EPA Project Number: 0210826.014

Land Use Implications of Biofuels:

GTAP Approach

Final Report

January 2010

Prepared for

U.S. Environmental Protection Agency

Office of Transportation and Air Quality

1200 Pennsylvania Avenue, NW

Washington, DC 20460.

Prepared by

Dileep K. Birur

Robert H. Beach

Environmental, Technology and Energy Economics Program 

Global Climate Change and Environmental Sciences Unit

RTI International

3040 Cornwallis Road, P.O. Box 12194

Research Triangle Park, NC 27709-2194.

Table of Contents

											      Page

  TOC \o "1-3" \h \z \u    HYPERLINK \l "_Toc251849897"  Table of
Contents	  PAGEREF _Toc251849897 \h  i  

  HYPERLINK \l "_Toc251849898"  List of Tables	  PAGEREF _Toc251849898
\h  ii  

  HYPERLINK \l "_Toc251849899"  List of Figures	  PAGEREF _Toc251849899
\h  iii  

  HYPERLINK \l "_Toc251849900"  1.	Overview	  PAGEREF _Toc251849900 \h 
1  

  HYPERLINK \l "_Toc251849901"  2.	Model Background	  PAGEREF
_Toc251849901 \h  1  

  HYPERLINK \l "_Toc251849902"  2.1.	Structure of GTAP Model	  PAGEREF
_Toc251849902 \h  2  

  HYPERLINK \l "_Toc251849903"  2.2.	Aggregation of Sectors and Regions	
 PAGEREF _Toc251849903 \h  4  

  HYPERLINK \l "_Toc251849904"  2.3.	Land Endowment in the GTAP-BIO
Model	  PAGEREF _Toc251849904 \h  6  

  HYPERLINK \l "_Toc251849905"  2.4.	Land Supply in the GTAP-BIO Model	 
PAGEREF _Toc251849905 \h  8  

  HYPERLINK \l "_Toc251849906"  3.	Key Parameter Assumptions and
Qualifications	  PAGEREF _Toc251849906 \h  8  

  HYPERLINK \l "_Toc251849907"  3.1	Sensitivity of Key Parameters	 
PAGEREF _Toc251849907 \h  9  

  HYPERLINK \l "_Toc251849908"  3.2	Qualifications	  PAGEREF
_Toc251849908 \h  10  

  HYPERLINK \l "_Toc251849909"  4.	Experimental Design	  PAGEREF
_Toc251849909 \h  10  

  HYPERLINK \l "_Toc251849910"  5.	Results from Corn-Ethanol Experiment	
 PAGEREF _Toc251849910 \h  12  

  HYPERLINK \l "_Toc251849911"  6.	Results from Soybean-Biodiesel
Experiment	  PAGEREF _Toc251849911 \h  15  

  HYPERLINK \l "_Toc251849912"  7.	Conclusions	  PAGEREF _Toc251849912
\h  18  

  HYPERLINK \l "_Toc251849913"  References	  PAGEREF _Toc251849913 \h 
19  

  HYPERLINK \l "_Toc251849914"  Appendix A: Aggregation Tables	  PAGEREF
_Toc251849914 \h  45  

  HYPERLINK \l "_Toc251849915"  Appendix B: Land Supply	  PAGEREF
_Toc251849915 \h  48  

 

List of Tables

Table 								    			          Page

  TOC \h \z \t "Table Style" \c    HYPERLINK \l "_Toc251931010"  Table
1. Variation in key parameters driving the general equilibrium results
on biofuels.	  PAGEREF _Toc251931010 \h  22  

  HYPERLINK \l "_Toc251931011"  Table 2. The U.S. biofuel scenarios
implemented in the GTAP model.	  PAGEREF _Toc251931011 \h  23  

  HYPERLINK \l "_Toc251931012"  Table 3. Change in agricultural output
due to an increase in corn-ethanol production in the U.S.	  PAGEREF
_Toc251931012 \h  24  

  HYPERLINK \l "_Toc251931013"  Table 4. Change in commodity price due
to an increase in corn-ethanol production in the U.S.	  PAGEREF
_Toc251931013 \h  25  

  HYPERLINK \l "_Toc251931014"  Table 5. Impact of an increase in U.S.
corn-ethanol production on bilateral trade (Change in import volume)	 
PAGEREF _Toc251931014 \h  26  

  HYPERLINK \l "_Toc251931015"  Table 6. Change in land use and land
cover due to an increase in corn-ethanol production in the U.S.	 
PAGEREF _Toc251931015 \h  27  

  HYPERLINK \l "_Toc251931016"  Table 7. SSA estimates of change in
harvested area of crops due to an increase in corn-ethanol production in
the U.S.	  PAGEREF _Toc251931016 \h  28  

  HYPERLINK \l "_Toc251931017"  Table 8. SSA estimates of change in land
cover due to an increase in corn-ethanol production in the U.S.	 
PAGEREF _Toc251931017 \h  29  

  HYPERLINK \l "_Toc251931018"  Table 9. Change in agricultural output
due to an increase in biodiesel production in the U.S.	  PAGEREF
_Toc251931018 \h  30  

  HYPERLINK \l "_Toc251931019"  Table 10. Change in commodity price due
to an increase in biodiesel production in the U.S.	  PAGEREF
_Toc251931019 \h  31  

  HYPERLINK \l "_Toc251931020"  Table 11. Impact of an increase in U.S.
biodiesel production on bilateral trade (Ch. in import volume)	  PAGEREF
_Toc251931020 \h  32  

  HYPERLINK \l "_Toc251931021"  Table 12. Change in land use and land
cover due to an increase in biodiesel production in the U.S. (million
acres).	  PAGEREF _Toc251931021 \h  33  

  HYPERLINK \l "_Toc251931022"  Table 13. SSA estimates of change in
harvested area of crops due to an increase in biodiesel production in
the US (million acres).	  PAGEREF _Toc251931022 \h  34  

  HYPERLINK \l "_Toc251931023"  Table 14. SSA estimates of change in
land cover due to an increase in biodiesel production in the US (million
acres).	  PAGEREF _Toc251931023 \h  36  

  HYPERLINK \l "_Toc251931024"  Table A1. Aggregation of sectors in the
GTAP-BIO-1 Model	  PAGEREF _Toc251931024 \h  45  

  HYPERLINK \l "_Toc251931025"  Table A2. Aggregation of Regions in the
GTAP-BIO-1 model.	  PAGEREF _Toc251931025 \h  47  

 

List of Figures

Figure 								    			          Page

  TOC \h \z \t "Figure Style" \c "Figure"    HYPERLINK \l
"_Toc252542623"  Figure 1.  An illustrative overview of the GTAP model	 
PAGEREF _Toc252542623 \h  3  

  HYPERLINK \l "_Toc252542624"  Figure 2a. Crop cover change due to an
increase in U.S. corn-ethanol production (million acres)	  PAGEREF
_Toc252542624 \h  37  

  HYPERLINK \l "_Toc252542625"  Figure 2b. Pasture cover change due to
an increase in U.S. corn-ethanol production (million acres)	  PAGEREF
_Toc252542625 \h  38  

  HYPERLINK \l "_Toc252542626"  Figure 2c. Forest cover change due to an
in crease in U.S. corn-ethanol production (million acres)	  PAGEREF
_Toc252542626 \h  39  

  HYPERLINK \l "_Toc252542627"  Figure 3. Crop cover change due to an
increase in U.S. corn-ethanol production (Ha/billion BTU)	  PAGEREF
_Toc252542627 \h  40  

  HYPERLINK \l "_Toc252542628"  Figure 4a. Crop cover change due to an
increase in U.S. biodiesel production (million acres)	  PAGEREF
_Toc252542628 \h  41  

  HYPERLINK \l "_Toc252542629"  Figure 4b. Pasture cover change due to
an increase in U.S. biodiesel production (million acres)	  PAGEREF
_Toc252542629 \h  42  

  HYPERLINK \l "_Toc252542630"  Figure 4c. Forest cover change due to an
increase in U.S. biodiesel production (million acres)	  PAGEREF
_Toc252542630 \h  43  

  HYPERLINK \l "_Toc252542631"  Figure 5. Crop cover change due to an
increase in U.S. biodiesel production (Ha/billion BTU)	  PAGEREF
_Toc252542631 \h  44  

  HYPERLINK \l "_Toc252542632"  Figure B1. Land supply in the GTAP-BIO
models.	  PAGEREF _Toc252542632 \h  48  

 

Land Use Implications of Biofuels: GTAP Approach

Overview

The U.S. Environmental Protection Agency (EPA) has undertaken a life
cycle assessment (LCA) focused on greenhouse gas (GHG) emissions
associated with different types of renewable fuels as required by the
Energy Independence and Security Act of 2007 (EISA).  One important
component of this LCA is a careful accounting of GHG emissions resulting
from land use change.  The purpose of this study is to examine the
global land use implications of biofuels production in a general
equilibrium framework.  The Global Trade Analysis Project (GTAP) model
(Hertel ed. 1997), a multi-region multi-sector computable general
equilibrium model, is widely used for global-scale assessment of
economic policies.  The use of the GTAP data base and model has been
increasing with the growing research interests in international trade
policies, energy policies, climate change, etc.  The GTAP framework
ensures complete accounting of the impacts of these topical policy
issues.  With the ease of access to the standard GTAP model, which is
publicly available, there are numerous GTAP-based models in use around
the world.  However, the GTAP Center has a mechanism to control the
quality of these applications through a peer-review process.  In recent
years, a version of the model was developed to explicitly account for
substitution between energy commodities (Burniaux and Truong, 2002). 
Lee et al. (2005) developed another version of the model which
explicitly accounts for global competition for land across alternative
uses in agro-ecological zones (AEZs).  More recently Birur et al. (2008)
developed the GTAP-BIO model by combining the above two frameworks and
introducing biofuels as substitutes for petroleum products.  This study
covers biofuels that are produced from agricultural feedstocks such as
corn ethanol, sugarcane ethanol, and oilseed-based biodiesel.  More
recently, the California Air Resource Board (CARB) has used the GTAP-BIO
model for its rulemaking on a Low Carbon Fuel Standard for biofuels
(CARB, 2009).  The modeling approach used for this analysis is discussed
in detail in the next section.

Model Background

 	With the growing research interest in biofuels, numerous recent
studies on the economics of biofuels have employed cost-accounting
procedures and/or partial equilibrium (PE) frameworks (Walsh et al.
2003; Msangi et al. 2006; Fargione et al. 2008; Searchinger et al.
2008).  However, these approaches do not capture the economy-wide
implications of a policy.  Also, PE approaches fail to address the
linkages between factor income and expenditures resulting from a policy.
 In contrast, the general equilibrium (GE) models can be used to check
theoretical and accounting consistency (e.g., market clearing conditions
are satisfied in input and output markets, consumers are on their budget
constraints, macro-economic accounting balances as indicated by balance
of payments conditions).  With the strengthening linkage between
agriculture and energy markets, biofuels production could impact food
security, international trade, the environment, and natural resources. 
The emerging linkage is a demand-pull from the energy sector to
agriculture, which will potentially increase agricultural commodity
demand due to the biofuel policies and high oil prices.  Such a linkage
can be fully captured in a GE model such as GTAP.  Literature on
economic modeling of biofuels suggest that for analyzing the long-term
consequences of biofuels, the GTAP model is a suitable economic tool to
link energy and crop demand (Kløverpris et al. 2008; Rajagopal and
Zilberman, 2007).  Several studies have adopted the GTAP data base
and/or GTAP model-based approaches for studying the economy-wide
implications of biofuel policies in the major biofuel producing regions
such as the EU and the U.S. (Banse et al. 2007; Eickhout et al. 2008;
Birur et al. 2009; Hertel et al. 2010).  The findings of these studies
offer varied perspectives, with differences in results due primarily to
differences in behavioral parameter assumptions and methodological
approach.  We will revisit these issues in Section 3.

Structure of GTAP Model

The GTAP model is a multi-commodity, multi-regional computable general
equilibrium model developed by Hertel (ed., 1997).  The model has been
documented in the ‘GTAP Book’ (Hertel ed., 1997) with a detailed
discussion on the underlying theory and derivation of the behavioral
equations involved in the model.  The standard GTAP model basically
assumes perfect competition in all markets with Walrasian adjustment. 
As represented in Figure 1 (Hertel, Tyner, and Birur, 2010), the
regional household collects all the income in its region and spends it
over three expenditure types – private household (consumer),
government, and savings, over a Cobb-Douglas utility function. A
representative firm maximizes profits in nested Constant Elasticity of
Substitution (CES) functions in a perfectly competitive market for each
industry/sector in each region and pays income to the regional household
for utilizing the endowment commodities (land, labor, capital, and
natural resources).

Furthermore, firms sell the final goods consequently produced by
combining the endowments with the intermediate inputs to the private
households and the government, and the investment goods to the regional
household.  In an open economy, firms also export the tradable
commodities and import the intermediate inputs from other regions (rest
of the world), but the model follows an Armington assumption, which
accounts for product heterogeneity.  The land endowment is imperfectly
mobile, while labor and capital are perfectly mobile within a region but
imperfectly mobile across regions.  Government spending is modeled by
using a Cobb-Douglas sub-utility function, which maintains constant
expenditure shares across all the sectors. 

 

Figure 1.  An illustrative overview of the GTAP model

Source: Hertel, Tyner, and Birur (2010).

Private household consumption is modeled by adopting a non-homothetic
Constant Difference of Elasticity (CDE) implicit expenditure function,
which allows for differences in income elasticities across commodities. 
Taxes (and subsidies) go as net tax revenues (subsidy expenditures) to
the regional household from private household, government, and the
firms.  As shown in Figure 1, the rest of the world gets revenues by
exporting to private households, firms and government.  These revenues
are spent on export taxes and import tariffs, which eventually go to the
regional household.

The standard closure of the GTAP model allows for equilibrium in all the
markets, all firms earn zero economic profits, the regional household is
on its budget constraint, global investment equals global savings, and
the sum of global exports and imports is zero.  The global trade balance
condition determines the world price of a given commodity. The
Cobb-Douglas utility function of the regional household allows for
maintaining constant budget shares.

Aggregation of Sectors and Regions

The data base used in the GTAP-BIO model versions is based on the GTAP
version 6 data base (GTAP v6) (Dimaranan, 2006 Ed.), which pertains to
the global economy for 2001.  The original data base has 57 sectors and
87 regions of the globe, but does not include any sectors explicitly
breaking out biofuels.  To create the data base used for the GTAP-BIO
model, Taheripour et al. (2007) split out three biofuels sectors from
existing sectors in the standard GTAP v6 data base: the grain based
‘ethanol-1’ sector from the food products sector (ofd) receiving
inputs from the cereal grains sector (gro); the sugar-based
‘ethanol-2’ sector out of the chemicals sector (crp) with inputs
from sugar-cane-beet (c_b) sector; and the biodiesel sector was created
from the vegetable oils and fats (vol) sector which gets input from the
oil-seeds (osd) sector.  The sales of biofuels are channeled through
household as well as intermediate demand.  Taheripour et al. (2009)
further modify the data base to incorporate byproducts of biofuels such
as dried distillers grains with solubles (DDGS) and vegetable oil cake
(soybean meal in the U.S. and rapeseed cake in the EU), and then augment
feed demand in the livestock sector by allowing for substitution between
biofuel byproducts and other animal feed.  

For the corn ethanol production scenario, we utilize the version of the
model used for the California LCFS (referred to in this report as
GTAP-BIO-1).  However, due to limitations on how the oilseed byproducts
were treated in this version of the model, we have used a different
version of the model for the biodiesel scenario.  Additional details on
this version of the model are documented in Taheripour et al. (2009, in
press) (referred to in this report as GTAP-BIO-2).  This version of the
model was not available for the corn ethanol scenario, but offers
advantages for the analysis of biodiesel expansion.  In the GTAP-BIO-1
model, biodiesel is produced from oilseeds along with the production of
by-products.  Since biodiesel can also be produced from processed
vegetable oil, Taheripour et al. (2009) split the vegetable oil sector
into refined and crude-vegetable oil sectors and allow for biodiesel to
be produced from crude-vegetable oil rather than directly from oilseeds.
 Unlike GTAP-BIO-1 where byproducts (oil-meal/cake) are produced along
with the production of biodiesel, biodiesel is produced from
crude-vegetable oil as the main input in the GTAP-BIO-2 version.  The
crude-vegetable oil is produced as a joint product along with
oil-meal/cake from the oilseeds sector.  This modification is expected
to result in a better assessment of the impacts of expanded U.S.
renewable fuels production, particularly the production of biodiesel
from soybean oil.  Because the oil content in soybeans is only about 18%
and much of the remainder is soybean-meal, which is used in animal feed,
large production of biodiesel using soybean oil as the feedstock is
expected to significantly impact feed markets. Therefore, we utilize the
GTAP-BIO-2 version of the model to analyze potential impacts of
increasing annual U.S. biodiesel production by 1 billion gallons.  

After incorporating the biofuel and byproducts sectors, the data base is
aggregated to permit focus on the sectors and regions of particular
interest.  In the GTAP-BIO-1 version of the model used in this report
for analyses of ethanol expansion, the data base is aggregated into 20
economic sectors and 18 regions (Table A1 and Table A2, respectively in
Appendix-A).  The sector aggregation was chosen such that we could focus
on the linkages among feedstocks, biofuels, energy 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.  However, in the GTAP-BIO-2 model, the
‘other grains’ sector has been kept disaggregated as
‘paddy-rice’ and ‘wheat’.  The ‘livestock’ sector is also
kept disaggregated as ‘ruminants’, ‘non-ruminants’, and
‘dairy-farms’.  This helps in keeping track of substitution of
byproducts for traditional feed across alternative livestock uses. 
Regional aggregation is similar to that of the GTAP-BIO-1 version but
has 19 regions in aggregate.  The next section focuses on incorporating
disaggregated land endowment across agro-ecological zones.

Land Endowment in the GTAP-BIO Model

In the standard 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.  In the GTAP-BIO model versions, land is treated as
the typical non-tradable endowment but is also further classified based
on agro-ecological zones (AEZs) in each region.  As defined by FAO and
IIASA (2000), AEZs are the classification of a parcel of land based on
soil and climate characteristics such as moisture, temperature, soil
type, etc.  Lee et al. (2005) first classify the AEZs based on “length
of growing period” (LGP) data provided by FAO and IIASA (2000).  The
LGP refers to the duration when soil moisture and temperature are
conducive to plant growth in a given year.  Land is classified into six
categories of LGP, each comprising 60 days of sufficient temperature and
moisture to grow crops.  Following Ramankutty and Foley (1999), Lee et
al. (2005) overlay six categories of LGP on three climate zones of the
world, classified as tropical, temperate, and boreal zones based on
absolute minimum temperature and growing degree days.  The resulting 18
AEZ categories, which account for heterogeneity of land in each region,
offer better characterization of the competition for land across
alternative uses.  The global land use data base used in the GTAP model
is compiled using three land use data sets: (i) the land cover data from
Ramankutty et al. (2005) which distinguishes forest, pastureland, and
cropland cover types, (ii) data on harvested land acreage and production
from Monfreda et al. (2008), and (iii) a data base that maps forestry
activity into the 18 AEZs as documented in Sohngen et al. (2009).  Lee
et al. (2009) utilizes these land use components and disaggregates land
rents in the GTAP data base on the basis of prices and yields.  Detailed
discussion on this aspect is given in the volume edited by Hertel, Rose,
and Tol (2009).

Since the GTAP data base corresponds to economic flows, the land
endowment is observed as rents in the land-using sectors in a given
region.  Lee et al. (2009) share out land rents for cropland,
pasture-cover and forest-cover based on the yearly economic activity in
a given AEZ.  They determine cropland rents based on value of crops
produced in a given AEZ using 0.5o grid level data on harvested crop
area and per hectare yield data offered by Monfreda et al. (2008).  Due
to this approach, the land rents can be treated as indicators of
productivity in each AEZ.  The temperate AEZs with a longer length of
growing period (LGP) are found to have the highest land rents worldwide,
followed by the tropical AEZs, and then boreal zones.  Also, the land
rents are largest in those AEZs where high value crops such as
vegetables, fruits and nuts, and irrigated crops such as paddy-rice and
sugarcane are grown.  

For determining land rents for the livestock sector, which includes the
economic activities of ruminants (cattle, sheep and goats), dairy
production, wool, and non-ruminants (pigs and poultry), Lee et al.
(2009) draw on the direct competition between these sectors with the
grazing land.  For this purpose, they utilize land cover (pasture cover)
data provided by Ramankutty et al. (2005) which explicitly shows the
availability of pasture/grazing land in each AEZ for all the regions in
the world.  For computing livestock sectors’ land rent, ideally it is
computed based on revenues resulting from growing forage crops.  But the
major constraint to this approach is there is no explicit ‘forage
crop’ sector in the GTAP data base that would indicate the crop yield
and hence the revenues.  Therefore, Lee et al. (2009) utilized the data
on average coarse grain yield in each AEZ and multiplied it by the
pasture land cover hectares.  Due to this approach, the aggregated land
rent of livestock sectors is smaller than the aggregated land rents of
agricultural crops.  For example, in the U.S. the average cropland cash
rent is $70/acre and that of pastureland is only $9/acre, clearly
indicating the marginality of pasture land.  

Similarly, Lee et al. (2009) compute the AEZ level forest cover land
rent by using information on timberland land rent and timberland area
offered by Sohngen et al. (2009).  The land rents for the forestry
sector in the GTAP data base are distinguished as ‘natural resource’
rents.  Lee et al. split this ‘natural resource’ sector rents based
on the AEZ level forest rent shares.  For computing the AEZ level forest
rent shares, those authors exclude the inaccessible forest area as these
forests do not generate any rents in principle.  The resulting per
hectare land rents indicate that forest land rents are slightly smaller
than that of aggregated pasture sector rents indicating their
marginality.  

Land Supply in the GTAP-BIO Model

Since the land endowment is split into AEZs, Lee et al. (2005) made an
assumption that the land is mobile across uses within an AEZ, but
immobile across the 18 AEZs as the crops grown are climate and soil
specific.  In line with Hertel et al. (2009), in the GTAP-BIO model
versions, the land mobility is effectively restricted across alternative
uses within a given AEZ, by using a constant elasticity of
transformation (CET) function.  In principle, the land-owners maximize
total returns to land by optimal mix among alternative uses, in two
tiers (Figure B1 in Appendix-B).  In the first stage, the land-owner
makes the optimal allocation of a given parcel of land under crops,
pasture or commercial forest, while the choice of crops is made in the
second stage (five categories of crops: cereal grains, oilseeds,
sugar-crops, other grains (paddy-rice and wheat), and other
agriculture).  The ease with which the land-AEZi is transformed across
different crops is governed by the elasticity of transformation, ETRAE-2
(assumed to have a base value of -0.5) and across different land covers
by ETRAE-1 (assumed to have a base value of -0.2).  As the value of
these parameters rise in absolute terms, the degree of sluggishness
diminishes, possibly driving the land rents across alternative uses
together.  In other words, ETRAE-2 influences the extent of change in
cropping pattern and ETRAE-1 influences the extent of land conversion.

For instance, if we boost corn-ethanol production in the U.S., the
resulting rise in corn price is shared among all the factors of
production.  As a result, the land rents attract more land into cereal
grain production, which is taken from alternative uses.  The land supply
function is nested in the value added nest of the firms’ production
structure which follows constant elasticity of substitution (CES) form. 
The land supply structure is discussed in detail in Birur et al. (2008)
and the role land transformation elasticities in a biofuel policy
experiment is examined in Hertel et al. (2010).  

Key Parameter Assumptions and Qualifications

In addition to land transformation elasticities discussed in the
previous section, there are other key parameters that drive the land use
change results due to biofuels production (Table 1).  Yield elasticity
(YDE_Target) is a parameter which determines the change in yield of a
crop in response to change in price of that crop.  With the increment in
biofuels production, the supply of feedstock has to come either from the
diversion of land under other uses or increased yields and/or expansion
of land area under the feedstock crop.  Keeney and Hertel (2008) examine
the issue of crop yield response in greater detail.  The GTAP-BIO model
versions use a long-run elasticity of yield response to price of 0.25 as
recommended by those authors and is calibrated to reach this targeted
yield response by adjusting the elasticity of substitution in crop
production.  International trade in commodities also determines the land
use change in a given region. The trade elasticities (ESUBD) in the GTAP
model determine the substitutability of imports among all the exporting
regions.  In other words, as the domestic price of a commodity changes
in a given region, ESUBD determines the extent to which an importer will
substitute to an alternate exporter who can supply at a lower price.

In addition to key parameters listed in Table 1, CARB (2009) assumes
there is an effective cropland adjustment (ETA).  As a result, crop
yields in the newly converted land are lower than the yields obtained on
regular cropland already in production.  In the CARB analysis, it is
assumed that for every one acre of additional cropland, two acres of
other land covers are to be converted (ETA=1/2 = 0.5).  More recently,
Taheripour et al. (2009) assumed the value of ETA as 0.66 implying that
for every two acres of cropland, three acres of other land are to be
converted to grow crops.  We follow the latter to keep our comparisons
consistent. 

Sensitivity of Key Parameters

Since the parameters discussed above are the key inputs that drive the
general equilibrium results, there are uncertainties associated with
these parameter values.  For exploring this uncertainty, we undertake
systematic sensitivity analysis (SSA) in which the model is re-solved
for different draws from the underlying parameter distributions
presented in Table 1.  Monte Carlo analysis is the standard approach to
this problem. However, it is impractical for large scale models.
Therefore, Arndt (1996) recommends a systemic sensitivity analysis (SSA)
with Gaussian Quadrature (GQ) numerical integration technique, which
gives robust results with only few draws from the distribution of random
variables.  Pearson and Arndt (2000) employ this technique in the GTAP
framework using Stroud Quadrature, which requires the model to solve
only 2N times where N is the number of varying parameters/variables.

Hertel et al. (2010) follow the approach given by Pearson and Arndt
(2000) and use symmetric triangular distributions to approximate the
underlying distribution of the key parameters, which permits us to
characterize the parameter distribution through the mean and lower end
point.  The mean parameter values and end points obtained from the
literature are as given in Table 1. The last column of the table shows
the amount of variation which is the amount that the parameter is
displaced from its central point to its endpoint.  In the triangular
distribution, the amount of variation is equivalent to the standard
deviation multiplied by the square root of six.  In our biofuel policy
scenarios we simultaneously vary all the parameters listed in Table 1
and the model solves 84 times holding the solution at each point to
calculate mean and standard deviation of the model variables.  The
results reported in this study are the mean values and confidence
intervals computed based on SSA.

Qualifications

Both the versions of the GTAP-BIO model include only first-generation
biofuels.  Due to the absence of nascent feedstock sectors in the GTAP
data base, second generation biofuels are not included in this study.

Since this study is based on the static GTAP model, we cannot capture
dynamic market interactions.

Conversion of land from pasture or forest covers involve accessibility
cost which we have not captured in this model.

The current structure of the GTAP-BIO model versions classifies
shrubland, savanna grassland, built-up land, and other land as
“unmanaged” and treats it as exogenously fixed in the model.  The
“managed” categories refer to cropland, pasture, and forest. This
would rule out any possibility of conversion of “unmanaged” land
categories.

The scenarios included in this analysis are only a rough approximation
of the renewable fuels standard required by EISA, because we have only
analyzed the impact of increasing one renewable fuel volume, rather than
analyzing the impact of the entire RFS2 and then backing out the impact
of one fuel at a time.  Furthermore, the corn ethanol scenarios do not
get close to the projected volume of 15 billion gallons.

Experimental Design

Before implementing the biofuel scenarios in the GTAP-BIO model
versions, we first perform historical analysis as offered by Birur et
al. (2008) which validates the model as well as projects the biofuel
economy for 2006.  For implementing the historical analysis those
authors consider three key factors that were responsible for the biofuel
boom in the U.S. and EU during the 2001-2006 period.  

A major factor is the rise in annual average real price of crude oil
from $25.29/barrel in 2001 to $59.69/barrel in 2006.  The 136% rise in
crude oil price escalated gasoline prices, which further influences the
price of biofuels.

Another factor that was responsible for the ethanol boom, particularly
in the U.S., was the phase out of methyl tertiary-butyl ether (MTBE), a
petroleum derived additive used as octane enhancer in the oil industry. 
As MTBE was phased out, it was replaced by ethanol, the other recognized
additive at a rate of about 49.24% during the five year period.

Other factors that encouraged the biofuels industry to expand rapidly
were the subsidies and tax credits offered by governments.  The U.S. has
subsidized biofuels for three decades.  Several member states in the
European Union also announced tax credits to the biofuel industry during
the 2001-2006 period.  The ad valorem equivalent of subsidies and tax
credits to biofuels industry in these two regions were also considered
for the historical validation experiment. 

After implementing the historical experiment involving the key biofuel
drivers listed above, we start from the ex-post 2006 biofuel economy and
implement the corn-ethanol and biodiesel scenarios separately.  As
mentioned earlier, for the corn ethanol analysis we use the GTAP-BIO-1
model and for biodiesel, we use the GTAP-BIO-2 version.  In both
versions of the model the resulting simulated 2006 biofuel economy
baseline has 4.252 billion gallons of corn-ethanol, 0.140 bg of
biodiesel, and 0.303 bg of imported sugar-ethanol in the U.S.  As
summarized in Table 2, we conducted two analyses based on increases in
biofuels volumes specified by EPA.  In the corn-ethanol scenario, we
increase the quantity of corn-ethanol production by 2 bg (47% increase)
in the U.S. while keeping production of all other biofuels at 2006
level.  Similarly, we implement the biodiesel scenario by increasing
quantity of biodiesel production in the U.S. by 1 bg (714% increase)
while all other biofuels production remain constant.

Results from Corn-Ethanol Experiment

Impact on Production:  In this section, we discuss the impact of
increasing U.S. corn ethanol production by 2 billion gallons per year on
some of the important variables related to biofuel economy.  As ethanol
production is exogenously increased in the U.S., as expected, production
of cereal grains in the U.S. rises by 7 million tonnes so as to meet the
new demand for feedstock from the ethanol industry (Table 3).  Most of
this rise is attributed to corn as 87% of cereal grains area and 94% of
cereal grains revenue sales constitute corn in the U.S. during 2006. 
Model simulations for the baseline experiment indicate that about 18% of
the corn produced in the U.S. would be used in the U.S. ethanol industry
in 2006, with the share increasing to 25.3% of U.S. corn production in
the post corn-ethanol scenario.  As seen from Table 3, due to
competition for land, production slightly declined in all other crop
sectors such as oilseeds (-0.76 mt), other grains (-0.96 mt),
sugar-crops (-0.13 mt), and other agriculture (-1.83 mt) due to the rise
in corn production.  As the domestic use of corn increases in the U.S.,
export sales decline slightly.  The model predicted U.S. export sales of
cereal grains decline from 26.3% of U.S. corn production in 2006 to
25.1% in the ex-post scenario.  The rest of the world responds to this
smaller decline in export supply as it leads to a slight rise in
commodity prices in the international market.  Except for sugar-crops,
all the other agricultural sectors in the rest of the world indicate a
gain in production. Production of sugar-crops in Brazil decline by 0.66
mt mainly because Brazil ends up exporting more oilseeds to partially
offset the decrease in U.S. oilseeds exports to the rest of the world.  
In all, the 2 bg corn-ethanol production in the U.S. leads to a
permanent rise in cereal grains production by 8 mt globally.  

Impact on Commodity Prices: Table 4 reports the simulated percent
changes in market price in important regions and for the world price in
agricultural and energy sectors.  It is important to note that, since we
have included only corn-ethanol shock in this exercise and not all the
exogenous variables that affect the global economy, our model predicted
prices apply only to the post-2006 biofuel economy.  The market price
for cereal grains in the U.S. rose by 1.95% over the baseline where the
sectoral prices are denoted as numeraire and equal to 1.  The change in
price of all other agricultural sectors was positive but relatively
small.  

Impact on Bilateral Trade: The corn-ethanol impact in the domestic
market that we discussed above could have repercussions around the world
through trade linkages.   Table 5 depicts bilateral trade in some of the
key commodities such as cereal grains, oilseeds and crude oil. 
Interestingly, only the U.S. shows a modest decline in exports of cereal
grains by about $143 million.  The main regions that registered a
decline in imports were Japan, Latin-America, and Middle-Eastern
countries (these regions are folded into Rest of the World to keep the
table concise).  The oilseeds sector is an important competitor for
cereal grains particularly in the U.S.   Even the modest decline in
oilseeds production in the U.S. resulted in a decline in exports by $113
million, mainly to the European Union, China, and other Asian regions. 
This trend is partially offset by an increase in Brazilian oilseeds
exports by $21 million.  Other agricultural commodities did not register
any significant change in trade patterns.  

Impact on Land-Use and Land-Cover:  As we discussed above, the
corn-ethanol experiment boosts demand for ethanol feedstocks and due to
competition for limited land resources, the price of other crops also
rise.  These higher crop prices transmit across borders, implying higher
profitability and hence a shift in cropping pattern towards the crop in
greater demand.  The corn-ethanol driven change in land use and land
cover across all the regions are presented in Table 6.  As seen from the
table, harvested area under cereal grains in the U.S. expanded by 2.22
million acres, part of it came at the cost of other major land using
sectors such as other grains (paddy-rice and wheat: -0.98 mill. acres)
and oilseeds (-0.64 mill. acres).  The demand for corn spilled over to
the rest of the world, resulting in a small positive change in acreage
under cereal grains in most of the regions, adding up to 2.66 million
acres globally.  Some of the decline in oilseeds acreage in the U.S. was
offset by Canada, Sub Saharan Africa, and Brazil due to which oilseeds
acreage declined only by -0.09 million acres, globally.  However, not
much response was reported from the rest of the world to mitigate the
reduced acreage under other grains in the U.S. which lead to global
reduction of -0.92 million acres under paddy-rice and wheat.  After
accounting for all the cropping pattern adjustments, about 1.39 million
acres of additional cropland were required to meet the demand.  

As discussed earlier, in the GTAP-BIO model, expansion in crop cover is
allowed to come from pastureland and accessible forest covers.  The
model predicted expansion in cropland in the U.S. (0.58 mill. acres)
comes from decline in accessible forests (-0.22 mill. acres) and pasture
lands (-0.36 mill. acres).  The only other regions which showed
significant expansion in cropland were Sub Saharan Africa (0.20 mill.
acres), Canada (0.15 mill. acres), and the EU (0.12 mill. acres).  While
much of the cropland expansion in Africa came from a decline in pasture
cover (-0.24 mill. acres), in Canada and EU it was due to a decline in
forest cover by 0.10 and 0.07 million acres, respectively.  The forest
is mainly sold to the lumber sector (for example, in the GTAP data base
90% of forest sector sales are made to the lumber sector in the U.S. and
the corresponding percentage for Canada is 70%), therefore most of the
other regions did not report much change in forest cover.  Instead, most
of the cropland expansion came from a decline in pasture cover (Table
6).  On the global scale, the 1.39 million acres of additional cropland
came from a decline in pasture and forest cover by 1.16 and 0.23 million
acres, respectively.  

Sensitivity of Impact on Land:  As discussed previously, since the key
parameters listed in Table 1 drive the general equilibrium results on
impacts of biofuel production, we also report the results from a
systematic sensitivity analysis on change in harvested acreage (Table 7)
and land cover (Table 8).  As depicted in Table 7, the mean value of
change in cereal grains acreage was 2.17 million acres which ranged
between 1.66 and 2.69 million acres in the U.S., when all the key
parameters are varied simultaneously. The model reported no significant
variation in the range of cereal grains acreage in the rest of the
regions and globally the cereals grains acreage ranged between 2.25 and
3.0 million acres.  Global oilseeds acreage indicated a positive upper
bound, suggesting that the decline in U.S. oilseeds acreage (-0.46 mill.
acres) due to corn-ethanol production may be completely offset by other
regions leading to a marginal increase in global oilseeds acreage (0.05
mill. acres).  However, the other land using sectors did not indicate
significant variation in their harvested acreage.  

Sensitivity on expansion of cropland to other land cover is presented in
Table 8.  The SSA results on crop cover indicate that U.S. corn-ethanol
production may require as low as 0.98 million acres of additional
cropland and the same could be 1.78 million acres on the upper scale
subjected to variability in parameter values.  It is clear from the
table that although most of the additional cropland came from pasture
cover (-1.15 mill. acres), the SSA results indicate a range of pasture
cover change between -1.50 and -0.83 million acres.  However, all the
significant variation in the change in forest cover was observed in
Canada and EU regions.  For a better understanding of these results,
changes in cropland, pasture cover, and forest cover with error bars are
depicted in Figures 2a, 2b, and 2c, respectively.  The SSA exercise
suggests that much of the variation in the land cover was reported in
the U.S. and not in the rest of the regions.  Hence  the theory that
variation in land transformation elasticities drive the impact on land
use though the role of trade elasticities is not undermined.  These
sensitivity results prove useful especially when accounting for land
conversion due to biofuels production.  

Apart from the sensitivity of the key parameters, the magnitude of the
shock and the type of biofuel also drives the general equilibrium
results.  For relative comparison purpose, we have reported land
required for the production of corn-ethanol normalized on an energy
basis.  As seen from Figure 3, production of one billion BTUs of
corn-ethanol in the U.S. requires about 3.70 hectares of cropland
globally, which ranges from 1.93 to 5.42 hectares subjected to
sensitivity of the key parameters.

Results from Soybean-Biodiesel Experiment

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

Soybeans are the major feedstock used for production of biodiesel in the
U.S., but they contain only about 18% oil.  Thus, expanded biodiesel
production in this experiment results in a very large increase in demand
for the soybean feedstock.  Note that, as USDA reports, nearly 57% of
the global oilseed production basket constitutes soybean, followed by
rapeseed (12%), cottonseed (11%), peanuts (10%) and the remaining 10%
include palm-kernel, copra, sunflower, etc.  This clearly indicates the
extent of the impact that could result due to soybean derived biodiesel
production in the U.S.  The impact of biodiesel production on the rest
of the world is such that all of the regions indicate a positive change
in the production of oilseeds resulting in 9.46 million tonnes of global
production.  Malaysia and Indonesia end up producing 2.26 mt of oilseeds
basically to meet the demand for refined vegetable oil from EU and other
Asian regions.  Global production of cereal grains and wheat drop by
2.20 mt and 1.38 mt, respectively.  However, production of paddy-rice,
the other staple crop, does not get affected much.

Impact on Commodity Prices: Changes in regional market prices and world
prices in the agricultural and energy sectors due to biodiesel
production are presented in Table 10.  Keeping in mind that our
simulation experiment included only a biodiesel shock and not any
attempt to project the global economy in time, the model predicted a
wide range of implications on sectoral prices, particularly on the feed
industry.  As seen from the table, although the market price of oilseeds
in the U.S. increased by only 3.58%, the price of crude-vegetable oil,
which is the major component of biodiesel in the model, rose by 111% in
the U.S. market.  Since a large portion of soybean constitutes
oil-meal/cake (VOBP sector), the production of which is complementary to
biodiesel, expanded biodiesel production resulted in a 51% drop in its
market price.  Since oil-meal is mainly used by the livestock industry
as animal feed, it substitutes for processed-feed leading to a drop in
the latter’s market price by 10% in the U.S.   Though there is a mixed
spillover of market price to other regions, the world price of oilseeds
rises by 1.44% and that of crude vegetable oil by 17%.  

Impact on Bilateral Trade: The changes in market prices due to biodiesel
production discussed above have implications on bilateral trade.  Table
11 depicts change in bilateral trade volume in selected commodities
after implementing the biodiesel scenario.  As seen from the middle
panel of the table, exports of oilseeds from the U.S. fall by $208
million and this deficit is adequately met in the international market
by Brazil ($163 million) and Canada ($101 million).  Overall, the world
trade in oilseeds rises by $269 million.  Though U.S. cereal grains
production decreases due to biodiesel production, exports of cereal
grains fall only $17 million.  

Impact on Land-Use and Land-Cover:  As the U.S. biodiesel production
escalates demand for oilseeds, the changing crop prices drive land use
changes in all the regions.  Table 12 depicts change in harvested area
across the six crop categories and change in land cover across the three
cover types.  In these simulations, 1 bg of increased biodiesel
production required a permanent shift in oilseed acreage by 4.18 mill.
acres in the U.S. and 11 mill. acres globally.  The cropping pattern
changes in the U.S. were larger than that of any other regions.  The
acreage under coarse grains, wheat, and other agriculture fell by 1.73,
1.09, and 1.29 million acres, respectively in the U.S.  As reported by
USDA acreage data, nearly 80% of the oilseeds area planted in the U.S.
constitutes soybeans acreage, followed by cottonseed which forms about
15%.  Therefore, any change in the oilseeds sector in the U.S. may be
treated as soybeans.  Due to this model predicted pattern essentially no
additional cropland was required in the U.S. to meet the demand for
oilseed.  Oilseed acreage increased mainly in the EU (1.27 mill. acres),
followed by Canada, Brazil and other South American regions.  Globally,
harvested acreage under cereal grains, wheat, other agriculture, and
paddy-rice, fell by 2.70, 2.23, 2.32, and 0.56 million acres,
respectively.

	After adjusting cropping pattern across all the regions, the model
predicted an additional 3.01 million acres of cropland to meet the U.S.
oilseeds demand.  Globally, this additional cropland mainly came from
pasture cover (-2.24 mill. acres) and accessible forests (-0.77 mill.
acres).  However, due to the complementary nature of feed and pasture in
the livestock industry, the model predicted an increase in pasture-cover
by 0.72 million acres which mainly came from accessible forests (-0.69
mill. acres) in the U.S.  In contrast, most other regions indicated an
expansion in cropland that mainly came from the decline in pasture
cover.  

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

Given the range of harvested acreage adjustments, the mean cropland
requirement in the U.S. was -0.01 mill. acres with a range of -0.14 to
0.13 million acres (Table 14).  This indicates that though U.S. cropland
acreage actually went down in the biodiesel experiment, subjected to
sensitivity of the key parameters, it could even demand additional
cropland to meet the demand for oilseeds.  It is clear from Table 14
that pasture-cover change in all regions was sensitive to variation in
the key parameters.  A large increase in forest cover change was
predicted only in the U.S., EU, and Canada.  The model predicted
oilseeds acreage in Brazil ranged from 0.61 to 1.01 mill. acres, most of
this came from the decline in pasture-cover without any large impact on
forest cover.  It is interesting to note that China, Eastern Europe, and
some of the South American regions indicated positive change in forest
cover.  However, these regions reported a decline in pasture cover due
to U.S. biodiesel production.  These variations are depicted as error
bars in Figure 4a, 4b, and 4c, corresponding to land cover change in
crops, pasture, and forest, respectively. 

The SSA exercise on the biodiesel experiment suggests that subjected to
variation in the parameters there is a range in the requirement of
additional cropland across the regions including the U.S.  Figure 5
depicts the hectares required to produce one billion BTUs of biodiesel
normalized on an energy basis.  As shown in the figure, about 11
hectares of cropland is required to produce 1 billion BTU of biodiesel. 
Most of this land comes from regions other than the U.S.  However the
error bars indicate that that results are sensitive to the parameters,
so that the total hectares required could even range between 3.6 to 18.4
hectares per billion BTU of biodiesel.

Conclusions

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

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Science, 319 (5867): 1238-1240.

Sohngen, B., C. Tennity, M. Hnytka and Karl Meeusen (2009) “Global
Forestry Data for the Economic Modeling of Land Use”, Chapter-3 in T.
Hertel, S. Rose and R. Tol (eds.) Economic Analysis of Land Use in
Global Climate Policy, forthcoming from Routledge, also available as
GTAP Working Paper # 41, Center for Global Trade Analysis, Purdue
University, West Lafayette, IN, USA.

Taheripour, F., D.K. Birur, T.W. Hertel, and W.E. Tyner (2007)
‘Introducing Liquid Biofuels into the GTAP Data Base’ GTAP Research
Memorandum # 11. Center for Global Trade Analysis, Purdue University,
West Lafayette, IN, USA.

Taheripour, F., Hertel T. W., Tyner, W. E., Beckman J. F., & Birur,
D. K. (2009, in press). Biofuels and their by-products: Global economic
and environmental implications. Biomass and Bioenergy.

Msangi, S., T. Sulser, M. Rosegrant, R. Valmonte-Santos, and C. Ringler
(2006) “Global Scenarios for Biofuels: Impacts and Implications.”
International Food Policy Research Institute (IFPRI), Washington, D.C.

Walsh, M., D.G. de la Torre Ugarte, H. Shapouri, and S.P. Slinsky (2003)
“Bioenergy Crop Production in the United States: Potential Quantities,
Land Use Changes, and Economic Impacts on the Agricultural Sector,”
Environmental and Resource Economics, 24(4): 313-333.

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

 	Parameters	Lower bound	Mean	Upper bound	Standard Deviation	Amount of
Variation:  SD*(6^0.5)

1.	Yield elasticity (YDE_Target)	0	0.25	0.5	0.102	0.25

2.	Elasticity of transformation across land cover types (ETRAE-1)	-0.04
-0.2	-0.36	0.065	0.16

3.	Elasticity. of transformation across cropland (ETRAE-2)	-0.1	-0.5	-1
0.163	0.40

4.	Armington CES elasticity of substitution for domestic and imported
(ESUBD):

	a.  Coarse Grains	0.75	1.30	1.85	0.225	0.55

	b.  Other Grains	2.66	4.75	6.84	0.853	2.09

	c.  Oilseeds	2.05	2.45	2.85	0.163	0.40

	d.  Sugarcane	1.70	2.70	3.70	0.408	1.00

 	e.  Other Agri	1.57	1.89	2.21	0.129	0.32

	b1.  Paddy Rice	3.05	5.05	7.05	0.816	2.00

	b2.  Wheat	2.35	4.45	6.55	0.857	2.10



Source: Adapted from Hertel, Tyner, and Birur (2010).

Table 2. The U.S. biofuel scenarios implemented in the GTAP model.

	Billion gallons

	Corn-Ethanol	Biodiesel	Sugar-Ethanol

2006 baseline	4.252	0.140	0.303

Corn-ethanol Experiment:	+2.00 (47.04%)	0.140	0.303

Biodiesel Experiment:	4.252	+1.00

(714.29%)	0.303



Table 3. Change in agricultural output due to an increase in
corn-ethanol production in the U.S.

									(million tonnes)

Regions	Cereal grains	Oilseeds	Other grains	Sugar crops	Other Agri

U.S.A.	7.24	-0.76	-0.96	-0.13	-1.83

Canada	0.08	0.06	0.00	0.00	0.02

EU-27	0.09	0.03	0.07	0.02	0.18

Brazil	0.09	0.12	0.00	-0.66	0.01

Japan	0.00	0.00	0.01	0.00	0.00

China-Hong Kong	0.18	0.07	0.07	0.01	0.02

India	0.00	0.01	0.04	0.02	0.02

Latin American EEx.	0.15	0.11	0.00	-0.04	-0.12

Rest of Latin Am.  	0.02	0.02	0.01	-0.04	-0.01

EE & FSU EEx.	0.03	0.01	0.02	0.01	0.06

Rest of Europe	0.04	0.01	0.04	0.00	0.02

Middle Eastern N Africa EEx.	0.04	0.00	0.05	0.00	-0.06

Sub Saharan EEx.	-0.01	0.06	0.02	0.00	0.07

Rest of North Africa & SSA	0.03	0.00	0.00	0.01	0.00

South Asian EEx.	0.01	0.18	0.02	0.00	0.00

Rest of High Income Asia	0.00	0.00	0.00	0.00	0.00

Rest of Southeast & South Asia	0.01	0.03	0.03	0.01	0.00

Oceania countries	0.04	0.01	0.00	0.01	0.02

Global Total	8.05	-0.02	-0.57	-0.79	-1.58



Table 4. Change in commodity price due to an increase in corn-ethanol
production in the U.S.

									(percent change)

Sectors	Market Price	World Price

	USA	Canada	EU-27	Brazil

	Cereal Grains	1.95	0.21	0.08	0.17	0.48

Other Grains	0.43	0.17	0.08	0.13	0.07

Oilseeds	0.80	0.25	0.12	0.16	0.25

Sugarcane	1.08	0.24	0.06	0.07	0.12

Other Agri	0.35	0.12	0.04	0.07	0.08

Proc Livestock	0.11	0.05	0.00	0.01	0.03

Ethanol1	0.98	-3.42	-0.04	0.01	0.80

DDGS	-0.22	12.95	0.41	0.17	0.09

Biodiesel	0.53	0.11	0.10	0.06	0.21

Oil_Cake	0.06	0.05	0.01	0.03	0.03

Ethanol2	-0.03	-0.09	-0.08	0.00	-1.18

Coal	0.06	0.04	0.05	0.04	0.03

Oil	-0.87	-0.75	-0.66	-0.64	-0.70

Gas	-0.04	-0.06	-0.04	-0.08	-0.05

Oil_Pcts	-0.64	-0.61	-0.58	-0.53	-0.58

Electricity	-0.02	-0.02	-0.02	-0.05	-0.06





Table 5. Impact of an increase in U.S. corn-ethanol production on
bilateral trade (Change in import volume)

														         										($ millions)

 	Exporters: (post-2006)	Coarse Grains	Oilseeds

 

US	EU	RoW	Total Exports	US	EU	RoW	Total Exports

1	US	0	-4	-139	-143	0	-34	-79	-113

2	Canada	-3	0	2	-1	1	0	9	10

3	EU-27	-1	0	12	11	0	0	2	2

4	Brazil	0	0	7	7	0	8	12	21

5	Japan	0	0	0	0	0	0	0	0

6	China-Hong Kong	0	0	9	9	0	1	5	6

7	India	0	0	0	0	1	1	3	4

8	Latin American Energy Exporters	0	-1	10	9	0	0	9	9

9	Rest of Latin America & Caribbean	-1	0	2	1	0	0	3	4

10	EE & FSU Energy  Exp	0	0	4	5	0	3	1	4

11	Rest of Europe	0	0	1	1	0	0	0	1

12	Middle Eastern N Africa energy exporters	0	0	1	1	0	1	0	1

13	Sub Saharan Energy exporters	0	0	1	1	0	1	3	4

14	Rest of North Africa & SSA	0	0	4	4	0	0	0	0

15	South Asian Energy exporters	0	0	0	0	0	0	1	1

16	Rest of High Income Asia	0	0	0	0	0	0	0	0

17	Rest of Southeast & South Asia	0	0	1	0	0	0	1	1

18	Oceania countries	0	0	5	5	0	0	2	2

	Total 	-5	-5	-81	-90	3	-19	-29	-44



Note: Change in volume of exports of coarse grains, and oilseeds from
all the 18 regions to the US, EU, and Rest of the World were evaluated
at initial market prices.   



Table 6. Change in land use and land cover due to an increase in
corn-ethanol production in the U.S.

(million acres)

Regions	Change in harvested area	Change in land cover

	CrGrains	Oilseeds	Sugar crops	OthGrains	OthAgri	Cropland	Forest	Pasture

U.S.A.	2.22	-0.64	-0.01	-0.98	-0.02	0.58	-0.22	-0.36

Canada	0.05	0.10	0.00	0.00	0.00	0.15	-0.10	-0.05

EU-27	0.04	0.03	0.00	0.03	0.02	0.12	-0.07	-0.04

Brazil	0.05	0.08	-0.04	0.00	-0.02	0.06	0.00	-0.07

Japan	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

China-Hong Kong	0.06	0.06	0.00	0.00	-0.09	0.02	0.01	-0.03

India	0.00	0.01	0.00	0.00	-0.01	0.00	0.00	0.00

Latin American EEx.	0.07	0.06	-0.01	-0.02	-0.05	0.06	0.00	-0.06

Rest of Latin Am.  	0.02	0.01	-0.01	0.00	-0.01	0.02	0.02	-0.04

EE & FSU EEx.	0.03	0.03	0.00	0.01	0.01	0.09	0.09	-0.17

Rest of Europe	0.03	0.01	0.00	0.01	0.00	0.03	-0.02	-0.02

Middle Eastern N Africa EEx.	0.03	0.01	0.00	0.01	-0.03	0.02	0.00	-0.02

Sub Saharan EEx.	-0.01	0.11	0.00	0.04	0.06	0.20	0.04	-0.24

Rest of North Africa & SSA	0.03	0.01	0.00	0.00	-0.01	0.03	0.00	-0.02

South Asian EEx.	0.00	0.03	0.00	0.00	-0.03	-0.01	0.01	-0.01

Rest of High Income Asia	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Rest of Southeast & South Asia	0.00	0.01	0.00	0.00	-0.01	0.00	0.00	0.00

Oceania countries	0.04	0.01	0.00	-0.02	0.00	0.03	0.00	-0.03

Global	2.66	-0.09	-0.06	-0.92	-0.19	1.39	-0.23	-1.16



Table 7. SSA estimates of change in harvested area of crops due to an
increase in corn-ethanol production in the U.S.

(million acres)

Regions	CrGrains	Oilseeds	Sugar crops	OthGrains	Other Agri

	Mean	Confidence Interval (95%)	Mean	Confidence Interval (95%)	Mean
Confidence Interval (95%)	Mean	Confidence Interval (95%)	Mean	Confidence
Interval (95%)



Lower	Upper

Lower	Upper

Lower	Upper

Lower	Upper

Lower	Upper

U.S.A.	2.17	1.66	2.69	-0.62	-0.79	-0.46	-0.01	-0.01	-0.01	-0.95	-1.37
-0.53	-0.03	-0.08	0.02

Canada	0.05	0.01	0.09	0.09	0.05	0.14	0.00	0.00	0.00	0.00	-0.04	0.04	0.00
-0.02	0.02

EU-27	0.04	-0.01	0.09	0.03	0.00	0.05	0.00	0.00	0.00	0.02	-0.01	0.05	0.02
0.01	0.03

Brazil	0.05	0.02	0.08	0.08	0.04	0.12	-0.03	-0.04	-0.03	0.00	-0.01	0.00
-0.02	-0.04	-0.01

Japan	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00

China-Hong Kong	0.06	0.03	0.10	0.05	0.02	0.08	0.00	0.00	0.00	0.00	0.00
0.00	-0.09	-0.12	-0.06

India	0.00	0.00	0.00	0.01	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.01	-0.01
-0.01	0.00

Latin American EEx.	0.07	0.02	0.12	0.06	0.03	0.10	-0.01	-0.01	0.00	-0.02
-0.04	0.00	-0.04	-0.06	-0.03

Rest of Latin Am.  	0.02	0.01	0.04	0.01	0.00	0.01	-0.01	-0.01	0.00	0.00
-0.01	0.01	-0.01	-0.02	0.00

EE & FSU EEx.	0.03	0.02	0.05	0.03	0.02	0.04	0.00	0.00	0.00	0.01	-0.01
0.03	0.01	0.00	0.02

Rest of Europe	0.03	0.02	0.04	0.01	0.00	0.01	0.00	0.00	0.00	0.00	0.00
0.01	0.00	-0.01	0.01

Middle Eastern N Africa EEx.	0.04	0.02	0.05	0.01	0.00	0.01	0.00	0.00
0.00	0.01	0.00	0.02	-0.03	-0.05	-0.02

Sub Saharan EEx.	-0.01	-0.03	0.01	0.11	0.07	0.14	0.00	0.00	0.00	0.04
0.02	0.05	0.06	0.03	0.09

Rest of North Africa & SSA	0.03	0.01	0.04	0.01	0.00	0.01	0.00	0.00	0.00
0.00	0.00	0.00	-0.01	-0.01	0.00

South Asian EEx.	0.00	0.00	0.01	0.02	0.01	0.04	0.00	0.00	0.00	0.00	0.00
0.00	-0.03	-0.04	-0.02

Rest of High Income Asia	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00

Rest of Southeast & South Asia	0.00	0.00	0.01	0.01	0.01	0.02	0.00	0.00
0.00	0.00	0.00	0.00	-0.01	-0.02	-0.01

Oceania countries	0.04	0.02	0.06	0.01	0.01	0.02	0.00	0.00	0.00	-0.02
-0.05	0.01	0.00	-0.01	0.00

Global	2.63	2.25	3.00	-0.09	-0.23	0.05	-0.06	-0.07	-0.05	-0.90	-1.19
-0.61	-0.19	-0.29	-0.10



Table 8. SSA estimates of change in land cover due to an increase in
corn-ethanol production in the U.S.

														(million acres)

 

Regions

 	Crop Cover	Forest Cover	Pasture Cover

	Mean	Confidence Interval (95%)	Mean	Confidence Interval (95%)	Mean
Confidence Interval (95%)



Lower	Upper

Lower	Upper

Lower	Upper

U.S.A.	0.57	0.35	0.78	-0.21	-0.28	-0.14	-0.36	-0.51	-0.20

Canada	0.15	0.06	0.23	-0.10	-0.15	-0.04	-0.05	-0.08	-0.02

EU-27	0.12	0.05	0.18	-0.08	-0.12	-0.03	-0.04	-0.06	-0.02

Brazil	0.06	0.03	0.10	0.00	-0.01	0.01	-0.06	-0.09	-0.04

Japan	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

China-Hong Kong	0.02	0.01	0.04	0.01	0.00	0.02	-0.03	-0.06	-0.01

India	0.00	-0.01	0.01	0.00	-0.01	0.01	0.00	0.00	0.00

Latin American EEx.	0.06	0.03	0.09	0.00	0.00	0.01	-0.06	-0.10	-0.03

Rest of Latin Am.  	0.02	0.01	0.03	0.02	0.01	0.03	-0.04	-0.06	-0.02

EE & FSU EEx.	0.09	0.04	0.13	0.08	0.03	0.13	-0.17	-0.24	-0.10

Rest of Europe	0.03	0.02	0.05	-0.02	-0.02	-0.01	-0.02	-0.03	-0.01

Middle Eastern N Africa EEx.	0.02	0.01	0.03	0.00	0.00	0.00	-0.02	-0.03
-0.01

Sub Saharan EEx.	0.20	0.12	0.27	0.04	0.01	0.06	-0.24	-0.32	-0.16

Rest of North Africa & SSA	0.03	0.01	0.04	0.00	-0.01	0.00	-0.02	-0.04
-0.01

South Asian EEx.	-0.01	-0.01	0.00	0.01	0.00	0.02	-0.01	-0.01	0.00

Rest of High Income Asia	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Rest of Southeast & South Asia	0.00	0.00	0.01	0.00	0.00	0.00	0.00	-0.01
0.00

Oceania countries	0.03	0.01	0.05	0.00	0.00	0.01	-0.03	-0.05	-0.01

Global	1.38	0.98	1.78	-0.24	-0.36	-0.11	-1.15	-1.50	-0.83





Table 9. Change in agricultural output due to an increase in biodiesel
production in the U.S.

										(million tonnes)

	Cereal Grains	Oilseeds	Paddy Rice	Wheat	Sugar crops	Other Agri

U.S.A.	-1.82	3.61	-0.08	-0.55	-0.05	-2.60

EU-27	0.31	0.75	0.00	-0.52	0.04	-2.17

Brazil	-0.12	0.59	-0.01	-0.01	-0.38	-0.30

Canada	-0.18	0.35	0.00	-0.13	0.00	-0.64

Japan	0.00	0.00	0.00	0.00	0.00	0.01

China, Hong Kong	-0.18	0.24	-0.07	-0.01	-0.01	0.04

India	-0.02	0.11	-0.03	-0.04	-0.06	0.11

Caribbean and Central America	-0.05	0.13	0.01	0.00	0.07	0.01

South and Rest of America	-0.06	0.70	0.02	-0.05	0.14	-0.23

East Asia	0.00	0.00	0.00	0.00	0.00	0.01

Malaysia, Indonesia	-0.04	2.26	-0.03	0.00	-0.02	-0.02

Rest of Southeast Asia	-0.05	0.16	0.02	0.00	0.00	0.06

Rest of South Asia	0.00	0.01	0.00	-0.01	-0.01	0.02

Russia	-0.01	0.04	0.00	-0.01	0.00	0.01

Other Eastern Europe & FSU	0.00	0.04	0.00	0.00	0.00	0.10

Rest of Europe	0.00	0.00	0.00	0.00	0.00	0.03

Middle East & North Africa	0.00	0.05	-0.01	-0.02	0.02	0.23

SSA & Rest of Africa	0.01	0.36	-0.01	0.00	0.17	0.29

Oceania countries	0.01	0.07	0.00	-0.02	-0.01	0.25

Global Total	-2.20	9.46	-0.15	-1.38	-0.10	-4.80



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

									(percent change)

Sectors	Market Price	World Price

	USA	EU-27	Brazil	Canada

	Paddy_Rice	0.35	0.20	0.58	-0.02	0.14

Wheat	-0.09	0.31	0.33	0.10	0.15

CrGrains	0.27	0.51	0.44	0.11	0.22

Oilseeds	3.58	1.34	1.02	1.23	1.44

Sugar_Crop	0.60	0.41	0.48	0.34	0.28

OthAgri	0.43	0.36	0.47	0.21	0.23

Proc_Dairy	-0.88	0.00	0.09	-0.23	-0.25

Proc_Rum	-1.18	-0.05	0.11	-0.33	-0.44

proc_NonRum	-1.29	-0.13	0.07	-0.29	-0.36

Proc_Feed	-10.44	-0.70	0.15	-1.91	-2.71

Ethanol1	-0.94	-0.04	0.08	0.00	-0.88

DDGS	0.21	0.79	0.79	0.15	0.28

Cveg_Oil1	110.89	6.98	2.28	16.42	16.53

VOBP	-50.89	-12.02	-0.24	-15.11	-22.07

Biodiesel	56.44	5.44	0.40	2.77	28.57

Ethanol2	0.02	-0.02	0.13	-0.01	-0.69

Coal	-0.13	-0.02	-0.03	-0.04	-0.05

Oil	-0.35	-0.20	-0.18	-0.27	-0.23

Gas	-0.03	-0.02	-0.02	-0.05	-0.02

Oil_Pcts	-0.24	-0.16	-0.14	-0.19	-0.17

Electricity	-0.04	-0.02	-0.01	-0.03	-0.03





Table 11. Impact of an increase in U.S. biodiesel production on
bilateral trade (Ch. in import volume)

														        										 ($ millions)

 	Exporters: (post-2006)	Cereal Grains	Oilseeds

 

US	EU	RoW	Total Exports	US	EU	RoW	Total Exports

1	U.S.A.	0	1	-18	-17	0	-74	-134	-208

2	EU-27	-1	0	-7	-8	0	0	4	4

3	Brazil	0	1	-3	-2	0	101	62	163

4	Canada	-1	0	0	0	21	7	73	101

5	Japan	0	0	0	0	0	0	0	1

6	China, Hong Kong	0	0	2	3	1	12	30	43

7	India	0	0	0	0	5	4	13	22

8	Caribbean and Central America	0	0	0	0	4	0	8	13

9	South and Rest of America	-1	2	-3	-2	3	10	36	49

10	East Asia	0	0	0	0	0	0	2	2

11	Malaysia, Indonesia	0	0	0	0	0	0	0	-1

12	Rest of Southeast Asia	0	0	1	1	1	0	5	6

13	Rest of South Asia	0	0	0	0	0	0	2	2

14	Russia	0	1	0	1	0	4	0	4

15	Oth Eastern Europe & FSU	0	1	1	2	2	18	2	22

16	Rest of Europe	0	0	0	0	0	0	0	0

17	Middle East & North Africa	0	0	0	0	1	3	1	5

18	SSA & Rest of Africa	0	0	0	0	2	7	12	21

19	Oceania countries	0	0	0	0	7	3	10	20

	Total	-3	7	-25	-21	47	96	126	269



Note: Change in volume of exports of coarse grains and oilseeds from all
the 18 regions to the US, EU, and Rest of the World were evaluated at
initial market prices. 

 

Table 12. Change in land use and land cover due to an increase in
biodiesel production in the U.S. (million acres).

	Change in harvested area	Change in land cover

	Paddy Rice	Wheat	CrGrains	Oilseeds	Sugar crops	OthAgri	Cropland	Forest
Pasture

U.S.A.	-0.07	-1.09	-1.73	4.18	-0.03	-1.29	-0.03	-0.69	0.72

EU-27	0.00	-0.48	0.11	1.27	0.00	-0.33	0.57	-0.35	-0.22

Brazil	-0.02	-0.03	-0.16	0.80	-0.03	-0.14	0.41	-0.08	-0.34

Canada	0.00	-0.16	-0.12	0.92	0.00	-0.09	0.55	-0.08	-0.47

Japan	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

China, Hong Kong	-0.10	-0.05	-0.16	0.42	0.00	-0.08	0.03	0.12	-0.15

India	-0.04	-0.05	-0.06	0.30	0.00	0.02	0.16	-0.09	-0.07

Caribbean and Central America	0.01	0.00	-0.04	0.09	0.01	0.01	0.08	0.17
-0.25

South and Rest of America	-0.01	-0.19	-0.15	0.80	-0.01	-0.27	0.18	0.12
-0.29

East Asia	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	-0.01

Malaysia, Indonesia	-0.24	0.00	-0.08	0.46	-0.01	-0.11	0.02	-0.01	0.00

Rest of Southeast Asia	-0.06	0.00	-0.08	0.16	0.00	-0.01	0.00	0.01	-0.01

Rest of South Asia	0.00	-0.01	0.00	0.02	0.00	0.01	0.02	-0.01	-0.02

Russia	0.00	-0.05	-0.05	0.15	0.00	-0.06	-0.01	0.16	-0.15

Oth Eastern Europe & FSU	0.00	-0.01	0.00	0.13	0.00	0.01	0.12	0.01	-0.12

Rest of Europe	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01

Middle East & North Africa	0.00	-0.05	-0.02	0.16	0.00	0.02	0.11	0.00
-0.11

SSA & Rest of Africa	-0.04	-0.01	-0.18	0.91	0.01	-0.06	0.63	-0.09	-0.53

Oceania countries	0.00	-0.03	0.02	0.11	0.00	0.05	0.16	0.04	-0.19

Global	-0.56	-2.23	-2.70	10.88	-0.07	-2.32	3.01	-0.77	-2.24



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

 

Regions

 	Paddy Rice	Wheat	CrGrains

	Mean	Confidence Interval (95%)	Mean	Confidence Interval (95%)	Mean
Confidence Interval (95%)



Lower	Upper

Lower	Upper

Lower	Upper

U.S.A.	-0.07	-0.08	-0.05	-1.07	-1.42	-0.73	-1.69	-2.05	-1.34

EU-27	0.00	0.00	0.00	-0.47	-0.56	-0.39	0.11	0.02	0.21

Brazil	-0.02	-0.03	-0.01	-0.03	-0.04	-0.03	-0.16	-0.20	-0.12

Canada	0.00	0.00	0.00	-0.15	-0.23	-0.07	-0.11	-0.15	-0.08

Japan	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

China, Hong Kong	-0.10	-0.12	-0.07	-0.05	-0.07	-0.02	-0.16	-0.19	-0.12

India	-0.03	-0.07	0.00	-0.05	-0.06	-0.04	-0.06	-0.07	-0.04

Caribbean and Central America	0.01	0.01	0.01	0.00	0.00	0.00	-0.04	-0.05
-0.03

South and Rest of America	-0.01	-0.01	0.00	-0.18	-0.21	-0.16	-0.15	-0.17
-0.12

East Asia	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Malaysia, Indonesia	-0.24	-0.29	-0.18	0.00	0.00	0.00	-0.08	-0.10	-0.06

Rest of Southeast Asia	-0.06	-0.10	-0.02	0.00	0.00	0.00	-0.08	-0.10
-0.06

Rest of South Asia	0.00	-0.01	0.01	-0.01	-0.01	0.00	0.00	0.00	0.00

Russia	0.00	0.00	0.00	-0.05	-0.06	-0.03	-0.05	-0.06	-0.05

Oth Eastern Europe & FSU	0.00	0.00	0.00	-0.01	-0.06	0.04	0.00	-0.02	0.02

Rest of Europe	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Middle East & North Africa	0.00	0.00	0.00	-0.04	-0.08	-0.01	-0.02	-0.03
-0.01

SSA & Rest of Africa	-0.03	-0.04	-0.03	-0.01	-0.02	0.00	-0.17	-0.26
-0.07

Oceania countries	0.00	0.00	0.00	-0.03	-0.05	-0.01	0.02	0.01	0.03

Global	-0.54	 	 	-2.16	 	 	-2.63	 	 





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

 

Regions

 	Oilseeds	Sugar crops	Other Agri

	Mean	Confidence Interval (95%)	Mean	Confidence Interval (95%)	Mean
Confidence Interval (95%)



Lower	Upper

Lower	Upper

Lower	Upper

U.S.A.	4.10	3.06	5.14	-0.03	-0.03	-0.02	-1.25	-1.55	-0.96

EU-27	1.27	1.13	1.41	0.00	-0.01	0.01	-0.32	-0.41	-0.22

Brazil	0.81	0.61	1.01	-0.02	-0.04	-0.01	-0.14	-0.18	-0.09

Canada	0.92	0.77	1.07	0.00	0.00	0.00	-0.08	-0.15	-0.01

Japan	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

China, Hong Kong	0.42	0.32	0.53	0.00	0.00	0.00	-0.08	-0.12	-0.05

India	0.30	0.22	0.37	0.00	-0.01	0.00	0.02	-0.01	0.05

Caribbean and Central America	0.09	0.08	0.11	0.01	0.00	0.01	0.01	0.00
0.03

South and Rest of America	0.79	0.67	0.91	-0.01	-0.01	0.00	-0.26	-0.32
-0.21

East Asia	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.00

Malaysia, Indonesia	0.45	0.34	0.56	-0.01	-0.01	-0.01	-0.11	-0.13	-0.08

Rest of Southeast Asia	0.16	0.10	0.21	0.00	-0.01	0.00	-0.01	-0.03	0.00

Rest of South Asia	0.02	0.01	0.03	0.00	0.00	0.00	0.01	0.01	0.02

Russia	0.15	0.13	0.18	0.00	0.00	0.00	-0.06	-0.07	-0.04

Oth Eastern Europe & FSU	0.14	0.09	0.18	0.00	0.00	0.00	0.01	-0.02	0.05

Rest of Europe	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01

Middle East & North Africa	0.16	0.13	0.18	0.00	0.00	0.00	0.02	0.00	0.04

SSA & Rest of Africa	0.92	0.72	1.12	0.01	0.00	0.01	-0.04	-0.26	0.18

Oceania countries	0.11	0.10	0.13	0.00	0.00	0.00	0.06	0.01	0.10

Global	10.82	 	 	-0.07	 	 	-2.21	 	 



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

 

Regions

 	Crop Cover	Forest Cover	Pasture Cover

	Mean	Confidence Interval (95%)	Mean	Confidence Interval (95%)	Mean
Confidence Interval (95%)



Lower	Upper

Lower	Upper

Lower	Upper

U.S.A.	-0.01	-0.14	0.13	-0.66	-0.83	-0.49	0.67	0.45	0.89

EU-27	0.59	0.28	0.91	-0.37	-0.58	-0.15	-0.23	-0.33	-0.13

Brazil	0.43	0.20	0.67	-0.09	-0.20	0.01	-0.34	-0.49	-0.19

Canada	0.57	0.28	0.86	-0.10	-0.24	0.04	-0.47	-0.68	-0.27

Japan	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.00

China, Hong Kong	0.04	-0.01	0.09	0.12	0.05	0.19	-0.16	-0.26	-0.06

India	0.18	0.02	0.34	-0.10	-0.19	-0.01	-0.08	-0.15	-0.01

Caribbean and Central America	0.08	0.04	0.12	0.17	0.10	0.23	-0.25	-0.34
-0.15

South and Rest of America	0.18	0.08	0.29	0.12	0.04	0.20	-0.30	-0.48
-0.13

East Asia	0.00	0.00	0.00	0.01	0.00	0.01	-0.01	-0.01	-0.01

Malaysia, Indonesia	0.02	0.01	0.02	-0.01	-0.02	-0.01	0.00	-0.01	0.00

Rest of Southeast Asia	0.00	-0.01	0.00	0.01	0.00	0.02	-0.01	-0.02	0.00

Rest of South Asia	0.03	0.00	0.05	-0.01	-0.02	0.00	-0.02	-0.04	0.00

Russia	0.00	-0.04	0.03	0.16	0.03	0.30	-0.16	-0.28	-0.04

Oth Eastern Europe & FSU	0.14	0.01	0.26	0.00	-0.03	0.03	-0.14	-0.25
-0.03

Rest of Europe	0.00	0.00	0.01	0.00	0.00	0.00	-0.01	-0.01	0.00

Middle East & North Africa	0.12	0.04	0.19	0.00	0.00	0.00	-0.12	-0.19
-0.05

SSA & Rest of Africa	0.67	0.20	1.15	-0.11	-0.23	0.02	-0.57	-0.93	-0.21

Oceania countries	0.16	0.08	0.24	0.03	0.02	0.05	-0.20	-0.29	-0.10

Global	3.21	 	 	-0.83	 	 	-2.39	 	 





 

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

Note: This figure is based on the data reported in Table 5.

 

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

Note: This figure is based on the data reported in Table 5.

 

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

Note: This figure is based on the data reported in Table 5.

 

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

Note: This figure is based on the data shown below.

Cropcover change (Ha/billion BTU)

Corn-ethanol scenario:	Mean	Lower	Upper

USA	1.532	0.928	2.085

CAN	0.393	0.170	0.606

EU27	0.306	0.141	0.476

BRAZIL	0.171	0.077	0.261

JAPAN	0.007	0.003	0.012

CHIHKG	0.059	0.019	0.102

INDIA	-0.003	-0.038	0.038

LAEEX	0.165	0.084	0.245

RoLAC	0.042	0.017	0.067

EEFSUEX	0.229	0.114	0.350

RoE	0.087	0.046	0.128

MEASTNAEX	0.043	0.016	0.069

SSAEX	0.538	0.332	0.729

RoAFR	0.071	0.035	0.107

SASIAEEX	-0.017	-0.031	-0.001

RoHIA	0.000	0.000	0.000

RoASIA	0.004	-0.009	0.019

Oceania	0.081	0.028	0.133

GLOBAL	3.707	1.933	5.425



 

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

Note: This figure is based on the data reported in Table 8.

 

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

Note: This figure is based on the data reported in Table 8.

 

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

Note: This figure is based on the data reported in Table 8.

 

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

Note: This figure is based on the data shown below.

Crop-cover change (Ha/billion BTU)

Biodiesel scenario:	Mean	Lower	Upper

USA	-0.031	-0.490	0.428

EU27	2.035	0.967	3.103

Brazil	1.484	0.686	2.282

Canada	1.955	0.958	2.952

Japan	0.008	-0.006	0.023

China-hkg	0.146	-0.030	0.322

India	0.614	0.079	1.149

C_C_Amer	0.273	0.137	0.408

S_o_Amer	0.632	0.278	0.986

E_Asia	0.003	0.001	0.005

Mala_Indo	0.061	0.037	0.084

R_SE_Asia	-0.001	-0.019	0.017

R_S_Asia	0.095	0.007	0.183

Russia	-0.011	-0.140	0.117

Oth_CEE_CIS	0.464	0.041	0.887

Oth_Europe	0.013	0.004	0.022

MEAS_NAfr	0.398	0.151	0.646

S_S_AFR	2.309	0.680	3.937

Oceania	0.550	0.278	0.823

GLOBAL	10.997





Appendix A: Aggregation Tables

Table A1. Aggregation of sectors in the GTAP-BIO-1 Model

No.	Aggregated Sectors	GTAP sectors	Description

1	CrGrains	gro 	Other Grains: maize (corn), barley, rye, oats, other
cereals

2	OthGrains	pdr wht 	Paddy Rice: rice, husked and unhusked; Wheat: wheat
and meslin

3	Oilseeds	osd 	Oil Seeds: oil seeds and oleaginous fruit; soy beans,
copra

4	Sugarcane	c_b 	Cane & Beet: sugar cane and sugar beet

5	Livestock	ctl oap rmk wol 	Cattle: cattle, sheep, goats, horses,
asses, mules, and hinnies; and semen thereof; Other Animal Products:
swine, poultry and other live animals; eggs, in shell (fresh or cooked),
natural honey, snails (fresh or preserved) except sea snails; frogs’
legs, edible products of animal origin n.e.c., hides, skins and
furskins, raw , insect waxes and spermaceti, whether or not refined or
coloured; Raw milk; Wool: wool, silk, and other raw animal materials
used in textile

6	Forestry	frs 	Forestry: forestry, logging and related service
activities

7a	Ethanol1	eth1 	Ethanol1 (Grain based)

7b	DDGS	Byproduct of eth1	Distillers dried grains solubles

8	Ethanol2	eth2 	Ethanol2 (sugarcane based)

9a	Biodiesel	biod 	Biodiesel

9b	BDBP	Byproduct of biod	Oil-meal/cake

10	OthFoodPdts	voln ofdn 	Vegetable Oils: crude and refined oils of
soya-bean, maize (corn),olive, sesame, ground-nut, olive,
sunflower-seed, safflower, cotton-seed, rape, colza and canola, mustard,
coconut palm, palm kernel, castor, tung jojoba, babassu and linseed,
perhaps partly or wholly hydrogenated,inter-esterified, re-esterified or
elaidinised. Also margarine and similar preparations, animal or
vegetable waxes, fats and oils and their fractions, cotton linters,
oil-cake and other solid residues resulting from the extraction of
vegetable fats or oils; flours and meals of oil seeds or oleaginous
fruits, except those of mustard; degras and other residues resulting
from the treatment of fatty substances or animal or vegetable waxes;
Other Food: prepared and preserved fish or vegetables, fruit juices and
vegetable juices, prepared and preserved fruit and nuts, all cereal
flours, groats, meal and pellets of wheat, cereal groats, meal and
pellets n.e.c., other cereal grain products (including corn flakes),
other vegetable flours and meals, mixes and doughs for the preparation
of bakers’ wares, starches and starch products; sugars and sugar
syrups n.e.c., preparations used in animal feeding, bakery products,
cocoa, chocolate and sugar confectionery, macaroni, noodles, couscous
and similar farinaceous products, food products n.e.c.

11	ProcLivestoc	cmt omt mil 	Cattle Meat: fresh or chilled meat and
edible offal of cattle, sheep, goats, horses, asses, mules, and hinnies.
raw fats or grease from any animal or bird.; Other Meat: pig meat and
offal. preserves and preparations of meat, meat offal or blood, flours,
meals and pellets of meat or inedible meat offal; greaves; Milk: dairy
products.



Table A1. Aggregation of sectors in the GTAP-BIO-1 Model (continued)

[[

12	OthAgri	v_f pfb ocr pcr sgr b_t 	Veg & Fruit: vegetables,
fruitvegetables, fruit and nuts, potatoes, cassava, truffles,; Plant
Fibres: cotton, flax, hemp, sisal and other raw vegetable materials used
in textiles; Other Crops: live plants; cut flowers and flower buds;
flower seeds and fruit seeds; vegetable seeds, beverage and spice crops,
unmanufactured tobacco, cereal straw and husks, unprepared, whether or
not chopped, ground, pressed or in the form of pellets; swedes,
mangolds, fodder roots, hay, lucerne (alfalfa), clover, sainfoin, forage
kale, lupines, vetches and similar forage products, whether or not in
the form of pellets, plants and parts of plants used primarily in
perfumery, in pharmacy, or for insecticidal, fungicidal or similar
purposes, sugar beet seed and seeds of forage plants, other raw
vegetable materials; Processed Rice: rice, semi- or wholly milled;
Sugar; Beverages and Tobacco products.

13	OthPrimSect	fsh omn 	Fishing: hunting, trapping and game propagation
including related service activities, fishing, fish farms; service
activities incidental to fishing; Other Mining: mining of metal ores,
uranium, gems. other mining and quarrying

14	Coal	coa 	Coal: mining and agglomeration of hard coal, lignite and
peat

15	Oil	oil 	Oil: extraction of crude petroleum and natural gas (part),
service activities incidental to oil and gas extraction excluding
surveying

16	Gas	gas gdt 	Gas: extraction of crude petroleum and natural gas
(part), service activities incidental to oil and gas extraction
excluding surveying (part); Gas Distribution: distribution of gaseous
fuels through mains; steam and hot water supply

17	oil_pcts	p_c 	Petroleum & Coke: coke oven products, refined petroleum
products, processing of nuclear fuel

18	electricity	ely 	Electricity: production, collection and distribution

19	En_Int_Ind	crpn i_s nfm 	Chemical Rubber Products: basic chemicals,
other chemical products, rubber and plastics products; Iron & Steel:
basic production and casting; Non-Ferrous Metals: production and casting
of copper, aluminium, zinc, lead, gold, and silver

20	Oth_Ind_Se	tex wap lea lum ppp nmm fmp mvh otn ele ome omf wtr cns
trd otp wtp atp cmn ofi isr obs ros osg dwe 	Textiles: textiles and
man-made fibres; Wearing Apparel: Clothing, dressing and dyeing of fur;
Leather: tanning and dressing of leather; luggage, handbags, saddlery,
harness and footwear;  Lumber: wood and products of wood and cork,
except furniture; articles of straw and plaiting materials; Paper &
Paper Products: includes publishing, printing and reproduction of
recorded media; Petroleum & Coke: coke oven products, refined petroleum
products, processing of nuclear fuel; Non-Metallic Minerals: cement,
plaster, lime, gravel, concrete; Fabricated Metal Products: Sheet metal
products, but not machinery and equipment; Motor Vehicles: cars,
lorries, trailers and semi-trailers; Other Transport Equipment:
Manufacture of other transport equipment; Electronic Equipment: office,
accounting and computing machinery, radio, television and communication
equipment and apparatus; Other Machinery & Equipment: electrical
machinery and apparatus n.e.c., medical, precision and optical
instruments, watches and clocks; Other Manufacturing: includes
recycling; Water: collection, purification and distribution;
Construction: building houses factories offices and roads; Trade: all
retail sales; wholesale trade and commission trade; hotels and
restaurants; repairs of motor vehicles and personal and household goods;
retail sale of automotive fuel; Other Transport: road, rail ; pipelines,
auxiliary transport activities; travel agencies; Water transport; Air
transport; Communications: post and telecommunications; Other Financial
Intermediation: includes auxiliary activities but not insurance and
pension funding (see next); Insurance: includes pension funding, except
compulsory social security; Other Business Services: real estate,
renting and business activities; Recreation & Other Services:
recreational, cultural and sporting activities, other service
activities; private households with employed persons (servants); Other
Services (Government): public administration and defense; compulsory
social security, education, health and social work, sewage and refuse
disposal, sanitation and similar activities, activities of membership
organizations n.e.c., extra-territorial organizations and bodies;
Dwellings: ownership of dwellings (imputed rents of houses occupied by
owners).



Table A2. Aggregation of Regions in the GTAP-BIO-1 model.

No.	New Code	New Description	GTAP regions	Description of GTAP Regions  

1	USA	United States	usa 	United States of America

2	CAN	Canada	can 	Canada

3	EU27	European Union 27	aut bel dnk fin fra deu gbr grc irl ita lux nld
prt esp swe bgr cyp cze hun mlt pol rom svk svn est lva ltu 	Austria,
Belgium, Denmark, Finland, France, Germany, United Kingdom, Greece,
Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden,
Bulgaria, Cyprus, Czech Republic, Hungary, Malta, Poland, Romania,
Slovakia, Slovenia, Estonia, Latvia, Lithuania

4	BRAZIL	Brazil	bra 	Brazil

5	JAPAN	Japan	jpn 	Japan

6	CHIHKG	China, Hong Kong	chn hkg 	China, Hong Kong

7	INDIA	India	ind 	India

8	LAEEX	Latin American Energy Exporters	mex col ven arg 	Mexico,
Colombia, Venezuela, Argentina

9	RoLAC	Rest of Latin America + Caribbean	xna per xap chl ury xsm xca
xfa xcb 	Rest of North America, Peru, Rest of Andean Pact, Chile,
Uruguay, Rest of South America, Central America, Rest of Free Trade Area
of the Americas, Rest of the Caribbean.

10	EEFSUEX	EE & FSU Energy Exp	xef rus xsu 	Rest of EFTA, Russian
Federation, Rest of Former Soviet Union

11	RoE	Rest of Europe	che xer alb hrv tur 	Switzerland, Rest of Europe,
Albania, Croatia, Turkey

12	MEASTNAEX	Middle Eastern N Africa E Exp	xme tun xnf bwa 	Rest of
Middle East, Tunisia, Rest of North Africa, Botswana

13	SSAEX	Sub Saharan Energy Exporters	xsc mwi moz tza zwe xsd mdg uga
xss 	Rest of South African Customs Union, Malawi, Mozambique, Tanzania,
Zimbabwe, Rest of Southern African Development Community, Madagascar,
Uganda, Rest of Sub-Saharan Africa.

14	RoAFR	Rest of North Africa & SSA	mar zaf zmb 	Morocco, South Africa,
Zambia

15	SASIAEEX	South Asian Energy Exporters	idn mys vnm xse 	Indonesia,
Malaysia, Vietnam, Rest of Southeast Asia

16	RoHIA	Rest of High Inc Asia	kor twn 	Korea, Taiwan

17	RoASIA	Rest of Southeast & South Asia	xea phl sgp tha bgd lka xsa 
Rest of East Asia, Philippines, Singapore, Thailand, Bangladesh, Sri
Lanka, Rest of South Asia

18	Oceania	Oceania countries	aus nzl xoc 	Australia, New Zealand,  Rest
of Oceania





Appendix B: Land Supply

 

Figure B1. Land supply in the GTAP-BIO models.

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

 This analysis relies on the version of the model CARB used for the corn
ethanol assessment conducted in February 2009.

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

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

 PAGE   

  PAGE   \* MERGEFORMAT  iii 

  PAGE   \* MERGEFORMAT  1 

  PAGE   \* MERGEFORMAT  37 

  PAGE   \* MERGEFORMAT  48 

ETRAE2 =-0.5

Forest

Pasture

Cropland

Land-AEZi

Sugar-crops

Oilseeds

Coarse grains

ETRAE1 =-0.20

Land Supply in Value-Added Nest of Production Structure

Other agriculture

Other grains

