indirect land use change for biofuels: testing predictions and improving analytical methodologies
TRANSCRIPT
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 2 3 5e3 2 4 0
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Indirect land use change for biofuels: Testing predictions andimproving analytical methodologies
Seungdo Kim, Bruce E. Dale*
Department of Chemical Engineering and Materials Science, DOE Great Lakes Bioenergy Research Center, Michigan State University,
3900 Collins Road, Lansing, MI 48910, USA
a r t i c l e i n f o
Article history:
Received 27 January 2011
Received in revised form
21 April 2011
Accepted 25 April 2011
Available online 13 May 2011
Keywords:
Corn
Biofuel
Historical data
Indirect land use change
Renewable energy policy
Soybean
* Corresponding author. Tel.: þ1 517 353 677E-mail addresses: [email protected] (S.
0961-9534/$ e see front matter ª 2011 Elsevdoi:10.1016/j.biombioe.2011.04.039
a b s t r a c t
Current practices for estimating indirect land use change (iLUC) due to United States bio-
fuel production rely on assumption-heavy, global economic modeling approaches. Prior
iLUC studies have failed to compare their predictions to past global historical data. An
empirical approach is used to detect evidence for iLUC that might be catalyzed by United
States biofuel production through a “bottom-up”, data-driven, statistical approach. Results
show that biofuel production in the United States from 2002 to 2007 is not significantly
correlated with changes in croplands for corn (coarse grain) plus soybean in regions of the
world which are corn (coarse grain) and soybean trading partners of the United States. The
results may be interpreted in at least two different ways: 1) biofuel production in the United
States through 2007 (the last date for which information is available) probably has not
induced any indirect land use change, and 2) this empirical approach may not be sensitive
enough to detect indirect land use change from the historical data. It seems clear that
additional effort may be required to develop methodologies to observe indirect land use
change from the historical data. Such efforts might reduce uncertainties in indirect land
use change estimates or perhaps form the basis for better policies or standards for biofuels.
ª 2011 Elsevier Ltd. All rights reserved.
1. Introduction Several studies show that iLUC has the potential to be one
Indirect land use change (iLUC) due to biofuel production has
become a key issue in the ongoing ‘Food-versus-Energy’
debates. The basic concept of iLUC is that natural ecosystems
elsewhere might be converted to croplands to replace crops
(either ‘animal feed’ or ‘food’) that are lost due to biofuel
production. For example, changes in the United States (US)
corn supply caused by ethanol fuel production could eventu-
ally lead to increases in: 1) corn (coarse grain) areas in other
countries due to decline in US corn export or 2) croplands for
soybean in other countries due to a decline in US soybean
exports, resulting from conversion of soybean fields to corn-
fields in the United States.
7; fax: þ1 517 337 7904.Kim), [email protected] Ltd. All rights reserved
of the primary potential greenhouse gas (GHG) sources inwell-
to-fuel GHG emissions of biofuels [1e5]. Crop management
practices in newly converted croplands can also play an
important role in GHG estimates due to iLUC [6]. Even though
there is a general consensus within the biofuel community
that the iLUC effects are potentially important, a major
concern for iLUC is ‘uncertainty’, particularly due to the
amount and type of forest and grassland converted. These are
critical factors in determining GHG emissions associated with
iLUC. For example, Searchinger et al. [1] shows that about
9.1 ha of natural ecosystems are converted to croplands (52%
from forest, 46% from grassland and 2% from desert) due to
one TJ of ethanol fuel production, while the United States
(B.E. Dale)..
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 2 3 5e3 2 4 03236
Environmental Protection Agency (US EPA) [2] estimates about
4.4 ha TJ�1 for land conversion as iLUC (66% from forest and
34% from grassland). The California Air Resources Board
(CARB) [3] and the GREETmodel [4,5] estimate the iLUC effects
due to corn based ethanol via the Global Trade Analysis
Project (GTAP) model to determine the sizes and the locations
of natural ecosystems converted due to biofuels. Results from
CARB show the conversion of 5.1 ha TJ�1 of natural ecosys-
tems to croplands (26% from forest and 74% from grassland),
while GREET shows the conversion of 1.6 ha TJ�1 of natural
ecosystems to croplands (33% from forest and 67% from
grassland). These results show that the sizes, the types and
the locations of the natural ecosystems converted differ
greatly between the economic models chosen and their
assumptions.
CARB [3] uses an ethanol production increase of 50 hm3 in
its economic modeling, while the US EPA [2] uses a 10 hm3
ethanol production increase. CARB’s assumption of
a 50 hm3 ethanol production increase in its analysis implies
that an ethanol production increase dating from 2002 would
begin to trigger iLUC.
If biofuel production in the United States has indeed trig-
gered land conversion elsewhere, evidence for iLUC effects
should be observed in the historical data. The historical data
would include changes in the area of forest lands, total arable
lands and croplands for animal feed and food associated with
biofuel production in the United States. Few studies have
attempted to find evidence for iLUC from the historical data.
This study attempts to detect evidence for iLUC from the
historical data through a set of tests for a better understanding
of iLUC in policy or standard developments. The tests devel-
oped in this study identify regions of the world in which iLUC
due to US biofuel production might be observed from the
historical data. This analysis uses 19 geographical regions as
in Tyner et al.’s study [5] e Brazil (BRA), Canada (CAN), China
and Hong Kong (CHK), India (IND), Japan (JAP), United States
(USA), Central and Caribbean Americas (CCA), East Asia (EAS),
European Union 27(EUN), Malaysia and Indonesia (MAI),
Middle Eastern and North Africa (MEA), Oceania countries
(OCC), Other East Europe and Rest of Former Soviet Union
(OES), Rest of European Countries (REC), Rest of South Asia
(RSA), Rest of South East Asia (SEA), Russia (RUS), South and
Other Americas (SOA), and Sub Saharan Africa (SSA). The
details of these regions are given elsewhere [5]. The historical
data, including land use patterns and commodity grain
imports, associated with those 19 regions are investigated to
determine regions where iLUC has occurred.
2. Material and methods
The analysis proposed in this study is an empirical approach
to detect indirect land use change due to US biofuel produc-
tion, particularly corn based ethanol and soybean biodiesel.
This is a “bottom-up”, data-driven, statistical approach based
on individual regions’ land use patterns and commodity grain
imports. This approach relies on very few assumptions and
tries neither to quantify nor to predict iLUC effects. According
to iLUC predictions [1e3], production of corn ethanol or
soybean biodiesel in the United States will lead to reduced
exports which will increase crop commodity prices which in
turn will catalyze land use change with potentially large
accompanying GHG releases. Thus biofuel production leads to
reduced exports which in turn lead to land use change. It is
this mechanism that we test here.
Predictions of iLUC are empirically tested using historical
data on the United States croplands, commodity grain exports
to specific regions and land use trends in those geographical
regions. We use the 1990s as a baseline when the United
States biofuel industry was very small and measure changes
against that baseline. In order for iLUC to occur in a specific
geographical region, it is postulated that the following five
conditions must be met simultaneously.
1. For a specific region, average areas of croplands used for
corn plus soybean production in the 2000s must increase
compared to the 1990s.
2. For a specific region, average areas of arable lands in the
2000smust also increase compared to the 1990s. Otherwise,
corn plus soybean areas are probably converted from other
cropland areas in that region. Biofuel production in the
United States is unlikely to be involved in those inter-
cropland conversion processes. Economic or other factors
such as domestic agricultural policies are more likely to be
responsible for those inter-cropland conversions than is US
biofuel production [7]. Since there is always some cost
associated with bringing new lands into agricultural
production, we assume that there would be no conversion
of natural ecosystems in regions where average areas of
arable lands in the 2000s are less than those in the 1990s.
3. For a particular geographical region, average areas of
natural ecosystem lands in the 2000s decline compared to
the 1990s. The natural ecosystem lands include forest, and
permanent meadows and pastures. Otherwise, no natural
ecosystems are converted to croplands to produce animal
feed or food that is lost due to US biofuel production.
4. For a particular region, average corn plus soybean imports
from United States in the 2000s decline significantly
compared to imports during the 1990s. Annual changes
that occur within average annual variation are taken to
mean ‘no significant decline’. iLUC due to biofuel produc-
tion is not likely to occur in regions within which mean
corn plus soybean imports from United States increase.
Up to this point, conditions 1e4 seem to be straightfor-
ward. Indirect land use changes due to US biofuel production
would not happen in regions that do not meet conditions 1e4.
The last condition (condition 5) determines whether iLUC
would be observed in the regions that meet conditions 1e4
using correlation tests. Changes in harvested areas for corn
and soybean for US biofuel productionwill lead to increases or
decreases in croplands for corn plus soybean in a specific
region if US biofuel production influences land use changes.
The correlation test between biofuel production and land use
changes can provide some clues about the relationships
between these factors.
5. Annual percentage changes in croplands for corn plus
soybean in a specific region are positively correlated with
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 2 3 5e3 2 4 0 3237
annual percentage changes in harvested areas for corn and
soybean for biofuel production (corn ethanol and soybean
biodiesel) in the United States. A negative correlation
suggests that annual percentage changes in croplands for
corn plus soybean decline with annual percentage changes
in harvested areas for corn and soybean for biofuel
production in the United States. Even though ‘correlation’
does not mean ‘causation’, ‘no correlation’ strongly
suggests ‘not related to each other’.
Year 2007 biofuel production in the United States is tested
using the above conditions to determine regions within which
iLUC effects are detected in the historical data. The 1990
baseline values are average values from 1992 to 1999. Average
values in the 2000s are taken from 2000 to 2008. Global infor-
mation on country-level croplands and natural ecosystems is
available at the Food and Agriculture Organization of the
United Nations e FAOSTAT [8]. The United States corn and
soybean trade data are obtained from the United States
Foreign Agricultural Service [9].
Even though crop price is a primary factor driving iLUC
effects in the global economicmodels, the amount of cropland
involved is used in the correlation tests. The historical data
show that annual percentage changes in corn prices in the
United States are moderately correlated with annual
percentage changes in croplands for US biofuel production.
This is illustrated in Fig. 1. Due to moderate correlations,
cropland would be more appropriate for the correlation tests
than crop price. Furthermore, corn price is also influenced by
fossil energy costs (crude oil price), weather conditions and
other factors [10,11].
Annual percentage change is defined as (Ai�Ai�1)/(Ai�1) %,
where Ai is area at year i. The reasons for using the annual
percentage changes instead of total cropland areas in this
analysis are that a) biofuel production accounts for only 1.5%
of global croplands for grain and oil seeds [12], b) the biofuel
industry is relatively young, and c) many other factors rather
than biofuel production (e.g., population pressure, food
consumption, economics, crop yields, commodity specula-
tion, etc.) are involved in the system. Therefore, correlation
tests with the total cropland areas would not be appropriate.
The annual change in croplands, not just percentage change,
is another feasible parameter for the correlation tests. Both
Fig. 1 e Annual percentage changes in corn price versus
annual percentage changes in croplands for biofuel
production in the United States (from 1997 to 2009).
parameters (i.e., annual percentage changes and annual
changes) are investigated in our correlation tests.
For the correlation tests, corn area used for corn based
ethanol production is estimated by Eq. (1).
EtOHi$
�Di
YDiþ ð1� DiÞ
YWi
�
Ci�1$bþ
EtOHi$
�Di
YDiþ ð1� DiÞ
YWi
�
Ci$ð100� bÞ
(1)
Where EtOHi is corn based ethanol production at year i. Di is
the dry milling share of total ethanol production. YDi is
ethanol yield in dry milling, while YWi is ethanol yield in wet
milling. Ci is corn yield at year i. b is a percentage of feedstock
harvested in the previous year involved in producing corn
based ethanol at year i. (100�b) is a percentage of feedstock
harvested at year i. Soybean area used for soybean diesel
follows the same algorithm. Biofuel yield (e.g., ethanol and
soybean diesel) and other data are obtained from literature
[4,13e15].
We assume that essentially 100% of US biofuel production
in a given year is derived from feedstock harvested in the
previous year (b ¼ 1). This implies that there is at least a two-
year lag between diversion of corn and soybean production to
biofuels and any possible iLUC effects. The time lag between
biofuel production and its possible iLUC effects is one year, in
order to account for planting and harvesting decisions. For
example, the iLUC effects due to corn and soybeans produced
in 2005 and converted to biofuels in the US would occur in
2007 in the regions of interest, while the iLUC effects due to
the 2006 diversion of corn and soybean production to the 2007
biofuel production increase in the United States would occur
in 2008. In a sensitivity analysis, we investigate the effect of
the fraction of feedstock harvested in the previous year
involved in biofuel production in a given year.
The possibility that coarse grains other than corn alone are
planted in the newly converted croplands is also investigated
in a sensitivity analysis (referred to as ‘coarse grain case’).
This coarse grain case in the sensitivity analysis affects
conditions 1, 4 and 5. Croplands for coarse grain plus soybean
production in condition 1 and coarse grain plus soybean
imports from United States in condition 4 are investigated. In
condition 5, the correlations between croplands for coarse
grain plus soybean in a specific region and harvested areas for
corn and soybean for biofuel production are tested.
3. Results
First, the correlations between domestic cropland and crop-
land used for biofuel production in the United States are
investigated to determine whether iLUC has occurred in the
United States e annual percentage changes in planted areas
for crops versus annual percentage change in croplands for
biofuel production. Results show that the annual percentage
change in planted areas for cotton, corn plus soybean and oats
are significantly correlated with the annual percentage
change in croplands for biofuel production in the United
States at p< 0.05. However, negative correlations are observed
only for cotton, which is not a major food source. This implies
that US biofuel production can reduce croplands for cotton,
Table 1 e Results from testing conditions 1e4 [O:satisfying, X: not satisfying, n.a.: not applicable].
Condition1
Condition2
Condition3
Condition4
Brazil O (O) O O O (O)
Canada O (X) X O X (X)
China and
Hong Kong
O (X) X X X (X)
India O (O) X X O (O)
Japan O (X) X O X (O)
United States O (X) X X n.a.
Central and
Caribbean
Americas
X (X) O O X (X)
East Asia X (X) X O O (O)
European
Union 27
X (X) X X O (O)
Malaysia and
Indonesia
X (X) O O X (X)
Middle Eastern
and North
Africa
X (X) O X X (X)
Oceania
Countries
O (O) O O O (O)
Other East
Europe and
Rest of
Former
Soviet Union
O (X) X X X (X)
Rest of
European
Countries
X (X) X X O (O)
Rest of South
Asia
O (O) X O X (X)
Rest of South
East Asia
O (O) O O X (X)
Russia O (X) X X O (O)
South and
Other
Americas
O (O) O O X (X)
Sub Saharan
Africa
O (O) O O O (X)
b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 2 3 5e3 2 4 03238
which is consistent with findings in the United States
Government Accountability Office’s report [11]. This result
also intensifies the rationale for condition 5. Pearson product-
moment correlation coefficients for individual crops up to
year 2009 are summarized in the supplementary material.
Even though year 2007 US biofuel production is tested for
conditions in other regions, the data availability in the United
States allows investigating year 2009 biofuel production to
determine the iLUC effects in the United States. No arable land
increases from the 1990s are observed in the United States.
Furthermore, no declines in natural ecosystem lands in the
United States have been observed since 1998. Therefore, the
US historical data do not indicate that iLUC occurred within
the 48 contiguous states as a result of US biofuel production.
The regional information on 18 regions (except for the USA)
is tested for conditions 1e4 first to identify any regions that
meet all four conditions. Results show that only three regions
satisfy conditions 1e4: Brazil, Oceania countries, and Sub
Saharan Africa (Brazil and Oceania countries in the coarse
grain case). These three regions (two regions in the coarse
grain case) would thus have potential for iLUC due to US
biofuel production and are tested through the correlation
analysis. Results from testing conditions 1e4 are listed in
Table 1. Results in parentheses reflect the coarse grain case.
The MAI region (Malaysia and Indonesia) is eliminated
from consideration because it shows no increases in crop-
lands for corn (coarse grain) plus soybean despite satisfying
conditions 2e4. About 44% of natural ecosystems converted in
the MAI region from 1992 to 2008 become croplands, while the
rest of the natural ecosystems converted (about 56%) become
urban or other lands. Palm oil, but not corn (coarse grain) or
soybeans, is the major crop planted in the converted natural
ecosystem followed by coffee, coca bean and natural rubber
from 1992 to 2008. On the contrary, average croplands for
soybean in the 2000s in the MAI region were reduced by 55%
compared to those in the 1990s. It is thus obvious that US
biofuel production does not play a great role in land use
changes in the MAI region.
Although the SEA (Rest of South East Asia) and the SOA
regions (South and Other Americas) meet conditions 1e3,
these two regions do not meet condition 4. The average corn
(coarse grain) plus soybean imports from United States in the
2000s in the SEA and the SOA region increased by 22% (20%)
and 30% (29%) compared to corn (coarse grain) plus soybean
imports during the 1990s, respectively. Rice and beans are the
major crops grown on increased arable lands in the SEA
region, while soybean is the major crop grown on increased
arable lands in the SOA region, particularly in Argentina. Large
increases in soybean areas in Argentina occurred from 2001 to
2005 because of its floating currency policy, biotechnology and
double cropping [7]. Therefore, US biofuel production does not
play a role in increasing soybean area in the SOA regions.
If the correlation is significant, three regions (two regions
in the coarse grain case) identified from conditions 1e4 (i.e.,
BRA, OCC and SSA) might potentially have converted natural
ecosystems to croplands to produce corn (coarse grain) and
soybean that were once imported from the United States. Both
Pearson product-moment and Spearman’s rank correlation
coefficients are used to determine whether the correlation
between the percentage changes of croplands for corn (coarse
grain) plus soybean in those regions and the percentage
changes in corn and soybean production dedicated to US
biofuel production is significant at p < 0.05. Results show that
there are no regions that have a significant correlation
(at p < 0.05), implying that the percentage change in cropland
for corn (coarse grain) plus soybean in regions which are corn
(coarse grain) and soybean trading partners of the United
States may not be significantly correlated with the percentage
change in croplands for biofuel production in the United
States up to the 2007 biofuel production increase. These
results provide strong evidence that either: 1) no iLUC has
occurred due to US biofuel production up through the end of
2007 or 2) that the empirical data cannot detect iLUC due to US
biofuel production.
Increased soybean production plays a major role in
increased croplands in Brazil. Policies, biotechnology and
growing soybean demand from China expanded soybean area
in Brazil [16,17]. Wheat accounts for about 57% of the arable
land increases from the 1990s in the OCC region (Oceania
Countries), followed by barley and rapeseed accounting for
b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 2 3 5e3 2 4 0 3239
27% and 14%, respectively. It is possible that declines in
soybean imports from the United States would lead to
increases in croplands for rapeseed in the OCC region,
particularly Australia. However, rapeseed production in
Australia began to increase in early 1990s. Better varieties,
improved agronomy, crop monitoring program (Canola
Check) and good prices are the primary reasons for the
expansion of croplands for rapeseed in Australia [18]. Most
increases in croplands for rapeseed in Australia occurred
during 1998 and 1999 [19]. Therefore, the expansion of rape-
seed production in the OCC region is not related to US biofuel
production. Population pressure is a major driver for agricul-
tural expansion in the SSA region (Sub Saharan Africa) [20,21],
where extensification is a dominant trend [22].
Results for the correlation tests are summarized in the
supplementary material. Even though the RSA region (Rest of
South Asia) has a significant correlation with the annual
percentage change in croplands for US biofuel production at
p < 0.05, the RSA region fails to meet other conditions 1e4.
Sensitivity analyses also show that using the annual changes
as parameters (instead of the annual percentage changes)
does not affect the findings. The coarse grain case also shows
the similar results, which are summarized in the supple-
mentary material.
4. Discussion and conclusions
Results from these empirical tests are likely to be controver-
sial. Our results can be interpreted in two different ways:
1. Biofuel production in the United States up through the end
of 2007 in all probability has not induced indirect land use
change.
There are two feasible dependent conclusions to that
might be drawn from this interpretation: 1) crop intensifica-
tion may have absorbed the effects of expanding US biofuel
production or 2) the effects of US biofuel production expan-
sion may be simply negligible, and not resolvable within the
accuracy of the data. Note that these results do not apply to
increased biofuels production after 2008. Data do not yet exist
to make comparable tests after 2008. It therefore appears that
not every unit of biofuel production implemented to date has
triggered indirect land use change. For example, the 1999
biofuel production increase (biofuel produced in a biofuel
production facility built in 1999) has apparently not triggered
indirect land use change, while we do not have information to
say one way or the other whether the 2010 biofuel production
increase triggered iLUC. In other words, the characteristics of
indirect land use changes due to 2010 biofuel production
increases may be quite different from those due to a 2015
biofuel production increase. This conclusion also suggests
that ongoing agricultural intensification can continue to
absorb the effects of biofuel production expansion without
inducing indirect land use change, at least up to some level.
2. A contrary interpretation is that this empirical test simply
fails to detect ongoing indirect land use change from the
historical data.
This interpretation implies that iLUC has occurred, but that
our analysis may not be adequate to detect it from the
historical data. Thus more sophisticated empirical
approaches should be developed to detect indirect land use
change from the historical data.
Another concern with our conclusions is based on the
completeness of the FAO statistics. Unfortunately, there are
no global data currently available that are as complete as the
FAO statistics. In fact, some global economic models used to
estimate iLUC also use the data from the FAO statistics. Thus,
this concern does not appear to be relevant. Either these data
are adequate for analysis or they are not, and if they are not,
then iLUC estimates based on them are invalid also, and the
discussion cannot proceed further.
Very few previous studies have attempted to find empirical
evidence for or against indirect land use change from the
historical data. Most previous studies [1e5] have relied on
global economic simulations. Therefore, we encourage addi-
tional effort to determine a potential threshold point for
indirect land use change, to developmethodologies to observe
indirect land use change in the historical data, or to determine
that no measurable iLUC is taking place in the United States.
Such efforts might reduce uncertainties in indirect land use
change estimates or form the basis for better policies or
standards for biofuels.
Acknowledgments
This work was funded by DOE Great Lakes Bioenergy
Research Center (www.greatlakesbioenergy.org) supported by
the US Department of Energy, Office of Science, Office of
Biological and Environmental Research, through Cooperative
Agreement DEFC02-07ER64494. Support was also provided by
the Michigan Agricultural Experiment Station.
Appendix. Supplementary data
Supplementary data associated with this article can be found,
in the online version, at doi:10.1016/j.biombioe.2011.04.039.
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