## Indirect Land Use Estimate of Expansion of Palm Oil Renewable Diesel/Biodiesel in South Asia Utilizing the Global Trade Analysis Project Model (GTAP)

December 2, 2010

For: Neste Oil

Air Improvement Resource, Inc. 47298 Sunnybrook Lane Novi, Michigan 48374 248-380-3140 airimprovement.com2 Acknowledgements AIR acknowledges Prof. Farzad Taheripour and Prof. Wally Tyner at Purdue University for their comments. AIR also acknowledges Mr. Steven Gust of Neste in providing much background information that further informed this analysis.3 Indirect Land Use Estimate of Expansion of Palm Oil Renewable Diesel/Biodiesel in South Asia Utilizing the Global Trade Analysis Project Model (GTAP) 1.0 Summary AIR utilized the GTAPSOYv2 model to estimate the indirect land use emissions of palm oil expansion in South Asia, particularly Malaysia and Indonesia. This work was performed for Neste Oil, which is constructing a Renewable Diesel plant with 800,000 tonnes/year (195 million gallon/year) capacity located in Singapore where the primary feedstock is palm oil. Renewable diesel is produced by hydrotreating the feedstock to produce a paraffinic hydrocarbon (no oxygen). The GTAPSOYv2 model is a version of the GTAP model developed by researchers at Purdue University to evaluate land use emissions from expansion of biodiesel from soybeans in the U.S. This model was utilized by CARB in estimating indirect land use emissions of soybean biodiesel in January of 2010. The estimated ILUC for soybean biodiesel ranged from 43 g/MJ to 93 g/MJ (mean of 66 g/MJ) across different experiments. Prior to using GTAPSOY for this work, we made a number of modifications to the data at the recommendation of researchers from Purdue, and also introduced a special shock to calibrate the model to prices in the South Asia region. After imposing proper modifications in the database, we did several experiments to estimate ILUC of biodiesel production from palm oil. First we run an experiment with the elasticity inputs that were used by California EPA. Two different palm oil Renewable Diesel/Biodiesel (RD/BD) shocks were simulated: 200% and 500%. The 200% shock results in an increase of 16.9 million gallons of RD/BD, and 500% resulted in an increase of 42.2 million gallons. The average emissions for the 200% shock were 24.3 g/MJ, and the average emissions for the 500% shock were 23.3 g/MJ. Drawing from a recent paper by Tyner, Taheripour, Zhuang, Birur, and Baldos for Argonne National Laboratory, we changed the yield with respect to area expansion in Malaysia and Indonesia to 0.88. This resulted in five unique scenarios instead of seven previously. The average emissions for the 200% shock were 13.8 g/MJ, and for the 500% shock were 13.1 g/MJ. The Woods Hole data for emission factors in GTAP assumes no carbon sequestration in the soil by palm oil plants. Several studies were reviewed that contained estimates of soil carbon sequestration by palm oil. This study utilized a value in the low end of the range of 55 T/ha (30-year value). When this factor is incorporated into the analysis, the land use emissions for the 200% and 500% shocks, utilizing the updated ETA values of 0.88, are 10.4 and 10.0 g/MJ.4

The ILUC estimates for RD/BD made from palm oil are much lower than for biodiesel made from soybean oil. Several factors may contribute to these results. One reason for this is that oilseed yields (i.e., palm fruit and palm oil) per acre in the South Asia region are 4.5 times higher than oilseed yields in the U.S. (these figures are obtained from GTAP database). Therefore, less land is needed in the South Asia than in the U.S. to produce an equivalent amount of oil. It is also possible that oilseed and vegetable oil markets react differently to changes in the demand for palm oil and soybean oil.

The ILUC estimates from GTAP are somewhat uncertain. There are a number of factors that could change these estimates. These include:  The emission factors for forest and pasture converted to crops  The key elasticity inputs  The fact that palm fruit crops usually expand on logged over forest  The improved incorporation of coproducts for palm fruit in GTAP, particularly for palm kernel oil5

2.0 Introduction In April 2009, the California Low carbon Fuel Standards program adopted by the California Air Resources Board established indirect land use values for corn ethanol and sugarcane ethanol. [1] The Global Trade Analysis Project Model (GTAP) was used to develop these land use estimates. [2] CARB ran this model with seven different input scenarios where various elasticities were modified. The indirect land use values were estimated as the average of these seven results, for both corn ethanol and sugarcane ethanol. The indirect land use estimate for corn ethanol is currently estimated at 30 g/MJ, and for sugarcane ethanol is 42 g/ MJ.

Because of some of the uncertainties in these estimates California EPA convened an expert work group (EWG) to address indirect land use emission further. [3] This group has met several times, and is due to report to the California Air Resources Board with their recommendations on changes to land use modeling by the end of 2010. In February 2010, CARB released its estimate of the indirect land emissions for biodiesel made from soybeans. [4] This modeling also used GTAP, and utilized the same seven input scenarios as used for corn. The estimated land use value for soybean biodiesel is 66 g/MJ. Palm oil, produced mainly in Malaysia and Indonesia, is also used to produce either biodiesel (usually FAME, or fatty acid methyl ester) or Renewable Diesel (hydrotreated vegetable oil). The BD/RD produced from palm oil is completely fungible with diesel fuel. In addition, there is evidence that BD/RD produced from palm oil reduces exhaust NOx emissions from diesel engines. [5] The yield of palm oil per hectare (acre) from palm plants is very high compared to other oilseeds. 1 Both crude palm oil (CPO) and palm kernel oil (PKO) are obtained. The CPO is normally used as a feedstock for fuel production while PKO, which is the higher value oil, has a range of uses. Table 1 below compares vegetable oil yields from different oilseeds. [6]

Table 1. Oil Yields for Various Oilseed Feedstocks Crop Yield (kg oil/hectare) Soybean 375 Mustard 481 Camelina 490 Jatropha 1590 Nuts 405-2010 Chinese Tallow 4000 Oil Palm 3700-4500 Algae Up to 8000 California EPA plans to estimate the indirect land use emissions of BD/RD made from palm oil in the near future. NESTE, an oil company with a Renewable Diesel plant in

1 The palm oil yield per hectare is as much as 10 times that of soybean oil.6

Singapore, approached AIR to study the indirect land use emissions of palm oil in Malaysia. AIR has the GTAP model used by Purdue and others to estimate land use changes, and has replicated all of the modeling done by Purdue for CARB on corn ethanol and soybean biodiesel indirect land use changes. This study presents results of AIR’s analysis of indirect land use changes of palm oil utilizing GTAP. This report is divided into the following sections:  Methodology  Results  Discussion7

3.0 Methodology This section is divided into the following subsections:  Starting GTAP model  Region  Data modifications  Size of RD/BD shock in SE Asia  Input elasticities 3.1 Starting GTAP model The model used in this analysis was the GTAPSOYv2 model (dated January 2010), obtained from the CARB website. 3.2 Region Malaysia is included in a region referred to in the GTAP model as SASIAEEX, which includes the following countries:  Malaysia  Indonesia  Malaysia  Vietnam  Brunei  Darussalam  Cambodia  Lao People's Democratic Republic  Myanmar, and  Timor Leste. We produced a map of this region with Malaysia and Indonesia, showing the AEZs of this region. The map is shown in Figure 1 below.8 Figure 1. South Asia AEZ Regions

Malaysia and Indonesia are almost entirely in AEZ6.

3.3 Data modifications The GTAPSOYv2 Core Data file (BASEDATA.HAR) is a version of the GTAP6 database that contains many essential arrays needed by the model. These arrays hold the 2001 equilibrium values for prices, taxes, and trade used for the economic portion of the model, plus land area, yields, crops, and biofuels data for the GHG portion of the model. In this database are some headers related to the quantities of biofuel production and consumption. Placeholder values (0 or 1) are used in those region which did not produce considerable amounts of biofuels in 2001. In order to improve the accuracy of the results for the palm biodiesel experiments, we replaced these figures with proper actual values. To accomplish this task we used the imputed biofuel prices obtained from the GTAP database. To calculate the imputed price for Ethanol1, Ethanol2, and Biodiesel we divided the monetary values of these commodities by their outputs in the US, Brazil and EU. Then we divided the monetary value of each of these commodities in SASIAEEX region, again obtained from GATP, by their imputed prices to obtain their outputs in that region in base year data. Results are shown in Table 2.9 Table 2. Estimation of SASIAEEX BIOFPRDCTN Quantities

GTAP Base Country SASIAEEX

Biofuel Name Production Value (Million $), CM04 Production

Quantity (Billion Gallons), BIOFPRDCTN Production

Value (Million $), CM04 Production

Quantity (Billion Gallons), BIOFPRDCTN Ethanol1 USA 2489.067 1.74972 0.1243768 8.743E-05 Ethanol2 Brazil 5324.572 3.612 0.02698631 1.831E-05 Biodiesel USA 12.25681 0.005 10.44827 4.262E-03 Similarly, the volumes of fuel usage (BIOOILG) are computed in Table 3. (“Oil_pcts” are refined oil products).

Table 3. Estimation of SASIAEEX BIOOILG Quantities

GTAP Base Country SASIAEEX

Fuel Name Production Value (Billion $), CM04 Usage (Billion

Gallons), BIOOIG Production

Value (Billion $), CM04 Usage (Billion

Gallons), BIOOILG Oil_pcts USA 147666. 206.3277 13904.23 1.943E+01 Ethanol1 USA 2489.067 1.74972 0.1243768 8.743E-05 Ethanol2 Brazil 5324.572 3.612 0.02698631 1.831E-05 Biodiesel USA 12.25681 0.005 10.44827 4.262E-03 GTAPSOYv2 also requires that the number of quadrillion BTUs in the fuel (BIOOIL) be known. The conversion factors and final values are given in Table 4.

Table 4. Estimation of SASIAEEX BIOOIL Quantities

Fuel Usage (Billion Gallons),

BIOOILG GTAP Conversion Factor Usage (Quadrillion BTU),

BIOOIL Oil_pcts 1.943E+01 0.125071 2.430E+00 Ethanol1 8.743E-05 0.084262 7.367E-06 Ethanol2 1.831E-05 0.07171 1.313E-06 Biodiesel 4.262E-03 0.12852 5.478E-04 The SASIAEEX BIOFPRDCTN, BIOOILG, and BIOOIL quantities computed in the above three tables were introduced into the Core Data file. The biodiesel introduced in the GTAP data base for the CARB experiments is tuned to the soy biodiesel production. To use that database in the palm oil biodiesel we need to tune the biodiesel industry of SASIAEEX to palm oil feedstock. The most important coefficient in biodiesel industry is the costs of vegetable oil and its share in total intermediate costs. To tune this coefficient in SASIAEEX we used the following10

commands in a pre-simulation experiment. Then we used the updated database obtained from this experiment to run the main experiments introduced in this report later on. Closure: swap qo("Biodiesel",Reg) = aoall("Biodiesel",Reg); Shock: shock afall("Cveg_oil1","Biodiesel","SASIAEEX") = XXX; The value for the shock (XXX) must be adjusted so that the cost percentage of crude vegetable oil to biodiesel production matches that in the SASIAEEX region in 2001. Neste’s analysis of the percent of crude vegetable oil price in the biodiesel price shows that this percentage is about 80% (see Attachment 1). AIR ran the GTAPSOYv2 model using the modified Core Data, special Closure, and special Shock with a value set to 100% (i.e., XXX=100%). The resulting output file reported a crude vegetable oil to biodiesel cost percentage of 80%. (This value can be found by opening the array VDFA (Firm’s Domestic Purchases at Agent’s Prices), setting the Column view, selecting the SASIAEEX region, and observing the array element corresponding to the intersection of Biodiesel and Cveg_oil1.) Since this value was the same as Neste determined, AIR accepted that the output file was now properly balanced and in equilibrium. This output file (suffixed with .upd) was then renamed and used as the new BASEDATA.HAR file to generate the GHG estimates found in this report.

3.3 Size of shock In its soybean biodiesel modeling, CARB used two different shocks. The first was developed from the percentage increase in biodiesel from 2001 to a volume at some time in the future. CARB assumed 0.005 billion gallons/year (bgy) of soybean biodiesel in 2001, and 1 bgy in the future. The percent increase therefore is 19,900 %. In this case, CARB was assuming that the biodiesel increase would be met entirely by increased production of biodiesel from soybeans. In the January 2010 GTAP_SOY model release, Purdue updated the database to 2005, when the U.S. biodiesel volume was 0.25 billion gallons per year. Thus the percent increase to 1 bgy for this model was a much lower 300%. Again, CARB assumed that the increase in biodiesel use in the U.S. would be met with an increase in biodiesel from soybeans.

We evaluated the indirect land use emissions of two different increases – a 200% increase and a 500% increase in RD/BD from oil palm in SE Asia. 3.4 Input Elasticities For the first round of modeling, we used input elasticities that were the same as used by CARB in the corn ethanol and soybean biodiesel modeling. These elasticities are shown in Table 5. [Source 4, Table 3 of Attachment 2]11 Table 5. Input Elasticities Used in First Round of GTAP Modeling Elasticity A B C D E F G Elasticity of yield wrt area expansion (ETA) 0.50 0.75 0.50 0.50 0.50 0.66 0.75 Crop yield elasticity 0.40 0.40 0.20 0.40 0.40 0.25 0.20 Elasticity of land transformation 0.20 0.20 0.20 0.30 0.10 0.20 0.20 Elasticity of harvested area response 0.50

There has been considerable discussion of these elasticities, particularly the elasticity of yield with respect to area expansion. A recent paper by Tyner, Taheripour, Zhuang, Birur, and Baldos derived much more detailed ETA values for 18 agricultural ecological zones (AEZ) and 19 world regions. [7] The values derived for Malaysia and Indonesia are 0.88 and 0.90. In the second round of modeling, we have changed ETA to 0.88. If this value is applied to all seven scenarios in Table 5, there are five unique scenarios remaining, as shown in Table 6 (Scenarios A and B from Table 5, and Scenarios C and G are identical when ETA=0.9). We used the elasticities in Table 6 for the second round of modeling. Table 6. Input Elasticities Used in Second Round of GTAP Modeling Elasticity A, B C, G D E F Elasticity of yield wrt area expansion (ETA) 0.88 Crop yield elasticity 0.40 0.20 0.40 0.40 0.25 Elasticity of land transformation 0.20 0.20 0.30 0.10 0.20 Elasticity of harvested area response 0.512

4.0 Results

4.1 Results for 200% Shock The results utilizing the seven CARB elasticity inputs and the 200% shock are shown in Table 7. The results range from 16.4 g/MJ to 33.3 g/MJ. The average of these seven scenarios is 24.3 g/MJ. The 200% shock causes an increase in 16.9 million gallons of RD/BD in the region produced from palm oil. The GTAP model predicts that most of the forest and pasture conversion for the 200% expansion in bioidiesel in South Asia will occur outside of South Asia. Within south Asia, most of the land converted is pasture. However, there is also significant conversion of other crops such as coarse grains, sugarcane, and other crops to oil seeds. Examination of GTAP output for Scenario F (the scenario closest to the average of the seven scenarios) by AIR showed that the other crop conversions are nearly 3 times as high in percentage terms as the pasture conversions. In terms of the location of emissions, the largest land use change emissions are in the South Asia region, followed by Canada, the EU27, and Brazil. This is shown in more detail in Attachment 3. Table 7. GTAPSOYv2 Economic Inputs, Pass 1 (200%, CARB Inputs) Scenario A B C D E F G

Economic Inputs Malaysian RD/BD Shock (%) 200 200 200 200 200 200 200 Elasticity of yield wrt area expansion 0.50 0.75 0.50 0.50 0.50 0.66 0.75 Crop yield elasticity 0.40 0.40 0.20 0.40 0.40 0.25 0.20 Elasticity of land transformation 0.20 0.20 0.20 0.30 0.10 0.20 0.20 Elasticity of harvested acreage response 0.50 Trade elasticity of crops Central Model Results Total land converted (1000 ha) 3.77 2.51 4.95 4.30 2.76 3.47 3.30 * Forest land (1000 ha) 1.62 0.84 2.01 1.60 1.39 1.22 1.03 * Pasture land (1000 ha) 2.15 1.67 2.94 2.70 1.36 2.25 2.27 SASIAEEX land converted (1000 ha) 1.09 0.73 1.21 1.39 0.67 0.89 0.81 * Forest land (1000 ha) 0.97 0.62 1.07 1.24 0.58 0.77 0.69 * Pasture land (1000 ha) 0.13 0.11 0.14 0.15 0.09 0.12 0.12 SASIAEEX RD/BD Change (million gal) 16.9 LUC carbon Intensity (gCO2/MJ) 27.3 16.4 33.3 30.9 20.0 22.3 20.013

These land use results are less than the land use results for RD/BD derived from soybeans, which ARB has estimated at 66 g/MJ. We hypothesize that the reason the ILUC are lower for palm oil is due to the much higher yields from oilseeds in the South Asia region than in the U.S. For example, oilseed yields in the U.S. are in the 30-40 bu/acre range. In AEZ6 of SASIAEEX, however, oilseed yields are 182 bu/acre, or 4.5 times the yields in the U.S. Table 1 also shows much higher oil yields per hectare than many other crops, which not only accounts for the oilseed yield difference, but also the oil yield difference per unit volume of oilseeds. Thus, less land needs to be converted in SASIAEEX than in the U.S. for producing vegetable oil and biodiesel. The market reaction to changes in demand for soybean and palm oil could also be factors influencing the land use. Oilseed yields in the GTAP model for both U.S. and SASIAEEX by AEZ are shown in further detail in Attachment 2. The results for the 5 unique scenarios using the yield with respect to area expansion of 0.88 (ETA = 0.88) are shown in Table 8. As expected, the emissions are lower than in Table 7, with average emissions of the 5 scenarios of 13.8 g/MJ. Table 8. GTAPSOYv2 Economic Inputs, Pass 2 (200%, ETA = 0.88) Scenario A, B C, G D E F

Economic Inputs Malaysian RD/BD Shock (%) 200 200 200 200 200 Elasticity of yield wrt area expansion 0.88 0.88 0.88 0.88 0.88 Crop yield elasticity 0.40 0.20 0.40 0.40 0.25 Elasticity of land transformation 0.20 0.20 0.30 0.10 0.20 Elasticity of harvested acreage response 0.50 Trade elasticity of crops Central Model Results Total land converted (1000 ha) 2.14 2.81 2.44 1.57 2.60 * Forest land (1000 ha) 0.61 0.73 0.43 0.68 0.70 * Pasture land (1000 ha) 1.53 2.08 2.01 0.89 1.91 SASIAEEX land converted (1000 ha) 0.62 0.69 0.79 0.38 0.67 * Forest land (1000 ha) 0.52 0.58 0.67 0.31 0.56 * Pasture land (1000 ha) 0.10 0.11 0.12 0.07 0.11 SASIAEEX RD/BD Change (million gal) 16.9 LUC carbon Intensity (gCO2/MJ) 13.2 16.0 14.2 10.4 15.2

4.2 Results for 500% Shock

The results for the 500% shock and the CARB scenarios are shown in Table 9. The 500% shock increases biodiesel by 42.2 million gallons. The emissions of these scenarios are slightly lower on an energy specific basis. The average of the 7 scenarios is 23.3 g/MJ.14

Table 9. GTAPSOYv2 Economic Inputs, Pass 1 (500% Shock, CARB Inputs) Scenario A B C D E F G

Economic Inputs Malaysian RD/BD Shock (%) 500 500 500 500 500 500 500 Elasticity of yield wrt area expansion 0.50 0.75 0.50 0.50 0.50 0.66 0.75 Crop yield elasticity 0.40 0.40 0.20 0.40 0.40 0.25 0.20 Elasticity of land transformation 0.20 0.20 0.20 0.30 0.10 0.20 0.20 Elasticity of harvested acreage response 0.5 Trade elasticity of crops Central Model Results Total land converted (1000 ha) 9.06 6.04 11.89 10.35 6.63 8.35 7.93 * Forest land (1000 ha) 3.96 2.07 4.89 3.92 3.39 2.98 2.51 * Pasture land (1000 ha) 5.11 3.97 7.00 6.43 3.24 5.37 5.42 SASIAEEX land converted (1000 ha) 2.66 1.77 2.93 3.36 1.64 2.16 1.95 * Forest land (1000 ha) 2.26 1.42 2.51 2.89 1.36 1.78 1.58 * Pasture land (1000 ha) 0.40 0.35 0.42 0.47 0.28 0.38 0.37 SASIAEEX RD/BD Change (million gal) 42.2 LUC carbon Intensity (gCO2/MJ) 26.2 15.7 32.0 29.7 19.3 21.3 19.0 Results for the 500% shock with ETA = 0.88 are shown in Table 10. The average of these scenarios is 13.1 g/MJ.15

Table 10. GTAPSOYv2 Economic Inputs, Pass 2 (500% Shock, ETA = 0.88)

Scenario A, B C, G D E F

Economic Inputs Malaysian RB/BD Shock (%) 500 500 500 500 500 Elasticity of yield wrt area expansion 0.88 0.88 0.88 0.88 0.88 Crop yield elasticity 0.40 0.20 0.40 0.40 0.25 Elasticity of land transformation 0.20 0.20 0.30 0.10 0.20 Elasticity of harvested acreage response 0.50 Trade elasticity of crops Central Model Results Total land converted (1000 ha) 5.15 6.76 5.88 3.77 6.26 * Forest land (1000 ha) 1.52 1.81 1.09 1.66 1.71 * Pasture land (1000 ha) 3.63 4.95 4.79 2.11 4.55 SASIAEEX land converted (1000 ha) 1.51 1.66 1.91 0.93 1.62 * Forest land (1000 ha) 1.17 1.31 1.52 0.69 1.27 * Pasture land (1000 ha) 0.34 0.35 0.39 0.24 0.35 SASIAEEX RD/BD Change (million gal) 42.2 LUC carbon Intensity (gCO2/MJ) 12.6 15.2 13.4 9.9 14.4 4.3 Additional Sensitivity Analysis

There are two additional items that should be considered in a detailed estimate of land use change emissions due to palm oil. The first is that over the past 5 years, palm oil in Malaysia has expanded primarily over a combination of logged-over forest and grassland, rather than virgin forest and grassland. We do not have data or statistics on the mix of the types of land converted, so it is difficult for us to take this factor into account. The emissions analysis up to this point has used the GTAP_SOY, which for forest assumes a certain mix of “tropical moist forest” (46.6%), “tropical seasonal forest” (40.2%), and “open forest” (13.2%). It is possible that the logged-over forest issue could be appropriately modeled utilizing a greater fraction of “open forest”, which has a lower carbon content than the other two forests. Thus, if this factor were included, it would likely lower the emission estimates due to land conversion for palm oil.

The second issue is that the Woods Hole emissions do not account for carbon sequestration in the crop that replaces newly converted land. The Woods Hole emissions contained in GTAP include the loss of carbon in above ground biomass, the loss of 25% of the below ground carbon, and foregone sequestration. No credit is given to carbon sequestration for the palm oil plants, either above or belowground. To investigate this further, Neste and AIR examined recent literature on carbon sequestration in palm oil plantations. [8,9,10,11] After land is prepared for palm oil, the seedlings are planted, and have a lifecycle of about 30 years. During the 30-year period, the plants mature, produce fruit, and accumulate substantial carbon in their trunks and root system. At harvest time, fresh fruit bunches (FFB) are harvested, and some of the16

palm fronds are cut and piled nearby. After 25-30 years, as yields decline, the above ground mass is cut down, chipped and used as mulch for the new plantings. The new plantings are generally placed where the palm fronds were piled from the old tree, because the soil in this area has an elevated carbon content due to decomposition from the fronds. When the new seedlings are planted after the first generation crop expires, the ground is not plowed. Thus the root mass from the first generation of plants is left to decompose, and adds to soil carbon content. Most of the above ground carbon eventually is converted to CO2, either through decomposition of the palm fronds or decomposition of the expired trunks. Soil carbon levels in mature palm plants in the Mathews et al study range from 55-81 T/Ha. The Wicke study reports soil carbon level of 40 T/ha. The Syahrinudin study estimates a soil carbon level of 183 T/ha. All of these levels are the levels after 25-30 years. For this analysis, we have used a value in the low end of the range of 55 T/ha (low end of the range for the Mathews et all study). Table 11 develops the impact of the 55 T/ha soil carbon level from palm oil plants on the Woods Hole emissions.

The first row of Table 11 contains the Woods Hole weighting factors for forest. The second row contains the above ground carbon lost upon conversion, in metric tons per hectare. The third row is the total below ground carbon, and the 4throw is 25% of the below ground carbon. GTAP assumes that 25% of the belowground carbon is lost upon conversion. The 5throw is the hardwood product factor (fraction of carbon stored hardwood products), which is set at zero. The sixth row is the carbon lost upon conversion, which is the total of the above ground carbon lost and 25% of the below ground carbon lost. The seventh row is GTAP’s estimate of the annual sequestration lost, and the eighth row is the annual amount multiplied by 30 years. The ninth row is the total carbon lost (sum of direct conversion and lost sequestration), and the tenth row is the total CO2 lost per ha. The eleventh row is the previous row multiplied by the weighting

Parameter Tropical Moist Forest Tropical

Seasonal Forest Open Forest Total Forest Temperate Grassland 1 - Wtg % 46.62 40.25 13.13 100 2 - Above ground C lost T/H 250 150 60 7 3 - Below ground C lost T/H 120 80 50 189 4 - 25% of Below C 30 20 12.5 47.25 5 - HWP factor 0 0 0 0 6 - Direct conversion C T/H 280 170 72.5 54.25 7 - Lost sequestration/year 1.07 0.78 0.36 0 8 - Lost sequestration/30 yrs 32.1 23.4 10.8 0 9 - Total carbon lost T/H 312.1 193.4 83.3 54.25 10 - Total CO2 lost 1142.3 707.8 304.9 198.6 11 - Wtd total CO2 lost 532.5 284.9 40.0 857.5 198.6 1 - Annual C stored by palm 1.83 1.83 1.83 1.83 2 - C stored by palm, 30 yrs 55 55 55 55 3 - Net carbon lost 257.1 138.4 28.3 -0.75 4 - Total CO2 lost 941.0 506.5 103.6 -2.7 5 - Wtd CO2 lost 438.7 203.9 13.6 656.2 -2.7

Table 11. Development of the Impact of Carbon Sequestration by Palm Oil Plants17

factor (first row). The total forest emission factor is 857 metric tons per hectare, and for grassland is 199 metric tons per hectare. The adjustment to these values for carbon stored above ground and below ground is shown in the next set of rows. The second row shows the total carbon stored over 30 years both aboveground and belowground based on the Wicke factors, the first row shows the annual levels dividing the second row by 30. The third row is the total carbon lost in the upper section of the table minus the carbon stored by palm over 30 years. The fourth row shows the total CO2 lost, and the fifth row shows the weighted CO2 lost. Taking the palm oil carbon storage into account, the forest losses are 656.2 metric tons per hectare, and grassland gains -2.7 metric tons per hectare instead of losing 198.6 metric tons per hectare. Impacts of this analysis are shown in Table 12.

Table 12. Impacts of Palm Oil Carbon Sequestration in Soil Shock GTAP Inputs Palm Oil Sequestration? Average Emissions

of Scenarios (g/MJ) 200% CARB No 24.3 500% CARB No 23.3 200% ETA = 0.88 No 13.8 500% ETA = 0.88 No 13.1 200% ETA = 0.88 Yes 10.4 500% ETA = 0.88 Yes 10.0 As shown in Table 12, the emissions for the 200% and 500% shocks with ETA = 0.88 drop to 10.0 and 10.4 g/MJ when the carbon sequestration of the palm oil plants is taken into account. This is a significant factor that should be accounted for. 4.4 Recommended ILUC for Malaysia RD/BD from Palm Oil Overall, we believe the estimates using ETA=0.88 utilize the most recent information developed by Purdue for estimating land use changes. And, we think palm oil carbonsequestration below ground should be included because palm oil plants are semiperennials with long lives, and current practices are to leave all of the root carbon in the ground. The range of the estimates for the 200%-500% shocks is 10.0-10.4 g/MJ. We believe a value in this range is appropriate at this time.18

5.0 Discussion ILUC estimates, even using GTAP, are quite uncertain. This section discusses some of the uncertainties with these estimates.

Forest and Pasture Emission Factors – There is considerable uncertainty over the emissions factors for forest and pasture converted (for both above ground and below ground carbon). The GTAP model relies on emission factors estimated from the Woods Hole data. EPA utilized a different data set, called the Winrock data. It is not known how these two datasets compare for Malaysia and Indonesia. Secondly, there is much evidence that palm oil tends to expand on land that has already been logged over. It is unlikely that this is accounted for in the Woods Hole emission factors for this region. Finally, palm oil has expanded on some peat land, and this land has very high conversion emissions. However, the amount of land converted from peat is thought to be small. Carbon Stored by Palm Fruit Crop– The GTAP model assumes that the crops planted onconverted pasture and forest do not store any carbon underground. Palm fruit is a semiperennial with a fairly long life of about 30 years. These plants would store considerable carbon in the soil, and this is not being accounted for in the Woods Hole emission rates currently being used by CARB. Size of RD/BD Increase Utilizing Palm Oil– The size of the shock appears to affect the magnitude of the emissions released, with large shocks producing somewhat less emissions.

Co-Product Credits– In addition to palm fruit oil, which is utilized to make RD/BD, the palm kernel produces high quality oil that can substitute for coconut oil and other oils used in cosmetics and other products. After the oil has been extracted from the kernel, the remaining kernel (palm kernel expeller) PKE is used for animal feed. The use of palm kernel oil will displace some coconut oil and the palm kernel expeller will displace some animal feed, leading to a land use credit. The palm kernel meal credit is already accounted for in GTAP, but the substitution of coconut oil by PK oil has not yet been incorporated into the model. Input Elasticities – The results show that the land use emissions are sensitive to the input elasticities, particularly ETA. We have accounted for this by using the CARB seven scenarios, augmented by setting ETA = 0.88 based on later data developed by Purdue.19 References 1. “Proposed Regulation to Implement the Low Carbon Fuel Standard” Volume I, Staff Report: Initial Statement of Reasons, March 5, 2009, CARB. 2. There are many references for the GTAP model on the GTAP model website under the “Resources” heading: https://www.gtap.agecon.purdue.edu/resources/tech_papers.asp 3. Please see the following California EPA website for materials relate to the Expert Working Group: http://www.arb.ca.gov/fuels/lcfs/workgroups/ewg/expertworkgroup.htm 4. “Land Use Change Effects for Soy Biodiesel and Renewable Diesel”, CARB,February 2010, found at: http://www.arb.ca.gov/fuels/lcfs/lcfsbackground.htm#modeling 5. Rantanen, Leena; Linnaila, Raimo; Aakko, Päivi; Harju, Tiina, “NExBTL - biodiesel fuel of the second generation”, SAE Society of Automotive Engineers, Powertrain & Fluid Systems Conference, San Antonio, 24 - 27 Oct. 2005, SAE Technical Paper 2005-01-3771. Warrendale (2005), Scania NMEC / 5th International Colloquium Fuels / Jan 12, 2005 6. Pruszko, “Alternative Feedstocks and Biodiesel Production”, presented at the Practical Biodiesel Blueprint Conference, January 23 and 24, 2007, Iowa State University Extension 7. Tyner, Taheripour, Zhuang, Birur, and Baldos, “Land Use Changes and Consequent CO2 Emissions Due to U.S. Corn Ethanol Production: A Comprehensive Analysis”, Department of Agriculture Economics, Purdue University, 2010. 8. Syahrinudin (2005): The potential of oil palm and forest plantations for carbon sequestration on degraded land in Indonesia. - Ecology and Development Series No. 28 9. Wicke, B., Dornburg, V., Junginger, M., and Faaij, A. (2008): Different palm oil production systems for energy purposes and their greenhouse gas implications. – Biomass and Bioenergy 32. 1322–1337 10. Mathews J., Tan T. H., and Chong K. M. (2010): Indication of Soil Carbon Augmentation In Oil Palm Cultivated Inland Mineral Soils of Peninsular Malaysia. – The Planter, Kuala Lumpur, 86 (1010): 293-31320

11. Murdiyarso, D., van Noordwijk, M., Wasrin, U. R., Tomich, T. P. and Gillison, A. N. (2002): Environmental benefits and sustainable land use options in the Jambi transect, Sumatra. Journal of Vegetation Science 13:429–438 (not found from literature sources but is referenced by many others)21 Attachment 1 Analysis of Feedstock Cost in Palm Oil Price The base year in GTAP should be balanced to reflect the proper local cost relationships between crude vegetable oil (Cveg_oil) and biodieesl. This step was accomplished via the following special Closure and Shock inputs: Closure: swap qo("Biodiesel",Reg) = aoall("Biodiesel",Reg); Shock: shock afall("Cveg_oil1","Biodiesel","SASIAEEX") = XXX; The value for the shock (XXX) must be adjusted so that the cost percentage of crude vegetable oil to biodiesel production matches that in the SASIAEEX region. A review of the VDFA array shows that CVeg_Oil is initially 88.9% of biodiesel. An analysis by NESTE of the ratio of palm oil to biodiesel is lower, at about 80%. Using an iterative method, we were able to get the model to obtain an 80% ratio using a shock to afall of 100%.22 Attachment 2 Oilseed Yields in GTAP-SOYv2 for the U.S. and SASIAEEX by AEZ

United States Agricultural Yields (bu/acre) Region CrGrains Oilseeds Sugarcane OthGrains OthAgri AEZ1 0.0 0.0 0.0 0.0 0.0 AEZ2 0.0 0.0 0.0 0.0 0.0 AEZ3 0.0 0.0 0.0 0.0 0.0 AEZ4 0.0 0.0 0.0 0.0 0.0 AEZ5 0.0 0.0 0.0 0.0 0.0 AEZ6 0.0 0.0 0.0 0.0 0.0 AEZ7 116.5 31.4 23.8 41.5 8.3 AEZ8 104.2 29.5 19.7 42.1 10.6 AEZ9 134.2 35.4 18.1 44.1 9.0 AEZ10 151.0 41.0 26.0 64.9 9.3 AEZ11 135.1 36.4 34.2 74.9 6.3 AEZ12 111.9 30.0 30.4 74.3 4.6 AEZ13 46.9 15.5 19.1 31.6 7.4 AEZ14 55.4 25.6 19.8 47.2 7.5 AEZ15 65.2 32.5 0.0 73.9 7.3 AEZ16 0.0 0.0 0.0 116.9 18.7 AEZ17 0.0 0.0 0.0 0.0 0.0 AEZ18 0.0 0.0 0.0 0.0 0.023 SASIAEEX Agricultural Yields (bu/acre) Region CrGrains Oilseeds Sugarcane OthGrains OthAgri AEZ1 0.0 0.0 0.0 0.0 0.0 AEZ2 0.0 0.0 0.0 0.0 0.0 AEZ3 0.0 0.0 0.0 0.0 0.0 AEZ4 43.8 38.1 17.5 56.1 1.6 AEZ5 48.5 36.2 20.4 61.6 1.8 AEZ6 51.3 182.4 25.3 69.5 1.4 AEZ7 0.0 0.0 0.0 0.0 0.0 AEZ8 0.0 0.0 0.0 0.0 0.0 AEZ9 0.0 0.0 0.0 0.0 0.0 AEZ10 50.2 23.6 15.6 46.3 2.1 AEZ11 34.7 11.5 23.3 49.0 1.6 AEZ12 36.0 16.5 22.4 60.8 2.0 AEZ13 0.0 0.0 0.0 0.0 0.0 AEZ14 0.0 0.0 0.0 0.0 0.0 AEZ15 59.1 2.9 0.0 50.7 0.5 AEZ16 39.3 11.1 0.0 47.6 1.9 AEZ17 0.0 0.0 0.0 0.0 0.0 AEZ18 0.0 0.0 0.0 0.0 0.024 Attachment 3 Contribution of Individual Regions to Land Use Changes

And Emissions The following two charts show the distribution of land changes and emissions by region within GTAP for a 200% shock to biodiesel in south Asia, for all seven scenarios using the CARB elasticity inputs that were used in GTAP-SOY by Purdue for CARB (Pass 1 means the CARB elasticity inputs, Pass 2, not shown, refers to the elasticity inputs for ETA in the 2010 Tyner et al paper). The first three charts are changes in crops, pasture and forest. The region with the largest crop change is SASIAEEX. The region with the largest pasture change is SSAEX. The region with the largest forest change is SASIAEEX. The last chart shows the distribution of land use change emission by region. The area with the largest land use change emissions is SASAIEEX, followed by Canada and the EU27. The chart below shows the definitions of the different regions in GTAP. No. Abbreviation Description 1 USA United States 2 CAN Canada 3 EU27 European Union 27 4 BRAZIL Brazil 5 JAPAN Japan 6 CHIHKG China, Hong Kong 7 INDIA India 8 LAEEX Latin AmericanEnergy Exporters 9 RoLAC Rest of LatinAmerica+Caribbean 10 EEFSUEX EE and FSU Energy Exp 11 RoE Rest of Europe 12 MEASTNAEX Middle Eastern N Africa E Exp 13 SSAEX Sub Saharan Energy Exporters 14 RoAFR Rest of North Africa & SSA 15 SASIAEEX South Asian Energy Exporters 16 RoHIA Rest of High Income Asia 17 RoASIA Rest of Southeast & South Asia 18 Oceania Oceania countries25



### G F E D C B A

-1 0 1 2 3 4 5

1000 Hectares USA CAN EU27 BRAZIL JAPAN CHIHKG INDIA LAEEX RoLAC EEFSUEX RoE MEASTNAEX SSAEX RoAFR SASIAEEX RoHIA RoASIA Oceania Total Crop LUC, Pass 1 (200%, CARB Inputs)

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## G F E D C B A

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0

1000 Hectares USA CAN EU27 BRAZIL JAPAN CHIHKG INDIA LAEEX RoLAC EEFSUEX RoE MEASTNAEX SSAEX RoAFR SASIAEEX RoHIA RoASIA Oceania Total Pasture LUC, Pass 1 (200%, CARB Inputs)

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-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5

1000 Hectares USA CAN EU27 BRAZIL JAPAN CHIHKG INDIA LAEEX RoLAC EEFSUEX RoE MEASTNAEX SSAEX RoAFR SASIAEEX RoHIA RoASIA Oceania Total Forest LUC, Pass 1 (200%, CARB Inputs)

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## G F E D C B A

-5 0 5 10 15 20 25 30 35

gCO2e/MJ USA CAN EU27 BRAZIL JAPAN CHIHKG INDIA LAEEX RoLAC EEFSUEX RoE MEASTNAEX SSAEX RoAFR SASIAEEX RoHIA RoASIA Oceania Total LUC Carbon Intensity, Pass 1 (200%, CARB Inputs)

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