Impact of Biofuel Production on World Agricultural Markets: 

 A Computable General Equilibrium Analysis*

by 

Dileep K. Birur, Thomas W. Hertel, and Wallace E. Tyner1 

Department of Agricultural Economics, Purdue University

GTAP Working Paper No. 53 

2008 

*

The earlier version of the paper was presented 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 GENERAL EQUILIBRIUM ANALYSIS 

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

Abstract 

This paper introduces biofuels sectors as energy 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 modified 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 biofuels and AEZs offers a useful 

framework for analyzing the growing importance of biofuels for global 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 calibrate 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 share of feedstock in 

biofuels and related sectors in accordance with the historical evidence between 2001 and 

2006 in the three major biofuel producing regions: US, EU, and Brazil. The results from 

the historical simulation reveal an increased production of feedstock with the replacement 

of acreage under other agricultural crops. As expected, the trade balance in oil sector 

improves for all the oil exporting 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 Ecological 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 Analyzing the Impact of Biofuel Boom ................ 59
iii 

List of Tables 

Table 1. The Major Biofuel Producers in the World during 2006 ................................... 40

Table 2. Aggregation of Sectors and 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 Model from Historical Evidence. .......................................... 44

Table 6. Impact of Biofuel Drivers on the Agricultural Production: 2001-2006 ............. 46

Table 7. Impact of Biofuel Drivers on Market 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 Drivers on Bilateral Trade ($ millions) ................................ 49

Table 10. Performance of Trade Balance due to Biofuel Drivers ($ billion). ................. 50

Table 11. Decomposition of Terms of Trade 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 Structure in the GTAP-E Model ........................... 53

Figure 4. Splitting the Three Types of Biofuels 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 across 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 AEZs under Other Agri Goods: 2001-2006. ....... 57

Figure 10. Change in Land Area across AEZs under Forestry: 2001-2006. .................... 57

Figure 11. Change in Land Area across AEZs under Pasture cover: 2001-2006. ........... 58
1 

Impact of Biofuel Production on World Agricultural Markets: A Computable 

General Equilibrium Analysis

1. Introduction 

Energy is an important factor of production 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 emphasizing 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 framework 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 issue 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 CO2 emissions. All these regions have had significant subsidies or 

mandates for renewable energy production from agricultural sources. The impacts of 

these subsidies and mandates reach far beyond the borders of these economies. The 

purpose of this paper is to assess the global and 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 affecting 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 domestic policies, CGE serves as an ideal 

framework to study potential repercussions. 

1.1 An Overview of Biofuel Markets 

Ethanol, a predominant biofuel, is produced today from sugarcane and cereal 

grains mainly corn. Biodiesel is produced from oilseeds or palm oil. 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 production (13.1 billion gallons). Brazil is the 
2 

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 towards ethanol in the 

United States. Similarly, world biodiesel production has grown at a rate of 35 percent per 

annum since 1991; the majority of the boom comes 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) with the production of 799 million gallons during 

2006, followed by the US (20%), France (11%), Italy (7%), and other countries. 

In Brazil, ethanol is produced mainly from sugarcane beginning during the 1970s 

in order to reduce dependence on foreign oil. However, the ethanol industry had a 

setback in the 1990s due to cheap crude oil (Regaldo and Fan, 2007). When oil prices 

began to soar again in the recent years, ethanol became a more attractive alternative to 

gasoline, aided by the launch of flex-fuel vehicles (FFVs) in 2003. Brazil has a 

comparative advantage in producing ethanol, mainly due to its availability of land and its 

favorable climate for sugarcane cultivation. As Martines-Filho, Burniquist, and Vian 

(2006) report, the total cost of ethanol production in Brazil was about $1.10 per gallon 

and that of US was between $2.01 to $3.96 per 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 still in an early stage, US imports from 

Brazil grew dramatically since 2004. Brazil 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 impediment 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 gasoline 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, France, Germany, Russia, and Canada 

mainly use corn as their main feedstock, whereas, 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) and 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, all 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 and a brief history of the GTAP-E model. 

Section-3 deals with the study approach comprising modifications in the GTAP-E model 

and modeling land use change, followed by section-4 which informs about the database 

employed for this study. Section-5 illustrates the historical analysis involving the major 

biofuel drivers, calibration of the key parameters, and validation of the model. Resulting 

impact of biofuel drivers on output, prices, trade, 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 biofuels are few, largely due to infancy of the industry and 

limitations on availability of data. 

Sims (2003) described the benefits of displacement 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 Thierfelder (2006) is one of the earlier ones to utilize 

the GTAP data base for analyzing the effects of substituting a biomass (switchgrass) for 

crude oil in petroleum production in the US, using a CGE framework. As switchgrass is 
4 

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 assumed that the output is purchased as an 

intermediate input by the petroleum industry. The results from a direct substitution of 

switchgrass for crude oil revealed an increase in world price for cereals, but decline in 

world price of other crops, livestock, and crude 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 model (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 ethanol composite. The ethanol composite 

comprised substitution between the feedstocks such as sugar-beet-cane, wheat, grain, and 

forestry sectors. In order to account for 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 adopted by Gohin and Moschini (2007) to 

analyze the potential impacts of full implementation of the European biofuel policy in 

EU-15 economy where the farm sector is finely 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 price and production. In addition, the 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 negatively affected as the production cost of compound feed 
5 

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 for the utility derived by the consumers from 

the cleaner environment due to biofuels. Several studies in the recent past have focused 

on modeling production of biomass or cellulosic ethanol in a long run, recursive-dynamic 

CGE framework (Reilly and Paltsev, 2007; Dixon, Osborne, and Rimmer, 2007). In this 

study we do not consider biofuel from cellulosic materials since it has not been produced 

commercially; rather, we focus on liquid biofuels 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-Energy 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-down1

 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 function has been employed in 

the model. First, the energy inputs are separated from the non-energy intermediate inputs 

in the production structure, and then the energy inputs are aggregated with capital in a 

composite, allowing for capital-energy substitution with other factors. One of the main 

assumptions of the standard GTAP production structure 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 elasticity of substitution between any primary factor 

and intermediates is the same. This assumption 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 CO2 and other 

green house gases (GHGs), Burniaux and Truong (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 internationally. In their model, reduction in CO2

emission can be achieved either through energy substitution or by output reduction. They 

used an aggregated database of eight sectors and eight regions keeping in view the 

emission policy analyses as per the 1997 Kyoto 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 Australia, reiterated their commitment. Thus, 

the GTAP-Energy model emerged with the main purpose of climate change policy 

analysis, such as GHG mitigation. However, 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 facilitate its modification by 

others. Therefore, we build on the McDougall/Golub 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 agricultural markets and land use change, we ignore the CO2

emissions module for this analysis. We consider both ethanol and biodiesel, the two 

prominent biofuels produced across the world 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 based, and biodiesel is vegetable oil based2

. The 

substitution of biofuels is represented by intermediate demand substitution as well as 

household substitution, which required appropriate modifications in the production and 

consumption structures, respectively. For analyzing 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 (AEZs), 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 GTAP-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 household 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, ethanol1, ethanol2, biodiesel 

HEGY_COMM : coal, oil, gas, electricity, hbiooil

AEZ_COMM: the 18 Agro-Ecological Zones 

CROP_COMM: Coarse grains, oilseeds, sugarcane, other grains, other agri. 

NCROP_COMM: All other non-crop sectors 

AGRLAND_COMM: Land-using agri commodities (CROP + GRAZE) 

LAND_COMM: All land-using sectors (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 Golub (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’ consumption3

. Private consumption assumes 

constant-difference of elasticities (CDE) functional form to accommodate nonhomothetic 

preferences and fully flexible functional form. 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 aggregated composite energy good including 

biofuels (henergy) and all other non-energy tradeables. The following is the linearized 

form of the demand equation (in percentage change form) as stipulated in Hertel (1997). 

∑ ∈

− = + − i CDE COMM

P Y qp i r pop r i k r pp k r i r yp r pop r _

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

Where; i, k ∈ CDE_COMM; (i, k,r) σ P and (i,r) σ Y 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 commodity 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 substitution between petroleum 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

i BIOOIL COMM

CSHHBIOIL pp hbiooil r pp k r _

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

qp(i,r) qp("hbiooil",r) (r) *[ pp(i,r) pp("hbiooil",r)] = −σ ELHBIOIL − (3) 

where i, j ∈BIOOIL_COMM; ΨCSHHBIOIL is the share of good i in cost to j of household 

biofuel-petroleum (hbiooil) sub-product; σ ELHBIOIL is the elasticity of substitution in 

hbiooil sub-consumption which is calibrated using historical evidence, which will be 

discussed in the subsequent sections. Equation (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

i HEGY COMM

CSHEGY pp henergy r j r pp j r _

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

qp(i,r) qp("henergy",r) (r) *[ pp(i,r) pp("henergy",r)] = −σ ELEGY − (5) 

where i, j ∈ HEGY_COMM; ΨCSHEGY is the share of good i in cost to j of household 

energy sub-product; σ ELEGY is the elasticity of substitution among energy commodities 

and the petroleum-biofuel composite. Typically, the energy demands are found to be 

relatively price-inelastic. Cooper (2003) estimates the short-run and long-run elasticities 

of demand for crude oil in 23 countries and concluded that demand for crude oil is highly 

insensitive to changes in price. The estimated short-run elasticities range from 0.001 to -
10 

0.109 and that of long-run elasticities range from 0.005 to -0.453. Following Beckman et 

al. (2008), we assume a uniform own price elasticity of 0.1 (σ ELEGY ) across all regions4. 

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 the ease with which additional levels of 

nesting can be added within the production and consumption structures. We take 

advantage of this feature to incorporate biofuels as well as land-use information as shown 

in Figure 3. This production tree represents how the firm combines its individual inputs 

to produce its output qo(i,s). Truong (1999) removed energy commodities from the 

intermediate input nest and introduced 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 (σ ELBIOOIL ) of 0. 

That is, we treat biofuels and petroleum sectors 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 set 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 0.25 
11 

The price of biooil energy sub-production is determined by equation (6) and the 

demand for inputs into biooil energy sub-production is given by equation (7) below. 

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

∑ ∈

= Ψ − k BIOOIL COMM

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

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

( , , ) ( , , ) (" ", , ) ( , )

pf i j r af i j r pf biooil j r

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

− −

= − + −σ

 (7) 

where i, k ∈ BIOOIL_COMM and j ∈ PROD_COMM ; ΨCSHBIOOIL 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 − k NCOAL COMM

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

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

( , , ) ( , , ) (" ", , ) ( , )

pf i j r af i j r pf ncoal j r

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

− −

= − + −σ

 (9) 

where i, k ∈ NCOAL_COMM and j ∈ PROD_COMM; ΨCSHNCOAL 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 elasticity of substitution (σ ELNCOAL ) of 

0.25. 

The non-electricity sub-production nest allows for substitution between coal and 

non-coal energy composite with an elasticity of substitution (σ ELNEL ) of 0.1. The 

equations (10) and (11) refer to the price of non-electricity energy sub-product and 

demand for input into non-electricity energy sub-production. 

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

∑ ∈

= Ψ − k NELY COMM

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

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

( , , ) ( , , ) (" ", , ) ( , )

pf i j r af i j r pf nely j r

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

− −

= − + −σ

 (11) 
12 

where i, k ∈ NELY_COMM and j ∈ PROD_COMM; ΨCSHNELY is the share of k in cost to j

of non-electricity energy sub-product. 

Further up in the production tree, the price of composite energy good and demand 

for input into energy sub-production are given by equations (12) and (13), respectively. 

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

∑ ∈

= Ψ − k ENY COMM

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

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

( , , ) ( , , ) (" ", , ) ( , )

pf i j r af i j r pf eny j r

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

− −

= − + −σ

 (13) 

where i, k ∈ ENY_COMM and j ∈ PROD_COMM ; ΨCSHENY is the share of k in cost to j

of energy sub-product. The elasticity of substitution between electricity and non-electric 

composite (σ ELEN ) used here is 0.1. 

The important sub-nest is the capital-energy composite which determines the 

following variables: 

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

∑ ∈

= Ψ − k KEN COMM

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

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

( , , ) ( , , ) (" ", , ) ( , )

pf i j r af i j r pf ken j r

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

− −

= − + −σ

 (15) 

where i, k ∈ KEN_COMM and j ∈ PROD_COMM; ΨCSHKEN is the share of i in cost to j of 

capital-energy sub-product, and the elasticity of substitution (σ ELKE ) employed here is 

0.1. Equation (14) indicates the price of capital-energy sub-product and equation (15) 

denotes demand for inputs into the capital-energy sub-production. Burniaux and Truong 

(2002) mention that in order to ensure capital and energy are complements in the shortrun, and substitutes in the long-run, the elasticity σ ELKE 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 − k VAEN COMM

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

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

( , , ) ( , , ) (" ", , ) ( , )

pf i j r af i j r pf vaen j r

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

− −

= − + −σ

 (17) 

where i, k ∈ VAEN_COMM and j ∈ PROD_COMM ; ΨCSHVAEN is the share of i in cost to 

j of value-added-energy sub-product; the CES substitution elasticity (σ ESUBVA ) to combine 

the primary factors of production. 

At the top-level nest, the firm combines value-added and intermediate inputs with an 

elasticity of substitution (σ ESUBT ) equal to 0. 

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

( , , ) ( , , ) ( , ) ( , ) ( )

pf i j r af i j r ps j r ao j r

qf i j r af i j r qo j r ao j r j 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 run crop-yield-response which is used to 

calibrate the elasticity of substitution for primary factors (σ ESUBVA ) and the elasticity of 

intermediate input substitution (σ ESUBT ) for the five crop sectors. 

3.2 Modeling Land Use change for Biofuels 

The growing importance of biofuels has created a huge demand for bio-feedstock. 

The energy demand coupled with demand for food 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 marginal land prone to erosion, forest, pasture 

land etc. under corn5

. Any tendency towards importing foreign-grown feedstocks could 

also result in massive displacement of agriculture and rain forest in the developing 

 

5

 For example, Kelly (2007) reports that California’s state law stipulates to increase 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 feedstocks consequently exacerbating the global warming. 
14 

countries. Leahy (2007) reports that biofuels are causing deforestation in Indonesia, 

Malaysia, and Thailand due to monocultures of oil palm6. 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, with 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 crops 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-Ecological-Zones (AEZs) as per U.N. Food and 

Agricultural Organization convention. In the 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 temperature regimes, soil type, topography, and 

knowledge on crop requirements7

. The AEZ data are derived based on six categories of 

60 day interval growth period in the world subdivided into three climatic zones (tropical, 

temperate, and boreal) using criteria based 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 harvested land cover and yields 

from Monfreda et al. (2008a, 2008b), and (iii) database which 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 rents 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, incorporating aforementioned rich land-use 

AEZ level database into the biofuels module would yield better, 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 composite good (sub-product) which allows for 

substitution between AEZs for a given use. 

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

∑∈

= Ψ − k AEZ COMM

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

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

( , , ) ( , , ) (" ", , ) ( , )

pf i j r af i j r pf land j r

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

− −

= − + −σ

 (20) 

where i, k ∈ AEZ_COMM and j ∈ PROD_COMM; ΨCSHAEZ 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,σ ESAEZ , which we assume to be very high 

(σ ESAEZ = 20). This is dictated by the homogeneity of the products being 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 restricted 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 the revenue function. The land-owner makes 
16 

optimal allocation of a given parcel of land under crops, pasture or commercial forest in 

the first stage, while the choice of crops is made in the second stage. Given that any 

increase in biofuel production would necessitate an increase in the supply of feedstock, 

which has to come from diversion of feedstock from other uses, increased 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 recommend a long-run yield response 

to price of 0.4, which we adopt here and calibrate 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 qo i r qo i r i r pm i r pm i r cropland = −ω + Ω land − cropland (21) 

The composite price for AEZ-land endowments is given by: 

( , ) ( , , ) * ( , , ) 1 pm i r i k r pm i k r es

k

land = ∑Ψ (22) 

The market price of AEZ-land endowment allocated to different crops 

( , ) ( , , ) * ( , , ) 2 pm i r i k r pm i k r es

k

cropland = ∑Ψ (23) 

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

clearing condition; ( , , ) 1 Ψ i k r is the revenue share of ith AEZ in kth land using sector 

(LAND_COMM); ( , , ) 1 Ψ i k r is the revenue share of ith AEZ in kth crop sector using land 

(CROP_COMM); ) pm (i, k,r es is the market price of AEZ-land endowment i used by 

producing sector j in region r. 

The sensitivity of land allocation across the three cover types is determined by the 

elasticity, Ω1 , in equation (21). For this parameter, 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 Lubowski, Plantinga, and 

Stavins (2006). The solution for qo (i,r) cropland obtained from (21) is distributed across 

non-crop land (forestry and pasture lands) in as below: 

( , , ) ( , ) ( , ) *[ ( , ) ( , , )] 1 qoes i j r qo i r i r pm i r pm i j r = −ω + Ω land − es (24) 
17 

where i,∈AEZ_COMM; j∈NCROP_COMM. 

The supply of cropland is allocated across crops as given by equation (25). 

( , , ) ( , ) ( , ) *[ ( , ) ( , , )] 2 qoes i j r qo i r i r pm i r pm i j r = cropland −ω + Ω cropland − es (25) 

where i ∈ AEZ_COMM; j ∈ CROP_COMM; Ω2 is the elasticity of transformation across 

crops, taken as -0.5 (FAPRI, 2004) which is the maximum acreage response elasticity for 

corn across different regions in the United States. The CET parameters Ω1 and Ω2 are 

non-positive and their absolute value increases in absolute terms as the degree of 

sluggishness diminishes possibly driving the rental rates across 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 (Dimaranan, ed., 2007) does not include explicit 

biofuels sectors. Taheripour et al. (2007) deal with this challenge by incorporating 

biofuel sectors into the GTAP data base using the available information on the patterns of 

sales and purchases for these sectors. As noted previously, the biofuel industry included 

in this study8 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 three 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 ethanol-1 sector from the food products sector (ofd) receiving 

inputs from the cereal grains sector (gro), the sugar based ethanol-2 sector out of 

 

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 CO2 emission from biofuels is not included in the 

database. 
18 

chemicals sector (crp) with inputs from sugar-cane-beet (c_b) sector; and biodiesel sector 

is created from the vegetable oils and fats (vol) sector which gets input from the oil-seeds 

(osd) sector. Thus the final disaggregated level has 60 sectors9

 and 87 regions. The sales 

of biofuels are channeled through household as well as intermediate demand. As 

discussed in the earlier section, we use 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 and the AEZ data depict the global economy 

for the year 2001. 

This data base is aggregated to permit focus on the sectors and regions of 

particular interest. For implementing the biofuels boom analysis, we aggregate the 

database into 20 economic sectors and 18 regions (Table 2). The sectors are aggregated 

such that we could focus on the linkages among feedstock, biofuels, energy commodities, 

and other important sectors. The regions are aggregated such that each continent is 

broadly divided into three categories: major energy consuming countries, major energy 

exporting countries, and all remaining countries in the continent. 

5. Historical Analysis 

Typically validation of a model involves testing if it can track historical 

developments in the economy. In the same spirit, we verify the model by projecting10

the biofuel economy from 2001 baseline (database) to depict 2006 scenario and compare 

the share of feedstock in biofuels and related sectors to the historical evidence. In doing 

so, we consider three key factors of the US biofuels boom: rise in petroleum prices, the 

replacement of MTBE11 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. 

10As 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 elements 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 derived 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 increased biofuels consumption and consequently, 

the usage of feedstocks has implications on trade and welfare. In a GTAP-based study, 

McDonald, Robinson, and Thierfelder (2006) showed 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 price (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 dropping back to attain an average 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 oil would translate directly into higher prices 

for gasoline (Behrens and Glover, 2006). It is clear from the table that not all the crude 

oil price shock had been transferred to gasoline market by 2006, as crude oil prices rose 

by 136% while the average real price of gasoline 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 and 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 approach to achieving the oil price rise, since 

we are primarily interested in the consequences of the price hike, not the causes. 

Specifically, to achieve the world price 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 closure13. 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 gasoline, and it comprises the first piece of 

our historical validation experiment. 

5.2 Phasing out of MTBE in the US 

With the passing of the US Clean Air Act of 1990, the vendors were required to 

have a minimum oxygen percentage in gasoline. 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 discretion 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. This was the death knell for MTBE as an 

additive, and led to its replacement with ethanol (Tyner, 2008). Production of biofuels 

has increased even in the net energy exporting countries due to mandatory use of ethanol 

as an octane enhancer14. 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 ethanol domestically. So in 

order to blend with gasoline, several countries have started to produce or import ethanol 

to abate the pollution due to the petroleum 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 gallons) in 2001 to 79% (4.86 billion gallons) in 

2006, which is about 125% increase during the six year period. However, with the 

 

13 The closure used in this model is the standard general 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 companies were free to meet the clean air 

rules either by using ethanol or reformulated gasoline. Thus, there are two effects 

occurring simultaneously from two policies which have led to an increase in use of 

ethanol as an additive in the US gasoline industry (oil_pcts). To figure out this effect, 

consider the intermediate demand equation (18) discussed earlier. Assuming the 

elasticity of substitution among intermediate inputs in oil_pcts sector (σ ESUBT ) = 0, and 

no change output augmenting technical change in the oil_pcts industry ( ao( j,r) = 0), the 

equation (18) takes the form: 

qf (i, j,r) = −af (i, j,r) + qo( j,r) (26)

where i and j refer to ethanol-1 and oil_pcts sectors, respectively in the r region (US); 

qf (i, j,r) is the demand for ethanol-1 in the oil_pcts sector in the US; qf (i, j,r) is the 

factor i (ethanol-1) augmenting technical change in sector j (oil_pcts) in the US; and 

qo( j,r) being the output of oil_pcts in the US. Equation (26) is in the percentage change 

form and its levels form is as below: 

( , , )

( , ) ( , , ) QF i j rQO j r AF i j r =

 (27) 

From equation (27) we compute change of AF from 2001 (AF0

) to AF in 2006 

(AF1

). During the 2001 to 2006, decline in MTBE in the oil_pcts sector (QF1) was 1.97 

billion gallons (Table 3). That means, if we index output to 1.0, then AF1

= 1/1.97 = 0.51 

(assuming no change in oil_pcts output QO) and AF0 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_pcts 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 primarily attributed to the subsidies and other 

incentives that the national governments offer to this infant industry. The biofuel 
22 

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 gallon 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 was 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 costs 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 changes in ethanol subsidies and argues that 

the recent ethanol boom is an unintended consequence of a fixed ethanol subsidy which 

was calibrated to $20 per barrel crude oil prices. Though the success of ethanol industry 

relies on relative corn and oil prices, the subsidy has remained fixed irrespective of the 

hike in crude oil and corn prices. As Tyner and Quear (2006) 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 states offer several incentive schemes such 

as excise-tax reductions or producer payments, 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, the 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 

subsidy15, 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 shock the output subsidy variable (to) in the US by -

10.93. Similarly, the power of ADV for biodiesel in the US has declined from 1.41 to 

1.31, by 7% during 2001-06. 

 

15 In GTAP jargon, the power of the ad valorem tax or subsidy (TOi) = 1+ ti, where ti is the ad valorem tax 

or subsidy rate expressed in percentage (Hertel, 1997). 
23 

The EU-27 has emerged as the largest producer of biodiesel in recent years. The 

major impetus behind this boom is the tax credit given to biofuel industry by the 

members states (MS). The EU directive allows MS a legal framework to differentiate 

taxation of energy products and this has resulted 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 only after 2002, and later other MS adopted 

different levels of biofuel tax credits. We compute a production 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 increase to more than $2.46 per gallon in 2012 (Godoy, 

2007). Since we focus on the 2001-2006 historical 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. However it is practically infeasible to obtain the observed 

data for all the exogenous variables on a global scale, we shock only the key biofuel 

drivers responsible for biofuel 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 parameters 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 petroleum products and liquid biofuels in final 

demand (σ ELHBIOIL ). Unfortunately, we do not have estimates for this parameter, which 

obviously plays a key role in our analysis. Ideally, we would 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 simpler, calibration approach for obtaining these 

elasticities of substitution. 
24 

We have assembled historical data on biofuel use for our three focus regions: US, 

EU and Brazil. By choosing the values for σ ELHBIOIL of 3.95, 1.65 and 1.35 for these three 

regions, we are able to successfully reproduce the 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 increasing biofuel use in response to higher fuel prices is more 

limited than in the US and the EU, where there 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,σ ELBIOOIL , 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) elucidate 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 substitution 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 from various studies discussed in the earlier 

sections. With these parameter values, we analyze 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 of 
25 

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 variables interested to biofuel economy. After running the three 

shocks together as discussed in the earlier sections, the results are presented in Table 5 in 

comparison with the corresponding historical data (recall that we are only simulating the 

impact of the bio-fuel related shocks, not all of the other developments that occurred over 

this period). Ethanol production 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% which indicates a significant improvement 

in yield due to a combination of technology and weather. The model prediction of coarse 

grain production is about 7% over this same period. An important criterion to assess the 

performance of the model is the change in the share of feedstock going to biofuels. As 

seen from the table, the model predicted corn share16 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% and 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 share17 by about 

9%. The model prediction must be negative, given the economic logic of the model, 

since price rises and export demand is downward sloping. However, over this historical 

period, in spite of increased usage of corn for ethanol production, US corn exports have 

grown moderately due to factors 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 historic and predicted share of corn exports as well. 
26 

rapid economic growth in Asia and depreciation of the US dollar. This discrepancy 

between predicted and observed exports also helps to explain the divergence between 

predicted and actual production of corn. Similarly, the other grains sector, which 

constitutes paddy rice and wheat, is predicted to see a decline of 3% in production, which 

is less than the actual decline in production for 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 historical production of sugar-based 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 value is 39% which is larger 

than the historic data. On the contrary, the 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 sugarcane in ethanol production matches 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. Biodiesel 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 (increase of 431%). Unfortunately, we could not find the 

historical data on the share of oilseeds used for biodiesel production 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 production, 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 shocks that we have included here should predict accurate 

output levels. With the exception of the discrepancies noted above, overall the model 

predicts the stylized facts about the structure of the energy, 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 change in output in terms of domestic and export components across 

various agricultural sectors. The impact of the three shocks on agricultural output in the 

United States reveals that the production increase for coarse 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 coarse grains production, interestingly 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 ethanol 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 agricultural 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 (subsidies 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 subtotals18 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 biofuel industry did not have 

any direct effect on agricultural markets in other 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% reduction 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 decomposing 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 decline 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 important 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 the impact of biofuel drivers 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 sugarcane 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 little stronger pinch in prices, possibly due to increase in 

demand as their disposable income goes up following the hike in oil price19. 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 producing 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 pressure 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 the 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 skyrocketed 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 over time (Spieldoch, 2007). Along these lines, there 

are several reasons such as drought in New Zealand 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 could be insignificant. 
29 

following the biofuels boom. The largest changes in coarse grain acreage (up to 10%) are 

in less-productive AEZs which contribute little to national coarse grain output. The 

productivity-adjusted change in land cover and crop harvested 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, respectively in the US 

The productivity-weighted acreage change in land-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 sector with -3.4% changes in acreage. Figures 6-9 plot the 

land use changes by region and AEZ for oilseeds, sugar crops, other grains, other agrigoods sectors and Figures 10-11 depict the change 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 comes from decline of 2.1% each in forest and 

pasture land-cover area. Depending on the AEZ, the overall productivity-weighted 

average for land used in oilseeds increased from 0.05% to 19% (Figure 6). This trend 

confirms Tyner and Caffe (2007) who estimate that non-food rapeseed 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 land 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% from forest land. As seen from Figure 8, the 

forestry sector gave up its acreage in all the regions except for Canada and Rest of the 

World. Brazil is the main region with productivity-weighted acreage loss of up to -7% 

each in forest land (Figure 10) and in pasture 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 acreage 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, which registers a decline of about 11% of land used across 

AEZs in each of these sectors. Overall, the Other Agri-goods sector 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 trade is presented as change in import volume for coarse 

grains, oilseeds, and other food products by the 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 global 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 change 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. Interestingly, 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 billion), whereas, the RoW imports increase by 

$2.71 billion. 

The commodity trade balance, by region, owing 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 trade 
31 

balance is to reduce oil imports, which can be clearly seen from the table as the oil and 

oil products portion of the trade balance improves. The trade balance in aggregate 

deteriorates for the US, with the largest contributors being manufacturing and services (-

$77 billion) and oil and oil products (-$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 aggregate which mainly comes from exports in 

manufacturing and services sector ($151 billion). 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 the 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) effect 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 billion 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, other grains, and coarse grains sectors. 

The effect of the six shocks on ToT basically depends on the magnitude of the 

change in oil price relative to the change in the export prices. A decomposition of ToT as 

contribution of change in world price, export price, and the import prices would be 

helpful. In the US, the world price interaction with oil and oil products was the major 

component contributing towards ToT loss. Although 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 that 
32 

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 positive, 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, environmentalists, and national governments across the world. 

In order to analyze the linkages between biofuels and agricultural 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 the model, we project 

the biofuel economy forward in time from 2001 to 2006, and compare the model 

predictions with historical evidence. Since it is not possible to introduce 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 additives, subsidies for ethanol and biodiesel in the US and 

EU. Using this historical simulation, we calibrate some of key elasticities of energy 

substitution between biofuels and petroleum products in each region. 

Based on only the six types of shocks related to biofuels, the model predictions 

match reasonably well with key historical evidence in the major biofuel producing 

regions. The results from the historical simulation revealed, that with higher crude oil 

prices biofuels are substituted for petroleum products. The biofuel drivers have driven up 

the demand for feedstocks in the three major 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 dynamic, there are several elements of the 

global production and trade in this sector which 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. for which we have not 

yet established linkages with biofuel sectors. Byproducts of biofuels are also crucial in 

determining global impacts of biofuel programs. Taheripour et al. (2008) introduce 

byproducts of corn-ethanol and biodiesel into the earlier version of this model and found 

that the model without byproducts 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 specific nature of the biofuel by-products and 

their use varies across feedstocks and proper incorporation 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 CO2 and other GHG emissions to 

permit a comprehensive assessment of the environmental impact of biofuels. In addition 

to looking at the GHG emissions impacts of biofuels, we plan to analyze their impact on 

poverty, as they provide a double-edged sword for the world’s poor – on the one hand 

raising food prices, while on the other, enhancing earnings opportunities in agriculture. 
34 

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40 

Tables and Figures 

Table 1. The Major Biofuel Producers 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. 
Table 2. Aggregation of Sectors and Regions used in the Model No New Code Sector Description GTAP old sectors No. New Code Region Description 1 CrGrains Cereal grains gro 1 USA United States 2 OthGrains Paddy and Wheat pdr wht 2 CAN Canada 3 Oilseeds Oil seeds osd 3 EU27 European Union 27 4 Sugarcane Sugar cane, sugar beet c_b 4 BRAZIL Brazil 5 Livestock Cattle,Animal pdts,Milk,Wool ctl oap rmk wol 5 JAPAN Japan 6 Forestry Forestry frs 6 CHIHKG China, Hong Kong 7 Ethanol1 Ethanol1 (corn based) eth1 7 INDIA India 8 Ethanol2 Ethanol2 (sugarcane based) eth2 8 LAEEX Latin AmericanEnergy Exporters 9 Biodiesel Biodiesel biod 9 RoLAC Rest of LatinAmerica+Caribbean 10 OthFoodPdts Other Food Products voln ofdn 10 EEFSUEX EE & FSU Energy Exp 11 ProcLivestoc Meat, Dairy products cmt omt mil 11 RoE Rest of Europe 12 OthAgri other agriculture goods v_f pfb ocr pcr sgr b_t 12 MEASTNAEX Middle Eastern N Africa E Exp 13 OthPrimSect OtherPrimary:Fishery & Mining fsh omn 13 SSAEX Sub Saharan Energy Exporters 14 Coal Coal coa 14 RoAFR Rest of North Africa & SSA 15 Oil Crude Oil oil 15 SASIAEEX South Asian Energy Exporters 16 Gas Natural gas gas gdt 16 RoHIA Rest of High Income Asia 17 oil_pcts Petroleum, coal products p_c 17 RoASIA Rest of Southeast & South Asia 18 electricity Electricity ely 18 Oceania Oceania countries 19 En_Int_Ind Energy intensive Industries crpn i_s nfm 20 Oth_Ind_Se Other industry and services 

tex wap lea lum ppp nmm fmp mvh otn ele ome omf wtr cns trd otp wtp atp cmn ofi isr obs ros osg dwe 
42 

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 price1: 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 subsidy2 % 1.34 1.20 -10.9% 

Biodiesel:

Average price $ / gallon 2.45 3.23 31.8% 

Federal Subsidy $ / gallon 1 1 

ADV equivalent of subsidy2 1.41 1.31 -7.0% 

EU-27

Ethanol3 :

Average Price $ / gallon 1.48 1.96 32.4% 

Tax credit $ / gallon - 0.995 

ADV equivalent of tax credit2 % 1.00 1.508 50.77% 

Biodiesel4

 :

Average Price $ / gallon 2.33 2.34 0.6% 

Tax credit $ / gallon - 1.898 

ADV equivalent of tax credit2 % 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) and Energy Efficiency and Renewal 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)

Canada 2 0 

EU-27 1.65 

Brazil 1.35 

Elasticity of substitution in AEZ 

production nest (ESAEZ) Japan 2 20 

China-Hong Kong 2 

India 2 

Scalar yield elasticity target 

(YDE_Target ) Latin American Energy 0.4 

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) 

Middle Eastern N -0.2 

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. 
Table 5. Validation of the Model from Historical Evidence in the US, Brazil, and EU-27. 

US. 

Ethanol-1 

Production 

Million 

gallons 

Model 

Prediction1: 

Ethanol 

Production 

($ million) 

Corn Area 

Million 

hectares 

Corn 

Production 

Million 

tonnes 

Model 

Prediction1: 

Coarse Grain 

Production 

($ million) 

Share of Corn 

for Ethanol 

Model 

Prediction1: 

Corn Share 

for Ethanol 

(% share) 

S

C

E

2001 1770 2489.30 30.28 241.38 20936.5 6.5% 6.8% 2002 2130 31.56 227.77 7.5% 2003 2800 31.44 256.28 11.0% 2004 3400 32.37 299.92 11.7% 2005 3900 32.71 282.31 14.5% 

2006 4855 6886.80 31.33 267.60 22335.0 20.2% 17.0% 

2007* 7123 37.15 316.50 23.0% 

%Ch 

2001-06 174.29 176.66 3.47 10.86 6.68 209.05 150.0 

%Ch 

2000-07* 46.71 18.58 18.27 

US 

Wheat Area 

Million 

hectares 

Wheat 

Production 

Million 

tonnes 

Rice Area 

Million 

hectares 

Rice 

Production 

Million 

tonnes 

Model 

Prediction1: 

Other Grain 

Production 

($ million) 

Soybean Area 

Million 

hectares 

Soybean 

Production 

Million 

tonnes 

M

Pre

O

Pr

($ 

2001 23.77 53.00 1.33 10.59 7304.8 29.63 78.67 12002 24.13 43.70 1.30 10.38 29.59 75.01 2003 24.86 63.81 1.21 9.84 29.36 66.78 2004 23.87 58.74 1.34 11.43 30.08 85.01 2005 22.89 57.28 1.35 10.99 28.86 84.00 2006 22.94 49.32 1.14 9.51 7070.8 30.21 86.69 1

2007* 25.63 71.30 

%Ch 

2001-06 -3.52 -6.95 -14.79 -10.21 -3.20 1.95 10.20 

%Ch 

2000-07* -15.15 -17.75 
BRAZIL 

Ethanol-2 

Production 

Million 

gallons 

Model 

Prediction1: 

Ethanol 

Production 

($ million) 

Sugarcane 

Area 

Million 

hectares 

Sugarcane 

Production: 

Million 

tonnes 

Model 

Prediction1: 

Sugarcane 

Production 

($ million) 

Share of 

Sugarcane for 

Ethanol 

production 

Model 

Prediction1: 

Sugarcane 

Share for 

Ethanol 

Share of 

Sugarcane 

for Sugar 

production 

2001 3609.0 5341.7 4.88 326.1 3189.4 43.3% 43.5% 55.7% 2002 3760.0 5.02 344.3 45.2% 53.8% 2003 4336.0 5.1 363.7 46.2% 52.8% 2004 4443.0 5.5 416.6 49.1% 49.9% 2005 4467.0 5.69 421.8 53.0% 46.0% 

2006 4491.0 7420.4 5.87 431.4 3737.4 51.0% 51.6% 48.0% %Ch 

2001-06 24.44 38.91 20.29 32.29 17.18 17.67 18.51 -13.76 

EU-27 

Biodiesel 

Production: 

Million 

gallons 

Model 

Prediction1: 

Biodiesel 

Production 

($ million) 

Oilseeds 

Area: 

Million 

hectares 

Oilseeds 

Production: 

Million 

tonnes 

Model 

Prediction1: 

Oilseed 

production 

($ million) 

Model 

Prediction1: 

Oilseed Share 

for Biodiesel 

Oilseed 

Imports: 

Million 

tonnes 

Oilseed 

Exports: 

Million 

tonnes 

2001 288 513.69 14.21 33.5 6905.1 6.50% 26.14 6.40 2002 384 14.13 31.5 25.27 6.76 2003 503 15.06 34.4 24.45 6.46 2004 708 14.83 38.6 21.69 7.00 2005 815 15.02 36.5 24.87 6.60 2006 1467 2729.56 15.82 37.9 8111.1 27.60% 28.05 6.19 

%Ch 2001-06 409.38 431.37 11.3 13.1 17.47 324.62 7.29 -3.38 

Note: 1 Model prediction values refer to results from crude oil price in all regions, additive demand in the US, and biofuel subsidy 

performed together. The model predictions for 2001 are the pre-shock values (from the basedata) and 2006 values are post-shock valuData Sources: Food and Agriculture Organization online database ; Economic Research Service, United States Dept of AgricAssociation; European Biodiesel Board; The São Paulo Sugar Cane Agro-industry Union (UNICA). 
46 

Table 6. Impact of Biofuel Drivers on the Agricultural Production in the 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 -0.1 
47 

Table 7. Impact of Biofuel Drivers on Market 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 8.93 
48 

Table 8. Change in Land Cover and Crop 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 -1.6 
Table 9. Impact of Biofuel Drivers on Bilateral Trade (change in import volume): 2001-2006 ($ millions) 

Exporters: 

Coarse Grains Oilseeds Othe

US EU RoW Total Exports US EU RoW Total Exports US E

1 US 0 -4 -174 -178 0 158 -81 77 0 -12 Canada 6 1 2 9 -3 19 -14 2 89 -3 EU-27 0 0 23 23 0 0 -130 -130 135 0 4 Brazil 0 4 22 26 0 179 -42 137 -8 -15 Japan 0 0 0 0 0 0 0 1 8 -6 China-Hong Kong 0 1 40 42 1 49 52 102 29 -7 India 0 1 5 6 9 31 72 112 54 48 Latin American Energy Exporters 0 3 22 25 -2 23 -72 -51 -168 -39 Rest of Latin America & Caribbean 7 4 10 22 1 31 20 51 37 -4

10 EE & FSU Energy Exp 0 6 32 38 1 63 7 70 -19 -211 Rest of Europe 0 0 3 3 0 8 2 10 5 -212 Middle Eastern N Africa energy exporters -1 -2 -7 -9 -1 -7 -5 -13 -41 -113 Sub Saharan Energy exporters -1 -2 -4 -6 -1 -2 -20 -23 -19 -414 Rest of North Africa & SSA 0 1 19 20 0 4 3 8 -1 -315 South Asian Energy exporters 0 0 1 1 0 2 3 5 8 -516 Rest of High Income Asia 0 0 0 0 0 0 0 1 -10 -117 Rest of Southeast & South Asia 0 0 6 6 0 2 15 17 138 8 18 Oceania countries 0 0 28 28 2 13 5 20 1 -1

Total 14 13 27 54 7 574 -185 395 238 -16

Note: Change in volume of exports of coarse grains, oilseeds, and other food products, from all the 18 regions to the US and EU, respectinitial market prices (trade volume changes in $ millions).
Table 10. Performance of Trade Balance due 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 

Trading Sectors Coarse 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 Other Grains 0.02 0.21 -0.04 -0.04 0.12 0.06 0.41 0.05 0.04 0.15 0.04 -1.47 -0.17 0.04 0.01 Oilseeds 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 Sugarcane 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 Livestock -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 Forestry 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 Ethanol-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 Ethanol-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 Biodiesel 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 Other Food Products -0.36 0.12 3.02 -0.04 0.09 0.23 0.45 -0.78 0.19 -0.57 0.13 -2.08 -1.07 0.09 0.06 Processed Livestock -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 Other Agri Commodities -0.39 0.03 1.72 -0.02 0.18 0.45 0.75 -0.54 0.53 -0.34 0.51 -2.84 -1.75 0.20 0.06 Oil & Oil Products -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 Other Primary sectors 

-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 

Manufacturing & 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 Total -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 

Note: For abbreviations of the regions, please refer to the corresponding serial numbers in Table 9.
51 

Table 11. Decomposition of Terms of Trade 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 -11 -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 -97126 
52 

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 composite

σELEGY = 0.1

Petro products 

(Oil_Pcts) 

σELHBIOIL = 2 to 3.95

Biofuels 

(Ethanol1, Ethanol2, Biodiesel)

Oil Gas Electricity

CES
53 

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

σESUBD

Capital-Energy

composite

σESUBVA

Labor Land

Region 1

Domestic

σESUBM

σ ELKE = 0.1

Non-electric 

Capital

Electric 

Coal

σELNEL = 0.1

Petroleum products & 

Biofuels composite Crude oil

σELNCOAL = 0.25

Non-coal

Petro products 

(Oil_Pcts) 

σELBIOOIL = 0

Biofuels 

(Ethanol1, Ethanol2, Biodiesel)

Composite Energy good

σELEN = 0.1

Natural 

gas

AEZi AEZ18

σESAEZ = 20

CES

Ω1 = -0.11

Crops Pasture Forest 

Crop-1 Crop-n 

Ω 2 = -0.5

Natural 

resources 
Figure 4. Splitting the Three Types of Biofuels in the GTAP Data Base1 1Please refer to Taheripour et al. (2007) for more details. 

GTAP Data Base Version 6 

(57 sectors x 87 regions) 

ofd 

(food products) 

crp 

(chemicals) 

vol 

(veg oils & fats) 

ofdn 

(ofd new) 

Ethanol1 

(Grain based) 

crpn 

(crp new) 

Ethanol2 

(sugar based) 

voln 

(vol ne

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 across 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 across 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 AEZs 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 across 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 across AEZs under Pasture cover: 2001-2006. 
59 

Appendix-1: Closure and Shocks for Analyzing 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","Oil_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,"USA") = USA subsidies ; 

Subtotal to(NSAV_COMM,"EU27") = EU subsidies ;