4300 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

What information collection activity or 

ICR does this apply to? 

Docket ID No. EPA–HQ–OA–2012– 

0033. 

Affected entities: Entities potentially 

affected by this action are members of 

the general public who may be 

contacted to participate in the study. 

Title: Willingness to Pay for Improved 

Water Quality in the Chesapeake Bay. 

ICR numbers: EPA ICR No. 2456.01, 

OMB Control No. 2012–new. 

ICR status: This ICR is for a new 

information collection activity. An 

Agency may not conduct or sponsor, 

and a person is not required to respond 

to, a collection of information, unless it 

displays a currently valid OMB control 

number. The OMB control numbers for 

EPA’s regulations in title 40 of the CFR, 

after appearing in the Federal Register 

when approved, are listed in 40 CFR 

part 9, are displayed either by 

publication in the Federal Register or 

by other appropriate means, such as on 

the related collection instrument or 

form, if applicable. The display of OMB 

control numbers in certain EPA 

regulations is consolidated in 40 CFR 

part 9. 

Abstract: On May 12, 2009 the 

President signed Executive Order 13508 

calling for the protection and restoration 

of the Chesapeake Bay. In response to 

the Executive Order and other 

considerations the Environmental 

Protection Agency established Total 

Maximum Daily Loads (TMDLs) of 

nitrogen, phosphorus, and sediment for 

the Chesapeake Bay. These TMDLs 

called for reductions of 25, 24, and 20%, 

respectively, of these pollutants (EPA 

2011). 

The Chesapeake Bay watershed 

encompasses 64,000 square miles in 

parts of six states and the District of 

Columbia. While efforts have been 

underway to restore the Bay for more 

than 25 years, and significant progress 

has been made over that period, the 

TMDLs are necessary to continue 

progress toward the goal of a healthy 

Bay. As might be expected, a program 

on this scale is likely to be expensive. 

A 2004 report on implementation of the 

‘‘tributary strategies’’ proposed under an 

earlier plan for Bay restoration 

estimated their cost at $28 billion in 

capital costs plus an additional $2.7 

billion dollars per year in perpetuity for 

operating and maintenance costs (Blue 

Ribbon Panel 2004). The watershed 

states of New York, Pennsylvania, 

Delaware, West Virginia, Virginia, and 

Maryland, as well as the District of 

Columbia, have developed Watershed 

Implementation Plans (WIPs) detailing 

the steps each will take to meet its 

obligations under the TMDLs. EPA has 

begun a new study to estimate costs of 

compliance with the TMDLs. While 

these costs may prove high, a multitude 

of benefits may also be anticipated to 

arise from restoring the Chesapeake Bay. 

It is important to put cost estimates in 

perspective by estimating corresponding 

benefits. 

EPA’s National Center for 

Environmental Economics (NCEE) is 

undertaking a benefits analysis of 

improvements in Bay water quality 

under the TMDLs, as well as of ancillary 

benefits that might arise from terrestrial 

measures taken to improve water 

quality. As part of this analysis, NCEE 

plans to conduct a broad-based inquiry 

into benefits using a state-of-the-art 

stated preference survey. Benefits from 

the TMDLs for the Chesapeake will 

accrue to those who live on or near the 

Bay and its tributaries, as well as to 

those who live further away and may 

never visit the Bay but have a general 

concern for the environment. The latter 

category of benefits is typically called 

‘‘non-use values’’ and estimating the 

monetary value can only be achieved 

through a stated preference survey. 

In addition, a stated preference survey 

is able to estimate ‘‘use values,’’ those 

benefits that accrue to individuals who 

choose to live on or near the Bay or 

recreate in the watershed. Stated 

preference surveys allow the analyst to 

define a specific object of choice or suite 

of choices such that benefits are defined 

in as precise a manner as feasible. While 

use benefits of water quality 

improvements in the Chesapeake Bay 

watershed will also be estimated 

through other revealed preference 

methods, the stated preference survey 

allows for careful specification of the 

choice scenarios and will complement 

estimates found using other methods. 

Participation in the survey will be 

voluntary and the identity of the 

participants will be kept confidential. 

Burden Statement: The annual public 

reporting and recordkeeping burden for 

this collection of information is 

estimated to average 0.5 hours per 

response. Burden means the total time, 

effort, or financial resources expended 

by persons to generate, maintain, retain, 

or disclose or provide information to or 

for a Federal agency. This includes the 

time needed to review instructions; 

develop, acquire, install, and utilize 

technology and systems for the purposes 

of collecting, validating, and verifying 

information, processing and 

maintaining information, and disclosing 

and providing information; adjust the 

existing ways to comply with any 

previously applicable instructions and 

requirements which have subsequently 

changed; train personnel to be able to 

respond to a collection of information; 

search data sources; complete and 

review the collection of information; 

and transmit or otherwise disclose the 

information. 

The ICR provides a detailed 

explanation of the Agency’s estimate, 

which is only briefly summarized here: 

Estimated total number of potential 

respondents: 1,500. 

Frequency of response: once. 

Estimated total average number of 

responses for each respondent: 1. 

Estimated total annual burden hours: 

750 hours. 

Estimated total annual costs: $15,975. 

This includes estimated respondent 

burden costs only as there are no capital 

costs or operating and maintenance 

costs associated with this collection of 

information. 

What is the next step in the process for 

this ICR? 

EPA will consider the comments 

received and amend the ICR as 

appropriate. The final ICR package will 

then be submitted to OMB for review 

and approval pursuant to 5 CFR 

1320.12. At that time, EPA will issue 

another Federal Register notice 

pursuant to 5 CFR 1320.5(a)(1)(iv) to 

announce the submission of the ICR to 

OMB and the opportunity to submit 

additional comments to OMB. If you 

have any questions about this ICR or the 

approval process, please contact the 

technical person listed under FOR

FURTHER INFORMATION CONTACT. 

Dated: January 18, 2012. 

Al McGartland, 

Office Director, National Center for 

Environmental Economics. 

[FR Doc. 2012–1809 Filed 1–26–12; 8:45 am] 

BILLING CODE 6560–50–P 

ENVIRONMENTAL PROTECTION 

AGENCY 

[EPA–HQ–OAR–2011–0542; FRL–9608–8] 

Notice of Data Availability Concerning 

Renewable Fuels Produced From Palm 

Oil Under the RFS Program 

AGENCY: Environmental Protection 

Agency (EPA). 

ACTION: Notice of data availability 

(NODA). 

SUMMARY: This Notice provides an 

opportunity to comment on EPA’s 

analyses of palm oil used as a feedstock 

to produce biodiesel and renewable 

diesel under the Renewable Fuel 

Standard (RFS) program. EPA’s analysis 

of the two types of biofuel shows that 


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Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 4301 

biodiesel and renewable diesel 

produced from palm oil have estimated 

lifecycle greenhouse gas (GHG) emission 

reductions of 17% and 11% respectively 

for these biofuels compared to the 

statutory baseline petroleum-based 

diesel fuel used in the RFS program. 

This analysis indicates that both palm 

oil-based biofuels would fail to qualify 

as meeting the minimum 20% GHG 

performance threshold for renewable 

fuel under the RFS program. 

DATES: Comments must be received on 

or before February 27, 2012. 

ADDRESSES: Submit your comments, 

identified by Docket ID No. EPA–HQ– 

OAR–2011–0542, by one of the 

following methods: 

• www.regulations.gov: Follow the 

on-line instructions for submitting 

comments. 

• Email: asdinfo@epa.gov. 

• Mail: Air and Radiation Docket and 

Information Center, Environmental 

Protection Agency, Mailcode: 2822T, 

1200 Pennsylvania Ave. NW., 

Washington, DC 20460. 

• Hand Delivery: Air and Radiation 

Docket and Information Center, EPA/ 

DC, EPA West, Room 3334, 1301 

Constitution Ave. NW., Washington DC 

20004. Such deliveries are only 

accepted during the Docket’s normal 

hours of operation, and special 

arrangements should be made for 

deliveries of boxed information. 

Instructions: Direct your comments to 

Docket ID No. EPA–HQ–OAR–2011– 

0542. EPA’s policy is that all comments 

received will be included in the public 

docket without change and may be 

made available online at 

www.regulations.gov, including any 

personal information provided, unless 

the comment includes information 

claimed to be Confidential Business 

Information (CBI) or other information 

whose disclosure is restricted by statute. 

Do not submit information that you 

consider to be CBI or otherwise 

protected through www.regulations.gov 

or asdinfo@epa.gov. The 

www.regulations.gov Web site is an 

‘‘anonymous access’’ system, which 

means EPA will not know your identity 

or contact information unless you 

provide it in the body of your comment. 

If you send an email comment directly 

to EPA without going through 

www.regulations.gov your email address 

will be automatically captured and 

included as part of the comment that is 

placed in the public docket and made 

available on the Internet. If you submit 

an electronic comment, EPA 

recommends that you include your 

name and other contact information in 

the body of your comment and with any 

disk or CD–ROM you submit. If EPA 

cannot read your comment due to 

technical difficulties and cannot contact 

you for clarification, EPA may not be 

able to consider your comment. 

Electronic files should avoid the use of 

special characters, any form of 

encryption, and be free of any defects or 

viruses. For additional information 

about EPA’s public docket visit the EPA 

Docket Center homepage at http:// 

www.epa.gov/epahome/dockets.htm. 

Docket: All documents in the docket 

are listed in the www.regulations.gov 

index. Although listed in the index, 

some information is not publicly 

available, e.g., CBI or other information 

whose disclosure is restricted by statute. 

Certain other material, such as 

copyrighted material, will be publicly 

available only in hard copy. Publicly 

available docket materials are available 

either electronically in 

www.regulations.go v or in hard copy at 

the Air and Radiation Docket and 

Information Center, EPA/DC, EPA West, 

Room 3334, 1301 Constitution Ave. 

NW., Washington, DC 20004. The Public 

Reading Room is open from 8:30 a.m. to 

4:30 p.m., Monday through Friday, 

excluding legal holidays. The telephone 

number for the Public Reading Room is 

(202) 566–1744, and the telephone 

number for the Air Docket is (202) 566– 

1742. 

FOR FURTHER INFORMATION CONTACT: 

Aaron Levy, Office of Transportation 

and Air Quality, Transportation and 

Climate Division, Environmental 

Protection Agency, 1200 Pennsylvania 

Ave. NW., Washington, DC 20460 (MC: 

6041A); telephone number: (202) 564– 

2993; fax number: (202) 564–1177; 

email address: levy.aaron@epa.gov. 

SUPPLEMENTARY INFORMATION: 

Outline of This Preamble 

I. General Information 

A. Does this action apply to me? 

B. What should I consider as I prepare my 

comments for EPA? 

1. Submitting CBI 

2. Tips for Preparing Your Comments 

II. Analysis of Lifecycle Greenhouse Gas 

Emissions 

A. Methodology 

1. Scope of Analysis 

2. Models Used 

3. Scenarios Modeled 

4. Analysis of Projected Land Use Changes 

in Indonesia and Malaysia 

5. Analysis of Palm Oil Mills 

B. Results of Lifecycle Analysis for 

Biodiesel From Palm Oil 

C. Results of Lifecycle Analysis for 

Renewable Diesel From Palm Oil 

D. Consideration of Lifecycle Analysis 

Results 

1. Implications for Threshold 

Determinations 

2. Consideration of Uncertainty 

I. General Information 

A. Does this action apply to me? 

Entities potentially affected by this 

action are those involved with the 

production, distribution, and sale of 

transportation fuels, including gasoline 

and diesel fuel or renewable fuels such 

as biodiesel and renewable diesel. 

Regulated categories include: 


tkelley on DSK3SPTVN1PROD with NOTICES
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1U.S. Environmental Protection Agency (EPA). 

2011. Summary of Modeling Inputs and 

Assumptions for the Notice of Data Availability 

(NODA) Concerning Renewable Fuels Produced 

from Palm Oil under the Renewable Fuel Standard 

(RFS) Program. Memorandum to Air and Radiation 

Docket EPA–HQ–OAR–2011–0542. 

This table is not intended to be 

exhaustive, but rather provides a guide 

for readers regarding entities likely to 

engage in activities that may be affected 

by today’s action. To determine whether 

your activities would be affected, you 

should carefully examine the 

applicability criteria in 40 CFR part 80, 

Subpart M. If you have any questions 

regarding the applicability of this action 

to a particular entity, consult the person 

listed in the preceding section. 

B. What should I consider as I prepare 

my comments for EPA? 

1. Submitting CBI. Do not submit this 

information to EPA through 

www.regulations.gov or email. Clearly 

mark the part or all of the information 

that you claim to be CBI. For CBI 

information in a disk or CD-ROM that 

you mail to EPA, mark the outside of the 

disk or CD-ROM as CBI and then 

identify electronically within the disk or 

CD-ROM the specific information that is 

claimed as CBI. In addition to one 

complete version of the comment that 

includes information claimed as CBI, a 

copy of the comment that does not 

contain the information claimed as CBI 

must be submitted for inclusion in the 

public docket. Information so marked 

will not be disclosed except in 

accordance with procedures set forth in 

40 CFR part 2. 

2. Tips for Preparing Your Comments. 

When submitting comments, remember 

to: 

• Identify the rulemaking by docket 

number and other identifying 

information (subject heading, Federal 

Register date and page number). 

• Follow directions—The agency may 

ask you to respond to specific questions 

or organize comments by referencing a 

Code of Federal Regulations (CFR) part 

or section number. 

• Explain why you agree or disagree; 

suggest alternatives and substitute 

language for your requested changes. 

• Describe any assumptions and 

provide any technical information and/ 

or data that you used. 

• If you estimate potential costs or 

burdens, explain how you arrived at 

your estimate in sufficient detail to 

allow for it to be reproduced. 

• Provide specific examples to 

illustrate your concerns, and suggest 

alternatives. 

• Explain your views as clearly as 

possible, avoiding the use of profanity 

or personal threats. 

• Make sure to submit your 

comments by the comment period 

deadline identified. 

II. Analysis of Lifecycle Greenhouse 

Gas Emissions 

A. Methodology 

1. Scope of Analysis 

On March 26, 2010, the 

Environmental Protection Agency (EPA) 

published changes to the Renewable 

Fuel Standard program regulations as 

required by 2007 amendments to CAA 

211(o). This rulemaking is commonly 

referred to as the ‘‘RFS2’’ final rule. As 

part of the RFS2 final rule we analyzed 

various categories of biofuels to 

determine whether the complete 

lifecycle GHG emissions associated with 

the production, distribution, and use of 

those fuels meet minimum lifecycle 

greenhouse gas reduction thresholds as 

specified by CAA 211(o) (i.e., 60% for 

cellulosic biofuel, 50% for biomassbased diesel and advanced biofuel, and 

20% for other renewable fuels). Our 

final rule focused our lifecycle analyses 

on fuels that were anticipated to 

contribute relatively large volumes of 

renewable fuel by 2022 and thus did not 

cover all fuels that either are 

contributing or could potentially 

contribute to the program. In the 

preamble to the final rule EPA indicated 

that it had not completed the GHG 

emissions impact analysis for several 

specific biofuel production pathways 

but that this work would be completed 

through a supplemental rulemaking 

process. Since the March 2010 final rule 

was issued, we have continued to 

examine several additional pathways 

not analyzed for the final rule. This 

Notice of Data Availability (‘‘NODA’’) 

focuses on our analysis of the palm oil 

biodiesel and palm oil renewable diesel 

pathways. The modeling approach EPA 

used in this analysis is the same general 

approach used in the final RFS2 rule for 

lifecycle analyses of other biofuels.1 The 

RFS2 final rule preamble and 

Regulatory Impact Analysis (RIA) 

provides further discussion of our 

approach. 

This Notice provides an opportunity 

to comment on EPA’s analyses of 

lifecycle GHG emissions related to the 

production and use of biodiesel and 

renewable diesel produced from palm 

oil feedstock. We intend to consider all 

of the relevant comments received. In 

general, comments will be considered 

relevant if they pertain to EPA’s analysis 

of lifecycle GHG emissions related to 

palm oil biofuels, and especially if they 

provide specific information for 

consideration in our modeling. When all 

relevant comments have been 

considered we intend to inform the 

public of any resulting revisions in our 

analyses or any other relevant 

information pertaining to our 


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2EPA. 2010. Renewable Fuel Standard Program 

(RFS2) Regulatory Impact Analysis. EPA–420–R– 

10–006. http://www.epa.gov/oms/renewablefuels/ 

420r10006.pdf. 

3For example, in the scenarios modeled only 1% 

of land use change GHG emissions originate in the 

United States. These results are discussed more 

below and in the supporting materials available 

through the docket. 

consideration of the comments received. 

Public notification regarding our 

consideration of comments could be 

accomplished in several formats, such 

as a Federal Register notice, a 

rulemaking action or a guidance 

document. The appropriate form of 

public notification will depend on the 

outcome of the public comment process 

and any reanalysis we deem 

appropriate. In the event that EPA does 

not significantly modify its analyses, no 

regulatory amendments will be 

necessary since the existing regulations 

currently do not identify any palm oilbased biofuel production pathways as 

satisfying minimum lifecycle GHG 

reduction requirements. 

2. Models Used 

EPA’s analysis of the palm oil 

biodiesel and renewable diesel 

pathways uses the same model of 

international agricultural markets that 

was used for the final RFS2 rule: the 

Food and Agricultural Policy and 

Research Institute international models 

as maintained by the Center for 

Agricultural and Rural Development at 

Iowa State University (the FAPRI–CARD 

model). For more information on the 

FAPRI–CARD model refer to the RFS2 

final rule preamble (75 FR 14670) or the 

RFS2 Regulatory Impact Analysis 

(RIA).2 These documents are available 

in the docket or online at http:// 

www.epa.gov/otaq/fuels/ 

renewablefuels/regulations.htm. The 

models require a number of inputs that 

are specific to the pathway being 

analyzed, including projected yields of 

feedstock per acre planted, projected 

fertilizer use, and energy use in 

feedstock processing and fuel 

production. The docket includes 

detailed information on model inputs, 

assumptions, calculations, and the 

results of our assessment of the lifecycle 

GHG emissions performance for palm 

oil biodiesel and renewable diesel. 

As in our analysis of sugarcane 

ethanol in the RFS2 final rule, we did 

not use the Forestry and Agricultural 

Sector Optimization Model (FASOM) in 

our analysis of palm oil biodiesel and 

renewable diesel. FASOM is a highly 

detailed partial equilibrium model of 

the United States agricultural and 

forestry sectors. In the RFS2 final rule 

FASOM was used to determine the 

domestic U.S. agricultural sector 

impacts of domestically grown biofuel 

feedstocks. As palm oil is not grown 

domestically in any significant volume, 

the FAPRI–CARD model was the only 

model of agricultural markets used in 

the analysis. Our modeling indicates 

that any impacts to U.S. agriculture 

from using palm oil for biofuel 

production are small in comparison to 

the international impacts.3 Therefore, 

we determined that for this analysis the 

FAPRI–CARD model is better suited for 

modeling domestic agricultural impacts 

and, as such, FASOM modeling is 

unnecessary. 

3. Scenarios Modeled 

To assess the impacts of an increase 

in renewable fuel volume from 

business-as-usual (what is likely to have 

occurred without the RFS biofuel 

mandates) to levels required by the 

statute, we established reference and 

control cases for a number of biofuels 

analyzed for the RFS2 final rulemaking. 

The reference case includes a projection 

of renewable fuel volumes without the 

RFS renewable fuel volume mandates. 

The control cases are projections of the 

volumes of renewable fuel that might be 

used in the future to comply with the 

volume mandates. The final rule 

reference case volumes were based on 

the Energy Information Administration’s 

(EIA) Annual Energy Outlook (AEO) 

2007 reference case projections. In the 

RFS2 rule, for each individual biofuel, 

we analyzed the incremental GHG 

emission impacts of increasing the 

volume of that fuel to the total mix of 

biofuels needed to meet the EISA 

requirements. Rather than focus on the 

GHG emissions impacts associated with 

a specific gallon of fuel and tracking 

inputs and outputs across different 

lifecycle stages, we determined the 

overall aggregate impacts across sectors 

of the economy in response to a given 

volume change in the amount of biofuel 

produced. For this analysis we 

compared impacts in the control case to 

the impacts in a new palm oil biofuel 

case. 

Our ‘‘control’’ case volumes are based 

on projections of a feasible set of fuel 

types and feedstocks. The control case 

for our modeling assumes no renewable 

fuel made from palm oil is used in the 

United States. For the ‘‘palm biofuel’’ 

case, our modeling assumes 

approximately 200 million gallons of 

biodiesel and 200 million gallons of 

renewable diesel from palm oil are used 

in the United States in the year 2022. 

The modeled scenario includes 1.46 

million metric tonnes (MMT) of crude 

palm oil used as feedstock to produce 

the additional 400 million gallons of 

palm oil biofuel in 2022. The projected 

lifecycle GHG emissions associated with 

this increased production and use of 

palm oil biofuel in 2022 are normalized 

per tonne of crude palm oil. The 

lifecycle GHG emissions per gallon of 

biofuel are then calculated based on the 

yields of biodiesel and renewable diesel 

per tonne of crude palm oil. 

Our volume scenario of 

approximately 200 million gallons of 

biodiesel and 200 million gallons of 

renewable diesel from palm oil in 2022 

is based on several factors including 

historical volumes of palm oil 

production, potential feedstock 

availability and other competitive uses 

(e.g., for food or export elsewhere 

instead of for U.S. transportation fuel). 

Our assessment is described further in 

the inputs and assumptions document 

that is available through the docket 

(EPA 2011). Based in part on 

consultation with experts at the United 

States Department of Agriculture 

(USDA) and industry representatives, 

we believe that these volumes are 

reasonable for the purposes of 

evaluating the impacts of producing 

biodiesel and renewable diesel from 

palm oil. 

The FAPRI–CARD model, described 

above, projects in which countries the 

palm oil will most likely be grown to 

supply these biofuel volumes to the U.S. 

based on the relative economics of palm 

oil production, yield trends in different 

regions and other factors. Palm oil is 

currently grown in several regions 

internationally but the vast majority, 

close to 90%, is produced in Indonesia 

and Malaysia. Our modeled scenario 

projects that Indonesia and Malaysia 

would be the primary suppliers of palm 

oil for use as biofuel feedstocks, with 

other regions, such as Africa, Thailand 

and South America, contributing much 

smaller amounts. Because we anticipate 

that the great majority of palm oil for 

use in biofuels would be produced in 

Indonesia and Malaysia our modeling 

efforts focus on evaluating the lifecycle 

GHG emissions associated with palm oil 

production in these countries. 

Table II–1 provides a summary of 

projected palm oil production in 2022 

according to the FAPRI–CARD model.4

As discussed above, in the palm biofuel 

case 1.46 MMT of additional palm oil is 

used as biofuel feedstock in 2022 as 

compared to the control case. We 

project that global palm oil production 

would expand by 0.562 MMT in the 

palm biofuel case; the remaining 

volume of palm oil for biofuel 

production would be diverted from 

other sectors, such as food and chemical 

uses. In response we project that 


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4 In the tables throughout this preamble totals 

may not sum due to rounding errors and negative 

numbers are commonly listed in parentheses. 

5Historical palm oil yields are based on data from 

USDA’s Production, Supply and Distribution (PSD) 

database and reports from USDA’s Global 

Agricultural Information Network (GAIN). 

production of other vegetable oils would 

increase to back fill the palm oil 

diverted to the biofuels industry (See 

Table II–2). Due to market-mediated 

responses vegetable oil production does 

not increase enough to make up for the 

full amount of palm oil diverted to 

biofuel production in the palm biofuel 

case. There are several explanations for 

this including demand substitution 

away from vegetable oils and towards 

other products such as grains, meat and 

dairy. For more information refer to the 

full results from the FAPRI–CARD 

model which are available through the 

docket. 

TABLE II–1—PROJECTED PALM OIL PRODUCTION IN 2022 

[Thousand metric tonnes] 

Control case Palm biofuel 

case Difference 

Indonesia ..................................................................................................................................... 31,254 31,575 321 

Malaysia ....................................................................................................................................... 25,992 26,196 204 

Rest of World ............................................................................................................................... 7,739 7,777 38 

World .................................................................................................................................... 64,986 65,548 562 

TABLE II–2—PROJECTED VEGETABLE OIL PRODUCTION IN 2022 

[Thousand metric tonnes] 

Control case Palm biofuel 

case Difference 

Palm Oil ....................................................................................................................................... 64,986 65,548 562 

Soybean Oil ................................................................................................................................. 308,553 308,620 67 

Rapeseed/Canola Oil ................................................................................................................... 68,845 68,963 118 

Other Vegetable Oils* .................................................................................................................. 28,219 28,317 97 

Total ...................................................................................................................................... 470,603 471,448 845 

* Includes cottonseed oil, peanut oil, sunflower oil and palm kernel oil. 

As shown in the tables above, the 

primary response in the scenarios 

modeled is to increase palm oil 

production in Malaysia and Indonesia. 

In our analysis, projected palm oil 

yields in 2022 are approximately 5 

tonnes per hectare in both Indonesia 

and Malaysia. The EPA projection for 

palm oil yields is an extension of the 

historical data trend forward to 2022, 

based on historical data from the 

USDA.5 Palm oil yields vary in other 

countries, but in general they are 

somewhat less than the yields achieved 

in Indonesia and Malaysia. (More 

information on projected palm oil yields 

is available in the inputs and 

assumptions document available 

through the docket.) Projected harvested 

areas of palm oil are reported in Table 

II–3. As discussed below, the land use 

change GHG emissions associated with 

the incremental expansion of palm oil 

areas in Indonesia and Malaysia are a 

focal point in our analysis. 

TABLE II–3—PROJECTED PALM OIL HARVESTED AREA IN 2022 

[Thousand harvested hectares] 

Control case Palm biofuel 

case Difference 

Indonesia ..................................................................................................................................... 6,179 6,243 63 

Malaysia ....................................................................................................................................... 5,202 5,242 41 

Rest of World ............................................................................................................................... 4,035 4,055 20 

World .................................................................................................................................... 15,416 15,504 124 

4. Analysis of Projected Land Use 

Changes in Indonesia and Malaysia 

As in our analysis of other feedstocks 

in the RFS2 final rule, we assessed what 

the GHG emissions impacts would be 

relating to palm oil production 

(including land use changes) due to the 

use of additional volumes of palm oil 

for biofuel production. Today’s 

assessment of palm oil as a biofuel 

feedstock considers GHG emissions 

from international land use changes 

related to the production and use of 

palm oil, and uses the same land use 

change modeling approach used in the 

final RFS2 rule for analyses of other 

biofuel pathways. However, given our 

focus today on the use of palm oil as a 

biofuel feedstock, this analysis for palm 

oil is more detailed and considers new 

data for Indonesia and Malaysia, 

including higher resolution satellite 

imagery and maps of relevant 

geographic features, such as the location 

of existing oil palm plantations, soil 

types, roads, etc. EPA decided to 

undertake a more detailed assessment of 


tkelley on DSK3SPTVN1PROD with NOTICES
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6USDA Foreign Agricultural Service (USDA– 

FAS). 2009. Indonesia: Palm Oil Production Growth 

To Continue. Commodity Intelligence Report. 

http://www.pecad.fas.usda.gov/highlights/2009/03/ 

Indonesia/. 

7 Integrity Applications Incorporated (IAI). 2010. 

High Resolution Land Use Change Analysis of Oil 

Palm in Sumatra and Kalimantan Circa 2010. 

Report to EPA. BPA–09–03. September 20, 2010. 

8 IAI. 2011. High Resolution Land Use Change 

Analysis for Sumatra and Kalimantan Circa 2000. 

Report to EPA. BPA–09–03. April 8, 2011. 

9Wahid, B. O., Nordiana, A. Aand Tarmizi, A., M. 

2005. Satellite Mapping of Oil Palm Land Use. 

MPOB Information Series. June 2005. 

10MPOB. 2010. Additional Information 

Requested by United States Environmental 

Protection Agency: Agricultural Input. Data 

submitted by MPOB. June 4, 2010. 

Malaysia and Indonesia as compared to 

other regions, based on a number of 

factors including the concentration of 

the palm oil industry in this region and 

the availability of new data on palm oil 

land use. 

The goal of our Indonesia and 

Malaysia land use change analysis is to 

estimate GHG emissions from the 

incremental expansion of palm oil 

plantations that would result from the 

increased demand for palm oil to 

produce the modeled 400 million 

gallons of biodiesel and renewable 

diesel (i.e., land use change GHG 

emissions in Indonesia and Malaysia in 

the palm biofuel case versus the control 

case). This analysis involved projecting 

the locations of future palm oil 

expansion, the types of land impacted 

and the resulting GHG emissions. First, 

we gathered spatially explicit data on 

factors that could be expected to 

influence the location of palm oil 

plantations. In our analysis the spatial 

data are analyzed using the GEOMOD 

land use change simulation model, 

described in more detail below, to 

project the locations of incremental 

palm oil expansion in the scenarios 

modeled. We used the latest available 

data to set land conversion GHG 

emissions factors for Indonesia and 

Malaysia. Finally, we considered the 

uncertainty in our estimates and factor 

that into our assessment of threshold 

determinations for palm oil biodiesel 

and palm oil renewable diesel. An 

overview of our Indonesia and Malaysia 

land use change analysis is provided 

below, including references to materials 

that are available through the docket 

which provide more details about all of 

the inputs, assumptions and results. 

A key input in our analysis is newly 

available data on the historic locations 

of palm oil cultivation. These data are 

important because they establish a 

baseline area where palm oil is 

currently grown or has been grown in 

recent years. Past changes in the 

location of palm oil plantations were 

evaluated using relevant spatial 

information to determine what 

geographic factors were correlated with 

the changes. We then used this new 

understanding to predict the locations 

of future expansion related to increased 

palm oil biofuel production. This 

section includes the following: 

• Description of data on the location 

of palm oil plantations in Indonesia and 

Malaysia; 

• Summary of the geographic data 

sources considered in our analysis; 

• Background on the GEOMOD model 

and our methodology for land use 

change projections; 

• Summary of projected locations for 

palm oil expansion; 

• Description of land use change 

emissions factors used in our analysis; 

and 

• Estimated land use change GHG 

emissions in the scenarios modeled. 

Data on the historic locations of palm 

oil plantations in Indonesia and 

Malaysia—For Indonesia a literature 

search was conducted which found an 

absence of available spatial data on the 

locations of palm oil plantations. To fill 

this data gap EPA developed such maps 

for the time period from 2000 to 2009 

using satellite imagery and other 

remotely sensed information. As 

described below, the mapping project 

required intensive effort in terms of both 

data analysis and visual inspection. To 

enhance data quality and mapping 

accuracy we limited the geographic 

scope of the project to the islands of 

Sumatra and Kalimantan where close to 

90% of Indonesia’s palm oil is known 

to be located.6 In recent years palm oil 

expansion has also been encouraged in 

more remote locations on the islands of 

Sulawesi and Papua, but as mentioned 

above our mapping efforts did not 

consider these islands. This source of 

uncertainty in our analysis is discussed 

in a reference document available 

through the public docket which 

describes our consideration of 

uncertainty. 

To map the location of palm oil 

plantations in Indonesia we leveraged 

data from the complete Landsat archive, 

high-resolution data via Google Earth, 

and data from the National GeospatialIntelligence Agency (NGA) Unclassified 

National Informational Library (UNIL), 

among others. Analysis of palm oil 

plantation areas using Landsat data was 

performed both visually and through an 

automated detection algorithm to ensure 

a robust analysis. The project mitigated 

cloud cover and data gaps, executed 

final plantation identification, and 

estimated the total area of medium- to 

large-scale oil palm plantations. Using 

high-resolution remote sensing data 

yielded an estimated ground cover area 

for oil palm of 3.2 million hectares in 

the year 2000 and 4.0 million hectares 

in the year 2009. Detailed 

documentation of the analysis as well as 

electronic maps showing the results are 

available through the docket.7 8

For Malaysia, data on the locations of 

palm oil plantations in 2003 and 2009 

were provided by the Malaysian Palm 

Oil Board (MPOB), an agency of the 

Malaysian government. The data were 

provided in the form of electronic maps 

showing mature and immature palm oil 

plantations. The map of 2003 palm oil 

plantations utilizes remote sensing data 

from the Landsat database,9 and the 

map of 2009 plantations is based on 

SPOT satellite images.10 The data show 

the location of roughly 3.8 million 

hectares of palm oil plantations in 2003 

and roughly 5.2 million hectares in 

2009. The original maps, in a format 

compatible with Geographic 

Information System (GIS) software, were 

provided under a claim of confidential 

business information (CBI) and then 

returned to the source. Therefore, the 

original files are not available for public 

review. However, based on our 

agreement with the MPOB, electronic 

image files depicting the maps are 

available for review in the public 

docket. 

Spatial analysis of land use change in 

Indonesia and Malaysia—In addition to 

the historic locations of palm oil 

plantations, our analysis considers other 

relevant geographic suitability factors 

for Indonesia and Malaysia. For our 

analysis of land use change in Indonesia 

fourteen factor maps were created: 

Elevation, precipitation, temperature, 

slope, soil type, land cover type in 2001, 

distance to roads, distance to rivers, 

distance to railroads, distance to 

settlements, distance to palm oil mills, 

peat soil location, land allocation (e.g., 

protected areas), and distance to 

existing plantations. For our analysis of 

Malaysia eleven factor maps were 

created: elevation, precipitation, 

temperature, slope, soil type, land cover 

type in 2001, distance to roads, distance 

to rivers, distance to railroads, distance 

to settlements, and distance to existing 

plantations. The factor maps were 

selected based on data availability and 

their relevance for projecting the 

location of future palm oil plantations. 

More details about the data used in our 

projections, including the source for 

each data element, are provided in 

technical reports available through the 


tkelley on DSK3SPTVN1PROD with NOTICES
4306 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

11Harris, N., and Grimland, S. 2011a. Spatial 

Modeling of Future Oil Palm Expansion in 

Indonesia, 2000 to 2022. Winrock International. 

Draft report submitted to EPA. 

12Harris, N., and Grimland, S. 2011b. Spatial 

Modeling of Future Oil Palm Expansion in 

Malaysia, 2003 to 2022. Winrock International. 

Draft report submitted to EPA. 

13Hall, C., A., S., Tian, H., Qi, Y., Pontius, R., G., 

Cornell, J., and Uhlig, J. 1995. Modeling spatial and 

temporal patterns of tropical land use change. 

Journal of Biogeography, 22, 753–757. 

14Pontius Jr., R. G., Cornell, J., and Hall, C. 2001. 

Modeling the spatial pattern of land-use change 

with Geomod2: application and validation for Costa 

Rica. Agriculture, Ecosystems & Environment 85 (1– 

3) p.191–203. 

15Harris, N. L, Petrova, S., Stolle, S., and Brown, 

S. 2008. Identifying optimal areas for REDD 

intervention: East Kalimantan, Indonesia as a case 

study. Environmental Research Letters 3: 035006. 

16Rashmi, M. and Lele, N. 2010. Spatial modeling 

and validation of forest cover change in Kanakapura 

region using GEOMOD. Journal of the Indian 

Society of Remote Sensing p. 45–54. 

17Harris et al. (2011a) and (2011b). 

18USDA–FAS (2009). 

19USDA–FAS. 2011. Malaysia: Obstacles May 

Reduce Future Palm Oil Production Growth. 

Commodity Intelligence Report. June 28, 2011, 

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

Malaysia/. 

docket.11 12 We welcome public 

comments on additional data sources for 

consideration in our modeling. 

To analyze the spatial data described 

above and use it to project the most 

likely locations for future palm oil 

expansion, we used a well-established 

land use change simulation model 

called GEOMOD. GEOMOD is a 

spatially explicit simulation model of 

land cover change that uses maps of biogeophysical attributes and of existing 

land cover to extrapolate the known 

pattern of land cover from one point in 

time to other points in time. GEOMOD 

was developed by researchers at the 

SUNY College of Environmental Science 

and Forestry with funding from the U.S. 

Department of Energy.13 It has been 

used to model land cover changes across 

the world in many different ecosystems 

including Costa Rica,14 Indonesia 15 and 

India.16

Using spatial data described above, 

the GEOMOD land use change 

simulation model was used to project 

the locations of future palm oil 

expansion in Indonesia and Malaysia 

until the year 2022. First, we created 

maps of factors that could influence 

where future palm oil expansion occurs, 

such as elevation, slope, proximity to 

roads, etc. Second, we compared the 

factor maps against a map of existing 

palm oil plantations in 2000 and 2003 

for Indonesia and Malaysia respectively 

to construct a series of suitability maps. 

In the calibration stage, for each 

suitability map the model assigned 

higher suitability values to locations 

that have a combination of 

characteristics similar to the land 

already cultivated in palm oil and low 

suitability values to locations that are 

less similar to existing palm oil areas. In 

the validation stage, each candidate 

suitability map was overlain with a map 

of existing plantations in the year 2009. 

Each suitability map was evaluated with 

a set of statistics to assess its ability to 

accurately project the location of palm 

oil areas from the first time period to the 

second time period, e.g., 2000 to 2009. 

After single factor suitability maps 

were tested, we used this information to 

create suitability maps from several 

combined factors and with different 

weighting schemes. Results from the 

validation procedures of each scenario 

were used to refine subsequent 

simulations until a simulation model 

achieved the best validation results. The 

best model was defined as the model 

that most accurately projects the 

location of palm oil expansion between 

the first and second time periods. When 

the best model was identified based on 

the validation exercises, we used this 

model to simulate expansion of oil palm 

plantations from 2000 to 2022 in 

Indonesia and from 2003 to 2022 in 

Malaysia. 

For this analysis 34 different 

suitability maps were created for 

Indonesia. After applying lessons 

learned from the Indonesia analysis we 

were able to narrow the field to 18 

different suitability maps for Malaysia. 

After all of the trials, in both countries 

the combined suitability map that 

weighted all of the factors equally 

performed the best across a number of 

accuracy metrics. For both countries the 

accuracy metrics for the selected 

suitability maps indicated good model 

performance. Thus, the suitability maps 

created by weighting all factors equally 

were chosen to simulate expansion of 

oil palm plantations to 2022 in 

Indonesia and Malaysia. More details 

about our GEOMOD analysis are 

provided in technical reports available 

through the docket.17

Projected land use changes in 

Malaysia and Indonesia—This section 

provides a summary of our results 

regarding projected land use changes in 

Indonesia and Malaysia. As discussed 

above, we used the FAPRI–CARD model 

to simulate a roughly 400 million gallon 

increase in palm oil biodiesel and 

renewable diesel production in 2022, 

resulting in additional palm oil 

harvested area in Indonesia and 

Malaysia of 63 and 41 thousand hectares 

respectively. Using the GEOMOD model 

we projected where the additional 104 

thousand hectares of palm oil would be 

located, what types of land cover would 

be impacted, and the extent of resulting 

peat soil drainage. 

Table II–4 summarizes the projected 

locations of palm oil crops in Indonesia 

and Malaysia in 2022. Our analysis 

considers 45 different administrative 

units in Indonesia and Malaysia, but 

here the results are summarized into 5 

aggregate regions. In the modeled 

scenario we project that close to 90% of 

the incremental palm oil expansion in 

Indonesia would occur in the 

Kalimantan region. This is consistent 

with USDA’s reporting that Kalimantan 

has been the fastest expanding region 

for palm oil over the last decade.18 In 

Malaysia we project that most of the 

incremental palm oil expansion would 

occur on the mainland, i.e., Peninsular 

Malaysia. USDA reports that almost all 

of the highly suitable land for palm oil 

production has already been developed 

in Malaysia. According to USDA, 

Sarawak has the most remaining 

development potential, but the available 

areas on Sarawak are primarily coastal 

peatlands and/or degraded inland forest 

with native claims,19 which makes these 

areas less desirable for cultivation due 

to complications arising from peat soil 

characteristics and land rights issues. 

Our modeling indicates that the most 

likely area for incremental expansion is 

on the mainland where existing 

plantations may be able to expand 

around the fringes in order to increase 

productive area. 

TABLE II–4—PROJECTED LOCATION OF PALM OIL IN INDONESIA AND MALAYSIA IN 2022 

[Thousand harvested hectares] 

Country Region Control case Palm biofuel 

case Difference 

Indonesia ................................ Kalimantan .............................................................................. 1,396 1,452 56 

Sumatra .................................................................................. 4,782 4,790 8 

Malaysia ................................. Peninsular Malaysia ................................................................ 3,016 3,048 32 


tkelley on DSK3SPTVN1PROD with NOTICES
Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 4307 

20Unnasch, S. S. T. Sanchez, and B. Riffel (2011) 

Well-to-Wheel GHG Emissions and Land Use 

Change Impacts of Biodiesel from Malaysian Palm 

Oil. Prepared for Malaysian Palm Oil Council. Life 

Cycle Associates Report LCA.6015.50P.2011. 21 Department of Statistics, Malaysia. Table 1.2 

Area Under Oil Palm Mature and Immature. MPOB 

Web site, http://econ.mpob.gov.my/economy/ 

annual/stat2009/Area1_2.pdf. Accessed December 

2011. 

22USDA–FAS (2011). 

23Unnasch et al. 

24Harris et al. (2011a), Table 9. 

25Harris et al. (2011b), Table 9. 

26Harris et al. (2011a) and (2011b). 

27USDA–FAS (2009) and (2011). 

28 Koh, L. P., Miettinen, J., Liew, S. C. & Ghazoul, 

J. 2011. Remotely sensed evidence of tropical 

peatland conversion to oil palm. Proceedings of the 

National Academy of Scientists of the United States 

of America, 108, 5127–5132. 

29Paramananthan, S. 2008. Tussle over Tropical 

Peatlands. Global Oils & Fats: Business Magazine. 

(5)3, 1–16. 

30Page, 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. 

TABLE II–4—PROJECTED LOCATION OF PALM OIL IN INDONESIA AND MALAYSIA IN 2022—Continued 

[Thousand harvested hectares] 

Country Region Control case Palm biofuel 

case Difference 

Sabah ...................................................................................... 1,351 1,357 6 

Sarawak .................................................................................. 834 837 3 

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.20

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 2009.21

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.22 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.23 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. We invite comment on 

whether we should account for 

incremental expansion in the area of 

immature palm oil plantations in our 

analysis, and if so on which factors 

should be considered in making such a 

projection. 

To evaluate land use change GHG 

emissions resulting from palm oil 

expansion we considered the soil and 

land cover types in the areas projected 

for conversion. Land cover types were 

determined based on MODIS satellite 

data, the same land cover data set that 

was used in the RFS2 final rule. 

According to our analysis, over the 

previous decade over 50% of palm oil 

has been grown on areas classified as 

forest in Indonesia,24 and the figure is 

over 60% in Malaysia.25 Table II–5 

shows the projected types of land cover 

impacted in Indonesia and Malaysia by 

incremental palm oil expansion in 2022 

in the scenarios modeled. We project 

that the forest and mixed land cover 

types would account for over 80% of the 

land cover impacted by palm oil 

expansion. (The mixed land cover 

category assumes equal shares of forest, 

grassland, shrubland and cropland.) 

These projections are in line with recent 

historical data,26 USDA reports 27 and 

peer-reviewed literature,28 which all 

indicate that much of the recent 

expansion in palm oil has been at the 

expense of tropical forest. 

TABLE II–5—PROJECTED LAND COVER

TYPES IMPACTED BY PALM OIL EXPANSION IN INDONESIA AND MALAYSIA IN 2022 

Land cover type Indonesia 

(%) 

Malaysia 

(%) 

Forest .................... 43 54 

Mixed .................... 38 35 

TABLE II–5—PROJECTED LAND COVER

TYPES IMPACTED BY PALM OIL EXPANSION IN INDONESIA AND MALAYSIA IN 2022—Continued 

Land cover type Indonesia 

(%) 

Malaysia 

(%) 

Shrubland ............. 0 0 

Savanna ................ 10 1 

Grassland ............. 1 1 

Cropland ............... 7 5 

Wetland ................. 1 3 

An even more critical factor in terms 

of estimating land use change GHGs in 

this region is the extent of tropical peat 

soil drained in order to prepare land for 

palm oil production. Almost all of the 

undisturbed tropical peat land in the 

world is located in Indonesia and 

Malaysia, with much smaller amounts 

also found in Philippines and 

Thailand.29 Undisturbed tropical peat 

swamp forest removes carbon dioxide 

(CO2) from the atmosphere and stores it 

in biomass and peat deposits. The 

incomplete decomposition of dead tree 

material under waterlogged, anaerobic 

conditions has led to slow accumulation 

of peat deposits over millennia, giving 

this ecosystem a very high carbon 

density. Typical estimates are that 

tropical peat soils sequester 

approximately 20 times more carbon 

than forest biomass on a per hectare 

basis.30

In their natural state, tropical peat 

lands are unfavorable for agricultural 

production compared to mineral soils, 

primarily because peat swamp has a 

ground water table that is at or close to 

the peat surface throughout the year. 

Despite these harsh conditions, peat 

swamps have recently been exploited to 

make room for agricultural and forest 

plantations as the global demand for 

food, wood and other resources has 


tkelley on DSK3SPTVN1PROD with NOTICES
4308 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

31Hooijer, A., Page, S., Canadell, J. G., Silvius, M., 

Kwadijk, J., Wo ̈ sten, H., & Jauhiainen, J. 2010. 

Current and future CO2 emissions from drained 

peatlands in Southeast Asia. Biogeosciences, 7, 

1505–1514. 

32Miettinen, J., Chenghua S., Liew, S.C. 2011. 

Two decades of destruction in Southeast Asia’s peat 

swamp forests. Frontiers in Ecology and the 

Environment. 

33 Koh et al. (2011). 

34Harris et al. (2011a), Table 22. 

35Harris et al. (2011b), Table 19. 

36Harris, N., Brown, S., and Grimland, S. 2009a. 

Global GHG Emission Factors for Various Land-Use 

Transitions. Winrock International. Report 

Submitted to EPA. April 2009. 

37Harris, N., Brown, S., and Grimland, S. 2009b. 

Land Use Change and Emission Factors: Updates 

since the RFS Proposed Rule. Winrock 

International. Report Submitted to EPA. December 

2009. 

38Harris, N. 2011. Revisions to Winrock’s Land 

Conversion Emission Factors since the RFS2 Final 

Rule. Winrock International report to EPA. 

39 2006 IPCC Guidelines for National Greenhouse 

Gas Inventories Volume 4 Agriculture, Forestry and 

Other Land Use. Chapter 5. http://www.ipccnggip.iges.or.jp/public/2006gl/vol4.html. 

40Harris (2011). 

41Page et al. 42Hooijer, A., M. Silvius, H. Wo ̈ sten and S. Page. 

2006. PEAT–CO2, Assessment of CO2 emissions 

from drained peatlands in SE Asia. Delft Hydraulics 

report Q3943. 

43Hooijer, 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. 

44Page et al., 53. 

increased.31 Some reasons that have 

been given for the recent development 

of peat swamps include that other 

suitable areas have already been used, 

advanced land conversion and drainage 

technologies have been developed, and 

in some cases seizing the swamps is less 

likely to result in native land disputes.32

Koh et al. found that approximately 6% 

of tropical peatlands in Indonesia and 

Malaysia had been converted to palm oil 

plantations by the early 2000s.33 Based 

on our analysis of 2009 data we find 

that palm oil plantations have been 

developed disproportionately on peat 

soils, which occupy 13% of the total 

land area in Indonesia (Sumatra and 

Kalimantan) but host 25% of palm oil 

plantations.34 For Malaysia, we estimate 

that in 2009 approximately 13% of palm 

oil plantations were on peat soils 

compared with only 8% of the country 

displaying that type of soil.35 Table II– 

6 summarizes our analysis regarding the 

historical and projected extent of palm 

oil on tropical peat soil. The values in 

the last row, projected incremental 

expansion in 2022, are used in our 

analysis. Taking the weighted averages 

for Indonesia and Malaysia, based on 

the data in Table II–4 and Table II–6, we 

project that 11.5% of incremental palm 

oil expansion in 2022 will occur on 

tropical peat lands in the scenarios 

modeled. 

TABLE II–6—PERCENT OF PALM OIL

PLANTATIONS ON PEAT SOIL, HISTORICAL AND PROJECTED

Year Indonesia 

(%) 

Malaysia 

(%) 

2009 (Historical) ... 22 13 

2022 (Projected) ... 15 10 

2022 (Projected Incremental Expansion) ............ 13 9 

Land use change emissions factors— 

In our analysis, GHG emissions per 

hectare of land conversion are 

determined using the emissions factors 

developed for the RFS2 final rule 

following IPCC guidelines.36 37 In 

addition, several updates have been 

made to refine our land use change 

emissions factors for Indonesia and 

Malaysia. First, average above and 

below ground carbon stocks in palm oil 

plantations were revised based on new 

data. Second, GHG emissions associated 

with draining peat soils were updated 

according to new studies which 

consider data from hundreds of new 

field measurements. Finally, estimated 

average forest carbon stocks were 

updated based on a new study which 

uses a more robust and higher 

resolution analysis. In this section we 

briefly describe each of these updates. 

More information is available in a 

technical memorandum available 

through the docket.38

Palm Oil Carbon Stocks. In the final 

RFS2 rule, carbon stocks in palm oil 

plantations after one year of growth 

were estimated to be 15 tonnes carbon 

dioxide-equivalent per hectare (tCO2e/ 

ha). This was based on Table 5.3 of the 

2006 IPCC Guidelines for Agriculture, 

Forestry and Other Land Use 

(AFOLU),39 which gives biomass stocks 

on oil palm plantations as 136 tCO2e/ha. 

The total carbon stock value reported by 

IPCC was divided by an assumed 15- 

year growth period to derive a linear 

growth rate. Our original analysis 

accounted for only one year of growth 

when estimating carbon storage on palm 

oil plantations. 

We have revised our analysis of palm 

oil carbon stocks in favor of a more 

accurate time-averaged approach, using 

average carbon stocks over the life of the 

plantation. Since a typical rotation 

period for palm oil is approximately 30 

years (e.g., 3–5 years as immature plus 

20–25 years of harvesting), this 

approach is more appropriate for our 

lifecycle analysis methodology as 

established in the RFS2 final rule, 

which considers land use change 

emissions over a 30-year period. A 

literature review of palm oil carbon 

stocks was conducted, and based on this 

review we modified the carbon stocks of 

palm oil plantations to a time-averaged 

value of 128 tCO2e/ha.40

Peat Soil Emissions Factors. 

Development of tropical peatland for 

palm oil production requires removal of 

the vegetative cover and typical 

drainage depths of 0.6 to greater than 

1.0 meter. Drainage is accomplished by 

construction of a network of deep canals 

and shallower ditches. Additionally, the 

peat surface is often compacted by the 

weight of heavy vehicles to improve its 

load-bearing characteristics and increase 

the stability of palm trees. These 

changes remove carbon from the 

peatland system by lowering the peat 

water table, ensuring continuous aerobic 

decomposition of organic material and 

greatly reducing preservation of new 

carbon inputs to the peat from biomass. 

As a result the peat swamp ecosystem 

switches from a net carbon sink to a 

large source of carbon emissions. On 

completion of a productive palm oil 

cycle, the plantation is typically 

renewed by land clearance, drainage 

and replanting.41

In the RFS2 final rule peat soil 

emissions in Indonesia and Malaysia 

were estimated based on a relationship 

developed by Hooijer et al. (2006) that 

correlates peat drainage depth with 

annual peat CO2 emissions.42 Assuming 

average drainage depth of 0.8 meters, 

average emissions from drained peat 

soils were estimated to be 73 tCO2 per 

hectare per year. 

For our palm oil analysis average peat 

soil emissions have been updated based 

on a newly available study (Hooijer et 

al. 2011) 43 which considers over 200 

subsidence measurements (more than 

were previously available for all 

peatlands in Southeast Asia combined), 

taken at various locations including 

palm oil and acacia plantations on peat 

soil.44 Earlier studies had assumed 

constant annual emissions over time 

following peat soil drainage. Hooijer et 

al. (2011) is the only source with 

enough data to calculate peat carbon 

emissions over various time scales. 

These data showed higher rates of 

emission in the years immediately 

following drainage. As such, average 

annual emissions are no longer derived 

as a function of drainage depth but are 

instead based on the time scale of 

analysis. 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 


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Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 4309 

45 Jauhiainen, J., Hooijer, A., & Page, S. E. (2011). 

Carbon Dioxide Fluxes in an Acacia Plantation on 

Tropical Peatland. Biogeosciences Discussions, 8, 

8269–8302. 

46Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, 

M., Mitchard, E.T.A., Salas, W., Zutta, B.R., 

Buermann, W., Lewis, S.L., Hagen, S., Petrova, S., 

White, L., Silman, M. and Morel, A. 2011. 

Benchmark map of forest carbon stocks in tropical 

regions across three continents. PNAS doi: 10.1073/ 

pnas.1019576108. 

47 Gallaun, H., Zanchi, G., Nabuurs, G.J., 

Hengeveld, G., Schardt, M., Verkerk, P.J. 2010. EUwide maps of growing stock and above-ground 

biomass in forests based on remote sensing and 

field measurements. Forest Ecology and 

Management 260: 252–261. 

48Mokany, K., R.J. Raison, and A.S. Prokushkin. 

2006. Critical analysis of root:shoot ratios in 

terrestrial biomes. Global Change Biology 12: 84–96. 

49Harris (2011). 

50 Verified Carbon Standard (VCS) methodology 

module VMD0005: Estimation of carbon stocks in 

the long-term wood products pool (CP–W), Sectoral 

Scope 14, http://www.v-c-s.org/methodologies/find. 

51Only two other countries have extraction rates 

above 20 m3/ha: India with 33 m3/ha and China 

with 22 m3/ha. 

52Winjum, J.K., Brown, S., Schlamadinger, B. 

1998. Forest harvests and wood products: Sources 

and sinks of atmospheric carbon dioxide. Forest 

Science 44: 272–284. 

53Harris (2011). 

reviewed studies of carbon emissions 

from peat drainage and concluded that 

this is the most robust estimate of 

emissions over a 30-year period. They 

noted that this estimate, which is based 

on subsidence measurements, closely 

matches estimates from similar recent 

studies which use other measurement 

techniques such as direct gas fluxes.45

Forest Carbon Stocks. For the RFS2 

final rule, international forest carbon 

stocks were estimated from several data 

sources each derived using a different 

methodological approach. Two new 

analyses on forest carbon stock 

estimation were completed since the 

release of the final RFS2 rule, one for 

three continental regions by Saatchi et 

al. 46 and the other for the EU by 

Gallaun et al. 47 We have updated our 

estimates based on these new studies 

because they represent significant 

improvements as compared to the data 

used in the RFS2 rule. Forest carbon 

stocks across the tropics are particularly 

important in our analysis of palm oil 

biofuels because palm oil is grown in 

tropical regions. In the scenarios 

modeled there are also much smaller 

amounts of land use change impacts in 

the EU related to palm oil biofuel 

production. As such, we took this 

opportunity to incorporate the improved 

forest carbon stocks data in both of these 

regions. 

Preliminary results for Latin America 

and Africa from Saatchi et al. were 

incorporated into the final RFS2 rule, 

but Asia results were not included due 

to timing considerations. The Saatchi et 

al. analysis is now complete, and so the 

final map was used to calculate updated 

area-weighted average forest carbon 

stocks for the entire area covered by the 

analysis (Latin America, sub-Saharan 

Africa and South and Southeast Asia). 

The Saatchi et al. results represent a 

significant improvement over previous 

estimates because they incorporate data 

from more than 4,000 ground inventory 

plots, about 150,000 biomass values 

estimated from forest heights measured 

by space-borne light detection and 

ranging (LIDAR), and a suite of optical 

and radar satellite imagery products. 

Estimates are spatially refined at 1-km 

grid cell resolution and are directly 

comparable across countries and 

regions. 

In the final RFS2 rule, forest carbon 

stocks for the EU were estimated using 

a combination of data from three 

different sources. Issues with this 

‘patchwork’ approach were that the 

biomass estimates were not comparable 

across countries due to the differences 

in methodological approaches, and that 

estimates were not spatially derived (or, 

the spatial data were not provided to 

EPA). Since the release of the final rule, 

Gallaun et al. developed EU-wide maps 

of above-ground biomass in forests 

based on remote sensing and field 

measurements. MODIS data were used 

for the classification, and 

comprehensive field measurement data 

from national forest inventories for 

nearly 100,000 locations from 16 

countries were also used to develop the 

final map. The map covers the whole 

European Union, the European Free 

Trade Association countries, the 

Balkans, Belarus, the Ukraine, Moldova, 

Armenia, Azerbaijan, Georgia and 

Turkey. 

For both data sources, Saatchi et al. 

and Gallaun et al., we added 

belowground biomass to reported 

aboveground biomass values using an 

equation in Mokany et al.48 More details 

regarding updated forest carbon stock 

estimates are available in a technical 

report to the docket.49

In our analysis, forest stocks are 

estimated for over 750 regions across 

160 countries. For some regions the 

carbon stocks increased as a result of the 

updates and in others they declined. For 

comparison, we ran our palm oil 

analysis using the old forest carbon 

stock values used in the RFS2 rule and 

with the updated forest carbon values 

described above. Using the updated 

forest carbon stocks decreased the land 

use change GHG emissions related to 

palm oil biofuels by only 0.1%. 

Harvested Wood Products. Another 

update that was incorporated into our 

analysis of Indonesia and Malaysia is 

related to harvested wood products 

(HWP). When forest is cleared a fraction 

of the vegetation is harvested as 

valuable timber for use in wood 

products such as sawn wood, wood 

panels, paper and paperboard. 

Accounting for HWP in our analysis 

involves estimating the amount of 

carbon that is sequestered in these wood 

products for at least the length of the 

analysis period (i.e., greater than 30 

years). For the final RFS2 rule we 

addressed the potential significance of 

the HWP pool and concluded that for 

most regions of the world the amount of 

carbon stored in wood products longterm was insignificant, especially when 

considering a timeframe of 30 years. 

Therefore, carbon storage in HWP was 

not incorporated into the emission 

factors for deforestation in the RFS2 

final rule. 

For this analysis we have estimated 

carbon storage in HWP for timber 

extraction in Indonesia and Malaysia. 

Our updated assessment is based on the 

approved Verified Carbon Standard 

methodology for estimation of carbon 

stocks in the long-term wood products 

pool.50 We undertook this update 

because based on our analysis Indonesia 

and Malaysia have the highest average 

timber extraction rates in the world, 

equaling 52 and 42 cubic meters per 

hectare (m3/ha), respectively.51 The 

fraction of extracted biomass that ends 

up as wood waste during production 

was estimated as a constant 19% based 

on Winjum et al.52 We also estimated 

the fraction of wood products which 

will be retired and oxidized to the 

atmosphere in 30 years or less after 

harvesting. After accounting for wood 

waste and carbon in products that will 

not last for more than 30 years, the 

remainder is assumed to be the carbon 

stored in HWP after 30 years. We 

estimate that on average the carbon 

stored in harvested wood products after 

30 years equals 3.0 and 1.9 tonnes of 

carbon per hectare of forest cleared (tC/ 

ha) in Indonesia and Malaysia, 

respectively. These values are quite 

small compared to the forest carbon 

stocks in the region, which are typically 

in the range of 150–200 tC/ha. For more 

details on our updated assessment of 

HWP refer to the technical report 

available through the docket.53

Land use change emissions results— 

Based on the analysis described above 

we estimated land use change GHG 

emissions related to the production and 

use of biodiesel and renewable diesel 

from palm oil feedstock. Most of the 

land use change emissions associated 

with these two biofuels occur in 


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4310 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

Indonesia and Malaysia. Table II–7 

includes the land use change GHG 

emissions results for the scenarios 

modeled, in terms of million metric 

tonnes of carbon-dioxide equivalent 

over 30 years (MMT CO2e/yr over 30 

yrs). These are the incremental 

emissions related to the production and 

use of approximately 400 million 

additional gallons of palm oil biofuels 

in the palm biofuel case compared to 

the control case. For Indonesia and 

Malaysia the emissions are broken out 

by land conversion category, showing 

that the dominant sources of emissions 

are from peat swamp drainage and forest 

clearing in these two countries. 

TABLE II–7—LAND USE CHANGE GHG EMISSIONS

[MMT CO2e/yr over 30 yrs] 

Source of emissions Indonesia Malaysia Rest of world 

Forest Clearing ............................................................................................................................ 0.33 0.46 NA 

Other Land Cover Clearing ......................................................................................................... (0.02) 0.03 ........................

Peat Soil Drainage ....................................................................................................................... 0.81 0.33 ........................

Total ...................................................................................................................................... 1.11 0.83 0.37 

5. Analysis of Palm Oil Mills 

A key part of our analysis focuses on 

palm oil mills where bunches of fresh 

palm fruit are separated into palm 

kernels, empty fruit bunches, and the 

remaining fruit which contains crude 

palm oil. This is a similar step to 

soybean crushing which is included in 

the soybean biodiesel lifecycle analysis 

in the RFS2 rule. EPA’s analysis for 

palm oil mills includes an assessment of 

the energy and materials flows for an 

average palm oil mill and the resulting 

lifecycle GHG emissions. 

Palm oil mills extract crude palm oil 

using steam for sterilization, mechanical 

stirring, screw presses and other 

filtering, purifying and drying 

processes. The main solid wastes from 

the process (i.e., empty fruit bunches, 

mesocarp fiber, shells) are commonly 

returned to the field as fertilizer or used 

as fuel to generate steam and electricity 

for use in the mill. The main liquid 

waste called palm oil mill effluent 

(POME) is a dark brown slurry 

containing waste water, plant oil, and 

debris from the palm fruit. To meet 

environmental standards for discharge 

into local waterways the POME is 

treated in a series of anaerobic lagoons 

or tanks. When the POME is digested it 

generates biogas containing various 

concentrations of carbon dioxide and 

methane. If POME is digested in open 

ponds or tanks, the methane and carbon 

dioxide is emitted to the atmosphere. 

Our analysis indicates that the methane 

emissions from POME digestion can 

represent a substantial portion of the 

lifecycle GHG emissions associated with 

palm oil biodiesel. However, if covered 

lagoons or closed digester tanks are 

used, at least some of this methane can 

be captured and then either flared or 

used to generate electricity and/or 

steam. This process converts methane, 

which has a high global warming 

potential (GWP) of 21, to CO2, which 

has a lower GWP of 1, thus preventing 

the higher impact methane from 

entering the atmosphere. 

Because POME methane emissions are 

an important part of the lifecycle GHG 

emissions associated with palm oil 

biofuels, we collected information 

specifically looking at the deployment 

of POME methane capture/use 

technologies at palm oil mills. 

According to a mandatory survey of 422 

Malaysian palm oil mills conducted by 

the Malaysian Palm Oil Board in 2010, 

38 mills were capturing POME biogas, 

34 mills had POME biogas capture 

projects under construction, and 47 

mills were in various stages of planning 

to implement biogas capture at some 

point between 2012 and 2020. Among 

the mills that are currently capturing 

POME biogas, 63% use closed tank 

digesters and 37% use covered lagoons. 

Forty percent of the mills that are 

capturing POME biogas destroy it with 

flaring, 34% use it to generate 

electricity, 5% use it to produce steam, 

and 21% employ combined heat and 

power to generate steam and electricity. 

Information about POME methane 

capture was also provided by the 

Indonesian Embassy. According to the 

information provided, 3.5% of 

Indonesia’s 608 palm oil mills are 

currently capturing POME biogas with 

an additional 2% of the mills in the 

process of constructing biogas capture/ 

use projects. Thus, we estimate that 33 

of Indonesia’s 608 mills have methane 

capture/use projects in operation or 

under construction. All of the mills that 

currently capture POME biogas have 

covered lagoons and use the captured 

methane to generate electricity, based 

on data provided by the Indonesian 

Embassy. 

We are using the data from the 

Malaysian survey of palm oil mills and 

the information provided by the 

Indonesian Embassy to derive the 

industry average used in our lifecycle 

analysis. Based on the information 

collected and described above, our 

assessment of the lifecycle GHG 

emissions from industry average palm 

oil mills assumes that 10% of palm oil 

mills capture the methane from 

anaerobic digestion of POME (i.e., 105 

mills capture methane out of 1,030 total 

mills in Indonesia and Malaysia). Of the 

mills that capture POME methane we 

assume, based on the data described 

above, that 27% of the mills flare 

captured methane, 55% use the 

methane for electricity generation, 3% 

use the methane to produce steam and 

14% use the methane to produce 

electricity and steam (the percentages 

do not sum to 100% due to rounding). 

We believe that deriving the industry 

average in this manner is reasonable 

because palm oil mills in Malaysia and 

Indonesia represent close to 90% of 

crude palm oil production, and we do 

not have any reason to believe that 

biogas capture rates would be different 

enough in the other palm oil producing 

regions to affect our determinations. 

As discussed above, our analysis is 

based on average practices at palm oil 

mills in Indonesia and Malaysia. This is 

because the vast majority of palm oil for 

biofuel production would be extracted 

in these two countries. If the portion of 

facilities capturing biogas outside of 

Malaysia and Indonesia is different than 

currently within Malaysia and 

Indonesia or if the methane capture/use 

efficiencies are different than assumed 

in our analysis, then the average GHG 

emissions from palm mill operations 

would be different and the overall GHG 

performance of the biofuels produced 

from palm oil would be different than 

determined in our analysis. Because the 

vast majority of palm oil biofuel 

production is likely to occur in 

Indonesia and Malaysia, the impact of 

these differences on our results would 

be minimized because our analysis 


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Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 4311 

54For more information about the Clean 

Development Mechanism, which is implemented 

under the United Nations Framework Convention 

on Climate Change, refer to: http:// 

cdm.unfccc.int/. 

55We note, however, that, based on our analysis, 

our proposed determinations regarding lifecycle 

GHG thresholds would not change even if we 

assumed that all of the methane capture projects 

being planned in Malaysia will come to fruition. 

See Section II.D.2 for more information. 

56Using the Web site: http://cdm.unfccc.int/ 

Projects/projsearch.html; six project title searches 

were completed with the keywords ‘‘palm’’, 

‘‘POME’’, ‘‘wastewater’’, ‘‘waste water’’, ‘‘biogas’’, 

and ‘‘methane.’’ Search results were then examined 

to determine which projects involved methane 

capture from anaerobic digestion of POME. 

57These 47 mills represent approximately 79% of 

the mills with operational methane capture and use 

projects, but only about 5% of all mills in Indonesia 

and Malaysia. 58Cross-checking the registered mills with an EC 

list of CERs surrendered under the EU ETS as of 

March 19, 2010 yielded no matches. Unfortunately, 

due to the design of their electronic databases, the 

European Commission was unable to verify for us 

whether any of the CERs generated by methane 

capture at palm oil mills have been purchased and 

used by European companies. Personal 

communication with Thomas Bernheim (European 

Commission) from September 23, 2011. 

looks at average palm oil production 

practices. 

For this analysis, we determined the 

percentage of facilities employing 

methane capture/use based on projects 

currently in operation or under 

construction (facilities in the planning 

stage are not included). The analysis 

does not include any projected increases 

in the number of facilities that will 

employ these technologies above and 

beyond those currently operating or 

being installed between now and 2022. 

We do not project an increase because 

we are not aware of a technical or 

economic basis for making such a 

projection. For example, we do not have 

a sufficient technical or economic basis 

for determining how many of the mills 

in Malaysia that are at some stage of 

planning methane capture and use 

projects will actually follow through 

with construction and operation. For 

Indonesia and other countries we have 

even less information about additional 

possible deployment of such projects. 

Methane capture and use as applied to 

palm oil mills is a relatively new 

technology which has not been widely 

adopted (i.e., 10% of mills are currently 

using this technology in Indonesia and 

Malaysia). At this time, adoption of 

methane capture and use technology is 

entirely done voluntarily; there are no 

laws requiring its deployment. 

There are no mandatory requirements 

to install methane capture and use 

technologies, and no other strong 

reasons on which to base a projection of 

increased adoption of these 

technologies. Methane capture and use 

involves clear and significant costs, both 

in terms of equipment purchase and 

installation as well as in routine 

maintenance. If the captured methane is 

flared, the only option for a facility to 

recoup a portion of its costs would be 

through some type of certified emission 

reduction credit program, such as 

through the CDM.54 Certification under 

the CDM, though, requires additional 

time and costs and after more than a 

decade of operation the incentives 

provided by the CDM have spurred 

limited adoption of biogas capture at 

palm oil mills, as evidenced by the data 

on adoption of methane capture and use 

technologies at palm oil mills in 

Malaysia and Indonesia discussed 

above. 

We recognize that in some cases, it 

may make economic sense to, at 

additional cost, install equipment for 

using the methane as a fuel to generate 

electricity. Currently, palm oil mills in 

remote areas which do not have access 

to grid electricity tend to burn waste 

palm material to generate necessary 

process energy. EPA does not have 

sufficient information on which to 

determine how many facilities will, for 

economic reasons, choose to replace 

current equipment using the burning of 

waste palm material with methane 

capture and electricity generation 

capacity. 

This lack of information and basis for 

projecting the increased use of methane 

capture and use contrasts to other cases 

where, in the context of performing 

lifecycle GHG emissions analysis for the 

RFS program, we have been able to 

project technology improvements 

through 2022. For example, we have 

many years of data demonstrating a 

gradual increase in crop yields per acre 

for palm oil. Additionally, we know that 

substantial research continues in further 

improvements to palm oil yields and 

that as new varieties of oil palm come 

on market farmers have a natural 

economic incentive to adopt the 

enhanced crop varieties. We are thus 

able to project with a reasonably high 

degree of confidence a rate of continued 

improvement in palm oil crop yield 

through 2022. By contrast, we 

determined that biodiesel production 

technologies are mature and therefore 

we do not predict any improvements in 

process technology. In sum, where we 

have had sufficient information to 

predict improvements in the general 

state of technology across the industry, 

we have done so, but where no such 

basis exists—such as for methane 

capture/use at palm oil mills—we do 

not include such projections in our 

analysis.55

At least some methane capture/use 

projects at palm oil mills in Malaysia 

and Indonesia are registered under the 

CDM, but our analysis does not treat 

emission reductions differently based 

on whether or not a palm oil mill’s 

methane capture/use project is CDMregistered. As defined in Article 12 of 

the Kyoto Protocol, the CDM allows a 

country with an emission-reduction or 

emission-limitation commitment under 

the Kyoto Protocol to implement 

emission-reduction projects in 

developing countries. Such projects can 

earn saleable certified emission 

reduction (CER) credits, each equivalent 

to one tonne of CO2, which can be 

counted towards meeting Kyoto targets. 

For example, CERs can be used for 

compliance purposes under the 

European Union’s (EU) Emissions 

Trading System (ETS). A CER from a 

palm oil methane destruction project in 

Malaysia, for example, could 

conceivably be used for compliance 

under the EU ETS. Under such a 

scenario, an argument could be made 

that counting the emission reductions 

from a ‘‘retired’’ CER as part of our 

lifecycle analysis would effectively be 

double counting the same emission 

reduction. While CDM’s project 

database states that 47 palm oil mills in 

Indonesia and Malaysia have methane 

capture/use projects registered with the 

CDM,56 57 we have been unable to verify 

that any CERs generated by methane 

capture/use at the relevant palm oil 

mills have actually been used to meet 

obligations under the EU ETS.58

However, even if all of the available 

CER credits for methane emissions 

reduction had been purchased and 

retired for compliance purposes (and 

were thus not counted in our analysis), 

this would increase our lifecycle GHG 

emission estimates by only a relatively 

small amount (on the order of 2%). A 

final factor informing our approach on 

this topic is uncertainty about whether 

the CDM and ETS programs will be 

extended in their current form. Based on 

our lack of evidence that relevant CERs 

had been purchased, the relative 

magnitude of the emissions in question, 

and general uncertainty about the future 

of the CDM and ETS programs, our 

approach for lifecycle analysis purposes 

is to treat emission reductions from 

CDM-registered palm oil projects as we 

treat any other emission reduction. 

While we believe we do not have a 

strong technical or economic basis 

treating them otherwise at this time, we 

ask for further comment on this topic. 

According to the MPOB, another 

potential practice that can avoid 

methane emissions from palm oil mills 

entails recovering the organic solids 


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4312 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

59MPOB (2010). 

60EPA (2011). 

61The 95% confidence interval around that 

midpoint results in range of a 4% increase to a 35% 

reduction compared to the 2005 diesel fuel 

baseline. 

from POME so that there is no anaerobic 

digestion and therefore no methane 

emissions.59 Unless the recovered solids 

are used to replace other products the 

GHG reduction benefits of this 

technology are likely to be less than 

reductions associated with methane 

capture/use for electricity generation. 

MPOB data suggests that methane 

avoidance has not been deployed at a 

significant number of palm oil mills. 

Because we do not have a strong 

technical or economic basis for 

projecting the deployment of this 

technology it is not considered in our 

lifecycle analysis. 

Our analysis also accounts for the coproducts from palm oil mills. We 

assume that the biomass co-products 

(e.g., mesocarp fiber and shells) are used 

for heat and energy, with remaining 

empty fruit bunches trucked back to the 

field for use as fertilizer. We also 

account for the palm kernel co-product 

and model the emissions related to 

transporting the palm kernels to a 

separate milling facility where palm 

kernel oil and palm kernel meal are 

produced. Our agricultural modeling 

accounts for the use of the palm kernel 

oil and meal in the food and feed 

markets. 

The docket includes a memorandum 

with more discussion of and 

justification for the data, inputs and 

assumptions used in our analysis of 

palm oil mills.60 EPA invites comment 

on all aspects of its modeling of 

lifecycle GHG emissions from palm oil 

mills, including all of the assumptions 

and data inputs used. 

B. Results of Lifecycle Analysis for 

Biodiesel from Palm Oil 

We analyzed the lifecycle GHG 

emission impacts of producing biodiesel 

using palm oil as a feedstock assuming 

the same biodiesel production facility 

designs and conversion efficiencies as 

modeled in RFS2 for biodiesel produced 

from soybean oil. Our analysis looks at 

biodiesel produced in Indonesia or 

Malaysia which is then shipped to the 

United States via ocean tanker. As such, 

GHG emissions associated with 

electricity used at biodiesel production 

facilities were determined based on the 

emissions factors for grid average 

electricity generation in Indonesia and 

Malaysia. 

As was the case for soybean oil 

biodiesel, production technology for 

palm oil biodiesel is mature and we 

have not projected in our assessment of 

palm oil biodiesel any significant 

improvements in plant technology; 

while unanticipated energy saving 

improvements would tend to improve 

GHG performance of the fuel pathway, 

there is no valid basis for projecting 

such improvements. Additionally, 

similar to soybean oil biodiesel 

production, we assumed that the coproduct glycerin would displace 

residual oil as a fuel source on an 

energy equivalent basis. 

As part of the RFS2 proposal we 

assumed the glycerin would have no 

value and would effectively receive no 

co-product credits in the soy biodiesel 

pathway. We received numerous 

comments, however, as part of the RFS2 

final rule stating that the glycerin would 

have a beneficial use and should 

generate co-product benefits. Therefore, 

the biodiesel glycerin co-product 

determination made as part of the RFS2 

final rule took into consideration the 

possible range of co-product credit 

results. The actual co-product benefit 

will be based on what products are 

replaced by the glycerin, or what new 

uses the co-product glycerin is applied 

to. The total amount of glycerin 

produced from the biodiesel industry 

will actually be used across a number of 

different markets with different GHG 

impacts. This could include for 

example, replacing petroleum glycerin, 

replacing fuel products (residual oil, 

diesel fuel, natural gas, etc.), or being 

used in new products that don’t have a 

direct replacement, but may 

nevertheless have indirect effects on the 

extent to which existing competing 

products are used. The more immediate 

GHG reductions from glycerin coproduct use will likely range from fairly 

high reductions when petroleum 

glycerin is replaced to lower reduction 

credits if it is used in new markets that 

have no direct replacement product, and 

therefore no replaced emissions. EPA 

does not have sufficient information 

(and received no relevant comments to 

the RFS2 proposal) on which to allocate 

glycerin use across the range of likely 

uses. EPA’s approach is to pick a 

surrogate use for modeling purposes in 

the mid-range of likely glycerin uses, 

and focus on the more immediate GHG 

emissions results tied to such use. The 

replacement of an energy equivalent 

amount of residual oil is a simplifying 

assumption determined by EPA to 

reflect the mid-range of possible 

glycerin uses in terms of GHG credits, 

and EPA believes that it is appropriately 

representative of GHG reduction credit 

across the possible range without 

necessarily biasing the results toward 

high or low GHG impact. Given the 

fundamental difficulty of predicting 

possible glycerin uses and impacts of 

those uses many years into the future 

under different market conditions, EPA 

believes it is reasonable to use its more 

simplified approach to calculating coproduct GHG benefit associated with 

glycerin production. To narrow this area 

of uncertainty in our analysis we invite 

commenters to submit data regarding 

the use of glycerin produced at biodiesel 

production facilities, and especially for 

glycerin produced at facilities that are 

based in Indonesia or Malaysia or that 

use palm oil as a feedstock. 

As with other EPA analyses of fuel 

pathways with a significant land use 

impact, our analysis for palm oil 

biodiesel includes a mid-point estimate 

as well as a range of possible lifecycle 

GHG emission results based on 

uncertainty analysis conducted by the 

Agency. The graph included below 

(Figure II–1) depicts the results of our 

analysis (including the uncertainty in 

our land use change modeling) for palm 

oil biodiesel produced via transesterification using natural gas as 

process energy, because this is the 

primary source of process energy at 

existing plants. The docket also 

includes pathway analyses assuming 

coal or biomass is used instead of 

natural gas for process energy. Because 

the trans-esterification process requires 

a relatively small amount of energy, our 

threshold determinations would remain 

the same for the palm oil biodiesel 

pathway regardless of whether natural 

gas, coal or biomass is used for energy 

in the biodiesel production process. 

Figure II–1 shows the results of our 

biodiesel modeling. It shows the percent 

difference between lifecycle GHG 

emissions for the modeled 2022 palm 

oil biodiesel, produced via transesterification using natural gas for 

process energy, and those for the 

petroleum diesel fuel 2005 baseline. 

Lifecycle GHG emissions equivalent to 

the statutory diesel fuel baseline are 

represented on the graph by the zero on 

the X-axis. The results for palm oil 

biodiesel are that the midpoint of the 

range of results is a 17% reduction in 

GHG emissions compared to the 2005 

diesel fuel baseline.61 As in the case of 

other biofuel pathways analyzed as part 

of the RFS2 rule, the range of results 

shown in Figure II–1 is based on our 

assessment of uncertainty regarding the 

location and types of land that may be 

impacted as well as the GHG impacts 

associated with these land use changes 

(See Section II.D.3. for further 

information). These results, if finalized, 


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Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 4313 

62Totals in the table may not sum due to 

rounding. 

would justify our determination that 

fuel produced by the modeled palm oil 

biodiesel pathway fails to meet the 20% 

reduction threshold required for the 

generation of conventional renewable 

fuel RINs. 

Table II–8 breaks down by stage the 

lifecycle GHG emissions for palm oil 

biodiesel in 2022 and the statutory 2005 

diesel baseline.62 Results are included 

using our mid-point estimate of land use 

change emissions, as well as with the 

low and high end of the 95% confidence 

interval. Net agricultural emissions 

include impacts related to changes in 

crop inputs, such as fertilizer, energy 

used in agriculture, livestock 

production and other agricultural 

changes in the scenarios modeled. Land 

use change emissions are discussed 

above in Section II.A.4. Emissions from 

fuel production include emissions from 

palm oil mills, palm kernel mills and 

the trans-esterification process to 

produce biodiesel. Fuel and feedstock 

transport includes emissions from 

transporting fresh fruit bunches, palm 

kernels, crude palm oil and finished 

biodiesel along each stage of the 

lifecycle. In our analysis we assume that 

palm oil is converted to biodiesel in 

Indonesia and Malaysia and then the 

biodiesel is transported via ocean tanker 

to the U.S. Transporting crude palm oil 

to the U.S. would result in greater GHG 

emissions because biodiesel has greater 

energy density than crude palm oil. 

TABLE II–8—LIFECYCLE GHG EMISSIONS FOR PALM OIL BIODIESEL

[kgCO2e/mmBtu] 

Fuel type Palm oil biodiesel 2005 Diesel baseline 

Net Agriculture (w/o land use change) ............................................................................................ 5 ....................................

Land Use Change, Mean (Low/High) .............................................................................................. 46 (28/66) ....................................

Fuel Production ................................................................................................................................ 25 18 

Fuel and Feedstock Transport ........................................................................................................ 4 * 


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4314 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

63For a similar discussion see Stratton R.W., 

Wong, H.M., Hileman, J.I., 2011. Quantifying 

Variability in Lifecycle Greenhouse Gas Inventories 

of Alternative Middle Distillate Transportation 

Fuels. Environmental Science & Technology. 45, 

4640. 

64 Kalnes, T.N., McCall, M.M., Shonnard, D.R., 

2010. Renewable Diesel and Jet-Fuel Production 

from Fats and Oils. Thermochemical Conversion of 

Biomass to Liquid Fuels and Chemicals, Chapter 18, 

p. 475. 65Pearlson, M.N., 2011. A Techno-Economic and 

Environmental Assessment of Hydroprocessed 

Renewable Distillate Fuels. http://dspace.mit.edu/ 

handle/1721.1/65508. 66Huo, H., Wang, M., Bloyd, C., Putsche, V., 

2008. Life-Cycle Assessment of Energy and 

Greenhouse Gas Effects of Soybean-Derived 

Biodiesel and Renewable Fuels. Argonne National 

Laboratory. Energy Systems Division. ANL/ESD/08– 

2. March 12, 2008. 

TABLE II–8—LIFECYCLE GHG EMISSIONS FOR PALM OIL BIODIESEL—Continued 

[kgCO2e/mmBtu] 

Fuel type Palm oil biodiesel 2005 Diesel baseline 

Tailpipe Emissions ........................................................................................................................... 1 79 

Total Emissions, Mean (Low/High) .......................................................................................... 80 (62/101) 97 

Midpoint Lifecycle GHG Percent Reduction Compared to Petroleum Baseline ............................. 17% ....................................

* Emissions included in fuel production stage. 

The docket for this NODA provides 

more details on our key model inputs 

and assumptions, e.g., crop yields, 

biofuel conversion yields, and 

agricultural energy use. These inputs 

and assumptions are based on our 

analysis of peer-reviewed literature and 

consideration of recommendations of 

experts from within the palm oil and 

biodiesel industries and those from 

USDA as well as the experts at Iowa 

State University who have designed the 

FAPRI-CARD models. EPA invites 

comment on all aspects of its modeling 

of palm oil biodiesel, including all 

assumptions made and modeling inputs. 

C. Results of Lifecycle Analysis for 

Renewable Diesel From Palm Oil 

Palm oil can also be used in a 

hydrotreating process to produce a slate 

of products, including diesel fuel, 

heating oil (defined as No. 1 or No. 2 

diesel), jet fuel, naphtha, liquefied 

petroleum gas (LPG), and propane. 

Since the RFS regulations define the 

term renewable diesel to include the 

products diesel fuel, jet fuel and heating 

oil (40 CFR 80.1401), the following 

discussion uses the term renewable 

diesel to refer to all of these products. 

(The terms diesel fuel or diesel fuel 

replacement are used to refer to only the 

diesel fraction of the hydrotreating 

output.) While any propane (also 

referred to as fuel gas) produced as part 

of the hydrotreating process will most 

likely be combusted within the facility 

for process energy, the other coproducts that can be produced (i.e., jet 

fuel, naphtha, LPG) are higher value 

products that could be used as 

transportation fuels or, in the case of 

naphtha, a blendstock for production of 

transportation fuel. The hydrotreating 

process maximized for producing a 

diesel fuel replacement as the primary 

fuel product requires more overall 

material and energy inputs than 

transesterification to produce biodiesel, 

but it also results in a greater amount of 

other valuable co-products, as listed 

above. The hydrotreating process can 

also be maximized for jet fuel 

production which requires even more 

process energy than the process 

optimized for producing a diesel fuel 

replacement and produces a greater 

amount of co-products per barrel of 

feedstock, especially naphtha. 

Our lifecycle analysis accounts for the 

various uses of the co-products from 

hydrotreating. There are two main 

approaches to accounting for the coproducts produced, the allocation 

approach and the displacement 

approach. In the allocation approach all 

the emissions from the hydrotreating 

process are allocated across all the 

different co-products. There are a 

number of ways to do this, but since the 

main use of the co-products would be as 

fuel products, we allocate based on the 

energy content of the co-products 

produced. So emissions from the 

process would be allocated equally to 

all the Btus produced. Therefore, on a 

per Btu basis all co-products would 

have the same emissions. The 

displacement approach would attribute 

all of the emissions of the hydrotreating 

process to one main product and then 

account for the emission reductions 

from the other co-products displacing 

alternative products. So for example, if 

the hydrotreating process is configured 

to maximize renewable diesel 

production all of the emissions from the 

process would be attributed to 

renewable diesel, but we would then 

assume the other co-products were 

displacing alternative products, for 

example, naphtha would displace 

gasoline, LPG would displace natural 

gas, etc. This assumes the other 

alternative products are not produced or 

used so we would subtract the 

emissions of gasoline production and 

use, natural gas production and use, etc. 

This would show up as a GHG emission 

credit associated with the production of 

the renewable diesel. 

To account for a hypothetical scenario 

where RINs are generated from the 

renewable jet fuel, heating oil, naphtha 

and LPG in addition to the diesel 

replacement fuel produced, we would 

not give the diesel replacement fuel a 

displacement credit for these coproducts. Instead, the lifecycle GHG 

emissions from the fuel production 

processes would be allocated to each of 

the RIN-generating products on an 

energy content basis. This has the effect 

of tending to increase the fuel 

production lifecycle GHG emissions 

associated with the diesel replacement 

fuel because there are fewer co-product 

displacement credits to assign than 

would be the case if RINs were not 

generated for the co-products.63 On the 

other hand, the upstream lifecycle GHG 

emissions associated with producing 

and transporting the plant oil feedstocks 

will be distributed over a larger group 

of RIN-generating products. Assuming 

each product (except propane) produced 

via the palm oil hydrotreating process 

would generate RINs results in higher 

lifecycle GHG emissions for diesel fuel 

replacement as compared to the case 

where the co-products are not used to 

generate RINs. This general principle is 

also true when the hydrotreating 

process is maximized for jet fuel 

production. As a result, the best GHG 

performance (i.e., least lifecycle GHG 

emissions) for palm-oil renewable diesel 

via hydrotreating will occur when none 

of the co-products are RIN-generating 

(i.e., only the diesel replacement fuel is 

used to generate RINs). 

We have evaluated information about 

the lifecycle GHG emissions associated 

with the hydrotreating process which 

can be maximized for renewable jet fuel 

or diesel production. Our evaluation 

considers information published in 

peer-reviewed journal articles and 

publicly available literature (Kalnes et 

al.,64 Pearlson,65 Stratton et al., Huo et 

al.66). Our analysis of GHG emissions 

from the hydrotreating process is based 


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Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 4315 

67We have also considered data submitted by 

companies involved in the hydrotreating industry 

which is claimed as confidential business 

information (CBI). The conclusions using the CBI 

data are consistent with the analysis presented here. 

68Based on Pearlson, Table 3.1 and Table 3.2. 

69Co-product displacement accounting is 

described further in the inputs and assumptions 

document available through the public docket for 

this notice. 

on the mass and energy balance data in 

Pearlson which analyzes a hydrotreating 

process maximized for diesel 

production and a hydrotreating process 

maximized for jet fuel production.67

These data are summarized in Table II– 

9.68

TABLE II–9—HYDROTREATING PROCESS TO PRODUCE RENEWABLE DIESEL FUEL

Maximized for diesel fuel 

production 

Maximized for jet fuel 

production 

Units (per gallon of 

fuel produced) 

Inputs 

Crude Palm Oil ............................................................................. 9.56 12.84 Lbs. 

Hydrogen ...................................................................................... 0.04 0.08 Lbs. 

Electricity ...................................................................................... 652 865 Btu. 

Natural Gas .................................................................................. 23,247 38,519 Btu. 

Outputs: 

Diesel Fuel ................................................................................... 123,136 55,845 Btu. 

Jet Fuel ........................................................................................ 23,197 118,669 Btu. 

Naphtha ........................................................................................ 3,306 17,042 Btu. 

LPG .............................................................................................. 3,084 15,528 Btu. 

Propane ........................................................................................ 7,454 9,881 Btu. 

Table II–10 compares lifecycle GHG 

emissions from hydrotreating for palmoil-based renewable diesel and jet fuel. 

The lifecycle GHG estimates for palmoil diesel and jet fuel are based on the 

input/output data summarized in Table 

II–9. For the scenarios analyzed, we 

assume that the LPG and propane coproducts do not generate RINs; instead, 

they are used for process energy 

displacing natural gas. We also assume 

that the naphtha does not generate RINs 

but is used as blendstock for production 

of transportation fuel displacing 

conventional gasoline. As discussed 

above, lifecycle GHG emissions per Btu 

of diesel or jet fuel would be higher if 

the naphtha or LPG were used to 

generate RINs. 

TABLE II–10—HYDROTREATING LIFECYCLE GHG EMISSIONS

[gCO2e/mmBtu] 

Process RIN-generating products Other 

co-products 

Hydrotreating 

emissions 

Hydrotreating Maximized for Diesel ...................................... Diesel ..................................... Naphtha ................................. 4,448 

Jet Fuel .................................. LPG.

................................................ Propane.

Hydrotreating Maximized for Jet Fuel ................................... Diesel ..................................... Naphtha ................................. (3,358) 

Jet Fuel .................................. LPG.

................................................ Propane.

In Table II–10 the process maximized 

for jet fuel production results in 

negative emissions at the hydrotreating 

stage. This is due to the displacement 

credits for co-products, especially 

naphtha, replacing conventional 

gasoline.69 As shown in Table II–9, the 

process maximized for jet fuel 

production requires significantly more 

crude palm oil per Btu of fuel output. 

Each additional pound of palm oil used 

in the process has related lifecycle GHG 

emissions associated with producing, 

processing and transporting the palm oil 

to the hydrotreating facility. As a result, 

when palm oil is used as the feedstock, 

the full lifecycle GHG emissions are 

greater for the process maximized for jet 

fuel when all of the stages of the 

lifecycle are factored into the analysis. 

Unless otherwise noted, the analysis of 

palm oil renewable diesel in this 

preamble refers to the first scenario in 

Table II–10: hydrotreating maximized 

for production of diesel fuel 

replacement. Supporting information for 

the values in Table II–10 is provided 

through the docket. 

As discussed above, for a process that 

produces more than one RIN-generating 

output we allocate lifecycle GHG 

emissions to the RIN-generating 

products on an energy equivalent basis. 

We then normalize the allocated 

lifecycle GHG emissions per mmBtu of 

each fuel product. Therefore, each RINgenerating product from the same 

process will be assigned equal lifecycle 

GHG emissions per mmBtu from fuel 

processing. For example, based on the 

lifecycle GHG estimates in Table II–10, 

for the hydrotreating process maximized 

to produce diesel fuel, the diesel and jet 

fuel both have lifecycle GHG emissions 

of 4,448 gCO2e/mmBtu. For the same 

reasons, the lifecycle GHG emissions 

from the diesel and jet fuel will stay 

equivalent if we consider upstream GHG 

emissions, such as emissions associated 

with palm oil cultivation and land use 

change. Lifecycle GHG emissions from 

fuel distribution and use could be 

somewhat different for the diesel and jet 

fuel, but since these stages produce a 

relatively small share of the emissions 

related to the full fuel lifecycle, the 

overall differences will be quite small. 

The results presented below include 

emissions related to transporting palm 

oil-based diesel fuel. 

We model the production technology 

for palm oil renewable diesel as mature 

and therefore have not projected in our 

assessment any significant 

improvements in plant technology. 

Unanticipated energy saving 


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4316 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

70The 95% confidence interval around that 

midpoint results in range of a 10% increase to a 

30% reduction compared to the 2005 diesel fuel 

baseline. 

71 In the table totals may not sum due to 

rounding. 

improvements would improve GHG 

performance of the fuel pathway, but at 

this time we do not have a strong 

technical basis for including any such 

improvements. 

Figure II–2 summarizes the results of 

our modeling of palm oil renewable 

diesel, with fuel production emissions 

allocated between the diesel fuel and jet 

fuel outputs and displacement credit 

given for the naphtha output. It shows 

the percent difference between lifecycle 

GHG emissions for palm oil renewable 

diesel produced in 2022 and those for 

the statutory petroleum baseline. 

Lifecycle GHG emissions equivalent to 

the diesel baseline are represented on 

the graph by the zero on the X-axis. The 

results for palm oil renewable diesel are 

that the midpoint of the range of results 

is an 11% reduction in GHG emissions 

compared to the diesel fuel baseline.70

As with Figure II–1, the range of results 

shown in Figure II–2 is based on our 

assessment of uncertainty regarding the 

location and types of land that may be 

impacted as well as the GHG impacts 

associated with these land use changes. 

These results, if finalized, would justify 

our determination that fuel produced by 

the modeled palm oil renewable diesel 

pathway fails to meet the 20% reduction 

threshold required for the generation of 

conventional renewable fuel RINs. 

Table II–11 breaks down by stage the 

lifecycle GHG emissions for palm oil 

renewable diesel in 2022 and the 

statutory diesel baseline.71 This table 

demonstrates the contribution of each 

stage and its relative significance. 

Results are included using our midpoint estimate of land use change 

emissions, as well as with the low and 

high end of the 95% confidence 

interval. Net agricultural emissions 

include impacts related to changes in 

crop inputs, such as fertilizer, energy 

used in agriculture, livestock 

production and other agricultural 

changes in the scenarios modeled. Land 

use change emissions are discussed 

above in Section II.A.4. Emissions from 

fuel production include emissions from 

palm oil mills, palm kernel mills and 

the hydrotreating process to produce 

renewable biodiesel. Fuel and feedstock 

transport includes emissions from 

transporting fresh fruit bunches, palm 

kernels, crude palm oil and finished 

renewable diesel along each stage of the 

lifecycle. 


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Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 4317 

TABLE II–11—LIFECYCLE GHG EMISSIONS FOR PALM OIL RENEWABLE DIESEL

[kgCO2E/mmBtu] 

Fuel type 

Palm oil 

renewable 

diesel 

2005 

diesel 

baseline 

Net Agriculture (w/o land use change) .............................................................................................................................. 5 ................

Land Use Change, Mean (Low/High) ................................................................................................................................ 47 (28/67) ................

Fuel Production .................................................................................................................................................................. 31 18 

Fuel and Feedstock Transport .......................................................................................................................................... 4 (*) 

Tailpipe Emissions ............................................................................................................................................................. 1 79 

Total Emissions, Mean (Low/High) ............................................................................................................................ 87 (68/107) 97 

Midpoint Lifecycle GHG Percent Reduction Compared to Petroleum Baseline ............................................................... 11% ................

* Emissions included in fuel production stage. 

The docket includes a memorandum 

which summarizes relevant materials 

used for the palm oil renewable diesel 

analysis. Described in the 

memorandum, for example, are the 

input and assumptions document and 

detailed results spreadsheets (e.g., 

agricultural impacts, agricultural energy 

use, FAPRI–CARD model results) used 

to generate the results presented. The 

input and assumptions document 

available through the docket describes 

many aspects of our analysis, including 

our co-product accounting approach. 

EPA invites comment on all aspects of 

its modeling of palm oil renewable 

diesel including all assumptions made 

and modeling inputs. 

D. Consideration of Lifecycle Analysis 

Results 

1. Implications for Threshold 

Determinations 

As discussed above, EPA’s analysis of 

the two types of biofuel shows that, 

based on the mid-point of the range of 

results, biodiesel and renewable diesel 

produced from palm oil have estimated 

lifecycle GHG emission reductions of 

17% and 11% respectively compared to 

the statutory petroleum baseline used in 

the RFS program. The results for palm 

oil biodiesel and for palm oil renewable 

diesel, if finalized, would justify 

treating these fuel pathways as failing to 

meet the minimum 20% lifecycle GHG 

reduction requirement in the RFS 

program for non-grandfathered biofuels. 

Our analysis applies to the modeled 

palm oil biodiesel and palm oil 

renewable diesel pathways regardless of 

their country of origin (See 75 FR 14793 

for a similar discussion regarding other 

pathways). We project that the vast 

majority of palm oil used to produce 

biofuels for use in the United States 

would be produced in Indonesia and 

Malaysia (See Table II–1). Although 

palm oil and palm oil biofuel 

production may occur in other countries 

that have not been specifically modeled, 

or may be supplied from countries in 

different proportions than we modeled, 

we anticipate their use would not 

impact our conclusions regarding the 

lifecycle GHG thresholds met by the 

palm oil biofuel pathways under 

consideration. The emissions of 

producing these fuels in other countries 

could be slightly higher or lower than 

what was modeled depending on a 

number of factors. Our analysis 

indicates that crop yields in other 

countries where palm oil would most 

likely be produced tend to be lower than 

Malaysia and Indonesia, pointing 

toward somewhat higher land use 

change and consequently potentially 

higher land use change GHG impacts. If 

the supply of palm oil from other 

countries were to reduce the amount of 

agricultural expansion in Indonesia and 

Malaysia, with potentially reduced 

amounts of peat soil drainage, as 

compared to the amount predicted in 

our modeling, this would tend to lower 

our estimate of GHG emissions per acre 

of land use change. Technologies for 

turning this palm oil into biofuel are 

well established and would be expected 

to be similar in different countries. 

Based on these offsetting land use 

impact factors, similar biofuel 

production technology, and the small 

amounts of palm oil for biofuel likely to 

come from other countries, we conclude 

that incorporating palm oil from other 

countries would not impact our 

threshold determinations. 

2. Consideration of Uncertainty 

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. 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 best-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 inherent 

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. Therefore, 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. 

Our analysis of land use change GHG 

emissions includes an assessment of 

uncertainty that focuses on two aspects 

of indirect land use change—the types 

of land converted and the GHG 

emissions associates with different 

types of land converted. These areas of 

uncertainty were estimated statistically 


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4318 Federal Register / Vol. 77, No. 18 / Friday, January 27, 2012 / Notices 

72The Monte Carlo analysis is described in EPA 

(2010a), Section 2.4.4.2.8. 

using the Monte Carlo analysis 

methodology developed for the RFS2 

final rule.72 Figure II–1 and Figure II–2 

show the results of our statistical 

uncertainty assessment. In analyzing 

both palm oil biofuel pathways, the 

midpoint results, and therefore the 

majority of the scenarios analyzed, fail 

to meet the 20% lifecycle GHG 

reduction requirement for nongrandfathered renewable fuels. 

We have also identified areas of 

uncertainty that are not explicitly 

addressed in our Monte Carlo analysis 

due to time considerations. These areas 

of uncertainty have been assessed with 

sensitivity analysis and qualitative 

inspection. A majority of the areas of 

uncertainty considered could result in 

higher actual lifecycle GHG emissions 

than estimated in our midpoint results. 

These aspects of our analysis include 

uncertainties regarding: the total area of 

projected incremental palm oil 

expansion; the percent of palm oil 

expansion impacting tropical peat 

swamp forests; and indirect emissions 

related to peat soil drainage, such as 

from an increased risk of forest fires or 

collateral drainage of nearby 

uncultivated land. For these areas of 

uncertainty it is our judgment that our 

midpoint estimates likely underestimate 

the actual amount of lifecycle GHG 

emissions, but it is unlikely that they 

overestimate the actual emissions. We 

have also identified a smaller number of 

uncertainties which could result in less 

actual emissions. For example, 

increased adoption of methane capture/ 

use technologies at palm oil mills and 

future government restrictions on peat 

soil development would likely result in 

less actual emissions than estimated in 

our midpoint results. Regarding 

methane capture and use projections, 

we conducted sensitivity analysis 

assuming that all mills use closed 

digester tanks with 90% methane 

capture efficiency, and convert the 

methane to electricity with 34% 

efficiency for export to the grid. In this 

sensitivity scenario, the mid-point 

results for palm oil biodiesel and 

renewable diesel are 42% and 36% 

reductions compared to the diesel 

baseline, respectively. Thus, even in 

this very optimistic scenario, neither of 

the palm oil biofuel pathways analyzed 

achieves a 50% GHG reduction. Our 

consideration of uncertainties in our 

lifecycle assessments is described 

further in a reference document 

available through the public docket. 

Based on the weight of evidence 

considered, and putting the most weight 

on our mid-point estimate results, the 

results of our analysis indicate that both 

palm oil based biofuels pathways would 

fail to qualify as meeting the minimum 

20% GHG performance threshold for 

qualifying 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. 

Based on the results of our analysis and 

considering key areas of uncertainty, the 

minimum 20% lifecycle GHG reduction 

requirements for non-grandfathered 

fuels under the RFS program is not 

achieved for the palm oil biofuel 

pathways evaluated. 

The docket for this NODA provides 

more details on all aspects of our 

analysis of palm oil biofuels. EPA 

invites comment on all aspects of its 

modeling of palm oil biodiesel and 

renewable diesel. We also invite 

comment on the consideration of 

uncertainty as it relates to making GHG 

threshold determinations. 

Dated: December 14, 2011. 

Margo T. Oge, 

Director, Office of Transportation & Air 

Quality. 

[FR Doc. 2012–1784 Filed 1–26–12; 8:45 am] 

BILLING CODE 6560–50–P 

ENVIRONMENTAL PROTECTION 

AGENCY 

[ER–FRL–9001–3] 

Environmental Impacts Statements; 

Notice of Availability 

Responsible Agency: Office of Federal 

Activities, General Information (202) 

564–7146 or http://www.epa.gov/

compliance/nepa/. 

Weekly Receipt of Environmental 

Impact Statements 

Filed 01/17/2012 Through 01/20/2012 

Pursuant to 40 CFR 1506.9. 

Notice 

Section 309(a) of the Clean Air Act 

requires that EPA make public its 

comments on EISs issued by other 

Federal agencies. EPA’s comment letters 

on EIS are available at: http://www.epa.

gov/compliance/nepa/eisdata.html. 

EIS No. 20120013, Final EIS, USFS, ID, 

Clearwater National Forest Travel 

Planning Project, Proposes to Manage 

Motorized and Mechanized Travel, 

Clearwater National Forest, Idaho, 

Clearwater, Latah and Shoshone 

Counties, ID, Review Period Ends: 

02/27/2012, Contact: Heather Berg 

(208) 476–4541. 

EIS No. 20120014, Revised Draft EIS, 

USFS, MT, East Deer Lodge Valley 

Landscape Restoration Management 

Project, To Conduct Landscape 

Restoration Management Activities, 

Additional Information Including the 

Addition of Alternative 3, Pintler 

Ranger District, Beaverhead Deerlodge 

National Forest, Powell and Deerlodge 

Counties, MT, Comment Period Ends: 

03/12/2012, Contact: Brent Lignell 

(406) 494–2147. 

EIS No. 20120015, Draft EIS, FTA, WA, 

Mukilteo Multimodal Project, To 

Improve the Operations, Safety and 

Security of Facilities Serving the 

Mukilteo-Clinton Ferry Route, 

Funding, USACE Section 10 and 404 

Permits, Snohomish County, WA, 

Comment Period Ends: 03/12/2012, 

Contact: Daniel Drais (206) 220–4465. 

EIS No. 20120016, Draft EIS, BLM, NV, 

Hycroft Mine Expansion Project, 

Proposes to Expand Mining Activities 

on BLM Managed Public Land and 

Private Land, Approval, Humboldt 

and Pershing Counties, NV, Comment 

Period Ends: 03/12/2012, Contact: 

Kathleen Rehberg (775) 623–1500. 

EIS No. 20120017, Draft EIS, FHWA, 

NY, Tappan Zee Hudson River 

Crossing Project, To Provide an 

Improved Hudson River Crossing 

between Rockland and Westchester 

Counties Funding, USACE Section 10 

and 404 Permits, Rockland and 

Westchester Counties, NY, Comment 

Period Ends: 03/15/2012, Contact: 

Jonathan D. McDade (518) 431–4125. 

EIS No. 20120018, Final EIS, FHWA, 

CA, State Route 76 South Mission 

Road to Interstate 15 Highway 

Improvement Project, Widening and 

Realignment Including Interchange 

Improvements, USACE Section 404 

Permit, San Diego County, CA, 

Review Period Ends: 02/27/2012, 

Contact: Manuel E. Sanchez (619) 

699–7336. 

Amended Notices 

EIS No. 20110350, Draft EIS, USFS, AZ, 

Rosemont Copper Project, Proposed 

Construction, Operation with 

Concurrent Reclamation and Closure 

of an Open-Pit Copper Mine, 


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