO 2 emission from biofuels is not included in the database.18 chemicals sector (crp) with inputs from suga r-cane-beet (c_b) sector; and biodiesel sector is created from the vegetable oils and fats (v ol) sector which gets input from the oil-seeds (osd) sector. Thus the final di saggregated level has 60 sectors 9 and 87 regions. The sales of biofuels are channeled through househol d as well as intermediate demand. As discussed in the earlier section, we us e information on land rents from GTAP-AEZ database (Lee et al ., 2005; Lee et al ., 2008) to disaggregate land endowment data across
18 AEZs. The GTAP version 6 data base a nd the AEZ data depict the global economy for the year 2001.
This data base is aggregated to pe rmit focus on the sectors and regions of particular interest. For implementing the biofuels boom analysis, we aggregate the
database into 20 economic sect ors and 18 regions (Table 2). The sectors are aggregated such that we could focus on the linkages am ong feedstock, biofuels, energy commodities, and other important sectors. The regions ar e aggregated such that each continent is broadly divided into three categories: majo r energy consuming countries, major energy exporting countries, and all remaini ng countries in the continent.

5. Historical Analysis
Typically validation of a model involve s testing if it can track historical developments in the economy. In the same spirit, we verify the model by projecting 10 the biofuel economy from 2001 baseline (databas e) to depict 2006 s cenario and compare
the share of feedstock in biofue ls and related sectors to the historical evidence. In doing so, we consider three key fact ors of the US biofuels boom: ri se in petroleum prices, the replacement of MTBE 11 by ethanol as gasoline additive, and the subsidies to the ethanol
and biodiesel industries in the US and EU. Each of these key factors is discussed in detail in the following sections.

9 In this study, we do not include the by-products from biofuel sectors in the database. 10 As Keeney and Hertel (2005) rightly point out, validating a GE model is fundamentally difficult as in principle the GE model endogenously determines all variables. Many disruptions in the world such as wars, droughts, financial crises, trade policy changes etc. though very important, it is virtually impracticable to include them in the model. Here we focus only on three key elem ents that are responsible for biofuels boom during 2001-2006 and ignore all other exogenous changes in the global economy during this period. 11 Methyl tertiary-butyl ether (MTBE), a petroleum de rived additive used as octane enhancer in the oil industry, was banned recently due to its highly toxic nature.19 5.1 Crude oil price shock
The biofuels industry has a close linkage with petroleum products. The price of biofuels is implicitly dictated by the price of the crude oil for which it substitutes. Higher crude oil prices act as an incentive for incr eased biofuels consumption and consequently, the usage of feedstocks has implications on trade and welfare. In a GTAP-based study, McDonald, Robinson, and Thierfelder (2006) sh owed substitution of biomass for crude
oil in the US could lead to a decline in the world crude oil price, thereby benefitting oil importers through terms of trade improvements. The study further indicated that the substitution will have indirect effects on the global agricultural markets due to exchange rate linkages. As seen from Table 3, the average annual real pr ice (in 2006$ using GDP deflator) of crude oil was $25.3/barrel during 2001 and it took a steep jump to reach $78 in August 2006, thereafter droppi ng back to attain an averag e price of $59.7/barrel for the year 2006, which was an increase of 136% over 2001. Crude oil accounts for 55% of gasoline cost and the higher prices for crude o il would translate directly into higher prices for gasoline (Behrens and Glover, 2006). It is clear from the table th at not all the crude oil price shock had been transf erred to gasoline market by 2006, as crude oil prices rose by 136% while the average real price of ga soline has increased by 78%. The rise in gasoline prices has also driven the price of ethanol in the US, which has increased by about 74%, but the biodiesel price increased by only 31.8% over this same period.
In this paper we focus on a key underlying driver of biofuel demand – namely the crude oil price – shocking this by the historically observed amount, and asking the model to predict the impacts on gasoline prices a nd hence biofuel demands. In practice, the reasons for the oil price increase over this period are quite complex and modeling oil price formation over time would take us well beyond the scope of this technical paper. Therefore, we adopt a simple, transparent ap proach to achieving the oil price rise, since we are primarily interested in the conseq uences of the price hike, not the causes. Specifically, to achieve the world pri ce change in crude oil, we swap pxwcom (“Oil”) 12 with exogenous aosec (“Oil”), the rate of technical change of the oil sector worldwide, in

12 pxwcom is the price index of global crude oil exports.20 the closure 13 . Thus, the model reduces oil production world-wide by an amount sufficient to cause world crude oil prices to rise by 136%. This is expected to boost the demand for liquid biofuels as a substitute for ga soline, and it comprises the first piece of
our historical vali dation experiment.

5.2 Phasing out of MTBE in the US
With the passing of the US Clean Air Ac t of 1990, the vendors were required to have a minimum oxygen percentage in gasoli ne. Although ethanol and MTBE were the two recognized additives, the petroleum derived MTBE gained predominance during 1990s due to its lower cost of production. While it played an important role in reducing ozone emissions in the US, MTBE was found to be a serious ground water contaminant. This led to a ban of MTBE by 20 States by 1999. The Energy Policy Act (EPA) of 2005 removed the oxygen requirements giving the oil companies freedom to meet the clean air rules subject to their discre tion and the US Environmental Protection Agency eliminated the oxygen requirement as of May 8, 2006, which removed the oil companies’ legal cover on MTBE-based ground water contamination. Th is was the death kne ll for MTBE as an additive, and led to its replacement with et hanol (Tyner, 2008). Pr oduction of biofuels
has increased even in the net energy exporting countries due to mandatory use of ethanol
as an octane enhancer 14 . For example, many Latin American countries, including the major energy exporter, Venezuela, have been importing ethanol from Brazil and recently began to develop a large scale production of sugarcane-based et hanol domestically. So in order to blend with gasoline, several countri es have started to produce or import ethanol to abate the pollution due to the petr oleum based non-biodegradable MTBE.
As seen from Table 3, production of MTBE oxygenates plummeted from 3.26 billion gallons in 2001 to 1.29 billion gallons in 2006, following the legislation to phase out MTBE. The mirror image of this decline in share of MTBE in the additive market from 65% in 2001 to 21% in 2006 is the rising share of ethanol in the additive market which escalated from 35% (1.76 billion gall ons) in 2001 to 79% (4.86 billion gallons) in 2006, which is about 125% increase during th e six year period. However, with the

13 The closure used in this model is the standard gene ral equilibrium closure, which allows full adjustment within each country (Appendix-1). 14 The octane number of ethanol is 112 and that of standard gasoline is 87 (Tyner, 2007).21 removal of the oxygen requirement, the oil co mpanies were free to meet the clean air rules either by using ethanol or reformulat ed gasoline. Thus, there are two effects occurring simultaneously from two policies wh ich have led to an increase in use of ethanol as an additive in the US gasoline indus try (oil_pcts). To figure out this effect, consider the intermediate demand equati on (18) discussed ear lier. Assuming the elasticity of substitution among intermediate inputs in oil_pcts sector ( ESUBT σ ) = 0, and no change output augmenting technical change in the o il_pcts industry ( ) , ( r

j ao = 0), the equation (18) takes the form:


### ) , ( ) , , ( ) , , ( r j qo r j i af r j i qf +

− = (26)

where i and j refer to ethanol-1 and oil_pcts sectors, respectively in the r region (US); ) , , ( r j i qf is the demand for ethanol-1 in the oil_pcts sector in the US; ) , , ( r j i qf is the factor i (ethanol-1) augmenting technical chan ge in sector j (oil_pcts) in the US; and ) , ( r

j qo being the output of oil_pcts in the US. Equation (26) is in th e percentage change
form and its levels form is as below:



### ) , , ( ) , ( ) , , ( r j i QF r j QO r j i AF = (27)
From equation (27) we compute change of AF from 2001 (AF 0 ) to AF in 2006 (AF 1 ). During the 2001 to 2006, decline in MTBE in the oil_pcts sector (QF 1 ) was 1.97 billion gallons (Table 3). That mean s, if we index output to 1.0, then AF 1 = 1/1.97 = 0.51 (assuming no change in oil_pcts output QO) and AF 0 in the initial period is 1. Therefore, percent change in AF = ((0.51 - 1)/1)* 100 = - 49 %. This change in average intensity of ethanol-1 use due to MTBE ban in the oil_pc ts sector is incorporated by shocking the factor augmenting technical change ( af ) variable by -49%. With this additive shock, we expect ethanol-1 production in the US to go up and also the production of feedstock (corn).

5.3 Subsidies for Biofuels
The rising popularity of biofuels is primar ily attributed to the subsidies and other incentives that the national governments offer to this infant industry. The biofuel22 industry in the US thrived mainly because of the steady government subsidy being offered to the industry for the past three decades. The Energy Tax Act of 1978 started a tax exemption of 40 cents per ga llon for ethanol and it rose to 60 cents per gallon under Tax Reforms Act in 1984. Eventually this federal subsidy came down to the present rate of 51 cents per gallon. Until 2004, the excise tax exemption policy has prevailed in the ethanol industry, but this wa s replaced by a blenders’ credit of $0.51 per gallon; both the policies essentially have the same effect

. Similarly, biodiesel in the US gets a blender’s tax credit of $1 per gallon. The feedstock cost s for biodiesel are generally higher than ethanol, which has led to a higher level of subsidy for biodiesel (Gray, 2006).
Tyner (2008) discusses the historical cha nges in ethanol subsidies and argues that
the recent ethanol boom is an unintended c onsequence of a fixed ethanol subsidy which was calibrated to $20 per barrel crude oil pr ices. Though the success of ethanol industry relies on relative corn and oil prices, the su bsidy has remained fixed irrespective of the hike in crude oil and corn pri ces. As Tyner and Quear (20 06) argue, instead of a price invariant fixed subsidy, a subsidy that varies with ethanol prices or input costs could stimulate greater ethanol production through substantial risk reduction.
Apart from the US Federal subsidy, 38 stat es offer several incentive schemes such as excise-tax reductions or producer pa yments, production tax credits, statewide mandates for use of biofuels, etc. (Kojima, Mitchell, and Ward, 2007). Koplow (2006) estimated the per gallon aggregate subsidy as $1.05 for ethanol and $ 1.54 for biodiesel for the year 2006. As seen from Table 3, th e real ethanol price has gone up from $1.48 per gallon in 2001 to $2.58 in 2006, which is an increase by 74%. Given the ethanol prices, we compute the power of the ad valorem equivalent (ADV) of $0.51 fixed subsidy 15 , which was found to be 1.34 in 2001 and declined to 1.20 in 2006, by 10.93%. This is the reduction in economic impact of subsidy which acts as a disincentive for the ethanol producers. Therefore we s hock the output subsidy variable ( to ) in the US by - 10.93. Similarly, the power of ADV for biodiesel in the US has dec lined from 1.41 to 1.31, by 7% during 2001-06.

15 In GTAP jargon, the power of the ad valorem tax or subsidy (TO i ) = 1+ t i , where t i is the ad valorem tax or subsidy rate expressed in percentage (Hertel, 1997).23 The EU-27 has emerged as the largest produc er of biodiesel in recent years. The major impetus behind this boom is the ta x credit given to bi ofuel industry by the members states (MS). The EU directive allows MS a legal framework to differentiate taxation of energy products and this has resu lted in implementing different levels of tax credits by the MS (Bendz, 2007). Germany was the first country to implement tax incentives for biofuels, which started on ly after 2002, and late r other MS adopted
different levels of biofuel tax credits. We compute a pr oduction weighted average of these tax credits in major biofuels producing countries in the EU-27. The ADV equivalent of tax credit for ethanol was found to be 1.508 for 2006 which is an increase of 50.77% over 2001 (ADV equivalent of no tax credit is 1) and that of biodiesel was 81.18% (Table 3). Interestingly, German government started collecting $0.34 per gallon tax on biodiesel from January 1, 2008 as it was losing large tax revenue from fossil diesel. This tax is will likely to incr ease to more than $2.46 per gallon in 2012 (Godoy, 2007). Since we focus on the 2001-2006 histori cal period for validation, we ignore the very recent developments in the biofuel industry.
In order to project the global economy in time, we need to shock all the exogenous variables in time. Ho wever it is practically infeas ible to obtain the observed data for all the exogenous variables on a gl obal scale, we shock only the key biofuel drivers responsible for biof uel boom in the US and EU, and focus only on the higher petroleum prices in the case of Brazil. We implement all the six experiments discussed above simultaneously and calibrate the parame ters to predict historical experience – focusing on the composition of the energy sector and the ensuing impacts on agriculture.

5.4 Calibration of Substitution Parameters

In our biofuels extension of the GTAP-E model, we have added a new parameter – the elasticity of substitution between petr oleum products and liquid biofuels in final demand ( ELHBIOIL σ ). Unfortunately, we do not have estimates for this parameter, which obviously plays a key role in ou r analysis. Ideally, we woul d like to estimate this using an econometric approach. However, the lack of adequate time-series or cross-section data on biofuels limits us to adopt a simp ler, calibration approach for obtaining these
elasticities of substitution.24 We have assembled historical data on bi ofuel use for our three focus regions: US, EU and Brazil. By choosing the values for ELHBIOIL σ of 3.95, 1.65 and 1.35 for these three regions, we are able to successfully reproduce th e increase in biofuel output in these three regions, based on the three shocks above. Note the relatively lower value for Brazil, which has a much higher market penetration of biofuels. The model is telling us that, at this point, the potential for in creasing biofuel use in response to higher fu el prices is more limited than in the US and the EU, where ther e is still scope for displacing fuel use in conventional vehicles. In the other regions of the world, we adopt the default value for this parameter of 2.0 (Table 4). In our subsequent analysis, we will not be changing biofuel policies in these other countries, so the importance of this parameter is less pronounced.
Note that the elasticity of substitution between biofuels and petroleum products, ELBIOOIL σ , in the petroleum sector is zero, by assumption (see above). This is because this portion of the biofuel demand is explicitly recognized as additive demand in the model. Tokgoz and Elobeid (2006) eluc idate that complementarity relationship between ethanol and gasoline dominates over substitution relationship, mainly due to current blending at 10% and the FFVs market form negligible portion of the US vehicle fleet. They further assume that the substituti on effect will continue to be limited until the
FFVs dominate the market.
Another important set of parameters that drive the biofuels economy are the landuse parameters (Table 4), which are adopted fr om various studies discussed in the earlier sections. With these parameter values, we an alyze the impact of six shocks performed simultaneously and compare model results with historical evidence.

5.5 Validation of the Model Having fully specified the biofuels model, we ask how well it does in capturing the observed changes over the period: 2001-2006. Of course, since we have calibrated the elasticity of substitution in consumption to give us the desired increase in biofuel production, examination of that variable is not a test of model performance. However, it is information to ask how well the model has predicted other changes in the structure of25 the economy. Since we have not projected the entire economy forward in time, we will
focus on the composition of the economy, not on the level of prices or quantities in the new equilibrium.

6. Impact of Biofuel Drivers on the Global Economy
In this section, we discuss the impact of the key biofuel drivers (see previous section) on some of the vari ables interested to biofuel economy. After running the three shocks together as discussed in the earlier sections, the result s are presented in Table 5 in comparison with the corresponding historical da ta (recall that we are only simulating the impact of the bio-fuel relate d shocks, not all of the other developments that occurred over this period). Ethanol produc tion in the US increased from 1.7 billion gallons in 2001 to 7.1 billion gallons in 2006, which is about 174% increase. Our calibrated model faithfully reproduces this change (177% during the same period).
Historically, the area under corn, the key feedstock in the US, has increased only by 3.5%, but production has gone up by 11% whic h indicates a significant improvement in yield due to a combination of technology a nd weather. The model prediction of coarse grain production is about 7% over this same pe riod. An important criterion to assess the performance of the model is the change in th e share of feedstock go ing to biofuels. As seen from the table, the model predicted corn share 16 going to ethanol in the 2001 database is about 6.8%, and it increased to 17% in 2006. This share is comparable with
the historical shares of 6.5% a nd 20.2% for 2001 and 2006 respectively.
Interestingly, the share of corn exports has increased historically by 5.5% over the period of 2001-06, but the model prediction indicates a decline in export share 17 by about 9%. The model prediction must be negative, given the ec onomic logic of the model, since price rises and export demand is downwar d sloping. However, over this historical period, in spite of incr eased usage of corn for ethanol production, US corn exports have grown moderately due to fact ors that are not included in our simulation. These include

16 As presented in Table 5, the model predicted share distinguishes domestic production and exports separately. The reason for not combining the domestic and exports together is, the historic share includes only domestic production in the total. 17 The above explanation applies to the differences in hi storic and predicted share of corn exports as well.26 rapid economic growth in Asia and deprecia tion of the US dollar. This discrepancy between predicted and observed exports also helps to explain the divergence between predicted and actual production of corn. Si milarly, the other grains sector, which constitutes paddy rice and wheat, is predicted to see a declin e of 3% in production, which is less than the actual decline in production fo r these crops. However, if we closely look at the historical data, there are huge annual fluctuations in area and production of these grains.
Moving down to the next section of Table 5, we see the predicted and historical results for Brazil. Here, we see that historic al production of sugar-bas ed ethanol (ethanol2) in Brazil went up from 3.6 billion gallons in 2001 to 4.5 billion gallons in 2006, which
is an increase of 24%. The corresponding model predicted valu e is 39% which is larger than the historic data. On the contrary, th e historic increase in sugarcane production is about 32%, but the equivalent value from the model is only 17%. However, the model predicted the share of sugarcan e in ethanol production matche s the historical data quite well: 43.5% in 2001 and 51.6% in 2006. Furthermore, the model predicts a huge increase of (605 %) sugarcane based ethanol from Brazil over the six year period.
The last panel of Table 5 compares the model predictions and historical observation for the European Union. Biodies el production in the EU increased from 288 million gallons to 1.47 billion gallons during 2001-06, an increase by 410%, which is reproduced by the model (incr ease of 431%). Unfortunately, we could not find the
historical data on the share of oilseeds used for biodiesel pr oduction for the entire European Union, but our model showed an astonishing increase from 6.5% in 2001 to 27.6% in 2006. The model predicts that this 20+ percentage point increase in share comes from a 17% increase in oilseed produc tion, a 6% decline in oilseeds exports from the EU, and from a 10% increase in oilseed imports. To mention again, we have not shocked the entire 2001 economy forward in time – just the biofuel drivers. Therefore we cannot expect that the six sh ocks that we have included here shoul d predict accurate output levels. With the exception of the di screpancies noted above, overall the model predicts the stylized facts about the structure of the en ergy, biofuel and agricultural economy reasonably well.27 6.1 Decomposition of Change in Output and Prices

A substantial increase in world crude oil price along with US and EU specific
biofuel incentive shocks can result in economy wide impacts across countries. Table 6 depicts the percent ch ange in output in te rms of domestic and export components across various agricultural sectors. The impact of the three shocks on agri cultural output in the
United States reveals that th e production increase for coar se grains comes from a 7.5%
increase in domestic demand combined with a small (-0.9%) decline in exports. If we
look at the drivers behind the increase in coar se grains production, in terestingly it is the crude oil price (5.6%) first and additive demand for ethanol (2.4%) secondly which
contributed to the increase in coarse grains production. The subsidy shock (declining ad
valorem equivalent of subsidy) acts as a disincentive to the et hanol industry and leads to a 1 percent decline in coarse grains, while outputs in all other sectors go up by small amounts. The production of all other agricu ltural commodities went down slightly, except for oilseeds. Production in the other grains sector drops by 3.2%, the majority of which was due to decline in exports (2.7%). Since we ran four (subs idies to ethanol and biodiesel are combined together in Table 6) simulations simultaneously, the changes attributed to each shock may be examined for the United States. These subtotals 18 for each shock (Table 6) reveal that the hike in oil price has a major impact on output of the non-coarse grains commodities.
The output changes in the European Union and Brazil are mainly due to the crude oil price shock. The other two shocks specific to the US bi ofuel industry did not have any direct effect on agricultural markets in othe r regions. With the rise in crude oil price, oil seed production goes up substantially ( 17.5%) in the EU which mainly comes from a
19% increase in domestic demand and 1% reduc tion in exports. Besides, production of other grains goes down by 1%, and all other sectors also experience a small production slump in the EU.

Banse et al . (2007) indicate that higher crude oil prices would make the feedstock more competitive in petroleum production in the EU. In the case of Brazil, though all

18 The concept of subtotals in GEMPACK jargon is defined as deco mposing the total effect of a group of shocks into contribution made by each individual shock. The theory of subtotals is given in Harrison, Horridge, and Pearson (2000).28 the agricultural sectors experience a dec line in production except for sugarcane (17% increase) as expected, it is interesting to note that the domestic demand for oilseeds goes down by 0.5%, while exports rise by 2.2%. From these results, we can conclude that higher crude oil prices have played an im portant role in boosting biofuels and their feedstock production, but have led to deterioration in the production of other competing crops and forestry sectors.
A glance at Table 7 shows th e impact of biofuel driver s on the market price across the sectors for the period 2001-06. The total effect of the biofuel drivers is that the medium run market prices for the biofuel feedstock go up by 9% for coarse grains in the US, 10% for oilseeds in the EU, and 11% for su garcane in Brazil. Interestingly, the price of ethanol and biodiesel went up by 17% and 13% , respectively in the US (which is much less relative to the crude oil price increase of 136%), while the same declined in the EU. The table also lists the change in market price in some of the major energy exporting regions as they experienced a li ttle stronger pinch in prices , possibly due to increase in demand as their disposable income goes up following the hike in oil price 19 . The change in consumer price index (CPI) as also given at the bottom of the table which indicates that the general rise in price level was lower in the biofuel produci ng regions compared to that of energy exporting regions.

6.2 Land Use and Land Cover Change across AEZs The rise in feedstock demand brings in more land under cultivation of that feedstock. The additional pre ssure to increase the feedstock output can lead to intensification of the crop that could bring higher yield. Figure 5 shows the percentage change in land area under coarse grains across the AEZs in th e world during 2001-06

19 Though several studies have indicated about substantial increase in food prices due to biofuel boom, interestingly our model does not capture this occurrence. Since the last quarter of 2006 and up until the first quarter of 2008, world has been witnessing considerable increase in agricultural commodity prices often attributed to increase in biofuel production (Alexander and Hurt, 2007; Westcott, 2007; von Braun, 2008). For example, when tortilla prices skyrockete d in Mexico in January 2007, some market analysts attributed the price hike to bio-ethanol-related corn shortages (Caesar, Riese, and Seitz, 2007). However, though U.S. corn prices due to biofuel production could bare an explanation, the real reason behind the tortilla price hike was the concentration in the Mexican corn flour and tortilla industry, and failure of trade policies that allowed dumping of corn into Mexico ov er time (Spieldoch, 2007). Along these lines, there are several reasons such as drought in New Zeala nd and Australia, increase in global demand for dairy products, etc which are attributable to increase in food and feed prices. Since we have not projected the economy for 2006, the model predicted change in market prices coul d be insignificant.29 following the biofuels boom. The largest cha nges in coarse grain ac reage (up to 10%) are in less-productive AEZs which contribute li ttle to national coarse grain output. The productivity-adjusted change in land cover and crop harveste d area over the period 2001- 06 is given in Table 8. The total change in crop land cover was 2.1% which came from a
0.5% and 1.6% decline in commercial forest and pasture land, respecti vely in the US The productivity-weighted acreage change in la nd-use for the coarse grains is about 5%,
which comes from contribution of land from all other crop sectors. The major decline was observed in other grains se ctor with -3.4% changes in ac reage. Figures 6-9 plot the land use changes by region and AEZ for oilsee ds, sugar crops, other grains, other agrigoods sectors and Figures 10-11 depict the ch ange in land cover under commercial forest and pasture land, respectively.
Table 8 reports that the impact of these biofuels drivers on oilseed acreage in the European Union is quite large -- 15%, which mainly comes at the cost of all other landusing sectors. The land-cover under crops rises by 4.4% in the EU (which is entirely due to oilseeds acreage expansion) and this come s from decline of 2.1% each in forest and pasture land-cover area. De pending on the AEZ, the ov erall productiv ity-weighted average for land used in oils eeds increased from 0.05% to 19% (Figure 6). This trend confirms Tyner and Caffe (2007) who estima te that non-food rapes eed area in France would increase from 1.5 million acres to more than 4 million acres by 2010, whereas the same will decrease from 2.2 million acres to 1.6 million acres for food purposes.
The acreage under sugar crops (sugarcane and sugar beet) in Brazil expands by 15% under this biofuel experiment, with the la nd mainly coming from pasture, forestry, and other agri-good sectors. The increase in demand for sugar crops pushed the productivity-weighted average change from 0.49% to more than 18%, depending on the AEZ (Figure 7). With 6% increase in crop land-cover in Brazil, about 4.7% of which came from pasture land-cover and about 1% fr om forest land. As seen from Figure 8, the forestry sector gave up its acreage in all th e regions except for Canada and Rest of the World. Brazil is the main region with produc tivity-weighted acreage loss of up to -7% each in forest land (Figure 10) and in pastur e land (Figure 11) depending on the AEZs.30 As discussed above, the major acreage loss is under other grains (paddy and wheat). As seen from Figure 8, reductions in productivity-weighted acreage range from - 12% and the gain across some AEZs is by only 6%. It is clear from Table 8 that, overall, paddy and wheat are the crops which lose acre age and production heavily in most of the regions except for Oceania countries and India. Figure 9 depicts percent change in land area under agri-goods sectors, wh ich registers a decline of about 11% of land used across AEZs in each of these sectors. Overall, the Other Agri-goods se ctor gave up land for producing biofuel feedstock crops.

6.3 Impact on Trade
Apart from understanding the domestic impacts of biofuel production, it is important to investigate the possible repercussions around the world. The impact of
biofuel drivers on bilateral tr ade is presented as change in import volume for coarse grains, oilseeds, and other food products by th e US, EU and all other regions combined, in Table 9. As seen from the table, the US coarse grain exports decline by $178 million but the other regions fill this gap in exports and in fact, the total volume of gl obal trade in coarse grains rises by $54 million. US exports of coarse grains to EU declines only by $4 million, while trade with rest of the world gets affected to a greater extent ($174 million). The middle panel of the table gives the ch ange in import volume for oilseeds which increases by $395 million. The EU-27 region imports oilseeds from all other regions, the majority of which comes from Brazil and the US. The US and EU exports of oilseeds to rest of the world decline sharply. India and Eastern Europe also emerge as net oilseed exporters in this experiment. Interestin gly, trade in the Other Food Products sector increases heavily by $1.29 billion. The increase in US imports mainly comes from rest of the Southeast and South Asia, and EU-27. However, EU imports from most of the regions decline drastically (by $1.65 billio n), whereas, the RoW im ports increase by $2.71 billion.
The commodity trade balance, by region, owi ng to the biofuel drivers is presented
in Table 10. Macro-economic trade balance requires that the current account must be equated to the capital account in each region . Any change in trade due to biofuel production is offset and the capital account adjusts. The main impact on the trade31 balance is to reduce oil imports, which can be clearly seen from the table as the oil and oil products portion of the tr ade balance improves. The trade balance in aggregate deteriorates for the US, with the largest cont ributors being manufact uring and services (- $77 billion) and oil and oil pr oducts (-$66 billion – due to the higher oil prices). The trade balance improves in the US in the case of oilseed and coarse grain exports. In contrast, the scenario for the European Union is quite different than that of the United States. EU trade balance improves in aggr egate which mainly comes from exports in manufacturing and services sector ($151 b illion). The agricultural trade balance particularly in the oilseeds sector deteriorates as imports surge in response to the strong demand by the biodiesel sector. Brazil has a modest trade balance with positive numbers coming from export of ethanol mainly to th e US and EU. Brazil gains by exporting a larger share of oilseeds to the EU. All the oil exporting regions exhibit a solid trade
balance in the oil and oil products sector.

6.4 Impact on Terms of Trade
The terms of trade (ToT) eff ect reported in Table 11, is negative for both the US and
the EU. The welfare effect from ToT loss is due to the transfer of wealth from the consuming region to the producing region. The ToT loss in the US mainly comes from oil and oil products sector (-$54 billion) and also from the other sectors in a smaller scale. The European Union also loses about $77 billio n by ToT effect in the oil and oil products sector. The only sectors with positive ToT effect in the EU region are other primary sectors and to a smaller extent, oilseeds, ot her grains, and coarse grains sectors.

The effect of the six shocks on ToT ba sically depends on the magnitude of the change in oil price rela tive to the change in the export prices. A decomposition of ToT as contribution of change in world price, e xport price, and the import prices would be helpful. In the US, the world price inter action with oil and oil products was the major component contributing towards ToT loss. Al though change in export price interaction is positive for most of the sectors particularly for coarse grains and other agri-sectors, it is of a smaller magnitude. But the change in import price component was relatively much smaller for all the sectors. Thus, we can see that these changes in the price interactions
contributed towards to the ToT effect. The ToT decomposition for the EU indicated that32 world price interaction with oil and oil products contribute to a ToT loss of $76.9 billion. Interestingly, the export price component at the aggregate level was negative and quite large. But the import price interaction was po sitive, the larger part of which was from manufacturing and services sector.

7. Conclusions and Directions for Future Research
Biofuels have been receiving greater attention in the recent years from researchers, industrialists, e nvironmentalists, and national governments across the world. In order to analyze the linkages between bi ofuels and agricultura l markets, we use a global, general equilibrium model. The GTAP-E model, supplemented to include biofuels, is useful in assessing the impact of the growing importance of biofuels on global changes in crop production, utilization, prices, factor movements, trade, etc. In this study we incorporate biofuels into the GTAP-Energy data base and to the production and consumption structure of the model. We also apply agro-ecological zones (AEZs) information for each of the land using sectors. For validation of th e model, we project the biofuel economy forward in time fr om 2001 to 2006, and compare the model predictions with histor ical evidence. Since it is not possible to in troduce all the changes to the global economy over this period, we focus on three key issues which are responsible for biofuel boom in recent years: the hike in crude oil prices, replacement of MTBE by ethanol in gasoline a dditives, subsidies for ethanol and biodiesel in the US and EU. Using this historical simulation, we ca librate some of key el asticities of energy substitution between biofuels and pe troleum products in each region.
Based on only the six types of shocks re lated to biofuels, the model predictions match reasonably well with key historical evidence in the major biofuel producing regions. The results from the historical si mulation revealed, that with higher crude oil prices biofuels are substituted for petroleum pr oducts. The biofuel dr ivers have driven up the demand for feedstocks in the three majo r producing regions, United States, Brazil and EU. As a result there is change in acreage towards corn in the US, oilseeds in EU, and sugarcane in Brazil, affecting the land area under paddy and wheat in all the regions. Brazil emerges as a leading oilseed exporter to the European Union.33 Since the biofuel industry is very dynami c, there are several elements of the global production and trade in this sector whic h are hard to replicate in the model. For example, the GTAP data base used in this study pertains to 2001 data, whereas several countries have started producing biofuels at a large scale only in the recent years, which is not possible to capture in our database . Many developing countries have started producing new biofuel feedstocks such as palm oil, jatropa, etc. fo r which we have not yet established linkages with biofuel sectors. Byproducts of biofuels are also crucial in determining global impacts of bi ofuel programs. Taheripour et al . (2008) introduce byproducts of corn-ethanol and biodiesel into the earlier ve rsion of this model and found that the model without byproduc ts overstates the impacts of biofuels on feedstock production and land use. This must be borne in mind when using the version of the model documented in this paper. Of course the sp ecific nature of the biofuel by-products and their use varies across feedstocks and proper in corporation of them into a model is a large exercise in its own right. Future work will focus on extending this model to incorporate key types of cellulosic ethanol , as well as incorporating CO 2 and other GHG emissions to permit a comprehensive assessment of the enviro nmental impact of biofuels. In addition to looking at the GHG emissions impacts of biof uels, we plan to analyze their impact on poverty, as they provide a double-edged sword for the wo rld’s poor – on the one hand raising food prices, while on the other, enha ncing earnings opportuni ties in agriculture.34 References :
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Tyner, W.E., and M. Caffe (2007) “US and Fr ench Biofuels Policy – Possibilities for the Future,” Paper presented at the OECD Workshop on Bioener gy Policy Analysis, Umea, Sweden, 22-23 January 2007.
Valdes, C. (2007) “Ethanol Demand Driving th e Expansion of Brazil’s Sugar Industry.” Sugar and Sweeteners Outlook, Economic Re search Service, United States Department of Agriculture, SSS-249, June 4, 2007. pp: 31-38. Valenzuela E., T.W. Hertel, R. Keeney, and J.J. Reimer (2007) “Assessing Global CGE Model Validity using Agricultural Price Volatility.” American Journal of Agricultural Economics , 89(2):383–397
von Braun, J. (2008) “Rising Food Pr ices: What Should be Done?” IFPRI Policy Brief
, April 2008. http://www.ifpri.org/pubs/bp/bp001.asp
Westcott, P. C. (2007) “ Ethanol Expansion in the United States: How will the Agricultural Sector Adjust? ” Economic Research Service, United States Department of Agriculture, FDS-07D-01, May 2007. Wilson, D. and J. Swisher (1993) “Explorin g the gap - Top-down versus Bottom-Up Analyses of the Cost of Mitigating Global Warming.” Energy Policy , 21(3): 249- 263. Yacobucci, B.D. and R. Schnepf, (2007) “Ethanol and Biofuels: Agriculture, Infrastructure, and Market Constraint s Related to Expanded Production.” CRS Report for Congress, Order Code RL 33928, March 16, 2007.40 Tables and Figures
Table 1. The Major Biofuel Produc ers in the World during 2006

Ethanol Production Biodiesel Production Million gallons Share (%) Million gallons Share (%) United States 4,856 37.3 Germany 799 41.4 Brazil 4,763 36.5 United States 385 20.0 China 1,083 8.3 France 223 11.6 India 486 3.7 Italy 134 7.0 France 251 1.9 United Kingdom 58 3.0 Germany 202 1.5 Austria 37 1.9 Russia 171 1.3 Poland 35 1.8 Canada 153 1.2 Czech Republic 32 1.7 Other countries 1,068 8.2 Other countries 227 11.8 World 13,033 100 World 1,929 100

Data sources: Earth Policy Institute, 2006; Renewable Fuels Association, 2007; European Biodiesel Board, 2007.41Table 2. Aggregation of Sectorsand Regions used in the ModelNo New Code Sector DescriptionGTAP old sectorsNo. New Code Region DescriptionComprising GTAP old regions1 CrGrains Cereal grainsgro1 USA United Statesusa2 OthGrains Paddy and Wheatpdr wht2 CAN Canadacan3 Oilseeds Oil seedsosd3 EU27 European Union 27aut bel dnk fin fra deu gbr grc irl ita luxnld prt esp swe bgr cyp cze hun mlt polrom svk svn est lva ltu4 Sugarcane Sugar cane, sugar beetc_b4 BRAZIL Brazilbra5 Livestock Cattle,Animal pdts,Milk,Woolctl oap rmk wol5 JAPAN Japanjpn6 Forestry Forestryfrs6 CHIHKG China, Hong Kongchn hkg7 Ethanol1 Ethanol1 (corn based)eth17 INDIA Indiaind8 Ethanol2 Ethanol2 (sugarcane based)eth28 LAEEXLatin AmericanEnergyExportersmex col ven arg9 Biodiesel Biodieselbiod9 RoLACRest ofLatinAmerica+Caribbeanxna per xap chl ury xsm xca xfa xcb10 OthFoodPdts Other Food Productsvoln ofdn10 EEFSUEX EE & FSU Energy Expxef rus xsu11 ProcLivestoc Meat, Dairy productscmt omt mil11 RoE Rest of Europeche xer alb hrv tur12 OthAgri other agriculture goodsv_f pfb ocr pcr sgr b_t12MEASTNAEXMiddle Eastern N Africa EExpxme tun xnf bwa13 OthPrimSect OtherPrimary:Fishery & Miningfsh omn13 SSAEX Sub Saharan Energy Exportersxsc mwi moz tza zwe xsd mdg uga xss14 Coal Coalcoa14 RoAFR Rest of North Africa & SSAmar zaf zmb15 Oil Crude Oiloil15 SASIAEEX South Asian Energy Exportersidn mys vnm xse16 Gas Natural gasgas gdt16 RoHIA Rest of High Income Asiakor twn17 oil_pcts Petroleum, coal productsp_c17 RoASIARest of Southeast & SouthAsiaxea phl sgp tha bgd lka xsa18 electricity Electricityely18 Oceania Oceania countriesaus nzl xoc19 En_Int_Ind Energy intensive Industriescrpn i_s nfm20 Oth_Ind_Se Other industry and servicestex wap lea lum ppp nmmfmp mvh otn ele ome omfwtr cns trd otp wtp atp cmnofi isr obs ros osg dwe42 Table 3. Major Drivers of Ethanol Boom in the US and EU-27

Units 2001 2006 Change 2001-2006 Average crude oil price 2006 $ / barrel 25.29 59.69 136.0% United States Average gasoline price 1 : 2006 $ / gallon 1.20 2.13 77.5% Fuel ethanol oxygenate production: billion gallons 1.76 4.86 3.10 MTBE oxygenate production: billion gallons 3.26 1.29 -1.97 Additives = Ethanol + MTBE: billion gallons 5.02 6.15 Share of MTBE to additives: 65% 21% -68% Share of ethanol to additives: 35% 79% 125% Decline in MTBE additive demand: = 1/-1.97 0.51 Increase in ethanol additive demand (- af ) = ((0.51 -1)/1) *100 -49.24%
Ethanol: Average price $ / gallon 1.48 2.58 74.3% Federal Subsidy $ / gallon 0.51 0.51 ADV equivalent of subsidy 2 % 1.34 1.20 -10.9% Biodiesel: Average price $ / gallon 2.45 3.23 31.8% Federal Subsidy $ / gallon 1 1 ADV equivalent of subsidy 2 1.41 1.31 -7.0% EU-27 Ethanol 3 : Average Price $ / gallon 1.48 1.96 32.4% Tax credit $ / gallon - 0.995 ADV equivalent of tax credit 2 % 1.00 1.508 50.77% Biodiesel 4 : Average Price $ / gallon 2.33 2.34 0.6% Tax credit $ / gallon - 1.898 ADV equivalent of tax credit 2 % 1.00 1.812 81.18%

Note: 1 Gasoline prices exclusive of taxes. 2 Ad valorem equivalent of federal subsidy in the US and tax credit in the EU-27. 3 Ethanol price and the tax credit in the EU refers to France market. 4 Biodiesel price and tax credits are production share weighted averages of major biodiesel producing

countries in the EU-27. Data Sources: Energy Information Administration (EIA) an d Energy Efficiency and Re newal Energy (EERE), US Department of Energy; Nebraska Ethanol Board, Lincoln, NE. Nebraska Energy Office, Lincoln, NE.43 Table 4. Key Elasticities of Substitution in Biofuels and Land Use Module.

Regions ELHBIOIL Key Parameters Constant across all regions and sectors US 3.95 Elasticity of substitution in Bio-Oil composite in production nest (ELBIOOIL) 0 Canada 2 EU-27 1.65 Brazil 1.35 Elasticity of substitution in AEZ production nest (ESAEZ) 20 Japan 2 China-Hong Kong 2 India 2 Scalar yield elasticity target (YDE_Target ) 0.4 Latin American Energy Exporters 2

Rest of Latin America & Caribbean 2 Elasticity of transformation for sluggish primary factor endowments (ETRAE) -0.0001 EE & FSU Energy exporters 2 Rest of Europe 2 Elasticity of transformation for land cover at the bottom of land supply tree (ETRAE1) -0.2 Middle Eastern N Africa energy exporters 2 Sub Saharan Energy exporters 2 Elasticity. of transformation for crop land in supply tree (ETRAEL2) -0.5 Rest of North Africa & SSA 2 South Asian Energy exporters 2 Rest of High Income Asia 2
Rest of Southeast & South Asia 2

Oceania countries 2

Note: ELHBIOIL refers to elasticity of substitution in Bio-Oil energy composite in private demand.44Table 5. Validation of the Model from Historical Evidence in the US, Brazil, and EU-27.US.Ethanol-1ProductionMilliongallonsModelPrediction1:EthanolProduction($ million)Corn AreaMillionhectaresCornProductionMilliontonnesModelPrediction1:Coarse GrainProduction($ million)Share of Cornfor EthanolModelPrediction1:Corn Sharefor Ethanol(% share)Share ofCorn forExportsModelPrediction1: CornShare for Exports(% share)20011770 2489.30 30.28 241.38 20936.5 6.5% 6.8% 19.4% 27.6%20022130 31.56 227.77 7.5% 17.5%20032800 31.44 256.28 11.0% 18.8%20043400 32.37 299.92 11.7% 15.3%20053900 32.71 282.3114.5% 19.5%20064855 6886.80 31.33 267.60 22335.0 20.2% 17.0% 20.5% 25.1%2007*7123 37.15 316.50 23.0%%Ch2001-06174.29 176.66 3.47 10.86 6.68 209.05 150.0 5.50 -9.06%Ch2000-07*46.7118.58 18.27USWheat AreaMillionhectaresWheatProductionMilliontonnesRice AreaMillionhectaresRiceProductionMilliontonnesModelPrediction1:Other GrainProduction($ million)Soybean AreaMillionhectaresSoybeanProductionMilliontonnesModelPrediction1:OilseedProduction($ million)ModelPrediction1: Chin Coarse Grainexports($ million)200123.77 53.00 1.33 10.59 7304.8 29.63 78.67 12772.25779.3200224.13 43.70 1.30 10.38 29.59 75.01200324.86 63.81 1.21 9.84 29.36 66.78200423.87 58.74 1.34 11.43 30.08 85.01200522.89 57.28 1.35 10.99 28.86 84.00200622.94 49.32 1.14 9.51 7070.8 30.21 86.69 12857.35601.12007*25.63 71.30%Ch2001-06-3.52 -6.95 -14.79 -10.21 -3.20 1.95 10.20 0.67-3.08%Ch2000-07*-15.15 -17.75Continued...45BRAZILEthanol-2ProductionMilliongallonsModelPrediction1:EthanolProduction($ million)SugarcaneAreaMillionhectaresSugarcaneProduction:MilliontonnesModelPrediction1:SugarcaneProduction($ million)Share ofSugarcane forEthanolproductionModelPrediction1:SugarcaneShare forEthanolShare ofSugarcanefor SugarproductionModelPrediction1:SugarcaneShare forSugarModelPrediction1:Ch inEthanol-2Exports($ million)20013609.0 5341.7 4.88 326.1 3189.4 43.3% 43.5% 55.7% 44.3% 137.720023760.0 5.02 344.3 45.2% 53.8%20034336.0 5.1 363.7 46.2% 52.8%20044443.0 5.5 416.6 49.1% 49.9%20054467.05.69 421.853.0% 46.0%20064491.0 7420.4 5.87 431.4 3737.4 51.0% 51.6% 48.0% 37.1% 970.7%Ch2001-0624.44 38.91 20.29 32.29 17.1817.67 18.51 -13.76 -16.29 604.88EU-27BiodieselProduction:MilliongallonsModelPrediction1:BiodieselProduction($ million)OilseedsArea:MillionhectaresOilseedsProduction:MilliontonnesModelPrediction1:Oilseedproduction($ million)ModelPrediction1:Oilseed Sharefor BiodieselOilseedImports:MilliontonnesOilseedExports:MilliontonnesModelPrediction1:Ch inOilseedimports($ million)ModelPrediction1:Ch inOilseedexports($ million)2001288 513.69 14.21 33.5 6905.1 6.50% 26.14 6.40 5440.41 1329.5200238414.13 31.5 25.27 6.76200350315.06 34.4 24.45 6.46200470814.83 38.6 21.69 7.00200581515.02 36.524.87 6.6020061467 2729.56 15.82 37.9 8111.1 27.60% 28.05 6.19 5996.5 1243.9%Ch2001-06409.38 431.37 11.3 13.1 17.47 324.62 7.29 -3.38 10.22 -6.44Note:1Model prediction values refer to results from crude oil price in all regions, additive demand in the US, and biofuel subsidy shocks in the US and EU,performed together. The model predictions for 2001 are the pre-shock values (from the basedata) and 2006 values are post-shockvalues.Data Sources: Food and Agriculture Organization online database ; Economic Research Service, United States Dept of Agriculture; Renewable FuelsAssociation; European Biodiesel Board; The São Paulo Sugar Cane Agro-industry Union (UNICA).46 Table 6. Impact of Biofuel Drivers on the Agri cultural Production in th e US, EU, and Brazil: 2001-2006 ( % change in output by sector)

Total Change

(%) Decomposed by Demand Decomposed by Driver Domestic Exports Additives Oil Price Subsidy-US Subsidy-EU US Ethanol-1 176.7 176.7 0.0 63.9 148.0 -34.9 -0.3 Coarse Grains 6.7 7.5 -0.9 2.4 5.6 -1.3 0.0 Other Grains -3.2 -0.5 -2.7 -1.1 -2.8 0.6 0.1 Oilseeds 0.7 0.1 0.6 -0.7 0.2 0.3 0.8 Sugarcane -0.9 -0.8 0.0 -0.2 -0.8 0.1 0.0 Other Agri -0.8 -0.7 -0.1 -0.2 -0.7 0.1 0.0 Livestock -1.2 -1.0 -0.1 -0.1 -1.1 0.1 0.0 Forestry -0.3 -0.3 0.0 -0.2 -0.3 0.1 0.0
EU-27
Biodiesel 431.4 431.4 0.0 -0.7 184.5 0.4 247.1 Coarse Grains 0.8 0.6 0.2 0.1 0.6 -0.1 0.2 Other Grains -1.1 -1.0 -0.1 0.1 -0.1 0.0 -1.1 Oilseeds 17.5 18.7 -1.2 0.2 7.8 -0.1 9.6 Sugarcane -0.5 -0.5 0.0 0.0 -0.2 0.0 -0.2 Other Agri -0.3 -0.6 0.3 0.0 0.0 0.0 -0.3 Livestock -0.5 -0.5 0.0 0.0 -0.4 0.0 -0.1 Forestry -1.5 -1.3 -0.2 0.0 -1.0 0.0 -0.5
Brazil
Ethanol-2 38.9 23.3 15.6 0.2 38.8 0.1 -0.2 Coarse Grains 0.7 -0.4 1.1 0.2 0.7 -0.1 -0.1 Other Grains 0.3 0.3 0.0 0.0 0.8 0.0 -0.5 Oilseeds 1.7 -0.5 2.2 0.2 -0.2 -0.2 1.8 Sugarcane 17.2 17.2 0.0 0.1 17.2 0.1 -0.1 Other Agri -1.8 -1.1 -0.7 0.0 -1.6 0.0 -0.1 Livestock 0.1 0.1 0.0 0.0 0.2 0.0 0.0 Forestry -2.0 -1.8 -0.2 0.0 -1.9 0.0 -0.147 Table 7. Impact of Biofuel Drivers on Ma rket Price across Selected Regions: 2001-2006

% Change in Market Price US EU-27 Brazil Latin American

Energy Exporters Middle Eastern North African Energy Exporters SubSaharan Energy Exporters Coarse Grains 8.68 6.98 5.9 6.32 13.54 10.03 Other Grains 6.35 6.39 5.43 6.77 12.63 9.04 Oilseeds 5.85 9.55 6.41 6.76 13.56 9.5 Sugarcane 5.08 5.48 10.96 5.81 14.47 9.07 Livestock 3.44 3.1 2.75 4.86 10.78 9.81 Forestry 4.19 7.00 8.29 5.13 17.77 16.71 Ethanol-1 16.87 -33.51 1.51 3.91 10.37 7.42 Ethanol-2 3.88 6.67 3.27 6.83 18.22 9.62 Biodiesel 12.75 -40.9 3.27 4.35 11.39 7.91 Other Food Products 1.92 -0.09 1.84 3.98 10.42 7.45
Processed Livestock 2.41 0.29 1.43 4.11 10.17 7.97 Other Agri Commodities 2.68 1.66 3.72 4.62 13.1 8.54
Other Primary sectors 2.62 3.27 3.44 5.51 11.69 8.55

Consumer Price Index (% ch) 2.99 2.71 3.01 5.40 12.22 8.9348 Table 8. Change in Land Cover and Cr op Area due to Biofuel Drivers: 2001-2006

Region Land Cover (% ch) Crop Harvested Area Change (%)
Crops Forest Pasture Coarse Grains Oilseeds Sugarcane Other Grains Other Agri
US 1.3 -0.3 -0.4 5.0 -0.6 -2.0 -3.3 -1.2

Canada 0.8 -0.1 -0.1 1.4 -0.1 -0.4 0.8 0.1

EU-27 1.9 -1.1 -1.2 -0.4 15.0 -1.4 -2.1 -0.8
Brazil 2.8 -0.5 -0.4 -0.3 0.7 15.5 -1.0 -2.1 Japan 0.4 -0.1 0.0 3.9 1.4 -0.8 0.6 -0.7 China-Hong Kong 0.0 0.5 -0.2 0.0 2.0 -0.4 -0.5 -0.5
India 0.4 -0.6 -1.9 -0.7 0.0 -0.5 1.0 0.2
Latin American Energy Exporters -0.1 -0.2 0.1 0.9 -0.9 0.3 -0.1 -0.2

Rest of Latin America & Caribbean 0.8 0.1 -0.3 0.5 1.6 0.6 1.1 -0.4

EE & FSU Energy Exp -0.3 0.1 0.1 -0.7 1.1 -2.0 0.3 -0.3
Rest of Europe -0.2 0.7 -0.6 -0.7 0.7 -0.2 0.2 -0.2
Middle Eastern N Africa energy exporters 0.9 -2.0 -0.2 1.2 -1.1 1.1 -0.6 1.7

Sub Saharan Energy exporters -1.9 -0.5 0.7 1.2 -2.0 -0.8 -3.5 -1.7

Rest of North Africa & SSA 1.3 0.2 -0.3 -0.1 2.6 -0.3 2.1 -0.4 South Asian Energy exporters 0.3 -0.3 0.3 -0.1 0.7 -0.3 0.0 0.0 Rest of High Income Asia -0.4 0.2 -0.8 -0.4 3.4 -18.8 0.1 -0.9

Rest of Southeast & South Asia 0.8 -0.8 -0.3 -0.3 0.8 -0.6 0.5 -0.2

Oceania countries 1.1 -0.1 -0.1 1.6 2.7 -1.5 3.9 -1.649Table 9. Impact of Biofuel Drivers on BilateralTrade (change in import volume): 2001-2006 ($ millions)Exporters:Coarse Grains Oilseeds Other Food ProductsUS EU RoWTotalExportsUS EU RoWTotalExportsUS EU RoWTotalExports1 US 0 -4 -174 -178 0 158 -81 77 0 -129 -85 -2142 Canada 6 1 2 9 -3 19 -14 2 89 -9 14 933 EU-27 0 0 23 23 0 0 -130 -130 135 0 2667 28014 Brazil 0 4 22 26 0 179 -42 137 -8 -144 40 -1125 Japan 0 0 0 0 0 0 0 1 8 -2 13 206 China-Hong Kong 0 1 40 42 1 49 52 102 29 -4 96 1217 India 0 1 5 6 9 31 72 112 54 43 292 3898Latin American EnergyExporters0 3 22 25 -2 23 -72 -51 -168 -347 -264 -7799Rest of Latin America &Caribbean7 4 10 22 1 31 20 51 37 -41 110 10610 EE & FSU Energy Exp 0 6 32 38 1 63 7 70 -19 -206 -170 -39511 Rest of Europe 0 0 3 3 0 8 2 10 5 -28 105 8212Middle Eastern N Africa energyexporters-1 -2 -7 -9 -1 -7 -5 -13 -41 -191 -215 -44713 Sub Saharan Energy exporters -1 -2 -4 -6 -1 -2 -20 -23 -19 -487 -154 -66014 Rest of North Africa & SSA 0 1 19 20 0 4 3 8 -1 -39 89 4915 South Asian Energy exporters 0 0 1 1 0 2 3 5 8 -54 42 -416 Rest of High Income Asia 0 0 0 0 0 0 0 1 -10 -10 -90 -11117 Rest of Southeast & South Asia0 0 6 6 0 2 1517 138 8 251 39818 Oceania countries 0 0 2828 2 13 5 20 1 -14 -32 -45Total14 13 27 54 7 574 -185 395 238 -1654 2709 1293Note: Change in volume of exports of coarse grains, oilseeds, and other food products, from all the 18 regions to the US and EU, respectively, were evaluated atinitial market prices (trade volume changes in $ millions).50Table 10. Performance of Trade Balancedue to Biofuel Drivers ($ billion).

Trading Regions 1 USA 2 CAN 3 EU27 4 BRAZIL 5 JAPAN 6 CHIHKG 7 INDIA 8 LAEEX 9 RoLAC 10 EEFSUEX 11 RoE 12 MEASTNAEX 13 SSAEX 14 RoAFR 15 SASIAEEX 16 RoHIA 17 RoASIA 18 Oceania TotalTradingSectorsCoarse Grains 0.26 0.02 0.04 0.07 -0.13 0.04 0.01 -0.01 0.00 0.05 0.00 -0.39 -0.02 0.02-0.01-0.02 0.01 0.05-0.01Other Grains 0.02 0.21 -0.04-0.040.12 0.06 0.41 0.05 0.04 0.15 0.04 -1.47 -0.17 0.04 0.01 0.09 0.20 0.22-0.06Oilseeds 0.40 0.05 -0.89 0.32 -0.07 0.01 0.11 -0.04 0.07 0.08 0.01 -0.12 0.00 0.01-0.01-0.01 0.04 0.04-0.03Sugarcane 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.00Livestock -0.16 0.09 -0.07 0.00 0.01 0.25 0.11 -0.08 0.03 -0.01 0.11 -0.49 -0.07 0.03 0.00 -0.02 0.07 0.14-0.07Forestry 0.04-0.01-0.13 0.00 -0.04 0.10 0.12 0.00 0.02 0.13 0.06 -0.07 -0.34 0.02 0.07 0.00 0.05 0.020.04Ethanol-1 -0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.00Ethanol-2 -0.78-0.07-0.02 0.86 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 0.000.00Biodiesel 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.00Other FoodProducts-0.36 0.12 3.02-0.040.09 0.23 0.45 -0.78 0.19 -0.57 0.13 -2.08 -1.07 0.09 0.06 -0.06 0.53-0.03-0.08ProcessedLivestock-0.76 0.21 2.28 0.24 0.19 0.27 0.23 -0.54 0.19 -0.29 0.16 -2.34 -0.57 0.06 0.09 0.02 0.37 0.06-0.14Other AgriCommodities-0.39 0.03 1.72-0.020.18 0.45 0.75 -0.54 0.53 -0.34 0.51 -2.84 -1.75 0.20 0.06 0.22 1.06 0.05-0.12Oil & OilProducts-66.09 2.35 -62.45 -1.36 -29.29 -8.04 -8.24 29.20 -3.15 48.79 -6.18 115.12 20.09 -2.11 6.07 -18.34 -12.3 -1.45 2.64Other Primarysectors-0.26 -0.56 4.37 0.24 0.72 -0.58 0.22 -0.34 0.22 -0.20 0.21 -2.62 -0.18 -0.66 -0.12 1.10 0.19 -1.05 0.71Manufacturing& services-76.59 0.04 151.43 0.03 27.49 5.35 7.96-41.35 1.49 -12.30 22.88 -99.38 -17.87 3.20 -4.90 18.43 11.37 -0.13 -2.88Total-144.7 2.50 99.25 0.30 -0.74 -1.89 2.12 -14.4 -0.4 35.48 17.92 3.33 -1.97 0.89 1.32 1.42 1.62 -2.1 0.00Note: For abbreviations of the regions, please refer to the corresponding serial numbers in Table 9.51 Table 11. Decomposition of Terms of Trad e for the US and EU ($ million).

US EU-27
World Price Export Price Import Price Total World Price Export Price Import Price Total
Coarse Grains 20 113 4 137 0 1 4 6 Other Grains -54 45 0 -9 -3 44 -19 22 Oilseeds -41 -7 2 -46 25 45 -28 43 Other Food Products -221 153 3 -65 258 -656 212 -186 Processed Livestock -316 161 -1 1 -166 -297 -256 174 -379 Other Agri Sectors -105 56 0 -49 429 -423 -25 -19 Biofuels 8 0 -12 -4 1 -16 16 1 Oil & Oil Products -53989 110 295 -53583 -76985 -133 -328 -77446
Other Primary sectors 457 26 -48 435 2948 -115 31 2864
Manufacturing & services -880 9462 -2518 6064 -7582 -20025 5575 -22032

Total -55122 10120 -2284 -47286 -81206 -21532 5612 -9712652 Figure 1. World Production of Ethanol and Biodiesel (million gallons) (Data sources: Earth Policy Institute, 2006; FAPRI, 2007)

Figure 2. Modification of Consumption Structure in the GTAP-E Model Household demand for private goods (CDE_COMM)

Energy Composite CDE

Non-Energy Commodities

Coal Petroleum products & Biofuels com p osite σ

ELEGY = 0.1

Petro products (Oil_Pcts) σ E L H

B IOIL = 2 to 3.95

Biofuels (Ethanol1, Ethanol2, Biodiesel) Oil Gas Electricity CES53 Figure 3. Modification of Production Structure in the GTAP-E Model Firm’s output

Imported

Region r Value added & Energy Non-energy intermediate inputs σ = 0

σ ESUBD

Capital -Energy composite σ

ESUBVA

Land Labor

Region 1 Domestic

σ ESUBM
σ ELKE = 0.1

Non-electric Capital

Electric

Coal σ

ELNEL = 0.1

Petroleum products &

Biofuels com p osite Crude oil σ ELNCOAL = 0.25 Non-coal

Petro products (Oil_Pcts) σ ELBIOOIL = 0

Biofuels (Ethanol1, Ethanol2, Biodiesel) Composite Energy good

σ

ELEN = 0.1

Natural gas AEZi AEZ18 σ

ESAEZ = 20 CES

Ω

1 = -0.11

Crops Pasture Forest

Crop-1 Crop-n Ω 2 = -0.5 Natural

resources54Figure 4. Splitting the Three Types ofBiofuels in the GTAP Data Base11Please refer to Taheripouret al. (2007) for more details.GTAP Data Base Version 6(57 sectors x 87 regions)ofd(food products)crp(chemicals)vol(veg oils & fats)All othersectors (54)ofdn(ofd new)Ethanol1(Grain based)crpn(crp new)Ethanol2(sugar based)voln(vol new)biod(Biodiesel)55 -10.46 (minimum) -0.09 0.74 (median) 1.64 8.17 (maximum) % Change in Land Area under Coarse Grains: 2001-2006 Figure 5. Change in Land Area acro ss AEZs under Coarse Grains: 2001-2006.

-2.17 (minimum) 0.05 1.20 (median) 2.36 19.17 (maximum) % Change in Land Area under Oilseeds: 2001-2006 Figure 6. Change in Land Area acr oss AEZs under Oilseeds: 2001-2006.56 -11.47 (minimum) -0.51 -0.12 (median) 0.49 18.74 (maximum) % Change in Land Area under Sugarcane: 2001-2006 Figure 7. Change in Land Area across AEZs under Sugar Crops: 2001-2006.

-12.02 (minimum) -0.26 0.43 (median) 1.35 6.34 (maximum) % Change in Land Area under Other Grains: 2001-2006 Figure 8. Change in Land Area across A EZs under Other Grains (Paddy & Wheat): 2001-06.57 -10.89 (minimum) -0.31 -0.09 (median) 0.24 2.63 (maximum) % Change in Land Area under Other Agri : 2001-2006 Figure 9. Change in Land Area across AEZs under Other Agri Goods: 2001-2006.

-6.92 (minimum) -0.94 -0.45 (median) 0.06 1.33 (maximum) % Change in Land Area under Forest: 2001-2006 Figure 10. Change in Land Area ac ross AEZs under Forestry: 2001-2006.58 -6.57 (minimum) -1.20 -0.53 (median) -0.03 3.14 (maximum) % Change in Land Area under Pasture: 2001-2006 Figure 11. Change in Land Area acro ss AEZs under Pasture cover: 2001-2006.59 Appendix-1: Closure and Shocks for Anal yzing the Impact of Biofuel Boom

! Oil Price, Additive, to - Shocks closure exogenous afall afcom afreg afsec ams aoall aoreg aosec atd atf atm ats au cgdslack dpgov dppriv dpsave endwslack incomeslack pemp pfactwld pop profitslack psaveslack qo(ENDW_COMM,REG) RCTAXB tm tms to tpd tpm tp tradslack tx txs ; Rest Endogenous ;
swap aosec("oil") = pxwcom("oil");

List of Shocks : Shock pxwcom("oil") = 136;
Shock afall("ethanol1","O il_pcts","USA") = -49;
Shock to("Ethanol1","USA") = -10.93;
Shock to("biodiesel","USA") = -7.00;
Shock to("Ethanol1","EU27") = 50.77;
Shock to("biodiesel","EU27") = 81.18;
Subtotal afall = MTBE ban ; Subtotal pxwcom = World oil price ; Subtotal to(NSAV_COMM,"U SA") = USA subsidies ; Subtotal to(NSAV_COMM,"E U27") = EU subsidies ;