[Federal Register Volume 77, Number 18 (Friday, January 27, 2012)]
[Notices]
[Pages 4300-4318]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2012-1784]
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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).
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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
[[Page 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: [email protected].
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
[email protected]. 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.gov 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: [email protected].
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:
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[GRAPHIC] [TIFF OMITTED] TN27JA12.000
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 biomass-based 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.
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\1\ U.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.
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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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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 oil-based 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.
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\2\ EPA. 2010. Renewable Fuel Standard Program (RFS2) Regulatory
Impact Analysis. EPA-420-R-10-006. http://www.epa.gov/oms/renewablefuels/420r10006.pdf.
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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.
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\3\ For 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.
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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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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]
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Palm biofuel
Control case case Difference
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Indonesia....................................................... 31,254 31,575 321
Malaysia........................................................ 25,992 26,196 204
Rest of World................................................... 7,739 7,777 38
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World....................................................... 64,986 65,548 562
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Table II-2--Projected Vegetable Oil Production in 2022
[Thousand metric tonnes]
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Palm biofuel
Control case case Difference
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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
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Total....................................................... 470,603 471,448 845
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\*\ 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.
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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.
\5\ Historical 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).
Table II-3--Projected Palm Oil Harvested Area in 2022
[Thousand harvested hectares]
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Palm biofuel
Control case case Difference
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Indonesia....................................................... 6,179 6,243 63
Malaysia........................................................ 5,202 5,242 41
Rest of World................................................... 4,035 4,055 20
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World....................................................... 15,416 15,504 124
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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
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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.
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\6\ USDA 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/.
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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 Geospatial-Intelligence
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
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\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.
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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.
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\9\ Wahid, B. O., Nordiana, A. Aand Tarmizi, A., M. 2005.
Satellite Mapping of Oil Palm Land Use. MPOB Information Series.
June 2005.
\10\ MPOB. 2010. Additional Information Requested by United
States Environmental Protection Agency: Agricultural Input. Data
submitted by MPOB. June 4, 2010.
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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
[[Page 4306]]
docket.11 12 We welcome public comments on additional data
sources for consideration in our modeling.
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\11\ Harris, N., and Grimland, S. 2011a. Spatial Modeling of
Future Oil Palm Expansion in Indonesia, 2000 to 2022. Winrock
International. Draft report submitted to EPA.
\12\ Harris, N., and Grimland, S. 2011b. Spatial Modeling of
Future Oil Palm Expansion in Malaysia, 2003 to 2022. Winrock
International. Draft report submitted to EPA.
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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 bio-geophysical 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\
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\13\ Hall, 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.
\14\ Pontius 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.
\15\ Harris, 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.
\16\ Rashmi, 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.
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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\
---------------------------------------------------------------------------
\17\ Harris et al. (2011a) and (2011b).
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\18\ USDA-FAS (2009).
\19\ USDA-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/.
Table II-4--Projected Location of Palm Oil in Indonesia and Malaysia in 2022
[Thousand harvested hectares]
----------------------------------------------------------------------------------------------------------------
Palm biofuel
Country Region Control case case Difference
----------------------------------------------------------------------------------------------------------------
Indonesia............................. Kalimantan.............. 1,396 1,452 56
Sumatra................. 4,782 4,790 8
Malaysia.............................. Peninsular Malaysia..... 3,016 3,048 32
[[Page 4307]]
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\
---------------------------------------------------------------------------
\20\ Unnasch, 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.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\22\ USDA-FAS (2011).
\23\ Unnasch et al.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\24\ Harris et al. (2011a), Table 9.
\25\ Harris et al. (2011b), Table 9.
\26\ Harris et al. (2011a) and (2011b).
\27\ USDA-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.
Table II-5--Projected Land Cover Types Impacted by Palm Oil Expansion in
Indonesia and Malaysia in 2022
------------------------------------------------------------------------
Indonesia Malaysia
Land cover type (%) (%)
------------------------------------------------------------------------
Forest.......................................... 43 54
Mixed........................................... 38 35
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\
---------------------------------------------------------------------------
\29\ Paramananthan, S. 2008. Tussle over Tropical Peatlands.
Global Oils & Fats: Business Magazine. (5)3, 1-16.
\30\ Page, S. E., Morrison, R., Malins, C., Hooijer, A., Rieley,
J. O. & Jauhiainen, J. 2011. Review of peat surface greenhouse gas
emissions from oil palm plantations in Southeast Asia (ICCT White
Paper 15). Washington: International Council on Clean
Transportation.
---------------------------------------------------------------------------
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
[[Page 4308]]
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.
---------------------------------------------------------------------------
\31\ Hooijer, A., Page, S., Canadell, J. G., Silvius, M.,
Kwadijk, J., W[ouml]sten, H., & Jauhiainen, J. 2010. Current and
future CO2 emissions from drained peatlands in Southeast Asia.
Biogeosciences, 7, 1505-1514.
\32\ Miettinen, 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).
\34\ Harris et al. (2011a), Table 22.
\35\ Harris et al. (2011b), Table 19.
Table II-6--Percent of Palm Oil Plantations on Peat Soil, Historical and
Projected
------------------------------------------------------------------------
Indonesia Malaysia
Year (%) (%)
------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\36\ Harris, N., Brown, S., and Grimland, S. 2009a. Global GHG
Emission Factors for Various Land-Use Transitions. Winrock
International. Report Submitted to EPA. April 2009.
\37\ Harris, 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.
\38\ Harris, N. 2011. Revisions to Winrock's Land Conversion
Emission Factors since the RFS2 Final Rule. Winrock International
report to EPA.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\39\ 2006 IPCC Guidelines for National Greenhouse Gas
Inventories Volume 4 Agriculture, Forestry and Other Land Use.
Chapter 5. http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html.
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\40\ Harris (2011).
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\41\ Page et al.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\42\ Hooijer, A., M. Silvius, H. W[ouml]sten and S. Page. 2006.
PEAT-CO2, Assessment of CO2 emissions from
drained peatlands in SE Asia. Delft Hydraulics report Q3943.
---------------------------------------------------------------------------
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
[[Page 4309]]
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\
---------------------------------------------------------------------------
\43\ Hooijer, A., Page, S. E., Jauhiainen, J., Lee, W. A.,
Idris, A., & Anshari, G. 2011. Subsidence and carbon loss in drained
tropical peatlands: reducing uncertainty and implications for
CO2 emission reduction options. Biogeosciences
Discussions, 8, 9311-9356.
\44\ Page et al., 53.
\45\ Jauhiainen, J., Hooijer, A., & Page, S. E. (2011). Carbon
Dioxide Fluxes in an Acacia Plantation on Tropical Peatland.
Biogeosciences Discussions, 8, 8269-8302.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\46\ Saatchi, 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. EU-wide maps of growing stock and
above-ground biomass in forests based on remote sensing and field
measurements. Forest Ecology and Management 260: 252-261.
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\48\ Mokany, K., R.J. Raison, and A.S. Prokushkin. 2006.
Critical analysis of root:shoot ratios in terrestrial biomes. Global
Change Biology 12: 84-96.
\49\ Harris (2011).
---------------------------------------------------------------------------
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 long-
term 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 (m\3\/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\
---------------------------------------------------------------------------
\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.
\51\ Only two other countries have extraction rates above 20
m\3\/ha: India with 33 m\3\/ha and China with 22 m\3\/ha.
\52\ Winjum, J.K., Brown, S., Schlamadinger, B. 1998. Forest
harvests and wood products: Sources and sinks of atmospheric carbon
dioxide. Forest Science 44: 272-284.
\53\ Harris (2011).
---------------------------------------------------------------------------
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
[[Page 4310]]
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
[[Page 4311]]
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.
---------------------------------------------------------------------------
\54\ For 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/.
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\55\ We 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.
---------------------------------------------------------------------------
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 CDM-registered. 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.
---------------------------------------------------------------------------
\56\ Using 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.
\57\ These 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.
\58\ Cross-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.
---------------------------------------------------------------------------
According to the MPOB, another potential practice that can avoid
methane emissions from palm oil mills entails recovering the organic
solids
[[Page 4312]]
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.
---------------------------------------------------------------------------
\59\ MPOB (2010).
---------------------------------------------------------------------------
Our analysis also accounts for the co-products 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.
---------------------------------------------------------------------------
\60\ EPA (2011).
---------------------------------------------------------------------------
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 co-product 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 co-product 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 co-product 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 trans-esterification 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 trans-esterification 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,
[[Page 4313]]
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.
---------------------------------------------------------------------------
\61\ The 95% confidence interval around that midpoint results in
range of a 4% increase to a 35% reduction compared to the 2005
diesel fuel baseline.
[GRAPHIC] [TIFF OMITTED] TN27JA12.001
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.
---------------------------------------------------------------------------
\62\ Totals in the table may not sum due to rounding.
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 5 ....................
use change)................
Land Use Change, Mean (Low/ 46 (28/66) ....................
High)......................
Fuel Production............. 25 18
Fuel and Feedstock Transport 4 *
[[Page 4314]]
Tailpipe Emissions.......... 1 79
-------------------------------------------
Total Emissions, Mean 80 (62/101) 97
(Low/High).............
Midpoint Lifecycle GHG 17% ....................
Percent Reduction Compared
to Petroleum Baseline......
------------------------------------------------------------------------
* 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 co-products
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 co-products 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 co-products. 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).
---------------------------------------------------------------------------
\63\ For 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.
---------------------------------------------------------------------------
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
[[Page 4315]]
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\
---------------------------------------------------------------------------
\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.
\65\ Pearlson, M.N., 2011. A Techno-Economic and Environmental
Assessment of Hydroprocessed Renewable Distillate Fuels. http://dspace.mit.edu/handle/1721.1/65508.
\66\ Huo, 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.
\67\ We 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.
\68\ Based on Pearlson, Table 3.1 and Table 3.2.
Table II-9--Hydrotreating Process to Produce Renewable Diesel Fuel
----------------------------------------------------------------------------------------------------------------
Maximized for diesel Maximized for jet fuel Units (per gallon of fuel
fuel production production 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
palm-oil-based renewable diesel and jet fuel. The lifecycle GHG
estimates for palm-oil 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 co-products 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]
----------------------------------------------------------------------------------------------------------------
Hydrotreating
Process RIN-generating products Other co-products 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.
---------------------------------------------------------------------------
\69\ Co-product displacement accounting is described further in
the inputs and assumptions document available through the public
docket for this notice.
---------------------------------------------------------------------------
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 RIN-generating 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
[[Page 4316]]
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.
---------------------------------------------------------------------------
\70\ The 95% confidence interval around that midpoint results in
range of a 10% increase to a 30% reduction compared to the 2005
diesel fuel baseline.
[GRAPHIC] [TIFF OMITTED] TN27JA12.002
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 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
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.
---------------------------------------------------------------------------
\71\ In the table totals may not sum due to rounding.
[[Page 4317]]
Table II-11--Lifecycle GHG Emissions for Palm Oil Renewable Diesel
[kgCO2E/mmBtu]
------------------------------------------------------------------------
Palm oil 2005
Fuel type renewable diesel
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 11% .........
Compared to Petroleum Baseline................
------------------------------------------------------------------------
* 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
[[Page 4318]]
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 non-grandfathered renewable fuels.
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\72\ The Monte Carlo analysis is described in EPA (2010a),
Section 2.4.4.2.8.
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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]
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