December 1 4 , 2011

MEMORANDUM
TO: Air and Radiation Docket EPA

- HQ

- OAR

- 2011

- 0542
SUBJECT: Cons ideration of Uncertainty Related to Lifecycle Greenhouse Gas (GHG) Emissions from Renewab le Fuels Produced from Palm Oil This memorandum provides addi tional information regarding consideration of un certainty in our modeling of the lifecycle GHG emissions associated with the palm oil

- based biodiesel and renewable diesel fuel produ ction pathway s anal yz ed in the Notice of Data Availability (NODA). Outline of thi s Document:
I. Introduction II. Stochastic Analy sis of Uncertaint y Related to Land Use Change GHG Emissions III. Uncertaint y Regarding the Total Area of Projected Palm Oil Expansion IV

. Uncertaint y Regarding Satellite Data and Palm Oil Maps V. Uncertaint y Regarding the Exte nt of Peat Soil Impact VI

. Uncertaint y Regarding Indirect Emissions from Peat Soil Drainage VII. Conclusions Based on Consideration of Uncertainties VIII. References**I. Introduction**

As discussed in the NODA preamble , b ecause of the inherent uncertaint y and the state of evolving science regarding lifecycle anal ys is of biofuels, any threshold determinations that EPA makes for palm oil biodiesel and renewable diesel will be based on an approach that considers the weight of evidence currentl y available (EPA 2011) . For thes e two pathway s the ev idence considered includes the mid

- point estimate as well as the range of results based on statistical uncertaint y and sensitivity anal ys es conducted b y the Agency . EPA will weigh all of the evidence available to it, while placing the greatest weight on the mid

- point estimate value for the scenarios anal yz ed. As part of our assessment of the two palm oil biofuel pathway s we have identified key areas of uncertaint y in our anal ys is. Although there is uncertainty in all portions of th e lifecycle modeling, we focused our uncertainty anal ys is on the factors that are the most uncertain and have the biggest impact on the results. For example, the energy and GHG emissions used by a natural gas - fired biodiesel plant to produce one gallon of biodiesel can be calculated through direct observations, though this will vary somewhat between individual facilities. The indirect, international emissions are the component of our anal ys is with the highest level of uncertaint y. For example, identifying what ty pe of land is converted internationally and the emissions associated with this land conversion are critical issues that have a large impact on the GHG emissions estimates. Thus , we focused our efforts on the international indirect land use change emissions and worked to manage the uncertainty around th ose impacts in three way s: (1) g etting the best information possible and updating our analy sis to narrow the uncertainty , (2) performing sensitivity

anal ys is around key

factors to test the impact on the results, and (3) establishing reasonable ranges of uncertainty and using probability

distributions within these ranges in threshold assessment. This reference document discuss es key areas of uncertainty

that we have identified in our anal ys is and consi der s their implications for making threshold determinations.
**II. Stochastic Analysis of Uncertainty Related to Land Use Change GHG Emissions**  In the RFS2 final rule (EPA 2010) we addressed uncertaint y in a number of way s, including using the best available information and conducting statistical uncertaint y anal ys is related to key areas of our anal ys is. Uncertaint y analysis in the RFS2 rule focused on two aspects of indirect land use change

– the t yp es of land converted and the GHG emissions associ ated with different ty pes of land use change. In particular, our uncertainty anal ys is focused on such specific sources of information as the satellite imaging used to inform our assessment of land use trends and the specific changes in carbon storage expe cted from a change in land use in each geographic area of the world modeled. These areas of uncertainty were estimated using Monte Carlo anal ys is. 1 The Monte Carlo anal ys is is described in Section 2.4.4 of the Regulatory Impact Analy sis (EPA 2010a) for the RFS2 final rule. We have used the same methodology to quantify

the uncertainty in the land use change GHG emissions related to the production and use of palm oil biofuels. The uncertainty

assessment for the RFS2 rule was updated for our palm oil ana ly sis by incorporating the new estimates for palm oil carbon stocks, forest carbon stocks and peat soils. Although these data updates have been incorporated, our uncertainty assessment does not

1 The Monte Carlo analysis is described in EPA (2010a), Section 2.4.4.2.8account for all of the improvements that were made in our analy sis of Indonesia and Malay sia.

For example, the uncertainty assessment does not consider our more detailed spatial modeling of Indonesia and Malay sia using the GEOMOD model. These changes were not incorporated because our Monte Carlo anal ys is was not d esigned to estimate uncertaint y in results from the GEOMOD model. As such, the high

- and low

- end of the range of results for Indonesia and Malay sia are based on uncertaint y in the land use change methodology used in the RFS2 rule. We believe this approac h provides a reasonable estimate of the range of uncertaint y in land use change GHG emissi ons in the scenarios anal yz ed. For the scenarios anal yz ed our mean estimate of land use change emissions is 2.31 MMT CO 2 e/y r over 30 yr s (See Table II

- 7 in the NODA preamble ). Based on our Monte Carlo assessment, the low end of the 95% confidence interval is 1.39 MMT CO 2 e per ye ar and the high end of the 95% confidence range is 3.31 MMT CO 2 e per year. As discussed in the NODA preamble , this range of results is consi dered in our assessment of threshold determinations for palm oil biodiesel and renewable diesel. As discussed below , we also consider other areas of uncertaint y that are not fully captured in our Monte Carlo anal ys is of land use change uncertaint y. The d ocket includes additional information about our anal ys is of land use change GHG emissions associated with the production and use of palm oil biofuels, including detailed information on modeling assumpti ons, calculations and results.
**III. Uncertainty**  **R egar**  **ding the Total A**  **rea of P**  **rojected P**  **alm O**  **il E**  **xpansion** As discussed in Section II.A.3 of the NODA preamble , we anal yz ed a scenario with roughly 400 million gallons of additional palm oil

- based biofuel production in 2022. In the modeled scenarios a substantial portion of the palm oil used for additional biofuel production in the palm biofuel case comes from palm oil volumes that are used for other purposes in the control case. As such, in our modeling the incremental area of palm oil expansion is moderated b y market

mediated responses, such as price

- induced yield effects and demand substitution toward the use of other vegetable oils. Other studies show that without market

- mediated responses, the land use change GHG emissions associated with palm oil biofuel production could be much higher. Fargione et al. calculated that the carbon debt related to biodiesel produced from palm oil grown on tropical rainforest or tropical peat swamp forest wou ld take 86 to 423 years to pay back. Similarly , Gibbs et al. found that carbon pay

back times for biofuels derived from palm oil grown on tropical peat swamp forest range from 587 to 918 y ears depending on crop yields. For comparison, EPA’s mid

- point esti mates correlate with carbon pay back periods of less than 30 y ears. These studies differ from EPA’s anal ys is because they onl y look onl y at direct land use change impacts, whereas our assessment also considers indirect effects. In this case considering in direct impacts reduces the amount of land use change emissions allocated to palm oil biofuels. This is because we project that a substantial portion of palm oil used for biofuel would be sourced from existing plantations. If a larger share of the increme ntal palm oil volume is produced by new plantati ons on previously

forested land the lifecycle GHG emissions for these pathway s would be larger. Another area of uncertainty related to the total amount of projected palm oil expansion is linked to the area o f palm oil in the baseline scenario. The established control case used in ourmodeling appears to be somewhat conservative in terms of palm oil expansion rates in Indonesia.

USDA staff estimate that mature palm oil area in Indonesia will be 7.2 million h ectares in 2012 (USDA

- FAS). For comparison, the control case used in our modeling projects that harvested palm oil area in Indonesia will be 6.2 million hectares in 2022. As discussed above, our modeling is focused on the incremental expansion between tw o scenarios, which minimizes to some extent the importance of the total area of palm oil in the baseline scenario. It is difficult to know how our analy sis would change if the baseline were revised to include a greater area of palm oil. I f the FAPRI

- CARD model were recalibrated to project a greater area of palm oil expansion in Indonesia in the baseline, it is possible that the model would also project more incremental expansion in the policy scenario. A final area of uncertainty regarding total palm oil area is related to accounting for harvested versus planted area. Following the lifecycle anal ysis methodology in RFS2 final rule, our anal ys is of land use change GHG emissions looks at the impacts associated with incremental expansion in harvested crop ar ea in the scenarios analyzed. Ty pically palm oil is harvested for the first time 3

- 5 years after planting, followed by approximately

20

- 25 years of annual harvesting before the cycle is repeated (Unnasch et al.) . This implies that in a steady state the r atio of immature (non

- harvested) area to harvested area would be about 12

- 25%. Data published by

MPOB shows that on average the ratio of immature to harvested area was 15% during the period from 1990 to 2009 (Department of Statistics, Malay sia)

. Projecti ng the amount of palm oil area that would be immature in 2022 depends on several factors such as expansion and replanting rates which can vary over time and by

geographic region. For example, high palm oil prices may induce growers to continue harvesting their old plantations despite decreasing yi elds. This is because growers do not want to miss selling palm oil during a period of high prices while they are waiting for their replanted crops to mature. In fact, this is the current situation in Malay sia whe re many growers have delayed replanting to take advantage of high palm oil prices (USDA

- FAS 2011a) . Furthermore, replanting rates could change based on technological developments. Currentl y, palm oil is replanted when it reaches 25 feet in height due to the length of the long sickle poles often used for harvesting (Unnasch et al.) . The development of new clonal varieties and harvesting techniques could increase the economicall y viable lifetime of palm oil plantations, and thus reduce the ratio of immature to harvested area. Accounting for the land use changes associated with expansion of immature as well as harvested areas of palm oil would be an additional source of land use change GHG emissions in our anal ys is

.

**IV. Uncertainty**  **R egarding**  **S atellite**  **D ata and**  **P alm**  **O il**  **Maps** As discussed in Section II.A.4 of the NODA preamble , our land use change anal ys is considers maps depicting the location of palm oil plantations in Indonesia and Malaysia over the last decade. The maps used in our anal ys is were derived from satellite imagery. Remote sensing anal ys is in this region is complicated by several factors. Perhaps the largest problem is related to cloud cover issues which obscure the satellite images and lead to classification errors. Anotherchalleng e is that man y of the land cover t yp es in this region, such as palm oil, natural forest,

timber forest, coconut and rubber plantations all look quite similar from outer space. Thus, even with high resolution data and intensive efforts it is difficult to e liminate classification errors (IAI) . The palm oil maps used in our anal ys is appear to have some anomalies, such as disappearance of plantations that are classified in the first time period but not in the second time period. Planting cycles and land aban donment may explain some of this effect, but given the rapid expansion in palm oil area it is likely that classification errors are also a contributing factor. In order to reduce the uncertaint y in our modeling, we assumed that areas classified as palm oi l in the first time period were also palm oil in the second time period (Harris et al. 2011a and 2011b)

. Another limitation in modeling for Indonesia, as discussed in Section II.4.A of the NODA preamble , is that our analy sis is constrained to the islands of Sumatra and Kalimantan. In order to focus our mapping efforts, the more remote islands of Sulawesi and Papua were left out of our anal ys is (IAI ) . This is an area of uncertaint y in our anal ys is and would change our results if palm oil expansion in Sulawesi and Papua causes different t ypes of land use change with different amounts of GHG emissions than the expansion in Sumatra and Kalimantan. Papua has extensive amounts of tropical peat swamp forests, so additional conversion in Papua may cause greater emissions than the land use change we modeled (Harris et al. 2011a) . On the other hand Sulawesi has scarcel y any peat soil area, so additional conversion in Sulawesi would likely results in fewer emissions than we modeled. We acknowledge that the exclusi on of Papua and Sulawesi in our modeling is a source of uncertainty , but given the countervailing impacts that they would have on our modeling, we doubt that our threshold determinations would change dramaticall y if they were both included. **V. Uncertainty R**  **egarding t he Extent of Peat Soil Impact**  Table II

- 6 in the NODA preamble shows the percent of palm oil plantations planted on peat soils in recent history

and as projected in the scenarios anal yz ed b y EPA. Based on our anal ys is the percent of palm oil p lanted on peat soil is projected to decrease in 2022 as compared to 2009. Furthermore, in the scenarios modeled the percent of palm oil on peat soil is projected to decrease for the incremental expansion in 2022 as compared to the total area of palm oil i n 2022. According to our projections 11.5% of incremental palm oil expansion in Indonesia and Malay sia in 2022 would be planted on peat soils. As discussed in Section II.A.4. of the NODA preamble , these results are based on our updated land use change mo deling for Indonesia and Malay sia. We believe that our modeli ng uses the best available data but we acknowledge the inherent uncertaint y in making complex projections such as this. In comparison with the historic data our projections appear to be conservative regarding the percent of palm oil plantations planted on peat soils. Based on our anal ys is, every 1% increase in the percent of palm oil planted on peat soil would increase lifecycle GHG emissions for each of the two palm oil biofuel pathway s by

approximately

2% compared to the statutory diesel baseline.
Another area of uncertainty which c ould impact the extent of peat soil drainage is the possibility of new government or industry policies to protect peat swamp forests. An effective ban on converting peat swamp forests to palm oil could reduce actual lifecycle GHG emissions substantially , especially

if the alternative was to develop non

- forested lands. We do not have astrong technical basis for projecting which policies will be implemented over the study time period, nor do we have a strong basis for determining the outcomes of such as po licy change. Thus, instead of projecting uncertain policy outcomes our assessment relies on historical data.
**VI. Uncertainty**  **R egarding**  **I ndirect**  **E missions from**  **P eat**  **S oil**  **D rainage**  Our anal ys is considers emissions related to draining peat swamp forests prio r to the establis hment of palm oil plantations. Based on Hooijer et al. (2011), our analy sis assumes that average emissions from peat soil drainage are 95 tCO2e/ha/y r over a 30

- ye ar time period. This is supported by

Page et al., who reviewed studies of c arbon emissions from peat drainage and concluded that this is the most robust estimate of emissions over a 30

- year period

. Based on uncertainties in the available data Page et al. also suggest that the likely peat CO 2 emissions should be represented b y th e minimum and maximum values of 54 to 115 tCO2e/ha/y r for ty pical drainage depths associated with palm oil cultivation. Based on sensitivity anal ys is, if we use the low end of the range for peat soil emissions, biodiesel and renewable diesel produced from palm oil have estimated lifecycle GHG emission reductions of 27 % and 21 % respectively for these biofuels compared to the statutory baseline petroleum

- based diesel fuel used in the RFS program . If we use the high end of the range for peat soil emissions, palm oil

- based biodiesel and renewable diesel have reductions of 12% and 6% respectively compared to the baseline. Page et al. also discuss other potential sources of emissions related to peat soil drainage that are not explicitly

included in their recommended peat soil emissions factor. One of the factors not included in their recommended emissions factors are peat soil oxidation emissions related to the lowering of water tables in peat swamp forests adjacent t o palm oi l plantations. Hooijer et al. estimate additional carbon loss of 33 tCO 2 e/ha/y r from drainage

- affected forests in the areas adjacent to the palm oil plantations. We do not include emissions for land adjacent to palm plantations because the emiss ions estimates for these lands are new and, as yet, poorl y constrained. Further, applying these estimates would require knowledge of the exact shape of future plantations in order to project the extent of land affected; this area would be very different i f the land were planted in one large plantation compared with many small, dispersed plantations. Accounting for emissions associated with oxidation in adjacent, drainage

- affected forests would add an additional source of land use change GHG emissions to o ur estimates. Our estimates also do not consider that degraded tropical peat lands are at a greater risk of forest fires which can spread and lead to massive amounts of GHG emissions (Couwenberg et al) . For lack of data , our estimate of peat soil emissions also does not account for dissolved organic carbon (DOC) and particulate organic carbon (POC), which represent an additional smaller amount of carbon that is leached out of the peat swamp ecos ys tem as a result of draina ge (Page et al.) . Due to uncertainties we also do not account for changes in methane or nitrous oxide fluxes related to draining peat swamp forest. 2 According to Couwenberg et al. an y changes in methane fluxes are likel y to be quite small compared to CO 2 fluxes. Nitrous oxide emissions related to fertilizer use is included in our assessments, but nitrous oxide fluxes related to peat soil drainage are not included. Such fluxes could be a significant source of additional emissions but they are not include d in our anal ys is because they tend to be erratic and difficult to link to any

2 We do account for nitrous oxide emissions related to fertilizer applications for palm oil cultivation.single factor (Page et al.) . In sum, there are several potential sources of indirect emissions from

peat soil drainage that are not factored into our analy sis. If included the se sources would increase lifecycle GHG emissions related to the two palm oil biofuel pathway s anal yz ed.
**VIII. Conclusions Based on C onsideration of**  **U ncertainties**  Based on the weight of evidence considered, and putting the most weight on our best estimat e results, the results of our anal ys is indicates that both palm oil based biofuels would fail to qualify as meeting the minimum 20% GHG performance threshold for conventional renewable fuel under the RFS program. This conclusion is supported by our midpoi nt estimates, our statistical assessment of land use change uncertaint y, as well as our consideration of other areas of uncertaint y. A majorit y of the areas of uncertainty that we have identified, and discussed above, would lead to higher actual lifecycle GHG emissions than estimated in our midpoint results. Some of these areas of uncertaint y appear to be fairly likel y to result in greater actual emissions and in some cases b y a substantial amount. In comparison, we identified a smaller number of uncerta inties which could result in less actual emissions, but these factors appear less likel y to reduce emis sions by

an equivalent amount.
**IX. References** Couwenberg, J., Dommain, R., Joosten, H. 2010. Greenhouse gas fluxes from tropical peatlands in south

- east Asia. Global Change Biology

. 16: 1715

- 1732. Department of Statistics, Malay sia. Table 1.2 Area Under Oil Palm Mature and Immature. MPOB Website, http://econ.mpob.gov.m y/ economy /annual/stat2009/Area1_2.pdf. Accessed December 2011 Fargion e, J., Hill, J., Tilman, D., Polasky , S., Hawthorne, P. 2008. Land Clearing and the Biofuel Carbon Debt. Science

. 319 (5867), 1235

– 1238. Gibbs, H. K., Johnston, M., Foley , J. A., Holloway , T., Monfreda, C., Ramankutty , N., & Zaks, D. (2008). Carbon pay

bac k times for cropbased biofuel expansion in the tropics: the effects of changing yi eld and technology

. Environment Research Letters , 3, 1

- 10. Harris, N., and Grimland, S. 2011a. Spatial Modeling of Future Oil Palm Expansion in Indonesia, 2000 to 2022. Wi nrock International. Draft report submitted to EPA. Harris, N., and Grimland, S. 2011b. Spatial Modeling of Future Oil Palm Expansion in Malay sia, 2003 to 2022. Winrock International. Draft report submitted to EPA. Hooijer, A., Page, S. E., Jauhiainen, J., L ee, W. A., I dris, A., & Anshari, G. 2011. Subsidence and carbon loss in drained tropical peatlands: reducing uncertainty and implications for CO2 emission reduction options. Biogeosciences Discussions , 8, 9311

- 9356 Integrity Applications In corporated ( IA I ) . 2011. High Resolution Land Use Change Anal ys is for Sumatra and Kalimantan Circa 2000. Report to EPA. BPA

- 09

- 03. April 8, 2011.Malay sian Palm Oil Board (MPOB) Website. Table 1.2 Area Under Oil Palm Mature and

Immature. http://mpob.gov.my . Accessed October 2010 Page, S. E., Morrison, R., Malins, C., Hooijer, A., Rieley , J. O. & Jauhiainen, J. 2011. Review of peat surface greenhouse gas emissions from oil palm plan tations in Southeast Asia (I CCT White P aper 15). Washington: International Council on Clean Transportation. U.S. Department of Agriculture Foreign Agricultural Service (USDA

- FAS). 2011. Indonesia Oilseeds and Products Update. August 13, 2011. GAIN Report 1133 U.S. Department of Agriculture Foreign Agricultural Service (USDA

- FAS). 2011 a . Malay sia: Obstacles May

Reduce Future Palm Oil Production Growth. June 28, 2011. http://www.pecad.fas.usda.gov/highlights/2011/06/Malay sia/ U.S. Environmental Protection Agency (EPA). 2011. Notice of Data Av ailability Concerning Renewable Fuels Produced from Palm Oil Under the RFS Program

. U.S. Environmental Protection Agency (EPA). 2010. Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program; Final Rule. 75 FR 14670. March 26, 2010. U.S. Environmental Protection Agency (EPA). 2010a. Renewable Fuel Standard Program (RFS2) Regulatory

Impact Anal ys is. EPA

- 420

- R

- 10

- 006. http://www.epa.gov/oms/renewablefue ls/420r10006.pdf