December 14, 2011

MEMORANDUM

TO: Air and Radiation Docket EPA-HQ-OAR-2011-0542

SUBJECT: Consideration of Uncertainty Related to Lifecycle Greenhouse Gas (GHG) 

Emissions from Renewable Fuels Produced from Palm Oil

This memorandum provides additional information regarding consideration of uncertainty in our

modeling of the lifecycle GHG emissions associated with the palm oil-based biodiesel and

renewable diesel fuel production pathways analyzed in the Notice of Data Availability (NODA).

Outline of this Document:

I. Introduction

II. Stochastic Analysis of Uncertainty Related to Land Use Change GHG Emissions

III. Uncertainty Regarding the Total Area of Projected Palm Oil Expansion

IV. Uncertainty Regarding Satellite Data and Palm Oil Maps

V. Uncertainty Regarding the Extent of Peat Soil Impact

VI. Uncertainty Regarding Indirect Emissions from Peat Soil Drainage

VII. Conclusions Based on Consideration of Uncertainties

VIII. References
I. Introduction

As discussed in the NODA preamble, because of the inherent uncertainty and the state of

evolving science regarding lifecycle analysis 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 currently available (EPA 2011). For these two pathways the evidence 

considered includes the mid-point estimate as well as the range of results based on statistical 

uncertainty and sensitivity analyses conducted by 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 analyzed. 

As part of our assessment of the two palm oil biofuel pathways we have identified key areas of 

uncertainty in our analysis. Although there is uncertainty in all portions of the lifecycle 

modeling, we focused our uncertainty analysis 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 analysis with the highest level of uncertainty. 

For example, identifying what type 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 those impacts in three ways: (1) getting 

the best information possible and updating our analysis to narrow the uncertainty, (2) performing 

sensitivity analysis 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 discusses key areas of uncertainty that we have 

identified in our analysis and considers 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 uncertainty in a number of ways, including 

using the best available information and conducting statistical uncertainty analysis related to key

areas of our analysis. Uncertainty analysis in the RFS2 rule focused on two aspects of indirect 

land use change – the types of land converted and the GHG emissions associated with different 

types of land use change. In particular, our uncertainty analysis 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 expected from a change in land use in each geographic area of 

the world modeled. These areas of uncertainty were estimated using Monte Carlo analysis.1 The 

Monte Carlo analysis is described in Section 2.4.4 of the Regulatory Impact Analysis (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 analysis 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 2.4.4.2.8
account for all of the improvements that were made in our analysis of Indonesia and Malaysia. 

For example, the uncertainty assessment does not consider our more detailed spatial modeling of 

Indonesia and Malaysia using the GEOMOD model. These changes were not incorporated 

because our Monte Carlo analysis was not designed to estimate uncertainty in results from the 

GEOMOD model. As such, the high- and low-end of the range of results for Indonesia and 

Malaysia are based on uncertainty in the land use change methodology used in the RFS2 rule. 

We believe this approach provides a reasonable estimate of the range of uncertainty in land use 

change GHG emissions in the scenarios analyzed.

For the scenarios analyzed our mean estimate of land use change emissions is 2.31 MMT 

CO2e/yr over 30 yrs (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 CO2e per year and the high 

end of the 95% confidence range is 3.31 MMT CO2e per year. As discussed in the NODA 

preamble, this range of results is considered in our assessment of threshold determinations for 

palm oil biodiesel and renewable diesel. As discussed below, we also consider other areas of 

uncertainty that are not fully captured in our Monte Carlo analysis of land use change 

uncertainty. The docket includes additional information about our analysis of land use change 

GHG emissions associated with the production and use of palm oil biofuels, including detailed 

information on modeling assumptions, calculations and results.

III. Uncertainty Regarding the Total Area of Projected Palm Oil Expansion

As discussed in Section II.A.3 of the NODA preamble, we analyzed 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 by marketmediated 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 would take 86 to 423 years to pay back. Similarly, Gibbs et al. found 

that carbon payback times for biofuels derived from palm oil grown on tropical peat swamp 

forest range from 587 to 918 years depending on crop yields. For comparison, EPA’s mid-point 

estimates correlate with carbon payback periods of less than 30 years. These studies differ from 

EPA’s analysis because they only look only at direct land use change impacts, whereas our 

assessment also considers indirect effects. In this case considering indirect 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 incremental palm oil volume is produced by new plantations on 

previously forested land the lifecycle GHG emissions for these pathways would be larger.

Another area of uncertainty related to the total amount of projected palm oil expansion is linked 

to the area of palm oil in the baseline scenario. The established control case used in our 
modeling 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 hectares 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 two 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 analysis would change if the baseline were revised to include a greater area of 

palm oil. If 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 analysis methodology in RFS2 final rule, our 

analysis of land use change GHG emissions looks at the impacts associated with incremental 

expansion in harvested crop area in the scenarios analyzed. Typically 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 

ratio 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, Malaysia).

Projecting 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 yields. 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 Malaysia where 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. Currently, 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 

economically 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 

analysis.

IV. Uncertainty Regarding Satellite Data and Palm Oil Maps

As discussed in Section II.A.4 of the NODA preamble, our land use change analysis considers 

maps depicting the location of palm oil plantations in Indonesia and Malaysia over the last 

decade. The maps used in our analysis were derived from satellite imagery. Remote sensing 

analysis 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. Another 
challenge is that many of the land cover types 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 eliminate classification errors

(IAI). The palm oil maps used in our analysis 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 abandonment 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 uncertainty in our modeling, we assumed that areas classified as palm oil 

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 analysis 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 

analysis (IAI). This is an area of uncertainty in our analysis and would change our results if 

palm oil expansion in Sulawesi and Papua causes different types 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 scarcely any peat soil area, so additional conversion in Sulawesi would likely 

results in fewer emissions than we modeled. We acknowledge that the exclusion 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 

dramatically if they were both included.

V. Uncertainty Regarding the 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 analyzed by EPA. Based on our analysis the 

percent of palm oil planted 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 in 2022. 

According to our projections 11.5% of incremental palm oil expansion in Indonesia and 

Malaysia 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 modeling for Indonesia and 

Malaysia. We believe that our modeling uses the best available data but we acknowledge the 

inherent uncertainty 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 analysis, 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 

pathways by approximately 2% compared to the statutory diesel baseline.

Another area of uncertainty which could 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 a 
strong 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 policy change. 

Thus, instead of projecting uncertain policy outcomes our assessment relies on historical data.

VI. Uncertainty Regarding Indirect Emissions from Peat Soil Drainage

Our analysis considers emissions related to draining peat swamp forests prior to the 

establishment of palm oil plantations. Based on Hooijer et al. (2011), our analysis assumes that 

average emissions from peat soil drainage are 95 tCO2e/ha/yr over a 30-year time period. This 

is supported by Page et al., who reviewed studies of carbon 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 CO2

emissions should be represented by the minimum and maximum values of 54 to 115 tCO2e/ha/yr 

for typical drainage depths associated with palm oil cultivation. Based on sensitivity analysis, 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 to palm oil plantations. Hooijer et al. 

estimate additional carbon loss of 33 tCO2e/ha/yr 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 emissions estimates for these lands are new and, as yet, poorly 

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 

if 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 our 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 ecosystem as a result of drainage (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. any changes in 

methane fluxes are likely to be quite small compared to CO2 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 included in our analysis 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 analysis. If included these sources would 

increase lifecycle GHG emissions related to the two palm oil biofuel pathways analyzed.

VIII. Conclusions Based on Consideration of Uncertainties

Based on the weight of evidence considered, and putting the most weight on our best estimate 

results, the results of our analysis 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 midpoint estimates, our statistical 

assessment of land use change uncertainty, as well as our consideration of other areas of 

uncertainty. A majority 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 uncertainty appear to be fairly likely to result in greater actual emissions 

and in some cases by a substantial amount. In comparison, we identified a smaller number of 

uncertainties which could result in less actual emissions, but these factors appear less likely to 

reduce emissions 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, Malaysia. Table 1.2 Area Under Oil Palm Mature and Immature. 

MPOB Website, http://econ.mpob.gov.my/economy/annual/stat2009/Area1_2.pdf. Accessed 

December 2011

Fargione, 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 payback times for cropbased biofuel expansion in the tropics: the effects of 

changing yield 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. Winrock International. Draft report submitted to EPA.

Harris, N., and Grimland, S. 2011b. Spatial Modeling of Future Oil Palm Expansion in Malaysia, 

2003 to 2022. Winrock International. Draft report submitted to EPA.

Hooijer, A., Page, S. E., Jauhiainen, J., Lee, W. A., Idris, 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 Incorporated (IAI). 2011. High Resolution Land Use Change Analysis for 

Sumatra and Kalimantan Circa 2000. Report to EPA. BPA-09-03. April 8, 2011.
Malaysian 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 plantations in Southeast Asia (ICCT White 

Paper 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). 2011a. Malaysia: 

Obstacles May Reduce Future Palm Oil Production Growth. June 28, 2011. 

http://www.pecad.fas.usda.gov/highlights/2011/06/Malaysia/

U.S. Environmental Protection Agency (EPA). 2011. Notice of Data Availability 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 Analysis. EPA-420-R-10-006. 

http://www.epa.gov/oms/renewablefuels/420r10006.pdf