for the World Economy
Review of IFPRI study
“Assessing
the Land Use Change Consequences of
European
Biofuel policies and its uncertainties”
Dr. Ruth Delzeit
Prof. Gernot Klepper Ph.D.
Dipl.-Kulturw. Mareike Lange
Study on the behalf of the
European Biodiesel Board
1 On behalf of the European Biodiesel Board, this review compares two studies by IFRI on
environmental effects of the EU biofuels mandate. The review is structured as follows: first, we
set both studies in the context of current literature, define important terms and discuss the ability
of models to capture land use change consequences. Second, we explain the model used in both
studies and its suitability to address land use change. In a third part, we show major differences in
results between the two studies, which causes we discuss in a fourth chapter. In chapter five,we
address the aspect of uncertainties in the model. We finally conclude with some critical remarks.
1.
Setting the study in the context of current literature
1.1.
The concept of dLUC and iLUC
For the implementation of an effective control instrument to reduce direct (dLUC) and indirect
land use change (iLUC) of biofuel feedstock production, the concept and mechanism of iLUC
must be understood elaborately. In practice, several policy proposals do not address this concept
correctly, therefore we start with a short definition how dLUC and iLUC are to be understood
and we use this definition to further elaborate our answers in the following.
The definition of dLUC is straight forward: direct land use change is the conversion of land,
which was not used for crop production before,into land used for a particular biofuel feedstock
production. The emissions caused by the conversion process can be directly linked to the biofuel
load and thus be allocated to the specific carbon balance of that biofuel.
ILUC is a market effect that materialises when biofuel feedstocks are increasingly planted on
areas already used for agriculturalproducts. Ceteris paribus this causes a reduction of the area
available for food and feed production and therefore leads to a reduction of food and feed supply
on the world market. If the demand for food remains on the same level and does not decline,
prices for food rise due to the reduced supply. These higher prices create an incentive to convert
formerly unused areas for food production since the conversion of these areas becomes
profitable at higher prices. This is the iLUC effect of the biofuel feedstock production. The iLUC
effect of biofuels comes about only through the price mechanism of the global or regional food
market. Therefore iLUC in this context is always direct land use change (dLUC) for food
production incentivised by the cross-price effects of an increased production of biofuel
feedstocks which then translates into an additional demand for so far unused land areas.
2 Figure 1
Figure 1 illustrates the concepts of different land use changes driven by an increased demand for
biofuel feedstocks. DLUC includes the conversion of food cropland, degraded land and
untouched nature. As described above,iLUC is a biofuel demand driven direct land use change of
untouched nature for food production. We here consider also efficiency gains from biofuel
production. Traditional bioenergy use, such as the use of firewood, can be replaced by biofuel
feedstocks or other bioenergy. This leads to efficiency increases as the advanced cultivation
methods and technologies lead to higher energy yields per hectare. In particular this accounts for
large areas in Africa, were crop based bioenergy production can replace tradition use of firewood.
To assess the concept of iLUC one has to keep in mind, that land use can only be understood
globally. The adjustment processes and the replacement of areas devoted to food production as
well as the expansion of cultivated land areas is governed by complex global processes. Global
demand and supply conditions as well as the regional support policies in the agricultural sector,
local infrastructure and economic structure as well as the geophysical suitability of areas for
agricultural production simultaneously determine land use decisions. As a consequence, the iLUC
of bioenergy production does not take place within the local community nor on a national level
but come about throughout the globe. The casual linkage of a single driver/producer of biofuels
to a particular indirect land use change is therefore impossible. Furthermore, the monitoring of
these global processes requires highly spatially disaggregated remote sensing technologies which
are still not available at global scale.
3 1.2.
General feature of models applied in the literature
There are various substitution effects and linkages between the agricultural sector and the energy
sector. Hence, computable general equilibriummodels (CGE) that address the world agricultural
market as well as repercussions with the world economy and energy markets are used to simulate
land use change caused by biofuels production. Another model type that can be applied to
capture land use changes are agricultural partial-equilibrium models. Compared to CGE models,
they havethe advantage of capturing the agricultural sector in more detail, but since they treat
changes in other sectors exogenously (e.g. biofuels do not have an impact on energy prices), they
are not able to consider feedback effects between different sectors.
Kretschmer & Peterson (2010) give an overview about the current status quo of modelling
biofuels. One finding is that all existing modelling frameworks represent the world economy
disaggregated into countries and regions. Furthermore, the frequently employed general
equilibrium models do not only model agricultural and energy sectors but represent a complete
sectoral structure for each economy. With countries and regions being linked via bilateral trade,
the effects of sectoral policies, such as those supporting biofuels, do not only emerge across
sectors within one economy but also internationally. The existing model refinements with respect
to land use also take on a global dimension and aim to better capture land use competition
globally. This has to go at the expense of sectoral detail.
While CGE models can help to understand effects of biofuel policies on crop and energy prices
or production changes for example, a distinction of policy effects on iLUC versus dLUC is not
possible. Since in CGE models, all markets are cleared simultaneously only the net land use
change can be addressed. Furthermore, the level of aggregation does not allow for the
representation of every production unit which makes the representation of the individual direct
land use change effect of that particular production unit impossible. Thus, a land use change
factor calculated on the basis of a CGE model will always include dLUC and iLUC effects of the
biofuel mandatewhich cannot be distinguished. The EC Renewable Energy Directive ( EC RED)
already
includesthe
calculation
ofthe
dLUC
effect
of
a
particular
production
activity.Consequently, when a factor based on land use effects from a CGE modelis added to the
calculation based on the EC RED, dLUC will partly be counted twice. The calculations of the
dLUC based on the EU RED are directly linked to the specific biofuel feedstock production,
whereas the land use change factor from the CGE model will allocate only regional average
dLUC emissions of the biofuel mandate to the specific biofuel production.
Both types of models CGE and partial equilibriumstart with a baseline scenario which simulates
current trends up to a certain target year. This baseline scenario is then used to compare scenarios
4 which are shocked with policy measures. It is therefore of high importance which assumptions
are made in the baseline scenario.
For the set up of a CGE model to analyse land use change effects, several working steps have to
be accomplished. First of all, biofuel sectors (as well as other bioenergy sectors) are not included
in the currently available social accounting matrices (SAM) – the most important database for a
CGE model. Furthermore, important biofuel feedstocks are aggregated in sectors and therefore
need to be singled out for an analysis of land use changes caused by biofuel production. The
procedures to generate consistent new SAMs already create some noise in the data.
Another crucial factor is the way how different types of land use can be substituted. This is
determined by a nesting structure, which can include a) different levels and b) different elasticities
of transformation between the different land use types within the levels of nesting. In many CGE
models, the constant elasticity of transformation (CET) approach is applied which allows land to
be transformed to different uses whereas the ease of transformation is characterised by the
elasticity of transformation. These elasticities are crucial in analysing land use change effects,
since they determine via price effects the ease to what extend land can be movedbetween
different types of uses.
Another important factor is the inclusion of by-products e.g. products such as dried distillers
grains with soluble (DDGS),and oil meals. They can be used as a substitute for feedstuff and
therefore, if included into the modelling exercise cause less land use change than if they were not
considered (see Taheripour et al. 2010). Again, the possibilityto substitute between different byproducts or feedstuffs is crucial for the net effect on land used for biofuel production.
Generally,CGE models do not include physical units such as hectaresof land 1 . Instead, they are
calibrated to the values of goods, e.g. to the value of a harvested product. The simulation
exercises are driven by relative price changes which then can be decomposed in change of prices
and quantities of a particular product. In the case of agricultural products this means that the
decomposition leads to quantities of produced and harvested area as the input quantity. Hence
changes in the land input refer to harvested areas, but they cannot tell whether an increase in
harvested area is due to additional land being brought into production or due to an intensification
of the cropping activity through double or multi-cropping. Without additional information about
the change in the extent of multi-cropping CGE models cannot identify the quantity of land that
is brought into the production of agricultural activities.
1
The database includes harvested areas. Since some fields can be harvested multiple times, the
land area of harvested areas is usually higher than the physical area – the land covered with
crops. 5 In the following section, we discuss these issues as well as the modelling structure and database
which is applied in the IFPRI study (2011).
2. Model structure and database
Modelling iLUC of bioenergy production requires a) to change the database and model structure
since important biofuel crops are aggregated within the GTAP database, which is used by most
CGE models, b) to develop a new methodology to capture land as a heterogeneous production
factor, andc) accurate data about land use change patterns and associated emission factors.
Biofuel sectors are not included in the currently available SAM – the most important database for
a CGE model. Therefore, biofuel production, consumption and trade needs to be disaggregated
by country directly from the respective SAM, ensuring that the national and global SAMs are kept
balanced. Additionally, by-products are included into the database. In the IFPRI study, this
disaggregation of feedstock is the most sophisticated in the CGE literature, since they do not
only disaggregate maize from the “other grains” sector but they also split the oilseed sector into
five separatesectors.
Land use is represented by using the GTAP-AEZ database. Land enters production of
agricultural goods as an input factor and is usually represented by land rents generated in AgroEcological Zones (AEZ). Land rents are generated by the activity on a given parcel of land during
a calendar year.Original data on land use in GTAP-AEZ is based on global land cover and land
use data bases documented in Monfreda et al. (2008) and Ramankutty and Foley (1999),
Ramankutty et al. (2008), as well as global forestry data by Sohngen and Tennity (2004). Values
on returns to 1) crop production, 2) livestock production on pasture and 3) timber area are used
to allocate land rents from the land sector of the GTAP database into 18 separate land sectors.
Lee et al. (2009) explain how existing land rents in the GTAP database are distributed
accordingly.
Since this land endowment enters the production functions for crops, land use change is driven
by price changes. In the IFPRI study, land use change can take place within the land types that
are represented by land rents in the database (they call it substitution effect) and land used for
crop production can be extended to other land types (expansion effect). Land typescomprisedin
the database are cropland, pastureland and managed forest. They all havesome economic value.
The possibility to convert land from one of these land uses to another is determined by
substitution possibilities: according to other studies using CGE modelsforcapturing land use
change, the Constant Elasticity of Transformation (CET) approach is applied (see e.g. Banse et al.
6 2008, Hertel et al 2010, Bouët et al. 2008). In these models, an increase in demand of one
product, e.g. wheat, leads to an increase in price and land is taken from another good, e.g. maize,
depending on the relative prices. If the elasticity between wheat and maize is high, land use
change will not result in large prices increases in case of a demand increase. If transformation
possibilities are low, a higher demand (e.g. caused by biofuel quotas) will raise prices for managed
land and affect other land uses (land expansion). Hence, elasticities for land substitution (within
managed land) influence the expansion into non-managed land.
In the IFPRI study it is assumed that when land rents increase (e.g. through an increase in
agricultural prices) new land is converted and taken into agricultural production. This is a strong
assumption -which is also noted by the authors of the study - since the econometric correlation
between cropland expansion and e.g. deforestation hasnot been shown to be statistically
significant. The literature, however, indicates that there is a positive link between land expansion
and the price level, whereas the size of the elasticity of land expansion is not known. The authors
are therefore forced to base these elasticities on ad-hocassumptions and perform sensitivity
analyses.
The nesting structurethe elasticities are used for is not displayed in the IFRI 2010 or 2011 study,
but we presume that it is the same nesting as in Valin et al. (2009) and Bouet et al. (2010),
whoapply the MIRAGE model.
The MIRAGE model considers by-products of bioethanol and biodiesel production. In general,
the value of by-products is already included in the database, but it is aggregated in certain sectors.
When modelling biofuels, by-products are therefore split into separate sectors.
By-products of biodiesel production are contained in the GTAP vegetable oil sector, which is
split into four new sectors (palm oil, soybean oil, sunflower oil and rapeseed oil) that can be used
for biodiesel production. For the four vegetable oil sectors, oil cakes are implemented as byproducts (note that they were contained in the sector before the splitting as an aggregate).
In case of by-products from bioethanol, DDGS is introduced for four bioethanol subsectors
(except bioethanol from sugar cane, where bagasse is assumed to generate an income of 6% of
production costs from electricity). These subsectors are blended into one output sector of
bioethanol. Al-Riffai et al. (2010) emphasise, “that no other DDGS production is modeled outside of the
production of ethanol. It means that the size of DDGS market is more restricted in the model than in the real
world and will be totally dependent on the evolution of the ethanol production sectors. It is quite different from the
production of meals wherein the vegetal oil production process itself generates oilcakes. Since the biodiesel sector is a
7 limited destination for the overall vegetal oil sectors, the effects of biodiesel policies are much more limited on these
markets.” (Al-Riffai et al. 2010, p.34).
Two substitution degrees are introduced into the model: with a high elasticity of substitution in
the first level of nesting structure, there is substitution between oil cakes based on their protein
content. In the second level of substitution, the aggregate of oil cakes, other types of feedstuff,
grains and DDGS can be substituted also based on their energy contents. We consider the
representation of by-products based on their energy content as anappropriate way of
implementing them into the model.
On the demand side for by-products, the modelling of livestock production allows for
intensification through substitution of livestock feed, including ethanol and biodiesel coproducts, with land. This was changed in the 2011 version of the report since “it has appeared that
the substitution between proteins coming from meals and DDGs was too limited. Still keeping a two level nested
CES in the livestock sector, we allow now for a strong substitution between DDGS and meals at a first level, and
then between the protein aggregates and the other feedstuff at a second level. ” (Laborde 2011, p.16).
We think that by-products of bioethanol and biodiesel are well treated by the model, since their
values are represented in the SAM and substitution effects have been improved. Substitution
according to the energy contents is also a good way of modelling by-products. Limitations of the
approach are mentioned by the authors: the DDGS market is more restricted compared to the
meals market, since meals where an input into the livestock sector before introducing biofuels
into the model.
Concerning the emissions associated with the conversion of five different land use types, the
model assumes standard values of the EC-RED which are based on the IPCC Guidelines for
National Greenhouse Gas Inventories 2006. The 20 year payback time for land use change
emission, which is assumed in both IFPRI studies is based on these two sources. The choice of
the payback time of 20 years is mainly justified by the carbon content in the mineral soil which is
assumed to stabilise on average 20 years after the conversion process. In practice this depends on
several factors such as climate, rainfall, tillage practice or fertilizer input. Emissions from biomass
occur to a major extend during the conversion process, and thus in the first year. Organic soils,
such as peatland soils, are not assumed to stabilise after 20 years but continuously emit carbon
dioxide to the atmosphere. Consequently, the choice of 20 years payback time for the modelling
framework represents an assumption about average values based on standard research results. We
therefore consider the 20 year assumption as appropriate.
8 3. Difference in results of key elements between the 2010 and 2011 versions of the IFPRI
report
Biofuel production is displayed in million tons for biodiesel and bioethanol per country in the
2010 study and in shares on production in energy content by feedstock in the 2011 study.
However, changes between the reference scenario (REF) and the biofuel scenario (MEU_BAU)
for the EU27 and the world are comparable. In general, bioethanol production in 2020 is smaller
in the 2011 study compared to the 2010 study, and biodiesel production is considerably higher: in
the 2010 version, the change in ethanol production between REF and MEU_BAU is +157% for
EU27and +7.6% in the world. In the 2011 version ethanol production in the EU27only increases
by 48.8% under the MEU_BAU scenario and it decreases by -5% in the world. Biodiesel
production increases by 11% in the EU27 and by 5% globally in the 2010 study, whereas in the
2011 study it decreases by 12% in the EU 27 and increases by 50% in the world (again comparing
REF and MEU_BAU scenario in 2020).
Also the structure of EU biofuels production by feedstock in 2020 differs considerably
betweenthe 2010 and 2011 study. In the case of biodiesel (see Table 1) compared to the
respective reference scenario,
the share of biodieselfrom sunflowers in the MEU_BAU
scenarioincreases stronger in the 2011 study compared to the change in share in the 2010 study.
Most notably, the share of biodiesel from rapeseed decreases much more in the 2011 study (14%) and the share of biodiesel from palm fruit increases stronger (+9.1%) in the 2011 study.
Table 1: Comparison of structure of biodiesel EU27 production in 2020
Share of
biodiesel by
feedstock
2010 study (Mtoe)
2011 study (energy content)
REF
MEU BAU
Difference
REF
MEU BAU Difference
Sunflower
4.7
5
+0.3
3.9
6.6
+2.7
Soybeans
31.7
32.8
+1.1
8.2
10.7
+2.5
Rapeseed
53.1
50.7
-2.4
78.2
64.1
-14.1
Palm fruit
10.5
11.5
+1
9.5
18.6
+9.1
9 The composition of bioethanol feedstocks in 2020 comparing the changes between REF and
MEU_BAU scenario also shows differences in the two studies. The share of bioethanol from
wheat decreases less in the 2011 study compared to the 2010 study, while the share of maize
feedstock is similar and the share of bioethanol from maize increasesslightly in the 2010 study but
decreases in the 2011 study (see Table 2).
Table 2: Comparison of Structure of EU 27 bioethanol production in 2020
Share of
bioethanol by
feedstock
2010 study (Mtoe)
2011 study (energy content)
REF
MEU BAU
Difference
REF
MEU BAU Difference
Wheat
50.6
45.6
-5
40.3
39.7
-0.7
Sugar_cb
41.2
45.2
+4
31.5
35.6
+4.1
Maize
8.2
9.2
+1
28.1
24.7
-3.4
Comparing the REF and MEU-BAU scenario,EU Biodiesel importsincrease considerably
under the 2011 study: while in the two main exporting regions LAC and IndoMalay, imports in
2020 increase by 10% in the 2010 study, in the 2011 study they rise by 186% in LAC and 280%
in IndoMalay.
Two effects can be observed for EUbioethanol imports:comparing the MEU_BAU to the REF
in the 2010 study, exports from Brazil and CAMCarib to the EU fivefold. Comparingthe
MEU_BAU to the REF in the 2011 study,EU bioethanol imports more than triple.Thus, changes
in bioethanol imports in the 2011 study are considerably lower compared to the 2010 study.
The change in land use by sectoris not easy to compare since in the 2010 report results on
main changes in crop production (non-EU 27) in 2020 are presentedby region in 1000t, and
information on crop land expansion is also provided by region. In contrast, the 2011 report uses
different units: here, a graph illustrates land use changes for main crops in 1000ha for the EU27
and the rest of the world. However, we can still detect some differences between the 2010 and
2011 studies. In the 2011 study, the land used for producing biodiesel feedstocks within the
EU27 increases mainly for rapeseed production (replacing wheat), and to a lower extend for
sunflowers. In the 2010 study thisincrease in area for rapeseed production is lower and wheat
production shows a small increase.
The changes between REF and MEU_BAU in land used for wheat and maize as bioethanol
feedstocks in the EU27 in 2020 in the 2011 study is negative, whereas the area used for sugar_cb
10 (mainly sugar beet) increases by about 190,000 ha. In the 2010 study, land for wheat and maize
production increases to a small extend and land used for sugar_cb increases to only about
170,000 ha.
In summary, in the 2011 study there is less land used for bioethanol production in the EU and
more land is used for biodiesel production in 2020. The effect on feedstock production and land
use on the world is discussed in the following section.
In the 2011 study, again comparing the REF and MEU_BAU scenario the increase in sugar_cb
production in the world by 2020 is considerably lowercompared to the 2010 study(422,000 ha in
the 2011 study; 9,1844,496ha in the 2010 study). The world rapeseed production increases by
1,242,000 ha in the 2011 study, 16,832.9 ha in the 2010 study.For palm fruit there is only
information for Brazil in the 2010 study (Al-Riffai 2010 table 5), but not for IndoMalay (the
biggest producers). We can therefore not compare it with the land area of the 2011 study
(916,000 ha).Land area used for sunflower production is 26,271 ha in the 2010 study, and is
simulated to reach 203,000 ha in the 2011 study.
Besides substitution effects, both studies also analyse expansion effects. Total cropland
expansion is almost double as high in 2011 study (1,73 mio ha) compared to the 2010 study
(about 0.9 mio ha). Main changes are:
Brazil: less expansion in 2010 review (6,866 km2) compared to 2011 review (4,870 km2)
EU 27: less expansion in 2010 review (460 km2) compared to 2011 review (1,050 km2)
CIS: more expansion in 2010 review (about 500km2) compared to 2011 review (3,900 km2)
SSA: more expansion in 2010 review (about 450km2) compared to 2011 review (2,300 km2)
LAC: more expansion in 2010 review (about 450 km2) compared to 2011 review (1520 km2)
Emissions related to land use differ considerably between the two studies: in the 2010 study
the sum of land use related emissions implied by the EU mandate in 2020cause anaverage LUC
factor of 17gCO2eq/MJ, mainly originated in Brazil (50-60% of world emissions) caused by
demand for sugar and soybeans. In the 2010 study, the EU27 is the second largest source of
direct emissions (about 10% of world emissions). Indonesia and Malaysia are minor suppliers of
biodiesel. Crop specific marginal indirect land use emissions per year for a 20 years life cycle
(with peatland effect) range between 16 and 54 gCO2/MJ for ethanol feedstocks (54 gCO2/MJ
for ethanol maize). Biodiesel crops show emissions between 50 (palm oil) and 75 (soybean)
gCO2/MJ. The peatland effect causes about 15% of total emissions from biodiesel.
11 In the 2011 study, emissions related to land use change caused by the EU mandate in 2020are
38.4 gCO2eq/MJ. They are not divided into direct and indirect LUC. Compared to the 2010 study,
emissions for bioethanol feedstock are lower: they range between 6.6 and 14.4 (wheat) gCO2/MJ
(note the major improvement for maize). In case of biodiesel feedstock, emissions are between
51.8 (sunflowers) and 55.8 gCO2/MJ, whereas palm fruit causes emissions of 54.3 gCO2/MJ. The
peatland effect causes about 34% of total emission from biodiesel.
4. Identification of critical assumptions and parameter changes causing the changes in
results
Table 3: Overview on critical assumptions
Category
Mandate
Ratio Ethanol/Diesel
Calibration of land supply
and demand elasticities
by-products
Peat land emissions
Crop specific LUC
How much peatland on
Palm oil expansion area
2010 study
17.6 Mtoe biofuels by 2020
(55% biodiesel, 45%
bioethanol)
Elasticities for nested CES and
CET functions from literature
(OECD 2003): all the same
Demand for by-product in a
two nested CES function,
elasticities of substitution
40grCO2/ha/year
Measured in tons CO2 / metric
ton and GJ of biofuel
10% Malaysia / 27% Indonesia
(Wetland International)
2011 study
27.2 Mtoe biofuels by 2020
(78% biodiesel, 22% bioethanol)
Calibrated to fit land price elasticities from the
FAPRI model: more realistic due to high
differences in prices
Same CES function, but higher elasticities
55grCO2eq/ha/year
Increase blending rate in the EU while
keeping consumption of all other feedstock in
the world constant -> only one feedstock is
used for new EU demand. Feedstock has to
be provided by demand displacement. Trade
surplus/deficit are maintained constant
33% (Edwards et al. 2010)
The major changes between the 2010 and 2011 study are assumptions about the biofuel mandate,
the biodiesel share in the mandate and the assumption about peatland conversion and its related
emissions.
Naturally, the adjustment of the mandate from 17.6 Mtoe to 27.2 Mtoe leads to a higher
demand for biofuels by the European Union. Furthermore, the share of biodiesel in this mandate
was adjusted to fit the National Renewable Energy Action Plans of the member states which
results in a much higher biodiesel share of 78% in the new study compared to 55% in the 2010
version. One major driver of this high biodiesel share is for example Germany, where biodiesel
has the highest share within the biofuel sector.
12 Additionally, the amount of palm fruit expansion on peatland driven directly or indirectly by
the biofuel mandate, was increased from 10% for Malaysia and 27% for Indonesia to 33% on
average for both countries. Edwards et al. (2010) provide an overview about the current
knowledge about the expansion patterns of palm fruit plantations into peatland in Malaysia and
Indonesia, which is indeed very limited because official statistics do not exist. Nevertheless, they
expect the expansion into peatland to increase over time because there are not many other areas
left for expansion. Different sources for different regions are cited which indicate rates that range
from 25% of palm concessions on peatland in Indonesia up to 80% deforestation on peatland in
some regions. The conclusion that the expansion rate is 33% made by Edwards et al. (2010)
needs to be treated as an educated guess as long as there are no better statistics available. In
section 3, the difference in the peatland effect between the two studies has been pointed out
(about 15% of total emissions from biodiesel in the 2010 study, 34% in the 2011 study). How
much of this difference can be attributed to the higher share in expansion rate and how much on
price effects cannot be distinguished.
In addition to the two major drivers (higher EU mandate and biodiesel shares; higher emissions
allocated to palm oil) palmoil is the cheapest vegetable oil on the market, which results in the
highest shares in the overall biodiesel supply in the 2011 study. A recent study by
Greenpeace Germany testing for biofuel admixtures in European filling stations found high
shares of palmoil in the biodiesel shares (up to 80% in Italy), showing that this resultis not
unrealistic. Thus, the higher share of palmoil in the biodiesel market together with the increased
emission factors increase substantially the emissions from land use change for most of the
biodiesel options. However, the price competitiveness of palmoil is based on empirical facts,
whereas the emissions from LUC are at best educated guesses.
Besides the direct effect of the price competitiveness and higher shares of palm oil in the
biodiesel sector, these factors play an important indirect role due to the standard modelling
structure. This is especially true for the elasticity of demand substitution between different
biofuel crops and the elasticity between intensification and land expansion for a particular
feedstock. These mechanisms are further explained in the following paragraphs:
On the supply side, the question ariseswhether the additional demand for biofuel, given the
demand for bioethanol and vegetable oils particularly from the food sector does not change, is
met by additional feedstock production of the same crop or through the supply from
other feedstocks,and thus by demand substitution. In other words, if the demand for rapeseed
increases due to biofuel production, how is the demand for vegetable oils from other uses met
which used rapeseed oil before? It could be met by additional rapeseed production or be
13 displaced by other vegetable oils, such as palm oil. This ratio of additional supply of the same
feedstock due to an increased biofuel demand differs between the different crops and is shown in
Table 10 of the 2011 report (p.47) (see Table 4 in this document). It shows that for sugarcane
and palm fruit, the additional supply is generated nearly totally by more sugar cane and palm fruit
as both are the cheapest options in their category whereas for soybean, only 40% of additional
supply will be generated by additional supply of soybeans. The rest of the demand will be met by
palm oil. This additional palm oil production, which is an indirect effect of the soybean
consumption for biodiesel production, will lead to further land expansion and thus to higher
LUC emission. These effects are shown in figure 14 of the 2011 study (see below). Thus, the high
biodiesel share, the price advantage of palm oil and the high associated LUC emission affect the
LUC emission values of most biodiesel options.The price competitiveness of palm oil leads
to the substitution of non-energy uses of oils towards palm oil. However, since these
demands cannot be met on current land areas devoted to palm oil production, there will be
expansion, i.e. iLUC for palm oil plantations. Since it is also assumed that an increased share
comes from converted peatland the accompanying iLUC emissions are particularly large. It must
be emphasised that many of the parameters which drive this result are empirically not well
established.
Table 4 Ratio Additional Supply/Biofuel demand
Sugar Beet Sugar Cane Maize Wheat Palm Fruit Rapeseed Soybeans Sunflower 94.40% 98.30% 56.69% 51.38% 96.6% 78.2% 40.3% 71.0% Figure 14 Crop specific LUC. Sources of emissions
14 Another mechanism that needs to be discussed concerns the question if the additional demand
for a particular feedstock (not for a biofuel as in the paragraph above), is met byan
intensification of agricultural activities on the land already in use or by expansion into
unused land. Increased fertilizer use, increases in the cropping intensities and additional labour
and capital inputs would result in an increase in productivity or the additional productioncan take
place through the conversion of unused land. The elasticities of substitution and transformation
which determine the relative importance of these different mechanisms were adjusted in the 2011
version in such a way that they reproduce more accurately the price elasticities in the literature, in
particular from the FAPRI model (Laborde 2011, p.16). One of the reasons may have been that
the 2010 version of the modelgenerated yield intensifications in some cases and/or large supply
responsesfor some agricultural commodities which were considered not to be realistic. It is not
specified for which commodities this was the case but one can assume that if the new modelling
reduces yield intensifications it is more likely that higher land conversion rates with higher land
use change emission will occur.
Concerning the topic of intensification versus expansion, the 2011 review provides the ratio
between intensification and cropland expansion for the different crops (Table12). Here again
palm oil has the highest share of new land expansion with 90% of the supply changes being
generated by land conversion. Thus,an increase of the demand for vegetable oils leads to a
comparatively higher land use change effect if it is met mainly by additional palm oil
production and not by other crops with fewer LUC emissions.
15 Nevertheless, one should mention here that the difference between the land expansion and the
intensification effect on emissions is not as large as simulated by the model. The reason is that
the model does not account for the emissions from the additional fertilizer input which could
partly set off positive impact of land intensification on the carbon balance compared to land
expansion.
Table 5 Intensification vs. Extensification ‐ Decomposition of supply changes. Crop simulationsby Laborde 2011 Feedstock: Factor increase Fertilizer Land use Feedstock: Factor increase Fertilizer Land use* Palm fruit Rapeseed EU27 World EU27 World 25% 8% 8% ‐15% 1% 6% 90% 91% 86% Sugar Beet Sugar Cane EU27 World EU27 World 12% 14% 2% 12% 86% 74% Soybean Sunflower EU27 World EU27 World 12% 14% 12% 15% 2% ‐2% 2% 2% 87% 88% 86% 83% Maize Wheat EU27 World EU27 World 11% 12% 12% 11% 1% 6% 0% 23% 88% 82% 88% 66% Source: Mirage‐Biof Simulations Note: This table shows the decomposition of the source of increase in production. Negative figures for fertilizers can occur due to regional composition effects. For instance if at the world the extension of production occurs mainly in regions with low fertilizer rate, the “world average” fertilizer use per unit of output decrease and the contribution of fertilizer to the average supply is negative. This mechanism is further boosted by the high biodiesel and palm oil share due to non-linearities
in the modelling. The most important reason for non-linearities is the modelling of concave CET
functions which is the standard modelling approach in the literature. This implies that in the
beginning, farmers will more easily substitute some units of their land to biofuel feedstock
production, but with increasing demand for one particular feedstock, they will keep part of their
crop diversity on their land already used for crop production and will go into new land to meet
the demand for additional biofuel feedstocks (p.45). This is in line with the behaviour observed in
reality, where a certain diversity of crops is kept even though some crops are more productive
than others. There are several reasons for this such as security against price volatilities of one
crop. Consequently, this implies that a higher biodiesel mandate with higher shares of palm
oil will lead to higher land expansion for palm oil due to the concavity of the CET
function. This again implies higher LUC emissions. Other reasons for non-linearities are for
example the assumed below-average productivity of new units of land or the non-linear capacity
of sectors and final consumer to reduce their consumption level of one feedstock.
Summarising we argue that
16 -
the assumption of the higher biodiesel share in the increased EU mandate,
-
the low prices of palm oil relative to other oils, thus
-
higher associated palm oil production, and
-
- due to the peatland assumption - high LUC emission
are the crucial assumptions for the changes in the overall differences in the LUC emission values
for ethanol and biodiesel. The following modelling features mainly boost this effect:
-
Non-energetic demand substitution of other vegetable oils by palm oil.
-
Relatively low intensification possibilities for palm oil production and thus higher
conversion rates of unused land.
-
Higher conversion rates for palm oil due to the high palm oil production caused by the
concavity of the CET function.
5. Robustness of the results
There are sufficient land types and in principle an understanding of the role of elasticities of land
expansion to study the general direction and mechanisms of land use change due to a biofuel
demand shock. The problem rather is whether there is sufficient empirical evidence to identify
the different drivers of land expansion which would then are aggregated into a regional elasticity.
It is without doubt that land expansion will be driven not only by global drivers such as prices
and demand but also by local factors, especially political ones. Laborde (2011) addresses the
degree by which some sources of uncertainty influence the results by carrying out a Monte Carlo
Simulation with the following parameters (for the range see Laborde (2011) p. 19):
• Shifter in the share of extension occurring in primary forest
• Shifter in intermediate demand price elasticity of agricultural inputs
• Ratio between yield on new cropland and average yield
• Elasticity of substitution between land and other factors (factor intensification)
• Elasticity of substitution between key inputs (feedstuff or fertilizer) and land (input
intensification)
• Elasticity of transformation of land (intermediate level)
• Land extension elasticities
17 The results of the Monte Carlo Simulation are shown in Table of Laborde (2011) p. 57
Table 6 Monte Carlo simulations Summary of the LUC factor (grCO2eq/MJ) Central scenario Full Mandate Wheat Maize Sugar Beet Sugar Cane Soybean Sunflower Rapeseed Palm Fr
Trade policy Status Quo 38.1 14.4 10.3 6.6 13.4 55.8 51.8 53.8 54.3 Mean Standard Deviation 5% percentile Median 95% percentile Max 38.4 6.9 24.4 38.8 50.4 54.5 Median 40.9 95% percentile 54.1 Max 60.1 Source: Mirage‐Biof Simulations 13.6 3.1 8.3 13.8 18.4 21.1 9.8 2.4 6.0 10.1 13.2 16.7 7.0 3.9 0.8 7.2 12.6 13.7 15.6 5.3 6.5 15.4 26.5 29.8 12.7 17.1 19.8 9.6 13.8 17.8 Trade Liberalization 5.2 19.6 10.6 35.4 11.7 40.4 55.9 9.4 38.4 56.3 73.9 79.6 52.7 11.4 30.6 53.5 72.0 80.8 54.6 14.9 28.2 54.9 80.7 89.8 53.8 4.3 47.1 54.0 60.3 64.6 57.4 76.3 82.1 54.9 74.7 83.4 56.2 82.5 92.2 55.0 62.2 65.5 To evaluate the implications of the range of uncertainty on the evaluation of the overall
sustainability of the different biofuel options, we calculated the range of total emission savings by
adding the well-to-wheel (WtW) emissions from the EU-RED to the land use change emission
results from the Monte Carlo Simulation. Figure 2 shows the results for the median and the 5%
and 95% density level. We differentiate the results for total values and for values including byproduct allocation according to the lower heating value.
Figure 2: Total emission savings (%) using Monte Carlo simulation results (from
Laborde 2011, p. 57) for LUC emission values and EU-RED values for WtW emissions
18 Taking into account land use change emissionsonly, all ethanol options would be sustainable
according to the EU emission minimum saving rules. For biodiesel, only the median values and
below would achieve the 35% level, with rapeseed having the highest degree of uncertainty.
An additional consideration of well-to-wheel emissions leads to sustainable production in terms
of the required 35% emission saving rule for all ethanol options, given that wheat is produced in
an efficient CHP plant. Due to possible productivity and efficiency gains in the production
process, the achievement of the 50%/60% emission is still partly speculative and the results in
figure 2 only account for the current typical production emission. Additionally, sugar cane values
are still without any allocation factor for possible additional power generation. The range of the
results due to uncertainty is the highest for sugar cane. Median values achieve the 50% emission
savings but the 95% bound does not. The 5% bound even surpasses the 60% emission savings. A
similar picture but with a smaller range of results applies to wheat and sugar beet. Thus, the
model is robust for the achievement of the 35% emission savings of wheat, sugarcane and sugar
beet ethanol but not robust for the achievement of the 50% and 60% emission savings. For corn,
results seem to be robust for the achievement of the 35% and the failure to achieve the 50% and
60%.
For all biodiesel options, taking into account by-product allocation or not, the typical well-towheel values of the EU-RED plus land use change emission values from the Laborde’s Monte
Carlo Simulation lead to higher emissions than the required 35% emission savings. These results
are robust. Thus, uncertainty does not have a major influence on whether the sustainability
requirements are met by the biodiesel options or not.Consequently, the high share of biodiesel
and peatland in land conversion shares matter much more for the overall carbon balance of
biodiesel than uncertainties in the model.
19 6. Critical remarks and summary:
The MIRAGE model by IFPRI used to address land use change caused by the European biofuel
mandate represents a sophisticated modelling approach in the field of CGE modelling. It uses
up-to-date data inputs and new methodological way to treat land and land use emissions on a
global scale. The studies from 2010 and 2011 both transparently reportthe assumptions made and
critical parameters chosen. Furthermore, in both studies room for improvement especially in
terms of land use substitution and expansion are highlighted, and the range of uncertainties is
addressed. Caveats for those parameters are given which are based on especially weak empirical
evidence.
Our review of the two studiespoints out that the differences in results are mainly caused by
changing the target for the EU biofuel consumption as well as the shares of bioethanol and
biodiesel. Furthermore, emissions caused by peat land conversion (by palm oil production)
combined with higher imports of feedstock for biodiesel production increase CO2emissions per
MJ for biodiesel. CO2 emissions per MJ bioethanol decreases, which is mainly caused by fewer
bioethanol imports from Brazil.
Comparing the two versions of the MIRAGE application highlights large uncertainties when
parameters or assumptions are changed. Since the models are mainly based on working
assumptions and not on econometrically generated data, when interpreting results one needs to
keep these limitations in mind. This holds true for example for the resulting overall land use
change emission values: based on the limited differentiation within one land category, the land
use change emission factors applied represent average values for the particular land use category.
A more precise representation of the land use change emission would require a much further
differentiation of different land categories in the model. This on the other hand, would require a
much more elaborate database of the spatial distribution of global land use. Nevertheless, the
analysis of the robustness of the Laborde (2011) model shows that the high share of biodiesel
and peatland in land conversion shares matter much more for the overall carbon balance of
biodiesel than uncertainties in the model.
One of the critical assumptions many of the new results are driven by concerns the assumed
expansion of palm oil production activities into peatland forest areas. Since this is formally illegal
according to Indonesian law, this assumptions relates not to market or technological factors but
to political factors, in this case even to the effective enforcement of existing regulations. It is
20 difficult to imagine that such parameters can be expected to be easy to determine, and that they
are stable over time.
The large impact of the palm oil activities on the results of the simulation model points out that it
is difficult to ascribe a particular climate impact of biofuels in general. Even a more detailed
analysis of iLUC would eventually only identify the fact that it is reasonable not to expand palm
oil production activities into peatlands. Since this is obvious in the first place, the model results
generate no new insight into the need to regulate land use conversion, especially those
conversions going into forests and peatland areas since they contain the largest carbon pools. It
would be interesting to simulate a policy option in which Indonesia and Malaysia control this
type of land conversion while keeping the other parameters constant in order to see how much
LUC emission would remain.
We agree with Laborde (2011) that what could be learned from the model is that the distribution
between ethanol and biodiesel play a major role and that thus this ratio should be under review
for the National Renewable Energy Action Plans. It is also shown that agricultural markets are
highly interconnected and that general trade policies for all agricultural products play a major
role.
Concluding the analysis of the two studies, we highlight that the IFPRI MIRAGE model
represents a sophisticated tool to analyse the overall impact of the European biofuel mandate and
the direction of changes caused by biofuel policies. Nevertheless, given the uncertainties for key
assumptions, absolute values gained from those models should not be taken for designing
policies in the form of iLUC emission factors for different crops and regions.
Given the difficulties to calculate the indirect land use change effects of different crops, we
recommend to generally increase the required minimum emission savings with respect to direct
land use change for all biofuel crops. This would have the same effect as imposing a general
iLUC emission factor on all biofuels. As a consequence, only the most efficient biofuel
feedstocks would stay in the market, which reduces the land use change impact of the
overallmandate.
Nevertheless, it is evident that iLUC cannot be controlled efficiently bycertifying biofuel activities
alone. Efficiency is here understood as the quality of an accounting system to ascribe the causal
effects of the chain of LUCs to the biofuel activity. As argued above, this is in principle
impossible. This problem could only be overcome if all agricultural activities are considered
within the GHG accounting system. In this case every land use change becomes by definition a
direct LUC. And this LUC may increase or decrease the stock of carbon in the area under
21 consideration; hence, the LUC would incur a carbon debt in the case of a loss and a carbon gain
in the case of an accumulation of carbon through the new land use practices.
This means the problem of iLUC is in fact only a problem of an incomplete carbon accounting
of land use practices where only biofuel activities are subject to such an accounting, but food
production or other bioenergy uses are neglected. If, in contrast, all land use practices (forestry,
animal grazing, food, fodder and bioenergy production) were subject to a carbon accounting
system, the burden of LUC would always be imposed on the activity that has replaced the
previous type of land use. All considerations about accounting for iLUC would be meaningless.
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