Brazil module addresses these comments. Since the Brazil module
contains data specific to six regions, this additional level of details
allows FAPRI-CARD to more accurately capture real-world responses to
higher agricultural prices. For example, double cropping (the practice
of planting a winter crop of corn or wheat on existing crop acres) is a
common practice in Brazil. Increased double cropping is feasible in
response to higher agricultural prices, which increases total
production without increasing land use conversion. The new Brazil
module also explicitly accounts for changes in pasture acres, therefore
accounting for the competition between crop and pasture acres.
Furthermore, the Brazil module explicitly models livestock
intensification, the practice of increasing the number of heads of
cattle per acre of land in response to higher commodity prices or
increased demand for land.
    In addition to modifying how pasture acres are treated in Brazil,
we also improved the methodology for calculating pasture acreage
changes in other countries. We received several comments through the
public comment period and peer reviewers supporting a better analysis
of the interaction between crops, pasture, and livestock. In the NPRM,
although we accounted for GHG emissions from livestock production
(e.g., manure management), we did not explicitly account for GHG
emissions from changes in pasture demand. In response to comments
received, our new methodology accounts for changes in pasture area
resulting from livestock fluctuations and therefore captures the link
between livestock and land used for grazing. Based on regional pasture
stocking rates (livestock per acre), we now calculate the amount of
land used for livestock grazing. The regional stocking rates were
determined with data on livestock populations from the UN Food and
Agricultural Organization (FAO) and data on pasture area measured with
agricultural inventory and satellite-derived land cover data. As a
result of this change, in countries where livestock numbers decrease,
less land is needed for pasture. Therefore, unneeded pasture acres are
available for crop land or allowed to revert to their natural state. In
countries where livestock numbers increase, more land is needed for
pasture, which can be added on abandoned cropland or unused grassland,
or it can result in deforestation. We believe this new methodology
provides a more realistic assessment of land use changes, especially in
regions where livestock populations are changing significantly. For
additional information on the pasture replacement methodology, see RIA
Chapter 2.
    Although the total amount of land use conversion is lower in the
FRM analysis compared to the NPRM analysis, the regional distribution
of this land use change has shifted. Due to the many changes made in
response to comments associated with agriculture and livestock markets,
Brazil is now much more responsive to changes in world biofuel and
agricultural product demand. As a result, a larger portion of the
projected land use change occurs in Brazil compared to the NPRM
analysis. Additional details on the geographical location of land use
change are included in Chapter 2 of the RIA.
ii. Type of Land Converted
    Based on a number of improvements in our analysis, the types of
land affected by biofuel-induced tend to be less carbon intensive
compared to the NPRM. Therefore, the net effect of our revisions to
this part of our analysis significantly reduced land use change GHG
emissions. The updated FAPRI-CARD Brazil model, discussed in the
previous section, showed more pasture expansion in the Amazon which
increased land use change emissions. However, the most important
revisions to this part of our international analysis, in terms of their
net effect on GHG emissions, were improvements that we made in our
modeling of the

interactions between livestock, pasture, crops and unused, or
underutilized, grasslands globally. In the NPRM we made the broad
assumption that international crop expansion would necessarily displace
pasture, which would require an equivalent amount of pasture to expand
into forests and shrublands. In the FRM analysis as discussed in the
previous section, we have linked international changes in livestock
production with changes in pasture area to allow for pasture
abandonment in regions where livestock production decreases as a result
of biofuel production. We also incorporated the ability of pasture to
expand onto unused, or underutilized, grasslands and savannas which on
a global basis reduced the amount of forest conversion compared to the
proposal. These revisions, as well as a quantitative uncertainty
assessment, are discussed in this section.
    In the same way that the amount and location of land use change is
important, the type of land converted is also a critical determinant of
the magnitude of the GHG emissions impacts associated with biofuel
production. For example, the conversion of rainforest to agriculture
results in a much larger GHG release than conversion of grassland. In
the proposed rule analysis we used two approaches, based on the best
available information to us at the time, to evaluate the types of land
that would be affected domestically and internationally. Domestically,
we used the FASOM model, which simulates rental rates for different
types of land (e.g., forest, pasture, crop) and chooses the land uses
that would produce the highest net returns. Internationally, we used
the FAPRI-CARD/Winrock analysis whereby historical land conversion
trends, as evaluated with satellite imagery, are used to determine what
types of land are affected by agricultural land use changes in each
country or sub-region.
    In the proposed rule we also explained several other options to
determine what types of land will be affected by biofuel-induced land
use changes, such as the use of general equilibrium models. EPA
specifically sought expert peer review input and public comment on our
approach and all of the analytical options for this part of the
lifecycle assessment. The expert peer reviewers agreed that EPA's
approach was scientifically justifiable, but they highlighted
problematic areas and suggested important revisions to improve our
analysis. The public comments received on this issue expressed a wide
range of views regarding EPA's approach. In general, the commenters
that objected to our analytical approach raised similar concerns as the
peer reviewers, such as the need for more data validation and
uncertainty assessment. As discussed below, we made significant
improvements to our analysis based on the recommendations and comments
we received. Based on the peer reviewers agreement that our general
approach is scientifically justifiable, and in light of the significant
improvements made, we think that our approach represents the best
available analysis of the types of land affected by biofuel-induced
land use changes. We did consider a range of other analytical options,
but based on all of the information considered and the requirements for
this analysis, we did not find any alternative approaches that are
superior at this time. As part of periodic updates to the lifecycle
analysis, we will continue to consider ways to improve this part of our
analysis, as well as the merits of alternate approaches.
    Domestic: In response to comments received, we made two major
improvements to the FASOM model for the final rulemaking. As discussed
in the NPRM and supported by comments, we were able to include the
forestry sector into the FASOM analysis. Only the agricultural sector
of FASOM was analyzed for the NPRM, due to the fact that the forestry
sector component was undergoing model modifications. For this FRM
analysis, we were able to use the fully integrated forestry and
agricultural sector model, thereby capturing the interaction between
agricultural land and forests in the U.S. In addition, the inclusion of
the forestry model allows us to explicitly model the land use change
impacts of the competing demand for cellulosic ethanol from
agricultural sources with cellulosic ethanol from logging and mill
residues. As a result of this modification, the FRM analysis includes
some land use conversion from forests into agriculture in the U.S. as a
result of the increased demand for renewable fuels.
    The second major modification we made in response to comments was
the disaggregation of different types of land included in FASOM. In the
proposed rulemaking, the FASOM model included three major categories of
land: cropland, pasture, and acres enrolled in the Conservation Reserve
Program (CRP). Although this categorization allowed for a detailed
regional analysis of land used to grow crops, acres used for livestock
production were not fully captured. We received comments requesting a
more detailed breakdown of land types in order to capture the
interaction between livestock, pasture, and cropland. Therefore, the
FASOM model now includes rangeland, pasture and forest land that can be
used for grazing. Since we also received comments that we should take
into account the potential for idle land to be used for other purposes
such as the production of cellulosic ethanol, FASOM now accounts for
the amount of land within each category that is either idle or used for
production.
    These two major modifications to the FASOM model now allow us to
explicitly track land transfers between various land categories in the
U.S. As a result, we can more accurately capture the GHG impacts of
different types of land use changes domestically. More detail and
results of the FASOM model can be found in Section V.B.1.b of the
preamble.
    International: The proposed rule included a detailed description of
the FAPRI-CARD/Winrock approach used to determine the type of land
affected internationally. This approach uses satellite data depicting
recent land conversion trends in conjunction with economic projections
from the FAPRI-CARD model (an economic model of global agricultural
markets) to determine the type of land converted internationally. In
the proposed rule we described areas of uncertainty in this approach,
illustrated the uncertainty with sensitivity analyses, and discussed
other potential approaches for this analysis. To encourage expert and
stakeholder feedback, EPA specifically invited comment on this issue,
held public hearings and workshops, and sponsored an independent peer-
review, all of which specifically highlighted this part of our analysis
for feedback. While there were a wide range of views expressed in these
forums, the feedback received by the Agency generally supported the
FAPRI-CARD/Winrock approach as appropriate for this analysis. For
example, all five experts that peer reviewed EPA's use of satellite
imagery agreed that it is scientifically justifiable to use historic
remote sensing data in conjunction with agricultural sector models to
evaluate and project land use change emissions associated with biofuel
production. Additionally, the peer reviewers and public commenters
highlighted problematic areas and suggested revisions to improve our
analysis. Below, we describe the key revisions that were implemented
which have significantly improved our analysis based on the feedback
received.
    FAPRI-CARD/Satellite Data Approach: As described above in

Section V.B.1.b, the FAPRI-CARD model was used to determine the amount
of land use change in each country/region in response to increased
biofuel production. Because the FAPRI-CARD model does not provide
information about what type of land is converted to crop production or
pasture, we worked with Winrock International to evaluate the types of
land that would be affected internationally. Winrock is a global
nonprofit organization with years of experience in the development and
application of the IPCC agricultural forestry and other land use
(AFOLU) guidance. For the proposed rule, we used satellite data from
2001-2004 to provide a breakdown of the types of land converted to crop
production. A key strength of this approach is that satellite
information is based on empirical observations which can be verified
and statistically tested for accuracy. Furthermore, it is reasonable to
assume that recent land use change decisions have been driven largely
by economics, and, as such, recent patterns will continue in the
future, absent major economic or land use regime shifts caused, for
example, by changes in government policies.
    As discussed above, all five of the expert peer reviewers that
reviewed our use of satellite imagery for this analysis agreed that our
general approach was scientifically justifiable. However, all of the
peer reviewers qualified that statement by describing relevant
uncertainties and highlighting revisions that would improve our
analysis. Some of the public commenters supported EPA's use of
satellite imagery, while other expressed concern. In general, both sets
of public commenters--those in favor and opposed--outlined the same
criticisms and suggestions as the expert peer reviewers. Among the many
valuable suggestions for satellite data analysis provided in the expert
peer reviews and public comments, several major recommendations
emerged: EPA should use the most recent satellite data set that covers
a period of at least 5 years; EPA should use higher resolution
satellite imagery; EPA's analysis should consider a wider range of land
categories; EPA should improve it's analysis of the interaction between
cropland, pasture and unused or underutilized land; and EPA's analysis
should include thorough data validation and a full assessment of
uncertainty. Below, we describe these and other recommendations and how
we addressed each of them to improve our analysis. Based on the peer
reviewers agreement that our general approach is scientifically
justifiable, and in light of the significant improvements made, we
think that our approach represents the best available analysis of the
types of land affected internationally.
    One of the fundamental improvements in this analysis since the
proposed rule is that it now provides global coverage. The analysis for
the proposed rule included satellite imagery for 6 land categories in
314 regions across 35 of the most important countries, with a weighted
average applied to the rest of the world. We have since completed a
global satellite data analysis including 9 land categories in over 750
distinct regions across 160 countries. This was an analytical
improvement that we committed to do in the proposed rule. As described
below, the other major analytical enhancements were conducted in
response to the many technical recommendations that we received as part
of the peer review and public comment process.
    All of the expert peer reviewers agreed that the version 4 MODIS
data set used in the proposed rule, which covers 2001-2004 with one
square-kilometer (1km) spatial resolution, was appropriate for our
analysis given the goals of the study at the time. However, almost all
of the reviewers strongly recommended using a data set covering a
longer time period. The reviewers argued that the 3-year time period
from 2001-2004 was too short to capture the often gradual, or
sequential, cropland expansion that has been observed in the tropics.
The short time period may also show unusual or temporary trends in land
use caused by short-term policy changes or market influences. The
reviewers suggested that remote sensing observations covering 5-10
years would be adequate to address these problems. The reviewers also
recommended that remote sensing observations should be as recent as
possible in order to capture current land use change drivers and
patterns (e.g., political systems, infrastructure, and protected
areas). To use the best available data and respond to the peer
reviewers' recommendations, the analysis was updated to include the
most recent MODIS data set, version 5, which covers the time period
2001-2007. MODIS land cover products are not available for years prior
to 2001, so it is not currently possible to analyze a time period
longer than six years (i.e., 2001-2007) with a single, or consistent,
data set. Thus, consistent with the peer review recommendations, we are
now using the most recent global data set which covers at least 5
years. There are other advantages to using the version 5 MODIS data,
such as improved spatial resolution, and robust data validation, which
are discussed below.
    There was strong agreement among the peer reviewers that higher
resolution satellite imagery would be an important improvement over the
1-km resolution data used in the proposed rule analysis. Higher spatial
resolution is especially useful in categorizing highly fragmented
landscapes. One of the reviewers hypothesized that land use change
driven by biofuel production would likely involve large parcels of
land, and thus 1-km resolution may be sufficient. However, all of the
reviewers agreed that higher resolution data would be preferable. A
number of the peer reviewers specifically said that the version 5 MODIS
data set, with 500 meter resolution, would be adequate. With four-times
higher spatial resolution than version 4, the peer reviewers
anticipated that the 500m imagery would classify less area of ``mixed
class'' land, thus providing a more detailed representation of the land
in that category. Consistent with the peer reviewer's recommendations
and with our goal to use the best available information, our analysis
was updated with the higher resolution version 5 MODIS data.
    Related to the issue of spatial resolution, the peer review experts
were asked whether they would recommend augmenting our global analysis
with even higher resolution data for specific regions where there is a
high degree of agricultural land use change. All of the peer reviews
agreed that this type of analysis would be worthwhile. In response to
this recommendation, we analyzed select geographic regions (e.g.,
Brazil, India) with the higher resolution 30m Landsat data set covering
2000-2005. The Landsat data set does not currently provide global
coverage, thus it was not an option for use in the full analysis;
instead, it was used as a way to check/validate the appropriateness of
the version 5 MODIS imagery. In general, the higher resolution data
showed similar land use change patterns as the MODIS data. The results
of this analysis are discussed further in Chapter 2 of the RIA.
    Another issue that we invited comments on was the re-classification
of the MODIS data from 17 land cover categories into 6 aggregated
categories (e.g., open and closed shrubland were both re-classified as
shrubland). The category aggregation was intended to remove unnecessary
complexity from the analysis. All five expert reviewers agreed that the
methodology used to re-classify land cover categories using
International Geosphere-Biosphere Programme (IGBP) land definitions was

sound; however, the reviewers recommended inclusion of more than 6
aggregated land categories. The reviewers specifically recommended the
addition cropland/natural vegetation mosaic, permanent wetlands, and
barren or sparsely vegetated land, all of which are now included in our
analysis. Consistent with these recommendations, there are 9 aggregate
land categories in our revised analysis: barren, cropland, excluded
(e.g., urban, ice, water bodies), forest, grassland, mixed (i.e.,
cropland/natural vegetation mosaic), savanna, shrubland and wetland.
These land cover categories capture all significant types of land
affected by agricultural land use changes. As described below in
Section V.B.2.b.iii, we also estimated carbon sequestrations for all of
these land categories. The impact of adding these land categories to
our analysis is discussed further in RIA Chapter 2.
    Another important addition to our analysis was consideration of the
types of land affected by changes in pasture area, and the interaction
of pasture land with cropland. In the proposed rule, we made a broad
assumption that the total land area used for pasture would stay the
same in each country or region. Thus, in the proposed rule, we assumed
that any crop expansion onto pasture would necessarily require an equal
amount of pasture to be replaced on forest or shrubland. We received a
large number of comments questioning these assumptions, and the expert
peer reviewers encouraged us to develop a better representation of the
interactions between cropland and pasture land. As described above in
Section V.B.2.6.i, the results from the FAPRI-CARD model are now used
to determine pasture area changes in each country or region. In regions
where we project that pasture and crop area both increase, the land
types affected by pasture expansion are determined using the same
analysis used for crop expansion. This new approach accounts for the
ability of pasture to expand on to previously unused, or underutilized,
grasslands and savanna. In regions where we project that crop and
pasture area will change in opposite directions (e.g., crop area
increases and pasture decreases) we assume that crops will expand onto
abandoned pasture, and vice versa. Our analysis also now accounts for
carbon sequestration resulting from crop or pasture abandonment. We
used our satellite analysis, which shows the dominant ecosystems and
land cover types in each region, to determine which types of ecosystems
would grow back on abandoned agricultural lands in each region. More
information about our analysis of pasture and abandoned agricultural
land are provided in RIA Chapter 2.
    A sub-set of the expert peer reviewers recommended combining the
historic satellite imagery with other information on land use change
drivers (e.g., transportation infrastructure, poverty rates,
opportunity costs) as an additional means to estimate the types of land
affected. Consideration of these types of information could potentially
address two conceptual issues with the use of satellite imagery in this
analysis: First, biofuel-induced land use change could affect different
types of land than the generic agricultural expansion captured by the
historic data; and second, future land use change patterns may differ
from historic patterns. Our concerns with the first issue are allayed
to some degree by one of the peer reviewers who observed, ``While it is
theoretically possible that the changes in land use resulting from
biofuel production occur in ecosystems or regions that would not be the
ones affected by other drivers, this doesn't appear very likely.''
\170\ Furthermore, the economic drivers of land use change are to a
large degree captured by the economic models that are used in our
analysis. For example, the FAPRI-CARD model considers economic drivers
in its projections of where and how much crop production will change as
a result of specifically biofuel-induced changes. The second issue is
also addressed to some degree by the FAPRI-CARD model which includes
baseline forecasts of future international agricultural, economic and
demographic conditions. Furthermore, as discussed above, we used the
most recently available satellite data sets in order to capture the
most current land use change drivers. Thus, while we think that these
issues are currently addressed to a scientifically justifiable degree
for the purposes of this analysis, we recognize that these are areas
for future investigation, and we have tried to capture the uncertainty
from these factors in uncertainty and sensitivity analyses as described
below.
---------------------------------------------------------------------------

    \170\ Peer Review Report, Emissions from Land Use Change due to
Increased Biofuel Production: Satellite Imagery and Emissions Factor
Analysis, July 31, 2009, p. 2.
---------------------------------------------------------------------------

    While EPA has made significant improvements to the methodology in
response to peer review comments, the use of satellite data for
forecasting land use changes is a key area of uncertainty in the
analysis. To facilitate substantive comments on the impact of
uncertainty in international land use changes, and how to address the
uncertainty, the proposed rule highlighted areas of uncertainty and
included multiple sensitivity analyses. For example, we presented a
range of lifecycle results assuming at the high-end that all land
conversion caused deforestation and at the low-end that biofuels would
cause no deforestation. Further, EPA sought input on this issue in
public hearings and workshops, and expert feedback through the
independent peer review. The feedback we received, both from experts
and the public, overwhelmingly supported a more systematic analysis of
the uncertainty in using satellite data to project biofuel-induced land
use change patterns. Additionally, commenters recommended more data
validation, especially regarding the satellite imagery. To respond to
these comments, we incorporated satellite imagery validation and
conducted a Monte Carlo analysis of the MODIS satellite data using
assessments provided by NASA to quantitatively evaluate the uncertainty
in our application of satellite imagery.
    One benefit of using the MODIS data set is that it is routinely and
extensively validated by NASA's MODIS land validation team. NASA uses
several validation techniques for quality assurance and to develop
uncertainty information for its products. NASA's primary validation
technique includes comparing the satellite classifications to data
collected through field and aircraft surveys, and other satellite data
sensors. The accuracy of the version 5 MODIS land cover product was
assessed over a significant set of international locations, including
roughly 1,900 sample site clusters covering close to 150 million square
kilometers. The results of these validation efforts are summarized in a
``confusion matrix'' which compares the satellite's land
classifications with the actual land types observed on the ground. We
used this information to assess the accuracy and systematic biases in
the published MODIS data. In general, the validation process found that
MODIS version 5 was quite accurate at distinguishing forest from
cropland or grassland. However, the satellite was more likely; for
example, to confuse savanna and shrubland because these land types can
look quite similar from space.
    Using the data validation information from NASA about which types
of land MODIS tends to confuse which each other, our Monte Carlo
analysis was able to account for systematic misclassifications in the
MODIS data set. Therefore, part of the Monte Carlo analysis can be
viewed as a way to correct and reduce the inaccuracies in the MODIS
data. After this correction is performed, the uncertainty in the
satellite data is no longer solely a

function of the accuracy of the satellite. Instead, the sizes of the
standard errors for each classification are also a function of the
sample sizes in the data validation exercise. For example, if NASA
validated every pixel on Earth, the corrected data set would be 100%
accurate, even if the original satellite data were only 50% accurate.
Similarly, although NASA reports that the overall accuracy of the MODIS
version 5 land cover data set is approximately 75%, the standard errors
after the Monte Carlo procedure are less than 5% for each aggregate
land category. These standard errors were used to quantify the
uncertainty added by the satellite data used in our analysis. This
procedure and the results are described in more detail in Chapter 2 of
the RIA.
    It should be noted that our assessment of satellite data
uncertainty did not try to fully quantify the uncertainty of using
historical data to make future projections about the types of land that
would be affected internationally. As noted above, we think it is
reasonable to assume that in general, recent land use change patterns
will continue in the future absent major economic or land use regime
shifts caused, for example, by changes in government policies. Thus,
our uncertainty assessment provides a reasonable estimate of the
variability in land use change patterns absent any fundamental shifts
in the factors that affect land use patterns. However, our uncertainty
assessment does not attempt to fully quantify the probability of major
shifts in land use regimes, such as the implementation of effective
international policies to curb deforestation.
    Some of the peer reviewers recommended a satellite imagery analysis
approach known as change detection, instead of the ``differencing''
approach used in the Winrock analysis. However, there was disagreement
among the peer reviewers on this point, with one peer reviewer saying
that thematic differencing between land cover maps generated for two
specific dates, as conducted in this study, provides the best approach
for detecting and analyzing land use pattern changes globally. In
general terms, the differencing method employed by Winrock compared
global land cover maps from 2001 and 2007 to evaluate the pattern of
land use change during this period. Thus, the differencing method shows
all of the land that changed categories, as well as all of the land
that stayed the same over this period. For change detection, instead of
using comprehensive land cover maps, the data set only shows land
categories that changed. One advantage of change detection is that it
is better suited to capture the sequential nature of land use changes,
e.g., a forest could be converted to savanna, then grassland and then
cropland. The differencing method that we employed lends itself more
readily to comprehensive global analysis, data validation, and
uncertainty assessment. Given the timeframe and priorities for our
analysis, we think that the differencing method provides the best
approach available at this time. However, we will continue to consider
alternative analytical techniques, such as change detection, for use as
part of periodic updates to this analysis.
    Some of the peer reviewers recommended additional alternative
technical approaches for satellite data and land use change analysis.
For example, some of the reviewers recommended the use of satellite
imagery to identify specific crop-types and rotations, and one reviewer
suggested that EPA develop a new interactive spatial model. The Summary
and Analysis of Comments document includes discussion of these and
other technical comments and recommendations that are not covered here.
iii. GHG Emissions Associated With Conversion
(1) Domestic Emissions
    GHG emissions impacts due to domestic land use change are based on
GHG emissions the FASOM model generates in association with land type
conversions projected in the model. In the proposed rule analysis,
estimates of land use change emissions were limited to conversion
between different types of agricultural land (e.g., cropland, fallow
cropland, pasture). The analysis did not allow for the addition of new
domestic agricultural land.
    In response to feedback EPA received during the public comment
period and based on commitments EPA made in the NPRM, several changes
and additions have augmented the analysis of domestic land use change
GHG emissions since the proposed rule analysis. The addition of the
forest land types and the interaction between cropland, pastureland,
forestland, and developed land to the FASOM model provides a more
complete emissions profile due to domestic land use change (see Section
V.B.4.b.ii). We have updated soil carbon accounting based on new
available data. Lastly, the methodology now captures GHG emission
streams over time associated with discrete land use changes.
    For agricultural soils, FASOM models GHG emissions associated with
changes in crop production acreage and with changes in crop type
produced. FASOM generates soil carbon factors for cropland and pasture
according to IPCC Agriculture, Forestry, and Other Land Use (AFOLU)
Guidelines. In the proposed rule, we committed to updating FASOM soil
carbon accounting for agriculture. Per our commitment, we have updated
FASOM soil carbon accounting for cropland and pasture using the latest
DAYCENT modeling from Colorado State University.
    In the proposed rule, EPA committed to incorporate the forestry
sector and the GHG emission impacts due to the land use interactions
between the domestic agricultural and forestry sectors into the FASOM
analysis. We received comment supporting the incorporation of the
forestry sector. By including the forestry sector in the FASOM domestic
model (see Section V.B.4.b.ii), we have incorporated GHG emission
impacts associated with change in forest above-ground and below-ground
biomass, forest soil carbon stocks, forest management practices (e.g.
timber harvest cycles), and forest products and product emission
streams over time. Forest carbon accounting in FASOM is based on the
FORCARB developed by the U.S. Forest Service and on data derived
largely from the U.S. Forest Service RPA modeling system.
    With the changes to FASOM discussed above, we also updated the
final calculation method of domestic land use change GHG emissions to
account for FASOM's cumulative assessment of GHG emissions and the
continuous (rather than discrete) nature of soil carbon and forest
product emissions. For each category of agricultural and forestry land
use emissions, we calculated the mean cumulative emissions from the
initial year of FASOM modeling (2000) to 2022. Changes in agricultural
and forest soil carbon and forest products have a stream of GHG
emissions associated with them in addition to the initial pulse
associate with a discrete instance or year of land use change. For each
of these categories FASOM calculates the emissions over time associated
with the mean land use change over a year. We included in total
domestic land use change emissions the annualized emission streams
associated with all agricultural soil, forest soil, and forest product
changes included in the mean cumulative emissions (2000-2022) for 30
years after 2022.

(2) International Emissions
    Based on input from the expert peer review and public comments, we
incorporated new data sources and made other methodological
improvements in our estimates of GHG emissions from international land
conversions. Some of these modifications increased land use change GHG
emissions compared to the NPRM, such as the consideration of carbon
releases from drained peat soils. Other modifications, such as more
conservative foregone sequestration estimates, tended to decrease land
use change GHG emissions. For example, our estimates of emissions per
acre of deforestation in Brazil tended to increase because of improved
data on forest biomass carbon stocks in that region. However, for
example, our deforestation estimates in China decreased, in part
because of new data on foregone forest sequestration. The net effect of
the revisions varied depending on the location and types of land use
changes in each biofuel scenario. The major changes to this part of our
analysis, including a quantitative uncertainty assessment, are
discussed in this section.
    To determine the GHG emissions impacts of international land use
changes, we followed the 2006 IPCC Agriculture, Forestry, and Other
Land Use (AFOLU) Guidelines.\171\ We worked with Winrock, which has
years of experience developing and implementing the IPCC guidelines, to
estimate land conversion emissions factors, including changes in
biomass carbon stocks, soil carbon stocks, non-CO2 emissions
from clearing with fire and foregone forest sequestration (i.e., lost
future growth in vegetation and soil carbon). In addition to seeking
comment on our analysis in the proposed rule, EPA organized public
hearings and workshops, and an expert peer review specifically
eliciting feedback on this part of the lifecycle analysis. All of the
expert peer reviewers generally felt that our analysis followed IPCC
guidelines and was scientifically justifiable; however, they did make
several suggestions of new data sources and recommended areas that
could benefit from additional clarification. Based on the detailed
comments we received, we worked with Winrock to make a number of
important revisions, which have significantly improved this part of our
analysis.
---------------------------------------------------------------------------

    \171\ 2006 IPCC Guidelines for National Greenhouse Gas
Inventories, Volume 4, Agriculture, Forestry and Other Land Use
(AFOLU). See http://www.ipcc-nggip.iges.or.jp/public/2006gl/
vol4.html.
---------------------------------------------------------------------------

    The proposed rule analysis included land conversion emissions
factors for 5 land categories in 314 regions across 35 of the most
important countries, with a weighted average applied to the rest of the
world. We augmented this analysis to provide global coverage, including
emissions factors for 10 land categories in over 750 regions across 160
countries. Other significant improvements included incorporation of new
data sources, emissions factors for peat soil drainage, sequestration
factors for abandoned agricultural land, and a full uncertainty
assessment considering every data input.
    Another significant improvement in our analysis was incorporation
of higher resolution soil carbon data. One of the expert peer reviewers
commented that the weakest part of EPA's international emissions factor
analysis for the proposed rule was the global soil carbon map that was
used because of its coarse resolution. To address this comment, we
incorporated the new Harmonized World Soil Database, released in March
2009. This dataset provides one square kilometer spatial resolution,
which is a major improvement compared to the proposed rule analysis.
This dataset also includes an updated soil map of China that the peer
reviewers recommended. Using this updated soil carbon data, the change
in soil carbon following conversion of natural land to annual crop
production was estimated following the 2006 IPCC guidelines. When land
is plowed in preparation for crop production the soil loses carbon over
time until a new equilibrium is established. To calculate soil carbon
emissions the IPCC approach considers both tillage practices and
agricultural inputs. Some of the peer reviewers expressed concern with
our annual soil carbon change estimates, which assumed a constant rate
of change over 20 years. However, for analytical timeframes greater
than 20 years, such as used in our lifecycle analysis, the peer
reviewers agreed that the our approach was scientifically justifiable.
More information about soil carbon stock estimates is available in
Chapter 2 of the RIA.
    The expert peer reviewers generally agreed that EPA's estimate of
forest carbon stocks followed IPCC guidelines and used the best
available data. They did, however, recommend that the analysis could be
updated with improved forest biomass maps as they become available.
Consistent with these suggestions, we incorporated improved forest
biomass maps for regions where they were available. More information
about the specific data sources used is available in RIA Chapter 2.
    In addition to estimating forest carbon stocks for each region,
EPA's analysis also includes estimates of annual forest carbon uptake.
When a forest is cleared the future carbon uptake from the forest is
lost; this is known as foregone forest sequestration. In the proposed
rule, to estimate annual forgone forest sequestration, we used IPCC
default data for the growth rates of forests greater than 20 years old.
The expert peer reviewers noted that these estimates could be refined
with more detailed information from the scientific literature. Many of
the public commenters were also concerned that EPA's approach
overestimated foregone sequestration because it did not adequately
account for natural disturbances, such as fires and disease. To address
these comments, our analysis has been updated with peer reviewed
studies of long-term growth rates for both tropical and temperate
forests. These estimates are based on long-term records (i.e.,
monitoring stations in old-growth forests for the tropics and multi-
decadal inventory comparisons for the temperate regions) and reflect
all losses/gains over time. These studies show that the old-growth
forests in the tropics that many once assumed to be in ``steady state''
(i.e., carbon gains equal losses) are in fact still gaining carbon. In
summary, our analysis now includes more conservative foregone forest
sequestration estimates that account for natural gains and losses over
time. More information about these estimates is provided in RIA Chapter
2.
    Another consideration when estimating GHG emissions resulting from
deforestation is that some of the wood from the cleared forest can be
harvested and used in wooden products, such as a table, that retain
biogenic carbon for a long period of time. Some commenters argued that
consideration of the use of harvested wood in products would decrease
land use change emissions and reduce the impacts of biofuel production.
As part of analysis for the proposed rule, we investigated the share of
cleared forest biomass that is typically used in harvested wood
products (HWP). However, we did not account for this factor in the
proposed rule after it was determined that HWP would have a very small
impact on the magnitude of land use change emissions. A number of
commenters expressed concern that we did not account for HWP, and they
argued that HWP would be more significant than we had determined.
However, in response to specific questions on this topic, all of the
expert

peer reviewers agreed that EPA had properly accounted for HWP and other
factors (e.g., land filling) that could prevent or delay emissions from
land clearing. One of the peer reviewers noted that forests converted
to croplands are generally driven by interests unrelated to timber, and
thus the trees are simply burned and exceptions are probably of minor
importance. To study this issue further, we looked at FAO timber volume
estimates for 111 developing countries, and published literature on the
share of harvested timber used in wood products and the oxidation
period for wood products, such as wood-based panels and other
industrial roundwood. Consistent with the peer reviewers' statements,
our analysis concluded that even in countries with high rates of
harvested timber utilization, such as Indonesia, a very small share of
harvested forest biomass would be sequestered in HWP for longer than 30
years. The details of our HWP analysis are discussed further in RIA
Chapter 2. This is an area for further work, but based on our analysis,
and the feedback from expert commenters, we do not expect that
consideration of HWP would have a significant impact on the magnitude
of GHG emissions from international deforestation in our analysis.
Furthermore, the range of outcomes from consideration of HWP is
indirectly captured in our assessment of forest carbon stock
uncertainty, which is described below.
    The land conversion emissions estimates used in our analysis
consider the carbon stored in crop biomass. In the proposed rule, we
used the IPCC default biomass sequestration factor of 5 metric tons of
carbon per hectare for annual crops, and applied this value to all
crops globally. The final rule analysis now distinguishes between
annual and perennial crops, with separate sequestration estimates for
sugarcane and oil palm determined from the scientific literature. The
peer reviewers suggested approaches to refine our biomass carbon
estimates for different types of annual crops, e.g., for corn versus
soybeans. However, we determined that adding crop-specific biomass
sequestration estimates would have a very small impact on our results,
because in general annual cropland carbon stocks range only from 3 to 7
tons per hectare and the average would likely be very close to the IPCC
default factor currently applied. This is an area for future work, but
we are confident that it would have very small impact. Furthermore, the
range of potential outcomes is captured in the uncertainty analysis
described below.
    Other issues that were covered in the expert peer review and public
comments included EPA's carbon stock estimates for grasslands, savanna,
shrublands and wetlands, and our assumptions about which regions use
fire to clear land prior to agricultural expansion. There is less data
available for these parameters relative to some of the other issues
discussed above, e.g., forest carbon stocks. Therefore, we worked to
use expert judgment to derive global estimates for these parameters. In
general, the peer reviewers thought that EPA's approach to these issues
was reasonable and scientifically justifiable. Some of the peer
reviewers recommended more resource-intensive techniques to refine some
of our estimates. For example, regarding the issue of clearing with
fire, one of the peer reviewers suggested that we could review fire
events in the historical satellite data to estimate where fire is most
commonly used. We carefully considered these suggestions, but did not
make significant revisions to our analysis of these issues. Our review
concluded that given the timeframe and goals of our analysis, the
approach used in the proposed rule was most appropriate. We recognize
that these are areas for future work, and we will consider new data as
part of periodic updates. Furthermore, our uncertainty analysis,
described below, considered the fact that these are areas where less
data is available.
    Other improvements in our analysis included the addition of
emissions from peat soil drainage in Indonesia and Malaysia, and
sequestration factors for abandoned agricultural land. Consistent with
the expert peer reviewers' recommendations, we considered a number of
recent studies to estimate average carbon emissions when peat soils are
drained in Indonesia and Malaysia (the countries where peat soil is
sometimes drained in preparation for new agricultural production). To
estimate annual sequestration on abandoned agricultural land we used
our foregone sequestration estimates and other data from IPCC. More
information about these estimates is available in RIA Chapter 2.
    As discussed in Section V.A.2, the uncertainty of land use change
emissions is an important consideration in EPA's threshold
determinations as part of this rulemaking. We conducted a full
assessment of the uncertainty in international land use change
emissions factors consistent with 2006 IPCC guidance.\172\ This
analysis considers the uncertainty in the every parameter used in our
emissions factor estimates. Standard deviations for each parameter were
estimated based on the quality and quantity of the underlying data. For
example, in our analysis the standard errors (as a percent of the mean)
tend to be smallest for forest carbon stocks in Brazil, because a large
amount of high quality/resolution data was considered to estimate that
parameter. Standard errors are largest for parameters that were
estimated by scaling other data, or applying IPCC defaults, e.g.,
savanna carbon stocks in Yemen. More detail about our estimate of
parameter uncertainty is available in RIA Chapter 2.
---------------------------------------------------------------------------

    \172\ 2006 IPCC Guidelines for National Greenhouse Gas
Inventories, Volume 1: General Guidance and Reporting, Chapter 3:
Uncertainties, available at http://www.ipcc-nggip.iges.or.jp/public/
2006gl/vol1.html.
---------------------------------------------------------------------------

    Following IPCC guidance, the uncertainties in the individual
parameters of an emission factor can be combined using either error
propagation methods (IPCC Tier 1) or Monte Carlo simulation (IPCC Tier
2). We used the Tier 2 Monte Carlo simulation method for this analysis.
Monte Carlo is a method for analyzing uncertainty propagation by
randomly sampling from the probability distributions of model
parameters, calculating the results of the model from each sample, and
characterizing the probability of the outcomes. An important
consideration for Monte Carlo analysis is the treatment of correlation,
or dependencies, among parameter errors. Strong positive correlation
among parameter errors will result in greater overall uncertainty. As a
simplified example, if the errors in our forest carbon stock estimates
are positively correlated, then if we are overestimating forest carbon
in one region we are likely overestimating forest carbon in every
region. We worked with Winrock to estimate the degree of correlation
among variables--both the correlation of one variable across space as
well as the correlation of one variable to any others used in the
analysis. This was done by considering dependencies in the underlying
data used to estimate each parameter. For example, our forest carbon
stock estimates are correlated across Russia because they were derived
from one biomass map covering Russia. However, forest carbon stocks in
Russia are not correlated with China, because they were derived from
separate biomass maps. This partial correlation approach tended to
reduce the overall uncertainty

associated with GHG emissions factor data.
    The information about the uncertainty in each parameter and the
degree of correlation across parameters was utilized in Monte Carlo
analysis to determine the overall uncertainty in our emissions factor
estimates. We used the Monte Carlo simulation to combine the emissions
factor and satellite data uncertainty for every biofuel scenario
analyzed. Uncertainty ranges varied across scenarios depending on the
types and locations of land use changes. For example, based on the
sources of uncertainty analyzed, the 95% confidence range for land use
change emissions (as a percent of the mean) was -27% to +32% for base
yield corn ethanol in 2022, and -56% to +76% for base yield soy
biodiesel in 2022.\173\ More details about this uncertainty analysis
are provided in RIA Chapter 2.
---------------------------------------------------------------------------

    \173\ The 95% confidence range indicates there is no more than a
5% chance the actual value is likely to be outside this range.
---------------------------------------------------------------------------

iv. Timeframe of Emission Analysis
    Based on input from the expert peer review and public comments, EPA
has chosen to analyze lifecycle GHG emissions using a 30 year time
period, over which emissions are not discounted, i.e., a zero discount
rate is applied to future emissions. The input we received and the
reasons for our use of this approach are described in this section.
    As required by EISA, EPA must determine whether biofuels reduce GHG
emissions by the required percentage relative to the 2005 petroleum
baseline. In the proposal the Agency discussed a number of accounting
methods for capturing the full stream of GHG emissions and benefits
over time. When accounting for the time profile of lifecycle GHG
emissions, two important assumptions to consider are: (1) The time
period considered and (2) the discount rate (which could be zero)
applied to future emissions streams. At the time of proposal, EPA
requested public comment on the choice of time frames and discounting
approaches for purposes of estimating lifecycle GHG emissions. Also, as
part of the peer review process, EPA requested comment from expert peer
reviewers on the choice of the appropriate time frames and discount
rates for the RFS2 analysis. Below is a summary of the comments we
received on these issues and how we address them in our analytical
approach.
    Time Period for Analysis: In the proposed rule, EPA highlighted two
time periods, 30 years and 100 years, for consideration in our
lifecycle analysis. The Agency discussed the relative advantages of
these, and other, time periods. In addition, the Agency sought comment
on whether it is appropriate to split the time period for GHG emissions
assessment based upon how long the biofuel would be produced (i.e., the
``project'' period) and the time period for which there would likely be
GHG emissions changes (i.e., the ``impact'' period). To encourage
expert and public comments on these issues, EPA held public hearings
and workshops and sponsored an expert peer review specifically focused
on this topic. The expert input and comments that we received included
many valuable points which guided our decisions about which time frame
should be the focus of our analysis. Below we summarize some of the key
arguments made by the peer reviewers and commenters, and how these
arguments factored into our choice of analytical approach.
    The expert peer reviewers discussed a number of justifiable time
periods ranging from 13 to 100 years for assessing lifecycle GHG
emissions. A subset of the reviewers said that EPA's analysis should be
restricted to 2010-2022 based on the years specified in EISA, because
these reviewers argued that EPA should not assume that biofuel
production will continue beyond 2022 at the RFS2 levels. The reviewers
said that longer time frames, such as 100 years, were only appropriate
if the Agency used positive discount rates to value future emissions.
Almost all of the peer reviewers said that a time frame of 20 to 30
years would be a reasonable timeframe for assessing lifecycle GHG
emissions. They gave several reasons for why a short time period is
appropriate: This time frame is the average life of a typical biofuel
production facility; future emissions are less certain and more
difficult to value, so the analysis should be confined insofar as
possible to the foreseeable future; and a near-term time horizon is
consistent with the latest climate science that indicates that
relatively deep reductions of heat-trapping gasses are needed to avoid
catastrophic changes due to a warming climate. The peer reviewers
suggested that while there is no unassailable basis for choosing a
precise timeframe the expected average lifetime of a biofuel production
facility is the ``most sensible anchor'' for the choice of a timeframe.
    There was support in the public comments for both the 30 year and
100 year time frames. A number of public commenters supported the use
of a 30 year time period, or less, and made arguments similar to those
of the expert peer reviewers. They argued that shorter time periods
give more weight to the known, more immediate, effects of biofuel
production and that use of longer time periods gives more weight to
activities that are much more uncertain, and that the 100 year
timeframe is inappropriate because it is much longer than the life of
individual biofuel plants.
    On the issue of whether to split the time period for GHG emissions
analysis into the ``project and ``impact'' periods, there was little
support for the use of a split time frame for evaluating lifecycle GHG
emissions by the peer reviewers or in the public comments. The peer
reviewers thought that it would be difficult to find a scientific basis
for determining the length of the two different time horizons. Also,
splitting the time horizon would necessitate consideration of the land
use changes following the end of the project time horizon such as land
reversion. However, the majority of expert peer reviewers did not think
it was appropriate to attribute potential land reversions, following
the project time frame, to a biofuel's lifecycle.
    Based upon the comments discussed above, EPA has decided to use a
30 year frame for assessing the lifecycle GHG emissions. There are
several reasons why the 30 year time frame was chosen. The full life of
a typical biofuel plant seems reasonable as a basis for the timeframe
for assessing the GHG emissions impacts of a biofuel, because it
provides a guideline for how long we can expect biofuels to be produced
from a particular entity using a specific processing technology. Also,
the 30 year time frame focuses on GHG emissions impacts that are more
near term and, hence, more certain. We also determined that longer time
periods were less appropriate because the peer reviewers recommended
that they should only be used in conjunction with positive discount
rates; but, for the reasons discussed below, we are using a zero
discount rate in our analysis. In addition, the 30 year time frame is
consistent with responses of the peer reviewers that EPA should not
split the time periods for analysis, or include potential land
reversions following the project time period in the biofuel lifecycle.
    Discounting: In the RFS2 Proposal, EPA highlighted two principal
options for discounting the lifecycle GHG emission streams from
biofuels over time. The first involved the use of a 2% discount rate
using the 100 year time horizon for assessing lifecycle GHG emissions
streams. The second option involved using a 30 year time horizon for
examining lifecycle GHG emissions

impacts. In the 30 year case, each GHG emission is treated equally
through time, which implicitly assumes a zero discount rate to GHG
lifecycle emissions streams. The issue of whether to discount lifecycle
GHG emissions was raised as a topic that EPA sought comment on in both
the peer review process and in public comments.
    EPA received numerous comments on the issue of whether the Agency
should be discounting lifecycle GHG emissions through time. While many
of peer reviewers thought that current GHG emissions reductions should
be more strongly weighted than future reductions, the peer reviewers
were in general agreement that a discount rate should only be applied
to a monetary unit, rather than a physical unit, such as GHG emissions.
Public commenters suggested that discounting is an essential part of
long term cost benefit analysis but it is not necessary in the context
of the physical aggregation of lifecycle GHG emissions called for in
the EISA. Further, public commenters expressed concerns that any
discount rate chosen by the Agency would be based upon relatively
arbitrary criteria.
    After considering the comments on discounting from the peer review
and the public, EPA has decided not to discount (i.e., use a 0%
discount rate) GHG emissions due to the many issues associated with
applying an economic concept to a physical parameter. First, it is
unclear whether EISA intended lifecycle GHG emissions to be converted
into a metric whose underpinnings rest on principals of economic
valuation. A more literal interpretation of EISA is that EPA should
consider only physical GHG emissions. Second, even if the principle of
tying GHG emissions to economic valuation approaches were to be
accepted, there would still be the problem that there is a lack of
consensus in the scientific community about the best way to translate
GHG emissions into a proxy for economic damages. Also, there is a lack
of consensus as to the appropriate discount rate to apply to GHG
lifecycle emissions streams through time. Finally, since EPA has
decided to base threshold assessments of lifecycle GHG emissions on a
30 year time frame, the issue of whether to discount GHG emissions is
not as significant as if the EPA had chosen the 100 year time frame to
assess GHG emissions impacts. More discussion of discount rates and
their impact on the lifecycle results can be found in Chapter 2 of the
RIA.
v. GTAP and Other Models
    Although we have used the partial equilibrium (PE) models FASOM and
FAPRI-CARD as the primary tools for evaluating whether individual
biofuels meet the GHG thresholds, as part of the peer review process,
we explicitly requested input on whether general equilibrium (GE)
models should be used. None of the comments recommended using a GE
model as the sole tool for estimating GHG emissions, given the limited
details on the agricultural sector contained in most GE models. The
peer reviewers generally supported the use of the FASOM and FAPRI-CARD
models for our GHG analysis given the need for additional detail
offered in the PE models, however several comments suggested
incorporating GE models into the analysis.
    Given these recommendations, we opted to use the GTAP model to
inform the range of potential GHG emissions associated with land use
change resulting from an increase in renewable fuels. As discussed in
the NPRM, there are several advantages to using GTAP. As a general
equilibrium model, GTAP captures the interaction between different
markets (e.g., agriculture and energy) in different regions. It is
distinctive in estimating the complex international land use change
through trade linkages. In addition, GTAP explicitly models land-use
conversion decisions, as well as land management intensification. Most
importantly, in contrast to other models, GTAP is designed with the
framework of predicting the amount and types of land needed in a region
to meet demands for both food and fuel production. The GTAP framework
also allows predictions to be made about the types of land available in
the region to meet the needed demands, since it explicitly represents
different types of land cover within each Agro-Ecological Zone.
    Like the peer reviewers, we felt that some of the drawbacks of the
GTAP model prevent us from using GTAP as the sole model for estimating
GHG emissions from biofuels. As discussed in the NPRM, GTAP does not
utilize unmanaged cropland, nor is it able to capture the long-run
baseline issues (e.g., the state of the economy in 2022). For our
analysis, the GTAP model was most valuable for providing another
estimate of the quantity and type of land conversion resulting from an
increase in corn ethanol and biodiesel given the competition for land
and other inputs from other sectors of the economy. These results were
therefore considered as part of the weight of evidence when determining
whether corn ethanol or biodiesel met the GHG thresholds.
    The quantity of total acres converted to crop land projected by
FAPRI-CARD were within the range of values projected by GTAP when
normalized on a per BTU basis, although there were differences in the
regional distribution of these changes. The land use changes projected
by GTAP were smaller than land use changes predicted by FAPRI-CARD,
which is primarily due to several important differences in the modeling
frameworks. First, the GTAP model incorporates a more optimistic view
of intensification options by which higher prices induced by renewable
fuels results in higher yields, not just for corn, but also for other
displaced crops. Second, the demands for other uses of land are
explicitly captured in GTAP. Therefore, when land is withdrawn from
these uses, the prices of these products rise and provide a certain
amount of ``push-back'' on the conversion of land to crops from pasture
or forest. Third, none of the peer-reviewed versions of GTAP currently
contain unmanaged cropland, thereby omitting additional sources of
land. Finally, the GTAP model also predicted larger increases in forest
conversion than the FAPRI-CARD/Winrock analysis, in part because the
GTAP model includes only three types of land (i.e., crops, pasture,
forest). As discussed in the FAPRI-CARD/Winrock section, there are many
other categories of land which may be converted to pasture and crop
land.
    As with all economic models, GTAP results are sensitive to certain
key parameter values. One advantage of this framework is that it offers
a readily usable approach to Systematic Sensitivity Analysis (SSA)
using efficient sampling techniques. We have exploited this tool in
order to develop a set of 95% confidence intervals around the projected
land use changes. Several key parameters were identified that have a
significant impact on the land use change projections, including the
yield elasticity (i.e., the change in yield that results from a change
in that commodity's price), the elasticity of transformation of land
supply (i.e., the measure of how easily land can be converted between
forest, pasture, and crop land), and the elasticity of transformation
of crop land (i.e., the measure of how easily land can be converted
between crops). Although the confidence intervals are relatively large,
in most cases the ranges do not bracket zero. Therefore, we conclude
that the impacts of the corn ethanol and soybean biodiesel mandates on
land use change are statistically significant. These confidence
intervals also bracket the FAPRI-CARD results. Additional

information on the GTAP results is discussed in RIA Chapter 2.
c. Feedstock Transport
    To estimate the GHG impacts of transporting corn from the field to
an ethanol production facility and transporting the co-product DDGS
from the ethanol facility to the point of use, we used the method
described in the proposed rule. We also did not change our estimates
for the transport of cellulosic biofuel feedstock and biomass-based
diesel feedstock.
    For sugarcane transport, we received the comment that the GREET
defaults used to estimate the energy consumption and associated GHG
emissions do not all reflect current industry practices. To address
this concern, we reviewed the current literature on sugarcane transport
and updated our assumptions on the distance sugarcane travels by truck
from the field to ethanol production facilities as well as the payload
and fuel economy of those trucks. We incorporated these revised inputs
into an updated version of the GREET model (Version 1.8c) in order to
estimate the GHG impacts of sugarcane transport. More details on these
updates can be found in Chapter 2 of the RIA.
    In the proposal, we discussed updating our analysis to incorporate
the results of a recent study detailing biofuel production locations
and modes of transport. This study, conducted by Oak Ridge National
Laboratory, modeled the transportation of ethanol from production or
import facilities to petroleum blending terminals. Since the study did
not explicitly address the transport of biofuel feedstocks, we did not
implement the results for this part of the analysis. However, we did
incorporate the results into our assessment of the GHG impacts of fuel
transportation. We will continue to examine whether our feedstock
transport estimates could be significantly improved by implementing
more detailed information on the location of biofuel production
facilities.
    We also discussed updating the transportation modes and distances
assumed for corn and DDGS to account for the secondary or indirect
transportation impacts. For example, decreases in exports will reduce
overall domestic agricultural commodity transport and emissions but
will increase transportation of commodities internationally. We did not
implement these secondary transportation impacts in this final rule.
While we do not anticipate that such impacts would significantly change
the lifecycle analysis, we plan to continue to look at this issue and
consider incorporating them in the future.
d. Biofuel Processing
    For the proposal the GHG emissions from renewable fuel production
were calculated by multiplying the Btus of the different types of
energy inputs at biofuel process plants by emissions factors for
combustion of those fuel sources. The Btu of energy input was
determined based on analysis of the industry and specific work done as
part of the NPRM. The emission factors for the different fuel types are
from GREET and were based on assumed carbon contents of the different
process fuels. The emissions from producing electricity in the U.S.
were also taken from GREET and represent average U.S. grid electricity
production emissions.
    We received comments on our approach and updated the analysis of
GHG emissions from biofuel process for the final rule specifically
regarding process energy use and the treatment of co-products.
    Process Energy Use: For the final rule we updated each of our
biofuel pathways to include the latest data available on process energy
use. For the proposal, one of the key sources of information on energy
use for corn ethanol production was a study from the University of
Illinois at Chicago Energy Resource Center. Between proposal and final
rule, the study was updated, therefore, we incorporated the results of
the updated study in our corn ethanol pathways process energy use for
the final rule. We also updated corn ethanol production energy use for
different technologies in the final rule based on feedback from
industry technology providers as part of the public comment period. The
main difference between proposal and final corn ethanol energy use
values was a slight increase in energy use for the corn ethanol
fractionation process, based on feedback from industry technology
providers.
    For the proposal we based biodiesel processing energy on a process
model developed by USDA-ARS to simulate biodiesel production from the
Fatty Acid Methyl Ester (FAME) transesterification process. We received
a number of comments from stakeholders that the energy balance for
biodiesel production was overestimating energy use and should be
updated. During the comment period USDA updated their energy balance
for biodiesel production to incorporate a different biodiesel
dehydration process based on a system which has resulted in a decrease
in energy requirements. This change was reflected in the energy use
values for biodiesel assumed in our final rule analysis which resulted
in reduced GHG impacts from the biodiesel production process.
    In addition, for the final rule we have included an analysis of
algae oil production for biodiesel based on ASPEN process modeling from
NREL.\174\ The analysis is for two major cultivation pathways (open
pond and photobioreactors) for a facility that can be feasibly
commercialized in the future, represented by a ``2022'' target
production. We coupled the algae oil production process (which includes
cultivation, harvesting, and extraction) with the biodiesel production
energy use from virgin oils energy use model under the assumption that
algae oil is similar enough to that of virgin oil.
---------------------------------------------------------------------------

    \174\ Davis, Ryan. November 2009. Techno-economic analysis of
microalgae-derived biofuel production. National Renewable Energy
Laboratory (NREL)
---------------------------------------------------------------------------

    For the cellulosic biofuel pathways, we updated our final rule
energy consumption assumptions on process modeling also completed by
NREL. For the NPRM, NREL estimated energy use for the biochemical
enzymatic process to ethanol route in the near future (2010) and future
(2015 and 2022).175 176 177 As there are multiple processing
pathways for cellulosic biofuel, we have expanded the analysis for the
FRM to also include thermochemical processes (Mixed-Alcohols route and
Fischer-Tropsch to diesel route) for plants which assume woody biomass
as its feedstock.
---------------------------------------------------------------------------

    \175\ Tao, Ling and Aden, Andy. November 2008. Techno-economic
Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR).
National Renewable Energy Laboratory (NREL).
    \176\ Aden, Andy. September 2009. Mixed Alcohols from Woody
Biomass--2010, 2015, 2022. National Renewable Energy Laboratory
(NREL).
    \177\ Davis, Ryan. August 2009. Techno-economic analysis of
current technology for Fischer-Tropsch fuels. National Renewable
Energy Laboratory (NREL).
---------------------------------------------------------------------------

    Under the imported sugarcane ethanol cases we updated process
energy use assumptions to reflect anticipated increases in electricity
production for 2022 based on recent literature and comments to the
proposal. One major change was assuming the potential use of trash
(tops and leaves of sugarcane) collection in future facilities to
generate additional electricity. The NPRM had only assumed the use of
bagasse for electricity generation. Based on comments received, we are
also assuming marginal electricity production (i.e., natural gas)
instead of average electricity mix in Brazil which is mainly
hydroelectricity. This approach assumes surplus electricity will likely
displace electricity which is normally dispatched last, in this case

typically natural gas based electricity. The result of this change is a
greater credit for displacing marginal grid electricity and thus a
lower GHG emissions profile for imported sugarcane ethanol than that
assumed in the NPRM. We also received public comment that there are
differences in the types of process fuel e.g. used in the dehydration
process for ethanol. While using heavier fuels such as diesel or bunker
fuel tends to increase the imported sugarcane ethanol emissions
profile, the overall impact was small enough that lifecycle results did
not change dramatically.
    Co-Products: In response to comments received, we included corn oil
fractionation and extraction as a potential source of renewable fuels
for this final rulemaking. Based on research of various corn ethanol
plant technologies, corn oil as a co-product from dry mill corn ethanol
plants can be used as an additional biodiesel feedstock source (see
Section VII.A.2 for additional information). Dry mill corn ethanol
plants have two different technological methods to withdraw corn oil
during the ethanol production process. The fractionation process
withdraws corn oil before the production of the DGS co-product. The
resulting product is food-grade corn oil. The extraction process
withdraws corn oil after the production of the DGS co-product,
resulting in corn oil that is only suitable for use as a biodiesel
feedstock.
    Based on cost projections outlined in Section VII.A, it is
estimated that by 2022, 70% of dry mill ethanol plants will conduct
extraction, 20% will conduct fractionation, and that 10% will choose to
do neither. These parameters have been incorporated into the FASOM and
FAPRI-CARD models for the final rulemaking analysis, allowing for corn
oil from extraction as a major biodiesel feedstock.
    Glycerin is a co-product of biodiesel production. Our proposal
analysis did not assume any credit for this glycerin product. The
assumption for the proposal was that by 2022 the market for glycerin
would be saturated due to the large increase in biodiesel production in
both the US and abroad and the glycerin would therefore be a waste
product. We received a number of comments that we should be factoring
in a co-product credit for glycerin as there would be some valuable use
for this product in the market. Based on these comments we have
included for the final rule analysis that glycerin would displace
residual oil as a fuel source on an energy equivalent basis. This is
based on the assumption that the glycerin market would still be
saturated in 2022 and that glycerin produced from biodiesel would not
displace any additional petroleum glycerin production. However, the
biodiesel glycerin would not be a waste and a low value use would be to
use the glycerin as a fuel source. The fuel source assumed to be
replaced by the glycerin is residual oil. This inclusion of a co-
product credit for glycerin reduces the overall GHG impact of biodiesel
compared to the proposal analysis.
e. Fuel Transportation
    For the proposed rule, we estimated the GHG impacts associated with
the transportation and distribution of domestic and imported ethanol
and biomass-based diesel using GREET defaults. We have upgraded to the
most recent version of GREET (Version 1.8c) for our transportation
analysis in the final rule.\178\ We made several other updates to the
method we utilized in the proposed rule. These updates are described
here and in more detail in Chapter 2 of the RIA.
---------------------------------------------------------------------------

    \178\ The method used to estimate the GHG impacts associated
with biodiesel transportation has not been changed since the
proposal. This method utilized an earlier version of the GREET
model.
---------------------------------------------------------------------------

    In the proposal, we noted our intention to incorporate the results
of a recent study by Oak Ridge National Laboratory (ORNL) into our
transportation analysis for the final rule. The ORNL study models the
transportation of ethanol from refineries or import facilities to the
petroleum blending terminals by domestic truck, marine, and rail
distribution systems. We used ORNL's transportation projections for
2022 under the EISA policy scenario to update our estimates of the GHG
impacts associated with the transportation of corn, cellulosic, and
sugarcane ethanol. Since the study did not address the distribution of
ethanol from petroleum blending terminals to refueling stations, we
continued to use GREET defaults to estimate these impacts.
    The ORNL study also did not address the transportation of imported
ethanol within its country of origin or en route to the import facility
in the United States. As in the proposal, we used GREET defaults to
estimate the impacts associated with the transportation of sugarcane
ethanol within Brazil. We updated the GREET default for the average
distance sugarcane ethanol travels by ocean tanker using recent
shipping data from EIA in order to account for both direct Brazilian
exports and the shipment of ethanol from countries in the Caribbean
Basin Initiative. We received several comments on the back-haul
emissions associated with ocean transport. For the final rule, we
assumed that these emissions were negligible.
f. Vehicle Tailpipe Emissions
    We updated the CO2 emissions factors for ethanol and
biodiesel to be consistent with those used in the October 30, 2009
final rulemaking for the Mandatory GHG Reporting Rule. These changes
caused the tailpipe GHG emission factors to increase by 0.8% for
ethanol and to decrease by 1.5% for biodiesel. Specific tailpipe
combustion values used in this final rule can be found in Chapter 2 of
the RIA. Estimates for CH4 and N2O were made
using outputs from EPA's MOVES model.
3. Petroleum Baseline
    For the proposed rule, we conducted an analysis to determine the
lifecycle greenhouse gas emissions for the petroleum baseline against
which renewable fuels were to be compared. We utilized the GREET model
(Version 1.8b), which uses an energy efficiency metric to calculate GHG
emissions associated with the production of petroleum-based fuels. We
received numerous comments regarding this approach.
    Petroleum baseline calculation from proposed rule: The GREET model
relies on using average values as inputs to estimate aggregate
emissions, rather than using site-specific values. Commenters noted a
number of GREET input values that they believed to be incorrect. These
included: energy efficiency values for crude oil extraction; methane
emission factors for oil production and flaring; transportation
distances for crude oil and petroleum products; and the oil tanker
cargo payload value. Commenters also noted that GREET does not account
for the energy consumption associated with crude oil transport in the
country of extraction.
    In addition, commenters stated that the crude oil import slate
assumed in the proposed rule was inconsistent with EIA crude oil
production and import data for 2005. Commenters also noted that the
gasoline and diesel mix that we used for the proposal did not match
with EIA prime supplier sales volume data. One specific comment focused
on the definition of low-sulfur diesel in GREET, where it is defined as
being 11 ppm sulfur content, which is inconsistent with EPA's
definition. As a result, in the proposed rule, all transportation
diesel produced in 2005 was assumed to be ultra-low sulfur diesel.

    We largely agree with the above comments. An updated version of the
GREET model (Version 1.8c) is available, and it may address some of the
issues raised by commenters. We considered using this new version of
GREET with updated input values from publically available sources to
determine the petroleum baseline for the final rule. However, we have
decided that using the 2005 petroleum baseline model developed by the
National Energy Technology Laboratory (NETL) \179\ would address the
commenters' concerns, and result in a more accurate and comprehensive
assessment of the petroleum baseline than we could obtain using the
GREET model.
---------------------------------------------------------------------------

    \179\ Department of Energy: National Energy Technology
Laboratory. 2009. NETL: Petroleum-Based Fuels Life Cycle Greenhouse
Gas Analysis--2005 Baseline Model.
---------------------------------------------------------------------------

    Use of NETL study for final rule petroleum baseline calculation: In
the proposed rule, we requested comment on using the NETL study for our
2005 petroleum baseline for the final rulemaking. We only received one
comment, which agreed that the NETL values were generally more accurate
and better documented than the values in GREET. However, the commenter
also stated that NETL's use of 2002 crude oil extraction data would
underestimate extraction emissions for 2005, and that it would be
inconsistent to use the GREET model for determining GHG emissions from
biofuels, but not for petroleum.
    We do not agree with the commenters' criticism of the NETL model.
We have not seen data that indicates that the GHG emissions associated
with crude oil extraction would be appreciably different in 2005 than
2002. EPA also believes that it is important to use the best available
tools to estimate a petroleum baseline that can be compared to
renewable fuels. The fact that some GREET emission factors are used in
the calculation of biofuel lifecycle GHG impacts is not a reason to use
the GREET model for the petroleum baseline analysis over what we feel
to be a better tool for the baseline calculation needed.
    NETL states that the goal of their study is to ``determine the life
cycle greenhouse gas emissions for liquid fuels (conventional gasoline,
conventional diesel, and kerosene-based jet fuel) production from
petroleum as consumed in the U.S. in 2005 to allow comparisons with
alternative transportation fuel options on the same basis (i.e., life
cycle modeling assumptions, boundaries, and allocation procedures).''
Unlike GREET, the NETL study utilized site-specific data, such as
country-specific crude oil extraction profiles and port-to-port travel
distances for imported crude oil and petroleum products. The NETL model
also accounts for NGLs and unfinished oils as refinery inputs, which is
not available in GREET.
    Thus, we believe that use of the NETL model addresses the
commenters' concerns with the GREET inputs used in the proposed rule.
We have also verified that the NETL model uses a crude oil input mix
and gasoline and diesel product slate consistent with EIA data for
2005.
    For the final rule, we have also updated the CO2
emissions factors to be consistent with other EPA rulemakings. EPA
recently revised the CO2 emission factors for gasoline and
diesel and used them in the September 28, 2009 proposed rule to
establish GHG standards for light-duty vehicles. These new factors are
slightly lower than those used in the RFS2 proposal and result in a
decrease in tailpipe GHG emissions of 0.4% for gasoline of 0.6% and for
diesel.
    Overall, with the switch to NETL and the updated tailpipe values,
the final petroleum baseline value calculated for the final rule
analysis does not differ significantly from what we calculated in the
proposed rule.
    Inclusion of estimate for land use change: Numerous commenters
raised the issue of land use change with regard to oil production, both
on a direct and indirect basis. The proposed rule analysis for baseline
petroleum emissions did not consider any land use change emissions
associated with crude oil extraction. For the final rule, we do not
consider land use emissions associated with road or other
infrastructure construction for petroleum extraction, transport,
refining, or upgrading, as the land use change associated with roads
constructed for crop and livestock production was also not included.
Furthermore, land use associated with natural gas extracted for use in
oil sands extraction or upgrading was also not considered, as the land
use change from natural gas extracted for biofuels production was not
considered.
    However, for the final rule we did consider the inclusion of land
use emissions associated with oil extraction. Using estimates for land-
use change from conventional oil production and oil sands in
conjunction with our data for the carbon intensity of land being
developed, we were able to determine GHG emissions associated with land
use change for oil production. Our analysis showed that the value was
negligible compared to the full petroleum lifecycle. More detail on
this analysis can be found in Chapter 2 of the RIA.
    Consideration of marginal impacts: We received several comments
stating that we did not use consistent system boundaries in our
comparisons of biofuels and petroleum-based fuels, in particular by
using a marginal assessment of GHG emissions related to biofuel, but
not doing so for baseline petroleum fuels. According to commenters, by
not assessing the marginal impacts of petroleum production, we
overestimated the GHG impacts of an increase in biofuel use in the
proposed rule. Commenters argued that a consistent modeling approach
would involve a marginal analysis for both biofuels and the petroleum
baseline.
    The reason the system boundaries used for threshold assessment in
the proposed rule and the final rule did not include a marginal
analysis of petroleum production was due to the definition of
``baseline lifecycle greenhouse gas emissions'' in Section 211(o)(1)(C)
of the CAA. The definitions of the different renewable fuel categories
specify that the lifecycle threshold analysis be compared to baseline
lifecycle greenhouse gas emissions, which are defined as:

    The term `baseline lifecycle greenhouse gas emissions' means the
average lifecycle greenhouse gas emissions, as determined by the
Administrator, after notice and opportunity for comment, for
gasoline or diesel (whichever is being replaced by the renewable
fuel) sold or distributed as transportation fuel in 2005.

    Therefore, the petroleum production component of the system
boundaries is specifically mandated by EISA to be based on the 2005
average for crude oil used to make gasoline or diesel sold or
distributed as transportation fuel, and not the marginal crude oil that
will be displaced by renewable fuel. Furthermore, as the EISA language
specifies that the baseline emissions are to be only ``average''
lifecycle emissions for this single specified year and volume, it does
not allow for a comparison of alternative scenarios. Indirect effects
can only be determined using such an analysis; therefore there are no
indirect emissions to include in the baseline lifecycle greenhouse gas
emissions.
    On the other hand, assessing the lifecycle GHG emissions of
renewable fuel is not tied by statute to the 2005 baseline and could
therefore be based on a marginal analysis of anticipated changes in
transportation fuel as would result from meeting the EISA mandates.

    Thus, Congress did not, as many commenters suggested, intend to
accomplish simply a reduction in GHG emissions as compared to the
situation that would exist in the future without enactment of EISA, as
would be the case if Congress had specified that EPA use a marginal
analysis in assessing the GHG emissions related to conventional
baseline fuels that the EISA-mandated biofuels would replace. Rather,
the statute specifies a logical approach for reducing the GHG emissions
of transportation fuel as compared to those emissions that occurred in
2005. Therefore, EPA has retained in today's final rule the basic
analytical approach (marginal analysis for biofuels and 2005 average
for baseline fuels) used in the proposed rule.

C. Threshold Determination and Assignment of Pathways

    As required by EISA, EPA is making a determination of lifecycle GHG
emission threshold compliance for the range of pathways likely to
produce significant volumes of biofuel for use in the U.S. by 2022.
These threshold assessments only pertain to biofuels which are not
produced in production facilities that are grandfathered
(grandfathering of production facilities is discussed at the end of
Section V.C).
    As described in Section I.A.3, because of the inherent uncertainty
and the state of the evolving science on this issue, EPA is basing its
GHG threshold compliance determinations for this rule on an approach
that considers the weight of evidence currently available. For fuel
pathways with a significant land use impact, the evidence considered
includes the best estimate as well as the range of possible lifecycle
greenhouse gas emission results based on formal uncertainty and
sensitivity analyses conducted by the Agency. In making the threshold
determinations for this rule, EPA weighed all of the evidence available
to it, while placing the greatest weight on the best estimate value for
the base yield scenario. In those cases where the best estimate for the
potentially conservative base yield scenario exceeds the reduction
threshold, EPA judges that there is a good basis to be confident that
the threshold will be achieved and is determining that the bio-fuel
pathway complies with the applicable threshold. To the extent the
midpoint of the scenarios analyzed lies further above a threshold for a
particular biofuel pathway, we have increasingly greater confidence
that the biofuel exceeds the threshold.
    EPA recognizes that the state of scientific knowledge in this area
is continuing to evolve, and that as the science evolves, the lifecycle
greenhouse gas assessments for a variety of fuel pathways will continue
to change. Therefore, while EPA is making regulatory determinations for
fuel pathways as required by the statute in this final rule based on
its current assessment, EPA is at the same time committing to further
reassess these determinations and the lifecycle estimates. As part of
the ongoing effort, we will ask for the expert advice of the National
Academy of Sciences as well as other experts and then reflect this
advice and any updated information in a new assessment of the lifecycle
GHG emission performance of the biofuels being evaluated today. EPA
will request that the National Academy of Sciences evaluate the
approach taken in this rule, and the underlying science of lifecycle
assessment and in particular indirect land use change, and make
recommendations for subsequent rulemakings on this subject. This new
assessment could in some cases result in new determinations of
threshold compliance compared to those included in this rule which
would apply to future production from plants that are constructed after
each subsequent rule.
    Nonetheless, EPA is required by EISA to make threshold
determinations at this time as to what fuels qualify for each of the
four different fuel categories and lifecycle GHG thresholds. In the
previous sections, we have described the analytical basis EPA is using
for its lifecycle GHG assessment. These analyses represent the most up
to date information currently available on the GHG emissions associated
with each element of the full lifecycle assessment. Notably these
analyses include an assessment of uncertainty for key parameters of the
pathways evaluated. The best estimates and ranges of results for the
different pathways can be used to help assess whether a particular
pathway should be considered as attaining the 20%, 50% or 60%
thresholds, as applicable. The graphs included in the discussion below
provide representative depictions of the results of our analysis
(including the uncertainty in the modeling) for typical pathways for
corn ethanol, biodiesel produced from soy oil and from waste oils, fats
and greases, sugarcane ethanol and cellulosic biofuel from switchgrass.
We have also conducted lifecycle modeling assessments for cellulosic
biofuel pathways using other feedstock sources, for biobutanol and for
two specific pathways for emerging biofuels that would use oil from
algae as their feedstock. Additional GHG performance assessment results
for other feedstock/fuel/technology combinations are also described
below as well as in the RIA Chapter 2.
    Below we consider the analytical results of scenarios and fuel
pathways modeled by EPA as well as additional appropriate information
to determine the threshold compliance for an array of biofuels likely
to be produced in 2022.
    Ethanol from corn starch: While EPA analyzed the lifecycle GHG
performance of a variety of ethanol from corn starch pathways (complete
results can be found in the RIA), for purposes of this threshold
determination we have focused the discussion on the impacts of those
plant designs that are most likely to be built in the future. We have
focused this discussion on new plant designs because production from
existing plants is grandfathered for purposes of compliance with the
20% lifecycle GHG threshold. Only new plants and expanded capacity at
existing plants need to comply with a 20% lifecycle GHG emissions
threshold to comply with the total renewable fuel mandate under the
RFS2.
    While we focus our lifecycle GHG threshold analysis on the new
plant designs most likely to be built through 2022, we also note that
some existing plant designs, although subject to the grandfathering
provisions, would not qualify if having to meet the 20% performance
threshold. For example, existing designs of ethanol plants using coal
as their process heat source would not qualify.
    As discussed in Section IV, EPA anticipates that by 2022 any new
dry mill plants producing ethanol from corn starch will be equipped
with more energy efficient technology and/or enhanced co-product
production than today's average plant. These predictions are largely
based on economic considerations. To compete economically, future
ethanol plants will need to employ energy saving technologies and other
value added technologies that have the effect of also reducing their
GHG footprint. For example, while only in limited use today, we predict
approximately 90% of all plants will be producing corn oil as a by-
product either through a fractionation or extraction process; it is
likely most if not all new plants will elect to include such
technology. We also predict that all will use natural gas, biomass or
biogas as the process energy

source.180 181 We also expect that, to lower their operating
costs, most facilities will sell a portion of their co-product DGS
prior to drying thus reducing energy consumption and improving the
efficiency and lifecycle GHG performance of the plant. The current
national average plant sells approximately 37% of the DGS co-product
prior to drying.
---------------------------------------------------------------------------

    \180\ Dry mill corn ethanol plants using coal as a process
energy source would not qualify as exceeding the 20% reduction
threshold as modeled. We do not expect plants relying on coal for
process energy to be built through 2022. However, if they were
built, they would need to use technology improvements such as carbon
capture and storage (CCS) technology. We did not model what the
performance would be if these plants also installed CCS technology.
    \181\ We do not believe new wet mill corn ethanol plants will be
built through 2022 since this design is much more complicated and
expensive than a dry mill plant. Especially since dry mill plants
equipped with corn oil fractionation will produce additional
supplies of food grade corn oil (one of the products and therefore
reasons to construct a wet mill plant), we see no near term
incentive for additional wet mill ethanol production capacity.
However, we have modeled the lifecycle GHG impact of ethanol
produced at a wet mill plant when relying on biomass as the process
energy source and have determined it would meet the 20% GHG
threshold. Therefore, this type of facility is also included in
Table V.C-6.
---------------------------------------------------------------------------

    In analyzing the corn ethanol plant designs we expect could be
built through 2022 using natural gas or biomass for process energy and
employing advanced technology, in all cases, the midpoint and therefore
the majority of the scenarios analyzed are above the 20% threshold.
This indicates that, based on the current modeling approaches and sets
of assumptions, we are over 50% confident the actual GHG performance of
the ethanol from new corn ethanol plants will exceed the threshold of
20% improvement in lifecycle GHG emissions performance compared to the
gasoline it is replacing.
    We are determining at this time that the corn ethanol produced at
such new plants (and existing plants with expanded capacity employing
the same technology) will exceed the 20% GHG performance threshold. A
complete listing of complying facilities using advanced technologies
and operating procedures is included in Table V.C-6.
    Figure V.C-1 shows the percent change in the lifecycle GHG
emissions compared to the petroleum gasoline baseline in 2022 for a
corn ethanol dry mill plant using natural gas for its process energy
source, drying the national average of 63% of the DGS it produces and
employing corn oil fractionation technology. Lifecycle GHG emissions
equivalent to the gasoline baseline are represented on the graph by the
zero on the X-axis. The 20% reduction threshold is represented by the
dashed line at -20 on the graph. The results for this corn ethanol
scenario are that the midpoint of the range of results is a 21%
reduction in GHG emissions compared to the gasoline 2005 baseline. The
95% confidence interval around that midpoint ranges from a 7% reduction
to a 32% reduction compared to the gasoline baseline.

[GRAPHIC] [TIFF OMITTED] TR26MR10.424

    Table V.C-1 below includes lifecycle GHG emissions broken down by
several stages of the lifecycle impacts for a natural gas dry mill corn
ethanol facility as compared to the 2005 baseline average for gasoline.
This table (and similar tables which follow in the discussion for other
biofuels) is included to transparently demonstrate the contribution of
each stage and their relative significance. Lifecycle emissions are
normalized per energy unit of fuel produced and presented in kilograms
of carbon-dioxide equivalent GHG emissions per million British Thermal
Units of renewable fuel produced (kg CO2e/mmBTU). The
domestic and international agriculture rows include emissions from
changes in agricultural production (e.g., fertilizer and energy use,
rice methane) and livestock production. The fuel production row
includes emissions from the fuel production or refining facility,
primarily from energy consumption. For renewable fuels, tailpipe
emissions only include non-CO2 gases, because the carbon
emitted as a result of fuel combustion is offset by the uptake of
biogenic carbon during feedstock production. Note, that while the table
separates the emissions into different categories, the results are
based on integrated modeling; therefore, one component can not be
removed without impacting the other results. For example, domestic land
use and agricultural sector emissions depend on the international
assumptions. If a case without international impacts were modeled, the
domestic results would likely be significantly different.
    The table includes our mean estimate of international land use
change emissions as well as the 95% confidence range from our
uncertainty assessment, which accounts for uncertainty in the types of
land use changes and the magnitude of resulting GHG emissions. The last
row includes mean, low and high total lifecycle GHG emissions based on
the 95% confidence range for land use change emissions. For the
petroleum baseline, the fuel production stage includes emissions from
extraction, transport, refining and distribution of petroleum
transportation fuel. Petroleum tailpipe emissions include
CO2 and non-CO2 gases emitted from fuel
combustion.

       Table V.C-1--Lifecycle GHG Emissions for Corn Ethanol, 2022
                             [kg CO2e/mmBTU]
------------------------------------------------------------------------
                                                                 2005
             Fuel type                       Ethanol           Gasoline
                                                               baseline
------------------------------------------------------------------------
Fuel Production Technology.........  Natural Gas Fired Dry   ...........
                                      Mill.
Net Domestic Agriculture (w/o land   4.....................  ...........
 use change).
Net International Agriculture (w/o   12....................  ...........
 land use change).
Domestic Land Use Change...........  -2....................  ...........
International Land Use Change, Mean  32 (21/46)............  ...........
 (Low/High).
Fuel Production....................  28....................           19
Fuel and Feedstock Transport.......  4.....................  ...........
Tailpipe Emissions.................  1.....................           79
                                    ------------------------------------
Total Emissions, Mean (Low/High)...  79 (54/97)............           98
------------------------------------------------------------------------

    While we are projecting technology enhancements which would allow
corn ethanol plants to exceed the threshold, plant designs which do not
include such advanced technology would not comply. For example, a basic
plant which is not equipped with combinations of advanced technologies
such as corn oil fractionation or dries more than 50% of its DGS is
predicted to not comply. While we do not expect such a basic, low
technology plant to be built nor existing plants to expand their
production without also installing such advanced technology, if this
were to occur, ethanol produced at such facilities would not comply
with the 20% threshold.
    Biodiesel from soybean oil: We analyzed the lifecycle GHG emission
impacts of producing biodiesel using soy oil as a feedstock for
compliance with a lifecycle GHG performance threshold of 50%. The
modeling framework for this analysis was much the same as used for the
proposal. However, as noted above, based on comments, updated
information and enhanced models, the results are significantly updated.
    As in the case of ethanol produced from corn starch, EPA has relied
on a weight of evidence in developing its threshold assessment for
biodiesel produced from soybean oil. In analyzing the base yield case,
the midpoint and therefore the majority of the scenarios analyzed
exceed the threshold. This indicates that based on currently available
information and our current analysis over the range of scenarios
considered, the actual performance of soy oil-based biodiesel likely
exceeds the applicable 50% threshold.
    The scenarios analyzed also indicate, based on current data, we are
at least 95% confident biodiesel produced from soy oil will have GHG
impacts which are better than the 2005 baseline diesel fuel. From a GHG
impact perspective, we therefore conclude that even in the less likely
event the actual performance of biodiesel from soy oil does not exceed
the 50% threshold, GHG emission performance of transportation fuel
would still improve if this biodiesel replaced diesel fuel.
    We are further confident that biodiesel exceeds the 50% threshold
since our assessment of biodiesel GHG performance does not include any
prediction of significant improvements in plant technology or
unanticipated energy saving improvements that would further improve GHG
performance. Additionally, our assumption that the co-product of
glycerin would only have GHG value as replacement for residual heating
oil could be conservative. While we have not analyzed the range of
potential uses of glycerin, potential uses of glycerin including as a
feedstock to the chemical industry could be higher in GHG benefit than
its assumed use as a heating fuel.
    Considering all of the above current information and analyses, EPA
concludes that biodiesel made from soy oil will exceed its lifecycle
GHG threshold. Further, we see no benefit in lowering the threshold to
as low as 40% as allowed under EISA as this will neither benefit
available supply nor GHG performance of the fuel. Therefore, the
threshold for this rule will be maintained at 50%.
    Figure V.C-2 shows the percent change in the typical 2022 soybean
biodiesel lifecycle GHG emissions compared to the petroleum diesel fuel
2005 baseline. Lifecycle GHG emissions equivalent to the diesel fuel
baseline are represented on the graph by the zero on the X-axis. The
50% reduction threshold is represented by the dashed line at -50 on the
graph. The results for soybean biodiesel are that the midpoint of the
range of results is a 57% reduction in GHG emissions compared to the
diesel fuel baseline. The 95% confidence interval around that midpoint
results in range of a 22% reduction to an 85% reduction compared to the
diesel fuel 2005 baseline.

[GRAPHIC] [TIFF OMITTED] TR26MR10.425

    Biodiesel from waste oils, fats and greases: The lifecycle
assessment of GHG performance for biodiesel produced from waste oils,
fats and greases is much simpler than comparable assessments for
biofuels made from crops. In the case of biodiesel made from waste
material, there is no land use impact so the agricultural assessments
required for crop-based biofuels are unnecessary. Without the
uncertainty concerns due to land use impacts, there was no need to
conduct an uncertainty analysis for biodiesel from waste oils, fats and
greases. The assessment methodology for biofuel from waste oils fats
and greases is much the same as that analyzed for the proposal. As was
the case for the proposal, the assessment of each element in the
lifecycle process is straight forward and includes collecting and
transporting the feedstock, transforming it into a biofuel and
distributing and using the fuel. Based on the lifecycle assessment for
this final rule, we are estimating biofuel from waste oils, fats and
greases result in an 86% reduction in GHG emissions compared to the
2005 baseline for petroleum diesel. As was the case for the assessment
included in the proposal, biofuel from these feedstock sources easily
exceeds the applicable threshold of 50%.
    Table V.C-2 below breaks down by stage the lifecycle GHG emissions
for soy-based biodiesel, biodiesel from waste grease feedstocks and the
2005 diesel baseline. The average 2022 biodiesel production process
reflected in this table assumes that natural gas is used for process
energy and accounts for co-product glycerin displacing residual oil.
This table demonstrates the contribution of each stage and their
relative significance.

                            Table V.C-2--Lifecycle GHG Emissions for Biodiesel, 2022
                                                 [kg CO2e/mmBTU]
----------------------------------------------------------------------------------------------------------------
                                                                     Soy-based     Waste grease     2005 Diesel
                            Fuel type                                biodiesel       biodiesel       baseline
----------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change)..................             -10               0  ..............
Net International Agriculture (w/o land use change).............               1               0  ..............
Domestic Land Use Change........................................              -9               0  ..............
International Land Use Change,..................................      43 (15/76)               0  ..............
Mean (Low/High).................................................

Fuel Production.................................................              13              10              18
Fuel and Feedstock Transport....................................               3               3  ..............
Tailpipe Emissions..............................................               1               1              79
                                                                 -----------------------------------------------
Total Emissions, Mean...........................................      42 (14/76)              14              97
(Low/High)......................................................
----------------------------------------------------------------------------------------------------------------

    Biodiesel from algae oil: We analyzed the lifecycle GHG emission
impacts of producing biodiesel from algae oil as a feedstock for
compliance with a lifecycle performance threshold of 50%. Our analyses
were based on technoeconomic modeling completed by NREL, as previously
discussed. The NREL modeling included algae cultivation, harvesting,
extraction, and recovery of algae oil. Algae oil is further assumed to
use the same oil to biodiesel production technology as soy oil, which
was updated based on enhanced models. As algae are expected to be grown
on relatively small amounts of non-arable lands, it is expected that
the land use impact will be negligible. Based on our current lifecycle
assessment of algae oil for the final rule, we are determining that
biodiesel from algae oil will comply with the lifecycle performance
advanced biofuel threshold of 50%.
    Ethanol from sugarcane: As is the case for other crop-based
biofuels, EPA considered the weight of evidence currently available
information in assessing the lifecycle GHG performance of this fuel. As
noted in Section I.A.3, this lifecycle GHG assessment includes
significant updates from the analysis performed for the proposal. We
have added pathways for sugarcane ethanol such that we now distinguish
sugarcane ethanol produced assuming most crop residue (leaves and
stalks) are collected and therefore available for burning as process
energy, or sugarcane produced without the extra crop residue being
collected nor burned as process energy. We also analyzed pathways
assuming the ethanol is distilled in Brazil or alternatively being
distilled in the Caribbean. We did not analyze a ``high yield'' case
for sugarcane as we did for corn and soy since we had no information
available suggesting there could be an appreciable range in expected
sugarcane yields.
    Based on the currently available information, the midpoint and thus
the majority of the scenarios analyzed exceed the 50% threshold
applicable to advanced biofuels. This indicates that based on currently
available information and our current analysis, it is more than 50%
likely that the actual performance of ethanol produced from sugarcane
exceeds the applicable 50% threshold.
    The analyses also indicate, based on current data, ethanol produced
from sugarcane will clearly have GHG impacts which are better than the
2005 baseline gasoline. From a GHG impact perspective, we therefore
conclude that even in the less likely event the actual performance of
sugarcane does not exceed the 50% threshold, GHG emission performance
of ethanol from sugarcane would be better than gasoline.
    We also considered what would happen if we determine that ethanol
from sugarcane does not comply with a 50% threshold due to the
relatively low risk that this biofuel will actually be below that
threshold. Based on our current analysis of available pathways for
producing advanced biofuel, we believe that it will be necessary to
include over 2 billion gallons of sugarcane ethanol in order to meet
the advanced biofuel volumes anticipated by EISA. If sugarcane ethanol
was not an eligible source of advanced biofuel and other unanticipated
sources did not become available, the standard for advanced biofuel
would have to be lower to the extent necessary to compensate for the
lack of eligible sugarcane ethanol. The lower amount of advanced
biofuel would then most likely be replaced with petroleum-based
gasoline. The replacement fuel would have a worse GHG performance than
the sugarcane ethanol. Therefore, GHG performance of the transportation
fuel pool would suffer.
    Considering the above, EPA has concluded that, based on currently
available information and our analysis, ethanol from sugarcane
qualifies as an advanced biofuel.
    Figure V.C-3 shows the percent change in the average 2022 sugarcane
ethanol lifecycle GHG emissions compared to the petroleum gasoline 2005
baseline. These results assume the ethanol is produced and dehydrated
in Brazil prior to being imported into the U.S. Lifecycle GHG emissions
equivalent to the gasoline baseline are represented on the graph by the
zero on the X-axis. The 50% reduction threshold is represented by the
dashed line at -50 on the graph. The results for this sugarcane ethanol
scenario are that the midpoint of the range of results is a 61%
reduction in GHG emissions compared to the gasoline baseline. The 95%
confidence interval around that midpoint results in a range of a 52% to
71% reduction compared to the gasoline 2005 baseline.

[GRAPHIC] [TIFF OMITTED] TR26MR10.426

    Table V.C-3 below presents results for sugarcane ethanol production
and use by lifecycle stage. This table demonstrates the contribution of
each stage and their relative significance. The fuel production
emissions include displacement of marginal Brazilian electricity
because electricity is generated with the sugarcane bagasse co-product.
As in similar previous tables, domestic emissions include all emissions
sources in the United States, with all other emissions--including
emissions from Brazil--presented in the international categories.

    Table V.C-3--Lifecycle GHG Emissions for Sugarcane Ethanol, 2022
                             [kg CO2e/mmBTU]
------------------------------------------------------------------------
                                            Sugarcane     2005 Gasoline
               Fuel type                     ethanol         baseline
------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use                0                0
 change)...............................
Net International Agriculture (w/o land              38                0
 use change)...........................
Domestic Land Use Change...............               1                0
International Land Use Change, Mean           4 (-5/12)                0
 (Low/High)............................
Fuel Production........................             -11               19
Fuel and Feedstock Transport...........               5                0
Tailpipe Emissions.....................               1               79
                                        --------------------------------
    Total Emissions, Mean (Low/High)...      38 (29/46)               98
------------------------------------------------------------------------

    Cellulosic Biofuels: In the proposal, we analyzed biochemical
cellulosic ethanol pathways from both switchgrass and corn stover, and
on that basis proposed that such cellulosic biofuels met the required
60% lifecycle threshold by a considerable margin. As described in
Section V.B, we have considerably updated our lifecycle analysis, and
have analyzed additional cellulosic biofuel pathways (i.e.,
thermochemical cellulosic ethanol and a

BTL diesel pathway). We analyzed the GHG impacts of each element of the
lifecycle for producing and using biofuels from cellulosic biomass, and
as for other fuel pathways, considered the range of possible outcomes.
    Figure V.C-4 shows the percent change in the average lifecycle GHG
emissions in 2022 for ethanol produced from switchgrass using the
biochemical process compared to the petroleum gasoline 2005 baseline.
Lifecycle GHG emissions equivalent to the gasoline baseline are
represented on the graph by the zero on the X-axis. The 60% reduction
threshold is represented by the dashed line at -60 on the graph. The
results for this switchgrass ethanol scenario are that the midpoint of
the range of results is a 110% reduction in GHG emissions compared to
the gasoline baseline. The 95% confidence interval around that midpoint
ranges from 102% reduction to a 117% reduction compared to the gasoline
baseline.
[GRAPHIC] [TIFF OMITTED] TR26MR10.427

    Table V.C-4 below shows lifecycle GHG emissions for cellulosic
ethanol produced from switchgrass (as depicted in Figure V.C-4, above)
and also corn residue by lifecycle stage, comparing these to the 2005
baseline gasoline. This table is included to demonstrate the
contribution of each stage and their relative significance. Results are
presented for the biochemical production technology depicted in Figure
V.C-4 above and also for thermochemical production technologies. The
fuel production emissions for the biochemical pathway include credit
for excess electricity generation at the fuel production facility.

                                            Table V.C-4--Lifecycle GHG Emissions for Cellulosic Ethanol, 2022
                                                                     [kg CO2e/mmBTU]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                        Fuel type                                   Switchgrass ethanol                      Corn residue
--------------------------------------------------------------------------------------------------------------------------------------   2005 Gasoline
                Fuel production technology                    Bio-chemical     Thermo-chemical      Bio-chemical     Thermo-chemical        baseline
--------------------------------------------------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change)...........                  6                  6                 11                 11                  0

Net International Agriculture (w/o land use change)......                  0                  0                  0                  0                  0
Domestic Land Use Change.................................                 -2                 -3                -11                -11                  0
International Land Use Change, Mean (Low/High)...........          15 (9/23)         16 1(9/24)                  0                  0                  0
Fuel Production..........................................                -33                  4                -33                  4                 19
Fuel and Feedstock Transport.............................                  3                  3                  2                  2                  0
Tailpipe Emissions.......................................                  1                  1                  1                  1                 79
                                                          ----------------------------------------------------------------------------------------------
    Total Emissions, Mean (Low/High).....................       -10 (-17/-2)         27 (20/35)                -29                  7                 98
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Table V.C-5 below presents lifecycle GHG emissions for cellulosic
diesel produced with a Fischer-Tropsch process by lifecycle stage.

                        Table V.C-5--Lifecycle GHG Emissions for Cellulosic Diesel, 2022
                                                 [kg CO2e/mmBTU]
----------------------------------------------------------------------------------------------------------------
                     Fuel type                          Switchgrass     Corn residue diesel
----------------------------------------------------       diesel      ---------------------     2005 Diesel
                                                    -------------------                            baseline
             Fuel production technology                  F-T diesel          F-T diesel
----------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change).....                  6                   11                    0
Net International Agriculture (w/o land use change)                  0                    0                    0
Domestic Land Use Change...........................                 -3                  -11                    0
International Land Use Change, Mean (Low/High).....          16 (9/24)                    0                    0
Fuel Production....................................                  5                    5                   18
Fuel and Feedstock Transport.......................                  3                    2                    0
Tailpipe Emissions.................................                  1                    1                   79
                                                    ------------------------------------------------------------
    Total Emissions, Mean (Low/High)...............         29 (22/37)                    9                   97
----------------------------------------------------------------------------------------------------------------

    Based on the currently available information, we conclude that all
modeled cellulosic biofuel pathways are expected to exceed the 60%
threshold applicable to cellulosic biofuels.
    Assessments of similar feedstock sources: In the proposal, we
indicated that although we did not specifically analyze all potential
feedstock sources, some feedstock sources are similar enough to those
modeled that we believe the modeled results could be extended to these
similar feedstock types. Comments received supported this approach and
the specific recommendations for similar feedstock designations as
proposed.
    For this final rule, consistent with what was proposed, we are
relying on modeling results and only expanding to additional pathways
where we have good information these additional pathways will have
lifecycle GHG results which either will not impact our overall
assessment of the performance of that fuel pathway or would have at
least as good as the modeled pathways. The agricultural sector modeling
used for our lifecycle analysis does not predict any soybean biodiesel
or corn ethanol will be imported into the U.S., or any imported
sugarcane ethanol from production in countries other than Brazil.
However, these rules do not prohibit the use in the U.S. of these fuels
produced in countries not modeled if they are also expected to comply
with the eligibility requirements including meeting the thresholds for
GHG performance. Although the GHG emissions of producing these fuels
from feedstock grown or biofuel produced in other countries has not
been specifically modeled, we do not anticipate their use would impact
our conclusions regarding these feedstock pathways. 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
analyses indicate that crop yields for the crops in other countries
where these fuels are also most likely to be produced are similar or
lower than U.S. values indicating the same or slightly higher GHG
impacts. Agricultural sector inputs for the crops in these other
countries are roughly the same or lower than the U.S. pointing toward
the same or slightly lower GHG impacts. If crop production were to
expand due to biofuels in the countries where the models predict these
biofuels might additionally be produced, this would tend to lower our
assessment of international indirect impacts but could increase our
assessment of the domestic (i.e., the country of origin) land use
impacts. EPA believes, because of these offsetting factors along with
the small amounts of fuel potentially coming from other countries, that
incorporating fuels produced in other countries will not impact our
threshold analysis. Therefore, fuels of the same fuel type, produced
from the same feedstock using the same fuel production technology as
modeled fuel pathways will be assessed the same GHG performance
decisions regardless of country of origin.
    We are also able to conclude that some feedstock types not
specifically modeled should be covered as we have good reason to
believe their performance would be better than the feedstock pathways
modeled. Thus for example, we can conclude that, as in the case of corn
stover which we have modeled as a feedstock source, cellulosic biofuel
produced from other agricultural waste will also have no land use
impact and would be expected to

have lifecycle GHG emission impacts similar enough to the modeled corn
stover feedstock pathway such that they would also comply. Similarly,
we have information on miscanthus indicating that this perennial will
yield more feedstock per acre than the modeled switchgrass feedstock
without additional GHG inputs such as fertilizer. Therefore we are
concluding that since cellulosic biofuel from switchgrass complies with
the cellulosic threshold of 60% reduction, fuel produced using
miscanthus and other perennial grasses will also surely comply.
    We are also determined that biofuel from separated yard and food
wastes (which may contain incidental and post-recycled paper and wood
wastes) satisfy biofuel thresholds. Separated food waste is largely
starch-based and thus qualifies for the advanced biofuel standard of