into monetary values. This presents significant challenges for
lifecycle GHG analysis because it is difficult to translate dynamic GHG
emissions into a single estimate of physical impacts, much less a
single estimate of monetized impacts. This is the case for a number of
reasons, including the complex physical systems associated with climate
change (e.g., the relationship between atmospheric degradation rates
with atmospheric carbon stocks) that may create non-constant marginal
damages from GHG emissions over time. Furthermore, converting lifecycle
GHG emissions into monetized impacts may be inconsistent with the EISA
definition of lifecycle GHG emissions provided above in Section VI.A.1,
which stipulates that lifecycle GHG emissions are the ``aggregate
quantity of greenhouse gas emissions * * * where the mass values for
all greenhouse gases are adjusted to account for their relative global
warming potential.''
    Another argument against discounting GHG emissions changes is the
concept of inter-generational equity, which argues that benefits or
damages affecting future generations merit just as much weight as
impacts felt by current generations. It is argued that this would
invalidate the practice of discounting emissions impacts that could
affect future generations.
    Finally, earlier in this section we discussed the possible ranges
of time frames for analyzing the GHG emissions impacts. For shorter
time frames such as 30 years, there would be less uncertainty in the
emissions stream so the benefit of discounting to address uncertainty
is also lessoned.
    Comments are requested on the concept of discounting a stream of
GHG emissions for the purpose of estimating lifecycle GHG emissions
from transportation fuels as specified in EISA.

Appropriate Level of Discount Rate

    As described in more detail in Section IX on GHG emission reduction
benefits, GHG emissions have primarily consumption effects and inter-
generational impacts, as changes in GHG emissions today will continue
to have impacts on climate change for decades to centuries. If a
discount rate is applied to future GHG emissions, an appropriate
discount rate should be based on a consumption-based discount rate
given that monetized climate change impacts are primarily consumption
effects (i.e., impacts on household purchases of goods and services). A
consumption-based discount rate reflects the implied tradeoffs between
consumption today and in the future. Discount rates of 3% or less are
considered appropriate for discounting climate change impacts, since
they reflect the long run uncertainty in economic growth and interest
rates and the risk of high impact climate damages that could reduce
economic growth.\297\
---------------------------------------------------------------------------

    \297\ Technical Support Document on Benefits of Reducing GHG
Emissions, U.S. Environmental Protection Agency, June 12, 2008,
www.regulations.gov (search phrase ``Technical Support Document on
Benefits of Reducing GHG Emissions'').

---------------------------------------------------------------------------

    When analyzing the GHG emissions associated with a 100 year time
period, we examined a variety of alternative discount rates (e.g., 0,
2, 3, 7 percent) to show the sensitivity of greenhouse gas emissions
estimates to the choice of the discount rate. A zero discount rate
estimates GHG emission impacts as if each ton of GHG emissions is
treated equally through time. Previous methodologies of lifecycle GHG
benefits have presented results using a zero discount rate.\298\
However, some of the climate change literature supports using a higher
discount rate, as described in Section IX.C. We show the 7% discount
rate for illustrative purposes; however climate change benefit analyses
from global long-run growth models typically use discount rates well
under 7% for standard analysis.\299\ High discount rates imply very low
values for the future GHG emission impacts resulting from today's
actions on the welfare of future generations. Therefore, lower discount
rates such as 2-3% are considered more appropriate for discounting long
term climate change impacts.\300\
---------------------------------------------------------------------------

    \298\ Searchinger et al., 2008.
    \299\ Tol, 2005.
    \300\ Newell and Pizer, 2003.
---------------------------------------------------------------------------

    In the analysis for this proposal we use a 2% discount rate to
assess the present value of GHG emissions changes which occur over a
100 year time frame. This discount rate is consistent with the Office
of Management and Budget (OMB) \301\ and EPA \302\ guidance and is one
of the discount rates that has been used in the literature to monetize
the impacts of climate change.\303\ EPA has considered this issue
previously, and after weighing the pros and cons of different values,
set forth a guidance document recommending using a range of consumption
based discount rates of 0.5-3%. OMB and EPA guidance on inter-
generational discounting suggests using a low but positive discount
rate if there are important inter-generational benefits and costs. In
selecting a 2% discount rate coupled with a 100 year emission stream
estimate, EPA would be recognizing the long term nature of the emission
impacts of biofuel production, the uncertainty in estimating these
emission impacts and their consequences plus the significance of nearer
term emission changes in avoiding future consequences. Other options
for intergenerational discounting have been discussed in the economic
literature, such as dealing with uncertainty by using a non-constant,
declining, or negative discount rate.\304\ Comments could consider how
discounting appropriately reflects the uneven emission of greenhouse
gases from biofuels over time, the uncertainty in predicting emissions
in more distant futures and the impacts these emissions could have on
climate change. Alternative approaches for inter-generational
discounting are described in Chapter 5.3 of the DRIA.
---------------------------------------------------------------------------

    \301\ OMB Circular A-4, 2003 provides a range of 1-3% for
consumption based discount rates.
    \302\ EPA Guidelines for Preparing Economic Analyses, 2000.
    \303\ Tol (2005, 2007).
    \304\ Newell and Pizer, 2003, Weitzman (1999, 2001), Nordhaus
(2008), Guo et al., (2006), Saez, C.A. and J.C. Requena,
``Reconciling sustainability and discounting in Cost-Benefit
Analysis: A methodological proposal'', Ecological Economics, 2007,
vol. 60, issue 4, pages 712-725.
---------------------------------------------------------------------------

    Because we are considering not discounting GHG emissions and in
particular since the justifications for discounting physical emissions
are not as strong for shorter time periods, in Section VI.C.2, we also
present the GHG emissions reductions associated with biofuels using a
30 year time period and no discount rate. Using a zero percent or no
discount rate implies that all emission releases and uptakes during
this time period are valued equally. For a shorter time period such as
thirty years, we are relatively certain of the emission trends.
Furthermore, all of these emissions occur in a relatively short period
of time so their impact on climate change and the consequences of that
climate change could all be considered the same regardless of whether
those emissions occurred early or late in this 30-year time period.
    We specifically invite comment on our use of a 2% discount rate
with a 100 year time period for analysis of lifecycle GHG emissions,
and our use of no discount rate in our analysis of GHG emissions over
30 years. We also invite comments on whether using physical science
metrics such as the actual quantities of climate forcing gasses in the
atmosphere, actual quantities of climate forcing gasses in the
atmosphere weighted by global warming potential (GWP), or cumulative
radiative forcing should be used to evaluate emissions over time.
Specifically, we seek comment on an approach for comparing GHG
emissions based on the time profile of the greenhouse gas emissions in
the atmosphere, and whether this approach would be consistent with the
legal definition of lifecycle GHG emissions in EISA. One such method is
the Fuel Warming Potential as outlined in a memo to the EPA from the
Union of Concerned Scientists which is available on the public docket
for this rulemaking.\305\ This approach is based on the ratio of the
cumulative radiative forcing between the biofuel and the gasoline case
over a specified time frame.
---------------------------------------------------------------------------

    \305\ See Memo to EPA, Office of Transportation and Air Quality
from Union of Concerned Scientists, Re: Treatment of Time in Life
Cycle Accounting, February 18, 2009.
---------------------------------------------------------------------------

    The EISA definition of lifecycle GHG emissions stipulates that the
mass values for all greenhouse gas emissions shall be adjusted to
account for their relative GWP. We are proposing to use the standard
100-year GWP's published in the IPCC Second Assessment
Report.306 307 We invite comment on whether it is
appropriate to discount GWP-weighted emissions and how such discounting
might appropriately apply across the several greenhouse gases.
---------------------------------------------------------------------------

    \306\ See http://www.ipcc.ch/ipccreports/assessments-reports.htm.
    \307\ O'Hare, Plevin, Martin, Jones, Kendal and Hopson; ``Proper
accounting for time increases crop-based biofuel's greenhouse gas
deficit versus petroleum''; Environmental Research Letters, 4 (2009)
024001.
---------------------------------------------------------------------------

    Furthermore, if alternative time periods for the production of
biofuels and the GHG impacts of biofuel production are considered as
discussed above, and the choice is made to discount GHG emissions, the
question that arises is: What discount rate or combination of discount
rates should be considered? For example, if ethanol production is
assumed to occur for 30 years and the GHG impacts are assumed to span
across 80-100 years, should a single discount rate be applied to the
emissions stream or alternative discount rates based upon the different
time frames? EPA is taking comment on whether and how to apply
discounting when different time frames between the production and long-
run GHG impacts are utilized to analysis biofuels. Specifically, EPA is
considering and requests comment on the option of using either no
discount rate or a 3% discount rate to assess those emissions that
occur during the relatively shorter time frame for biofuel use which
could phase out within 30 years as in our corn ethanol example and a 2%
discount rate over the reminder of the 100 years in assessing the
longer term GHG emissions impacts resulting from land use changes
related to biofuel production (including land reversion
considerations).
    EPA is considering a range of discount rates including zero or no
discounting for reasons as described above and requests comments on the
appropriate discount rate to use when assessing the stream of GHG
emission changes that are likely to result from biofuel production and
use. Other

options for intergenerational discounting have been discussed in the
economic literature, such as dealing with uncertainty by using a non-
constant, declining, or negative discount rate.\308\ Comments could
consider how discounting appropriately reflects the uneven release of
greenhouse gases from biofuels over time, the uncertainty in predicting
emissions in more distant futures and the impacts these emissions could
have on climate change. Alternative approaches for inter-generational
discounting are described in Chapter 5.3 of the DRIA.
---------------------------------------------------------------------------

    \308\ Newell and Pizer, 2003, Weitzman (1999, 2001), Nordhaus
(2008), Guo et al., (2006), Saez, C.A. and J.C. Requena,
``Reconciling sustainability and discounting in Cost-Benefit
Analysis: A methodological proposal'', Ecological Economics, 2007,
vol. 60, issue 4, pages 712-725.
---------------------------------------------------------------------------

    EPA recognizes that the time horizon for analysis and the treatment
of future emissions including the appropriateness of applying discount
factors are key factors in determining biofuel lifecycle GHG impacts;
therefore, we plan to organize an expert peer review of these issues
before the final rule.
c. Feedstock Transport
    The GHG impacts of transporting corn from the field to the ethanol
facility and transporting the co-product DGs from the ethanol facility
to the point of use were included in this analysis. The GREET default
of truck transportation of 50 miles was used to represent corn
transportation from farm to plant. Transportation assumptions for DGs
transport were 14% shipped by rail 800 miles, 2% shipped by barge 520
miles, and 86% shipped by truck 50 miles. The percent shipped by mode
was from data provided by USDA and based on Association of American
Railroads, Army Corps of Engineers, Commodity Freight Statistics, and
industry estimates. The distances DGs were shipped were based on GREET
defaults for other commodities shipped by those transportation modes.
The GHG emissions from transport of corn and DGs are based on GREET
default emission factors for each type of vehicle including capacity,
fuel economy, and type of fuel used. Similar detailed analyses were
conducted for the transport of cellulosic biofuel feedstock and
biomass-based diesel feedstock.
    As part of this rulemaking analysis we have conducted a more
detailed analysis of biofuel production locations and transportation
distances and modes of transport used in the criteria pollutant
emissions inventory calculations described in DRIA Chapter 1.6 and for
the cost analysis of this rule described in DRIA Chapter 4.2. Given the
timing of when the current analysis was completed we were not able to
incorporate this updated transportation information into our lifecycle
analysis but plan to do that for the final rule.
    Furthermore, the transportation modes and distances assumed for
corn and DGs do not account for the secondary or indirect
transportation impacts. For example, decreases in exports might reduce
overall domestic agricultural commodity transport and emissions but
might increase transportation of commodities internationally. We plan
to consider these secondary transportation impacts in our final rule
analysis.
d. Processing
    The GHG emissions estimates associated with the processing of
renewable fuels is dependent on a number of assumptions and varies
significantly based on a number of key variables. The ethanol yield
impacts the total amount of corn used and associated agricultural
sector GHG emissions. The amount of DGs and other co-products produced
will also impact the agricultural sector emissions in terms of being
used as a feed replacement. Finally the energy used by the ethanol
plant will result in GHG emissions, both from producing the fuel used
and through direct combustion emissions.
    As mentioned above, in traditional lifecycle analyses, the energy
consumed and emissions generated by a renewable fuel plant must be
allocated not only to the renewable fuel, but also to each of the by-
products. For corn ethanol production, our analysis avoids the need to
allocate by accounting for the DGs and other co-products directly in
the FASOM and FAPRI agricultural sector modeling described above. DGs
are considered a partial replacement for corn and other animal feed and
thus reduce the need to make up for the corn production that went into
ethanol production. Since FASOM takes the benefits from the production
and use of DGs into account (e.g., displacing the need to grow
additional crops for feed and therefore reducing GHG emissions in the
agricultural sector), no further allocation was needed at the ethanol
plant and all plant emissions are accounted for here.
    In terms of the energy used at renewable fuel facilities, there is
a lot of variation between plants based on the process type (e.g., wet
vs. dry milling) and the type of fuel used (e.g., coal vs. natural
gas). There can also be variation between the same type of plants using
the same fuel source based on the age of the plant and types of
processes included, etc. For our analysis we considered different
pathways for corn ethanol production. Our focus was to differentiate
between facilities based on the key differences between plants, namely
the type of plant and the type of fuel used. One other key difference
we modeled between plants was the treatment of the co-products DGs. One
of the main energy drivers of ethanol production is drying of the DGs.
Plants that are co-located with feedlots have the ability to provide
the co-product without drying. This has a big enough impact on overall
results that we defined a specific category for wet vs. dry co-product.
One additional factor that appears to have a significant impact on GHG
emissions is corn oil fractionation from DGs. Therefore, this category
is also broken out as a separate category in the following section. See
DRIA Chapter 1.4 for a discussion of corn oil fractionation.
    Furthermore, as our analysis was based on a future timeframe, we
modeled future plant energy use to represent plants that would be built
to meet requirements of increased ethanol production, as opposed to
current or historic data on energy used in ethanol production. The
energy use at dry mill plants was based on ASPEN models developed by
USDA and updated to reflect changes in technology out to 2022 as
described in DRIA Chapter 4.1.
    The GHG emissions from renewable fuel production are calculated by
multiplying the Btus of the different types of energy inputs by
emissions factors for combustion of those fuel sources. The emission
factors for the different fuel types are from GREET and are based
primarily on assumed carbon contents of the different process fuels.
The emissions from producing electricity are also taken from GREET and
represent average U.S. grid electricity production emissions. The
emissions from combustion of biomass fuel source are not assumed to
increase net atmospheric CO2 levels the CO2
emitted from biomass-based fuels combustion does not increase
atmospheric CO2 concentrations, assuming the biogenic carbon
emitted is offset by the uptake of CO2 resulting from the
growth of new biomass. Therefore, CO2 emissions from biomass
combustion as a process fuel source are not included in the lifecycle
GHG inventory of the ethanol plant.
e. Fuel Transport
    Transportation and distribution of ethanol, biomass-based diesel,
petroleum diesel and gasoline were also included in this analysis based
on GREET defaults. The GREET defaults for

both ethanol and gasoline transport from plant/refinery to bulk
terminals were used. The GREET defaults for both ethanol and gasoline
distribution from the bulk terminal to the service station were also
used.
    As with feedstock transport we have conducted a more detailed
analysis of fuel transport and distribution impacts for use in criteria
pollutant inventories (see DRIA Chapter 1.6) and for our cost analysis
described in DRIA Chapter 4.2. Due to the timing of this analysis we
were not able to incorporate the results in our proposed lifecycle
calculation but plan to do that for the final rule.
f. Tailpipe Combustion
    Combustion CO2 emissions for ethanol, biomass-based
diesel, petroleum diesel and gasoline were based on the carbon content
of the fuel. However, over the full lifecycle of the fuel, the
CO2 emitted from biomass-based fuels combustion does not
increase atmospheric CO2 concentrations, assuming the
biogenic carbon emitted is offset by the uptake of CO2
resulting from the growth of new biomass. As a result, CO2
emissions from biomass-based fuels combustion are not included in their
lifecycle emissions results. Net carbon fluxes from changes in biogenic
carbon reservoirs in wooded or crop lands are accounted for separately
in the land use change analysis as outlined in the agricultural sector
modeling above.
    When calculating combustion GHG emissions, however, the methane and
N2O emitted during biomass-based fuels combustion are
included in the analysis. Unlike CO2 emissions, the
combustion of biomass-based fuels does result in net additions of
methane and N2O to the atmosphere. Therefore, combustion
methane and N2O emissions are included in the lifecycle GHG
emissions results for biomass-based fuels.
    Combustion related methane and N2O emissions for both
biomass-based fuels and petroleum-based fuels are based on EPA MOVES
model results.
6. Petroleum Baseline
    To establish the lifecycle greenhouse gas emissions associated with
the petroleum baseline against which the renewable fuels were compared,
we used an updated version of the GREET model. Lifecycle energy use and
associated emissions for petroleum-based fuels in GREET is calculated
based on an energy efficiency metric for the different processes
involved with petroleum-based fuels production. The energy efficiency
metric is a measure of how many Btus of input energy are needed to make
a Btu of product. GREET has assumptions on energy efficiency for
different finished petroleum products as well as for different types of
crude oil.
    We are using the latest version of the GREET model for this
analysis (Version 1.8b) which includes recent updates to the energy
efficiencies of petroleum refining. To represent baseline petroleum
fuels we have used the 2005 estimates of actual gasoline and diesel
fuel used. For 2005, 86% of gasoline and 92% of diesel fuel was
produced domestically with the rest imported finished product. To
represent international production we assume the same GHG refinery
emissions from GREET as used domestically. We did not include indirect
land use impacts in assessing the lifecycle GHG performance of the 2005
baseline fuel pool as we believe these would insignificantly impact the
average performance assessment of the baseline. Additionally,
consistent with our assessment of energy security impacts, we did not
include as an indirect GHG impact the potential impact of maintaining a
military presence.
    GREET also has assumptions on the mix of energy sources used to
provide the energy input, which determine GHG emissions. For example if
coal, natural gas, or purchased electricity is used as an energy
source. The GHG emissions associated with petroleum fuel production are
based on the emissions from producing and combusting the input energy
sources needed, like GHG emissions from using natural gas at the
petroleum refinery. Non-combustion GHG sources like fugitive methane
emissions are added in where applicable.
    Based on the EISA requirements, we used the 2005 mix of crude as
the petroleum baseline. We developed emissions factors for those crude
types since they are not currently included in GREET. In 2005, 5% of
crude was Canadian tar sand, 1% was Venezuela extra heavy, and 23% was
heavy crude.
    For this proposal, we are using the average GHG emissions
associated with the 2005 petroleum baseline, as required by EISA.
However, we recognize that an additional gallon of renewable fuel
replaces the marginal gallon of petroleum fuel. To the extent that the
marginal gallon is from oil sands or other types of crude oil that are
associated with higher than average GHG emissions, replacing these
fuels could have a larger GHG benefit. Conversely to the extent the
marginal gallon displaced is from imported gasoline produced from light
crude, replacing these fuels would have a smaller GHG benefit. We
solicit comment on whether--strictly for purposes of assessing the
benefits of the rule (and not for purposes of determining whether
certain renewable fuel pathways meet the GHG reduction thresholds set
forth in EISA), we should assess benefits based on a marginal
displacement approach and, if so, what assumptions we should use for
the marginal displacements.
    In December 2008, the U.S. Department of Energy's National Energy
Technology Laboratory (NETL) released a report that estimates the
average lifecycle GHG emissions from petroleum-based fuels sold or
distributed in 2005.\309\ The estimates in the report are based on a
slightly different methodology than EPA's analysis of lifecycle GHG
emissions for the petroleum baseline. The NETL report is available on
the docket for this rulemaking. We invite comments on whether NETL's
analysis has significant implications for how EPA is estimating
petroleum baseline lifecycle GHG emissions.
---------------------------------------------------------------------------

    \309\ DOE/NETL. 2008. Development of Baseline Data and Analysis
of Life Cycle Greenhouse Gas Emissions of Petroleum-Based Fuels.
DOE/NETL-2009/1346.
---------------------------------------------------------------------------

7. Energy Sector Indirect Impacts
    Increased demand for natural gas to power corn ethanol plants could
have additional impacts on the U.S. energy sector. As demand for
natural gas increases, the use of natural gas in other sectors (e.g.,
electric generation) could decrease. For this analysis, we are using
the NEMS model to project the secondary or indirect impacts on the
energy sector. However, we were not able to include this analysis in
the GHG emissions estimates presented in this proposal. We hope to have
this analysis for the final rule. Additional details on the methodology
are included in the DRIA Chapter 2, and we invite comments on this
approach.
    We are assuming, for the proposal, that a gallon of renewable fuel
replaces an energy equivalent gallon of petroleum fuel. This analysis
presumes that petroleum-based fuels as they are currently produced will
continue to be used for transportation fuels and will be replaced on a
Btu for Btu basis. Many factors could affect this assumption including
advances in petroleum fuel technology, availability of other fossil
fuels for transportation use, and of course the supply and cost of
petroleum. We have not tried to analyze these potential impacts in this
rule. However we invite comment on such an approach.
    We have also not assessed whether expanded use of biofuels in the
U.S.

will impact the energy markets in other countries. For example,
reducing demand for petroleum-based fuel in the U.S. may reduce
worldwide petroleum prices and impact the use of petroleum in other
countries. We invite comment on how best to assess these potential
impacts and will attempt to do so for the final rule.

C. Fuel Specific GHG Emissions Estimates

    While the results presented in this section represent the most up-
to-date information currently available, this analysis is part of an
ongoing process. Because lifecycle analysis is a new part of the RFS
program, in addition to the formal comment period on the proposed rule,
EPA is making multiple efforts to solicit public and expert feedback on
our proposed approach. As discussed in Section XI, EPA plans to hold a
public workshop focused specifically on lifecycle analysis during the
comment period to assure full understanding of the analyses conducted,
the issues addressed and options that should be considered. We expect
that this workshop will allow the most thoughtful and useful comments
to this proposal and assure the best methodology and assumptions are
used for calculating GHG emissions impacts of fuels for the final rule.
Additionally we will conduct peer-reviews of key components of our
analysis. As part of ongoing analysis for the final rule, EPA will seek
peer review of: Our use of satellite data to project future land use
changes; the land conversion GHG emissions factors estimated by
Winrock; our estimates of GHG emissions from foreign crop production;
methods to account for the variable timing of GHG emissions; and how
models are used together to provide overall lifecycle GHG estimates.
    In addition to the refinements to the methodology that we plan to
undertake for the final rule, we also intend to update our results
periodically. EPA recognizes that the state of the science for
lifecycle GHG analysis will continue to evolve over time as new data
and modeling techniques become available and as there are improvements
in agricultural and renewable fuel production practices as well as new
feedstocks. We invite comments on the appropriate amount of time for
periodic review of the lifecycle assessment methodology, but we propose
that performing an update of the methodology every 3-5 years would be
appropriate. We would expect the first update to this analysis would
occur closer to 3 years. This timeframe would allow us to undergo a
formal review process after the final rule to ensure that this
methodology takes into account the most state-of-the-art science and
reflects the input of appropriate experts in this field. However, any
change in lifecycle methodology as contemplated here would not affect
the eligibility of biofuels produced at facilities covered by the
grandfathering provisions of EISA at section 211(o)(4)(g).
1. Greenhouse Gas Emissions Reductions Relative to the 2005 Petroleum
Baseline
    In this section we present detailed lifecycle GHG results for
several specific biofuels representing a range biofuel pathways. This
section also includes the results of sensitivity analysis for key
variables. The sensitivity of the time period and discount rate are
discussed below. In the rest of this section we focus on two sets of
lifecycle GHG results. One set of results that uses a 100 year time
period and 2% discount rate and a parallel set of results using a 30
year time period and a 0% discount rate. In Section IV.C.2 which
follows, we also present the results for some additional combinations
of time horizon for assessing GHG emission changes as well as assuming
other discount rates. Additional pathways, not included in the results
presented in this section, distinguishing other combinations of
feedstock and processing technologies have been evaluated. These
additional pathways are described in detail in the DRIA and are
included in these proposed regulations.
a. Corn Ethanol
    Table VI.C.1-1 presents the breakout of the net present value of
lifecycle GHG emissions per million British thermal unit (mmbtu) of
corn ethanol and gasoline. The results are broken out by lifecycle
stage. Values are shown for a standard dry mill corn ethanol plant in
2022 using natural gas for process energy and drying the co-product of
distillers grains (DGs). Results indicate where the major contributions
of GHG emissions are across the fuel lifecycle. Fuel processing and
indirect land use change are the main contributors to corn ethanol
lifecycle GHG emissions. Net domestic and international agricultural
impacts (w/o land use change) include direct and indirect impacts, such
as reductions in livestock enteric fermentation.
---------------------------------------------------------------------------

    \310\ For this proposal, our preliminary analysis suggests land
use impacts of petroleum production for the fuels used in the U.S.
in 2005 would not have an appreciable impact on the 2005 baseline
GHG emissions assessment. However, we expect to more carefully
consider potential land use impacts of petroleum-based fuel
production for the final rule and invite comment and information
that would support such an analysis.
    \311\ 2005 petroleum baseline fuel production includes crude oil
extraction, transportation, refining, and transport of finished
product.
    \312\ Ethanol tailpipe emissions include CH4 and
N2O emissions but not CO2 emissions as these
are assumed to be offset by feedstock carbon uptake.

        Table VI.C.1-1--Absolute Lifecycle GHG Emissions for Corn Ethanol and the 2005 Petroleum Baseline
                                                 [CO2-eq/mmBtu]
----------------------------------------------------------------------------------------------------------------
                                                                    Natural gas                     Natural gas
                                                   2005 Gasoline   dry mill with   2005 Gasoline   dry mill with
                                                     baseline         dry DGs        baseline         dry DGs
----------------------------------------------------------------------------------------------------------------
                 Lifecycle Stage                             100 yr 2%
                                                             30 yr 0%
----------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change)..             N/A        -499,029             N/A        -347,365
Net International Agriculture (w/o land use                  N/A         452,118             N/A         314,711
 change)........................................
Domestic Land Use Change........................             N/A          79,547             N/A          92,575
International Land Use Change...................       N/A \310\       1,911,391             N/A       1,910,822
Fuel Production \311\...........................         823,262       1,404,083         573,058         977,358
Fuel and Feedstock Transport....................   (see footnote         174,327  ..............         121,346
                                                            321)
Tailpipe Emissions \312\........................       3,417,311          37,927       2,378,800          26,400
???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
    Net Total Emissions.........................       4,240,674       3,560,365       2,951,858       3,095,846
----------------------------------------------------------------------------------------------------------------

    Table VI.C.1-1 demonstrates the importance of the discount rate and
time period analyzed as well as the importance of significance of
including GHG emissions from international land use changes. Assuming
100 years of corn ethanol produced in a basic dry mill ethanol
production facility and using a 2% discount rate results in corn
ethanol having a 16% reduction in GHG emissions compared to the 2005
baseline gasoline assumed to be replaced. In contrast, assuming 30
years of corn ethanol production and use and no discounting of the GHG
emission impacts results in predicting that corn ethanol will have a 5%
increase in GHG emissions compared to petroleum gasoline.
    As discussed in Section VI.B.2.a, EPA's interpretation of the EISA
statute compels us to include significant indirect emission impacts
including those due to land use changes in other countries. The data in
Table VI.C.1-1 indicate that excluding the international land use
change would result in corn ethanol having an approximately 60%
reduction in lifecycle GHG emissions compared to petroleum gasoline
regardless of the timing or discount rate used.\313\
---------------------------------------------------------------------------

    \313\ The treatment of emissions over time is not critical if
international land use change emissions are excluded because the
results without land use change are consistent over time. Therefore
the overall lifecycle GHG results do not vary with time or discount
rate assumptions.
---------------------------------------------------------------------------

    In Table VI.C.1-1, we project a standard dry mill ethanol plant in
2022 using corn as its feedstock, using natural gas for process energy,
and drying the co-product of distillers grains (DGs). Different corn
ethanol production technologies will have different lifecycle GHG
results. For example, due to its high carbon content, using coal as the
process energy source significantly worsens the lifecycle GHG impact of
ethanol produced at such a facility. On the other hand, replacing
natural gas with renewable biomass as the process energy source greatly
improves the GHG assessment.
    Other technology options are available to improve the efficiency of
ethanol facilities. Table VI.C.1-2 shows the impact that different corn
ethanol production process pathways will have on the overall lifecycle
GHG results. Table VI.C.2-2 shows that currently available technologies
could be applied to corn ethanol plants to reduce their net GHG
emissions.
    For example, a combined heat and power (CHP) configuration, used in
combination with corn oil fractionation, would result in a GHG
emissions reduction of 27% relative to the 2005 petroleum baseline over
100 years using a 2% discount rate, and a 6% reduction over 30 years
with no discounting. In addition, advanced technologies such as
membrane separation and raw starch hydrolysis could improve the
emissions associated with corn ethanol production even more
substantially. Combining all of these technologies in a state-of-the-
art natural gas powered corn ethanol facility would produce ethanol
that has approximately 35% less lifecycle GHG emissions than an energy
equivalent amount of baseline gasoline displaced over 100 years using a
2% discount rate and, by comparison a 14% reduction when accounting for
30 years of emission changes but applying no discounting. Details on
these different technologies are included in the DRIA Chapter 1.5.
    Table VI.C.1-2 also shows that the choice of drying DGs can have a
significant impact on the GHG emissions associated with an ethanol
plan, since drying the ethanol byproduct is an energy intensive
process. However, wet DGs are only suitable where a local market is
available such as a dairy farm or cattle feedlot, since wet DGs are
highly perishable.

Table VI.C.1-2--Lifecycle GHG Emissions Changes for Various Corn Ethanol
        Pathways in 2022 Relative to the 2005 Petroleum Baseline
------------------------------------------------------------------------
                                          Percent change
                                             from 2005    Percent change
   Corn ethanol production plant type        petroleum       from 2005
                                           baseline (100   baseline (30
                                              yr 2%)          yr 0%)
------------------------------------------------------------------------
Natural Gas Dry Mill with dry DGs.......             -16              +5
Natural Gas Dry Mill with dry DGs and                -19              +2
 CHP....................................
Natural Gas Dry Mill with dry DGs, CHP,              -27              -6
 and Corn Oil Fractionation.............
Natural Gas Dry Mill with dry DGs, CHP,              -30             -10
 Corn Oil Fractionation, and Membrane
 Separation.............................
Natural Gas Dry Mill with dry DGs, CHP,              -35             -14
 Corn Oil Fractionation, Membrane
 Separation, and Raw Starch Hydrolysis..
Natural Gas Dry Mill with wet DGs.......             -27              -6
Natural Gas Dry Mill with wet DGs and                -30              -9
 CHP....................................
Natural Gas Dry Mill with wet DGs, CHP,              -33             -12
 and Corn Oil Fractionation.............
Natural Gas Dry Mill with wet DGs, CHP,              -36             -15
 Corn Oil Fractionation, and Membrane
 Separation.............................
Natural Gas Dry Mill with wet DGs, CHP,              -39             -18
 Corn Oil Fractionation, Membrane
 Separation, and Raw Starch Hydrolysis..
Coal Fired Dry Mill with dry DGs........             +13             +34
Coal Fired Dry Mill with dry DGs and CHP             +10             +31
Coal Fired Dry Mill with dry DGs, CHP,                -5             +15
 and Corn Oil Fractionation.............
Coal Fired Dry Mill with dry DGs, CHP,               -13              +8
 Corn Oil Fractionation, and Membrane
 Separation.............................
Coal Fired Dry Mill with dry DGs, CHP,               -21              -1
 Corn Oil Fractionation, Membrane
 Separation, and Raw Starch Hydrolysis..
Coal Fired Dry Mill with wet DGs........              -9             +12
Coal Fired Dry Mill with wet DGs and CHP             -11             +10
Coal Fired Dry Mill with wet DGs, CHP,               -17              +3
 and Corn Oil Fractionation.............
Coal Fired Dry Mill with wet DGs, CHP,               -25              -4
 Corn Oil Fractionation, and Membrane
 Separation.............................
Coal Fired Dry Mill with wet DGs, CHP,               -30              -9
 Corn Oil Fractionation, Membrane
 Separation, and Raw Starch Hydrolysis..
Biomass Fired Dry Mill with dry DGs.....             -39             -18
Biomass Fired Dry Mill with wet DGs.....             -40             -19
Natural Gas Fired Wet Mill..............              -7             +14

Coal Fired Wet Mill.....................             +20             +41
Biomass Fired Wet Mill..................             -47             -26
------------------------------------------------------------------------

    As described in Sections VI.A and VI.B, there are a number of
parameters and modeling assumptions that could impact the overall
renewable fuel GHG results. The estimates in Table VI.C.1-2 are based
on the GHG emissions for a specific change in volumes analyzed in 2022
(12.3 to 15 Bgal). These volumes represent the change in corn ethanol
production that would occur in 2022 without and then with EISA mandates
in place. The GHG impact is then normalized to a per gallon or Btu
basis in relation to gasoline. These values are used to represent every
gallon of corn ethanol produced throughout the program.
    There are several important implications associated with this
methodology. First, this analysis focuses on the average impact of an
increase in fuel produced using a technology pathway and does not
distinguish the emission performance between biofuel production plants
using the same basic production technology and type of feedstock. Thus
it does not account for any incremental differences in facility design
or operation which may affect the lifecycle GHG performance at that
facility. Second, by focusing on 2022, this analysis does not track how
biofuel GHG emission performance may change over time between now and
2022. Third, the results presented here are based on the GHG impacts of
the volumes analyzed.
    For this proposal, we believe that using the emissions assessment
from a typical 2022 facility for each major technology pathway captures
the appropriate level of detail needed to determine whether a
particular biofuel meets the threshold requirements in EISA. To address
whether the GHG emissions vary significantly over time, we also
calculated corn ethanol lifecycle GHG emissions estimates in 2012 and
2017. As shown in Table VI.C.1-3, corn ethanol's lifecycle GHG
emissions reductions are fairly consistent regardless of which base
year is analyzed. This may be due to countervailing forces that
stabilize land use change emissions over the period of our analysis.
Crop yields increase over time (therefore reducing land use pressure),
but there is also increasing production of other renewable fuels that
require land for feedstock production (therefore increasing land use
pressure). Although we are proposing to use 2022 as the base year for
our lifecycle GHG emissions estimates, we invite comments on this
approach.

  Table VI.C.1-3--Corn Ethanol Lifecycle GHG Emissions Changes in 2012,
                             2017, and 2022
------------------------------------------------------------------------
                                          Percent change  Percent change
                                             from 2005       from 2005
          Scenario Description               petroleum       petroleum
                                           baseline (100   baseline (30
                                              yr 2%)          yr 0%)
------------------------------------------------------------------------
Corn Ethanol Natural Gas Dry Mill in                 -16              -3
 2012 with dry DGs......................
Corn Ethanol Natural Gas Dry Mill in                 -13              +9
 2017 with dry DGs......................
Corn Ethanol Natural Gas Dry Mill in                 -16              +5
 2022 with dry DGs......................
------------------------------------------------------------------------

    We also tested the impact of analyzing a larger change in corn
ethanol volumes on the GHG emissions estimates. Table VI.C.1-4 shows
the sensitivity of our analysis to the volume changes analyzed. Based
on this scenario, the GHG emissions estimates associated with a larger
change (6.3 Bgal) in corn ethanol volumes (8.7 Bgal to 15 Bgal) results
in lower GHG emission reductions. Additional details on these
sensitivity analyses are included in the DRIA Chapter 2.

 Table VI.C.1-4--Corn Ethanol Lifecycle GHG Emissions Changes Associated
                      With Different Volume Changes
------------------------------------------------------------------------
                                          Percent Change  Percent Change
                                             from 2005       from 2005
          Scenario Description               Petroleum       Petroleum
                                           Baseline (100   Baseline (30
                                              yr 2%)          yr 0%)
------------------------------------------------------------------------
Corn Ethanol Natural Gas Dry Mill in                 -16              +5
 2022 with dry DGs; 2.7 Bgal change in
 corn ethanol volumes...................
Corn Ethanol Natural Gas Dry Mill in                  -6             +14
 2022 with dry DGs; 6.3 Bgal change in
 corn ethanol volumes...................
------------------------------------------------------------------------

    The results presented in previous tables assume that managed
pasture (i.e., land actively used for livestock grazing) converted from
pasture to cropland would be replaced with new pasture in other areas.
The area of

managed pasture converted to cropland was estimated using satellite
data from Winrock and land cover data from GTAP. As a sensitivity
analysis, we also analyzed a scenario in which none of the pastureland
converted to cropland would be replaced if, for example, livestock
production could be more intensively developed on the remaining pasture
(see first row in Table VI.C.1-5). Similarly, we also calculated
results assuming that all pasture acres would be replaced (second row
in Table VI.C.1-5). Finally, the third row of Table VI.C.1-5 includes
lifecycle GHG results assuming that all of the land converted to
cropland would come from pasture and that none of that pasture would be
replaced, which is counter to the land use trends identified by the
Winrock satellite data. As can be seen, the assumption of pastureland
replacement can have a significant effect on the results. We ask for
comment on the best assumptions to be made when considering the need to
replace pasture that has been converted to crop production. We note
that the best decision on pasture land replacement may vary by country
or region due to such factors as the current intensity of use of
pasture land as well as trends in demand for pasture. DRIA Chapter 2
includes more details about the treatment of pasture conversion, and
sensitivity analysis of the types land use changes induced by corn
ethanol production.

 Table VI.C.1-5--Corn Ethanol Lifecycle GHG Emissions Changes Associated
             with Different Assumptions on Land Use Changes
------------------------------------------------------------------------
                                          Percent Change  Percent Change
                                             from 2005       from 2005
          Scenario Description               Petroleum       Petroleum
                                           Baseline (100   Baseline (30
                                              yr 2%)          yr 0%)
------------------------------------------------------------------------
Corn Ethanol Natural Gas Dry Mill in                 -34             -19
 2022 with dry DGs; 0% pastureland
 replaced...............................
Corn Ethanol Natural Gas Dry Mill in                  -2             +24
 2022 with dry DGs; 100% pastureland
 replaced...............................
Corn Ethanol Natural Gas Dry Mill in                 -48             -38
 2022 with dry DGs; grassland only
 conversion and 0% pastureland replaced.
------------------------------------------------------------------------

    DRIA Chapter 2 includes results for additional sensitivity analysis
of corn ethanol lifecycle GHG emissions. We also intend to conduct
additional sensitivity analysis for the final rule. We invite comment
on these assumptions.
b. Imported Ethanol
    Table VI.C.1-6 presents the breakout of lifecycle GHG emissions for
sugarcane ethanol compared to a 2005 petroleum baseline under different
discount rate and time horizon scenarios and land use assumptions. This
assessment was based on applying the same methodology as for other
biofuels including the assessment of both direct and indirect impacts
using the combination of FASOM, FAPRI and Winrock modeling results.
Virtually all the ethanol from sugarcane is expected to be imported
from Brazilian production. Applying the proposed FAPRI/Winrock
methodology to sugarcane ethanol production in Brazil predicts a large
increase in new acres planted, which has a relatively large impact on
overall GHG emissions. The impact is from both new sugarcane production
acres in Brazil resulting in land use change but also reduced commodity
exports from Brazil resulting in land use change in other countries.
    The proposed FAPRI/Winrock methodology predicts that new crop
acreage is converted from a range of land types. In contrast, some
studies suggest that sugarcane ethanol production can increase in
Brazil by relying on existing excess pasture lands and will not
significantly impact other land types.\314\ Table VI.C.1-6 provides the
range of lifecycle GHG emission reduction results under these different
assumptions of type conversion patterns. As a sensitivity analysis,
shows results for a scenario where none of the grassland converted to
cropland in Brazil would be replaced if, for example, livestock
production could be more intensively developed on the remaining pasture
(see second row in Table VI.C.1-6). The third row of Table VI.C.1-6
includes lifecycle GHG results assuming that in Brazil all of the land
converted to cropland would come from grassland and that none of that
grassland would be replaced. As can be seen in the table, the
assumption of pastureland replacement can have an important effect on
the results. DRIA Chapter 2 includes more details about the treatment
of pasture conversion, and sensitivity analysis of the types land use
changes induced by sugarcane ethanol production.
---------------------------------------------------------------------------

    \314\ Goldemberg, J.; Coelho, ST.; Guardabassi, PM. The
sustainability of ethanol production from sugarcane. Energy Policy.
2008. doi:10.1016/j.enpol.2008.02.028.

Table VI.C.1-6--Sugarcane Ethanol GHG Emission Changes Under Varied Land
 Use Assumptions and Varied Discount Rates and Time Horizons Relative to
                         2005 Petroleum Baseline
------------------------------------------------------------------------
  Land Use Change Scenario Description      (100 yr 2%)     (30 yr 0%)
------------------------------------------------------------------------
FAPRI/Winrock estimate with managed                  -44             -26
 pasture replacement....................
FAPRI/Winrock estimate with no pasture               -59             -45
 replacement in Brazil..................
Only grassland conversion in Brazil and              -64             -52
 no pasture replacement in Brazil.......
------------------------------------------------------------------------

    We are aware that recent land use enforcement policies in Brazil
may shift cropland expansion patterns (see also Section VI.B.5.b.iii).
We seek comment on both pasture conversion patterns and Brazil land use
enforcement policy impacts. We are conducting more detailed economic
modeling of the Brazilian agricultural sector by state for inclusion in
FAPRI to address pasture, enforcement and other assumptions for the
final rule. State level production data could be used in conjunction
with Winrock's state level satellite data, which may substantially
change the

estimates of the location and type of land being converted in Brazil
for the final rule.
    We have also assumed that sugarcane ethanol production relies on
burning bagasse as an energy source and that the process produces
excess electricity. We factor in credits from this excess electricity
based on offsetting the Brazilian electricity grid. As Brazil
implements limits on field burning of bagasse there may be additional
bagasse used at sugarcane ethanol plants and additional electricity
production. We plan to look at this further for the final rule
analysis.
c. Cellulosic Ethanol
    Given that commercially-viable cellulosic ethanol production is not
yet a reality, analysis of this pathway relies upon significant
assumptions regarding the development of production technologies. As
described in the previous section, our analysis assumed corn stover
required no international land use changes, since corn stover does not
compete with other crops for acreage in the U.S. Therefore, using corn
stover as a feedstock for cellulosic biofuel production would not have
an impact on U.S. exports. We assumed some of the nutrients would have
to be replaced through higher fertilizer rates on acres where stover is
removed; however, increased stover removal was also associated with
higher rates of reduced tillage or no tillage practices which results
in soil carbon increase. See Section IX.A for details. In addition,
cellulosic ethanol was assumed to be produced using the biochemical
process which is expected to produce more electricity from the lignin
in the feedstock than is required to power the ethanol plant, so excess
electricity can be sold back to the grid. See DRIA Chapter 2 for
additional details. This electricity provides a GHG benefit, which
results in GHG emissions reductions from fuel production as shown in
Table VI.C.1-7.

   Table VI.C.1-7--Absolute Lifecycle GHG Emissions for Corn Stover Cellulosic Ethanol and the 2005 Petroleum
                                                    Baseline
                                                 [CO2-eq/mmBtu]
----------------------------------------------------------------------------------------------------------------
                                                                    Corn stover                     Corn stover
                                                                      ethanol                         ethanol
                                                       2005          (selling          2005          (selling
                                                     Petroleum        excess         Petroleum        excess
                                                     baseline     electricity to     baseline     electricity to
                                                                       grid)                           grid)
----------------------------------------------------------------------------------------------------------------
                 Lifecycle Stage                            (100 yr 2%)
                                                            (30 yr 0%)
----------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change)..  ..............         178,862             N/A         124,503
Net International Agriculture (w/o land use       ..............               0             N/A  ..............
 change)........................................
Domestic Land Use Change........................  ..............         -78,448             N/A         -91,925
International Land Use Change...................  ..............               0             N/A               0
Fuel Production.................................         823,262        -875,424         573,058        -609,367
Fuel and Feedstock Transport....................  ..............         107,214  ..............          74,629
Tailpipe Emissions..............................       3,417,311          37,927       2,378,800          26,400
                                                 ---------------------------------------------------------------
    Net Total Emissions.........................       4,240,674        -629,870       2,951,858        -475,130
----------------------------------------------------------------------------------------------------------------

    Although switchgrass must compete with other crops for land in the
U.S., average switchgrass ethanol yields are on average higher than
corn ethanol yields (approximately 580 gallons/acre compared to 480
gallons/acre). Therefore, switchgrass would need approximately 20% less
land to produce the same amount of ethanol compared to corn. In
addition, FASOM predicts that switchgrass would generally be grown on
more marginally productive land. Since switchgrass is not projected to
displace crop acres with high yields, new switchgrass acres generally
would not have a large impact on exports. Therefore, the international
land use change impacts are modest. Like cellulosic ethanol from corn
stover, switchgrass ethanol is also assumed to produce excess
electricity that can be sold to the grid, therefore switchgrass
cellulosic ethanol results in relatively large lifecycle GHG reductions
compared to the replaced petroleum gasoline as shown in Table VI.C.1-8.

    Table VI.C.1-8--Absolute GHG Emissions for Switchgrass Cellulosic Ethanol and the 2005 Petroleum Baseline
                                                 [CO2-eq/mmBtu]
----------------------------------------------------------------------------------------------------------------
                                                                    Switchgrass                     Switchgrass
                                                                      ethanol                         ethanol
                                                       2005          (selling          2005          (selling
                                                     Petroleum        excess         Petroleum        excess
                                                     baseline     electricity to     baseline     electricity to
                                                                       grid)                           grid)
----------------------------------------------------------------------------------------------------------------
                 Lifecycle Stage                            (100 yr 2%)
                                                            (30 yr 0%)
----------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change)..  ..............        -470,620  ..............        -327,590
Net International Agriculture (w/o land use       ..............        -356,712  ..............        -248,301
 change)........................................
Domestic Land Use Change........................  ..............         -65,318  ..............         -76,015
International Land Use Change...................  ..............         423,097  ..............         424,094
Fuel Production.................................         823,262        -874,599         573,058        -608,793
Fuel and Feedstock Transport....................  ..............         136,663  ..............          95,129
Tailpipe Emissions..............................       3,417,311          37,927       2,378,800          26,400
                                                 ---------------------------------------------------------------

    Net Total Emissions.........................       4,240,674      -1,169,561       2,951,858        -715,076
----------------------------------------------------------------------------------------------------------------

    Cellulosic ethanol does not have nearly as significant an impact on
land use as other biofuels, therefore we did not calculate sensitivity
impacts of, for example, assuming full replacement of pasture versus no
pasture replacement which could be important in the lifecycle GHG
assessment of other biofuels. As the land use issue is not critical for
the cellulosic feedstock fuels in the scenarios we analyzed, the impact
of timing and discount rates also do not have a significant impact on
the overall results for cellulosic ethanol. Both of the cellulosic
ethanol pathways we examined, switchgrass and corn stover using
enzymatic processing, reduced lifecycle GHG emissions by significantly
more than the 60% threshold for cellulosic biofuel. Table VI.C.1-9
summarizes the lifecycle GHG results for cellulosic ethanol fuel
pathways.

 Table VI.C.1-9--Cellulosic Ethanol GHG Emission Changes From Different
 Feedstocks and Varied Discount Rates and Time Horizons Relative to 2005
                           Petroleum Baseline
                              [In percent]
------------------------------------------------------------------------
    Assumption--feedstock type         (100 yr 2%)         (30 yr 0%)
------------------------------------------------------------------------
Corn Stover.......................               -115               -117
Switchgrass.......................               -128               -121
------------------------------------------------------------------------

d. Biodiesel
    EPA's modeling predicts that soybean-based biodiesel production has
a large land use impact for two major reasons. Soybean biodiesel has a
relatively low gallon per acre yield (approximately 65 gal/acre for
soybean biodiesel versus 480 gal/acre for corn ethanol). Thus, the
impact of any land-use change tends to be magnified with soybean
biodiesel. Even when the higher Btu value of biodiesel is taken into
consideration, Btu/acre yields are still significantly lower for
biodiesel than for ethanol (approximately 97 gal/acre ethanol
equivalent). Furthermore, our analysis suggests that due to high world
wide demand for soybeans for food, cooking and other non-biofuel uses,
soybean and other edible oils used for biofuel are generally replaced
by production in other countries including production in tropical
climates where the GHG emissions released per acre of converted land
are highest. This indicates that soy-based biodiesel lifecycle GHG
emissions could be greatly reduced with the adoption of policies and
agricultural practices that limit the amount of tropical deforestation
induced by soy-based biodiesel production. DRIA Chapter 2 includes
sensitivity analyses about the types of land converted to crops as a
result of soy-based biodiesel production. Table VI.C.1-10 presents the
breakout of the absolute lifecycle GHG emissions for soybean biodiesel
and the petroleum diesel fuel baseline by lifecycle stage.

     Table VI.C.1-10--Absolute Lifecycle GHG Emissions for Soybean Biodiesel and the 2005 Petroleum Baseline
                                                 [CO2-eq/mmBtu]
----------------------------------------------------------------------------------------------------------------
                                                  2005 Petroleum      Soybean     2005 Petroleum      Soybean
                                                     baseline        biodiesel       baseline        biodiesel
----------------------------------------------------------------------------------------------------------------
                 Lifecycle Stage                            (100 yr 2%)
                                                            (30 yr 0%)
----------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change)..  ..............        -423,206  ..............        -294,586
Net International Agriculture (w/o land use       ..............         195,304  ..............         135,948
 change)........................................
Domestic Land Use Change........................  ..............          -8,980  ..............         -10,451
International Land Use Change...................  ..............       2,474,074  ..............       2,469,574
Fuel Production.................................         749,132         838,490         521,458         583,658
Fuel and Feedstock Transport....................  ..............         149,258  ..............         103,896
Tailpipe Emissions..............................       3,424,635          30,169       2,383,828          21,000
                                                 ---------------------------------------------------------------
    Net Total Emissions.........................       4,173,768       3,255,109       2,905,286       3,009,039
----------------------------------------------------------------------------------------------------------------

    Our analysis is based on a change in biodiesel volumes from 0.4
Bgal to 0.7 Bgal. Similar to the analysis we conducted for corn-
ethanol, we plan to run a sensitivity analysis on the impact of using
different volumes for the final rule.

    As discussed in Section VI.B.2.a, EPA's interpretation of the EISA
statute compels us to include significant indirect emission impacts
including those due to land use changes in other countries. The data in
Table VI.C.1-10 indicate that excluding the international land use
change would result in soy-based biodiesel having an approximately 80%
reduction in lifecycle GHG emissions compared to petroleum gasoline
regardless of the timing or discount rate used. The treatment of
emissions over time is not critical if international land use change
emissions are excluded because the results without land use change are
consistent over time. Therefore the overall lifecycle GHG results do
not vary with time or discount rate assumptions.
    In contrast, GHG emissions from waste oil and greases are assumed
to have no land use impacts. We assumed any land use change was
attributed to the original use of the feedstock, for example, soy oil
was produced for the purpose of using for cooking and the land required
to produce this cooking oil is properly attributed to that use.
Gathering and re-using the left over waste cooking oil would have no
additional land use impact. This lack of land use impact greatly
influences the lifecycle GHG analysis. Table VI.C.1-11 presents the
breakout of the absolute lifecycle GHG emissions for waste grease
biodiesel and the petroleum diesel fuel baseline by lifecycle stage.

  Table VI.C.1-11--Absolute Lifecycle GHG Emissions for Waste Grease Biodiesel and the 2005 Petroleum Baseline
                                                 [CO2-eq/mmBtu]
----------------------------------------------------------------------------------------------------------------
                                                       2005                            2005
                                                     Petroleum     Waste grease      Petroleum     Waste grease
                                                     baseline        biodiesel       baseline        biodiesel
----------------------------------------------------------------------------------------------------------------
                 Lifecycle Stage                            (100 yr 2%)
                                                            (30 yr 0%)
----------------------------------------------------------------------------------------------------------------
Net Domestic Agriculture (w/o land use change)..  ..............               0  ..............               0
Net International Agriculture (w/o land use       ..............               0  ..............               0
 change)........................................
Domestic Land Use Change........................  ..............               0  ..............               0
International Land Use Change...................  ..............               0  ..............               0
Fuel Production.................................         749,132         658,198         521,458         458,160
Fuel and Feedstock Transport....................  ..............         149,258  ..............         103,896
Tailpipe Emissions..............................       3,424,635          30,169       2,383,828          21,000
                                                 ---------------------------------------------------------------
    Net Total Emissions.........................       4,173,768         837,626       2,905,286         583,056
----------------------------------------------------------------------------------------------------------------

    Table VI.C.1-12 summarizes the lifecycle GHG results for biodiesel
fuel pathways. As the waste grease biodiesel is not assumed to have any
land use impact the choice of timing or discount rate does not impact
the waste grease biodiesel results. However, as the soybean biodiesel
is found to have a large land use impact the choice of timing and
discount rate has a big impact on the soybean biodiesel results.

Table VI.C.1-12--Biodiesel Lifecycle GHG Emission Changes From different
 Feedstocks and Varied Discount Rates and Time Horizons Relative to 2005
                           Petroleum Baseline
------------------------------------------------------------------------
    Assumption--feedstock type         (100 yr 2%)         (30 yr 0%)
------------------------------------------------------------------------
Soybean...........................               -22%                +4%
Waste Grease......................               -80%               -80%
------------------------------------------------------------------------

    Table VI.C.1-13 shows the sensitivity of our assessment for soy oil
biodiesel assuming 100% of the grassland converted to cropland is
replaced compared to an assumption that none of this grassland is
replaced for livestock grazing. DRIA Section <IP_ADDRESS> provides more
information about sensitivity analysis for the pasture replacement
assumptions.

 Table VI.C.1-13--Soy-Based Biodiesel GHG Emission Changes Under Varied
    Land Use Assumptions and Varied Discount Rates and Time Horizons
                   Relative to 2005 Petroleum Baseline
------------------------------------------------------------------------
 Assumption--land types available
          for conversion               (100 yr 2%)         (30 yr 0%)
------------------------------------------------------------------------
100% Pasture Replacement..........                -4%               +27%
No Pasture Replacement............               -45%               -27%
------------------------------------------------------------------------

2. Treatment of GHG Emissions Over Time
    As described in Section VI.B.5, changes in indirect land use
associated with increased biofuel production result in GHG emissions
increases that accumulate over a long time period. Since there is a
large release of carbon in the first year of land conversion, it can
take many years for the benefits of the biofuel to make up for these
early carbon emissions, depending on the specific biofuel in question.
Table VI.C.2-1 contains the payback period associated with several
types of biofuels and fuel production pathways. A payback period of 0
indicates that these pathways do not have land use change impacts and
therefore reduce emissions in the first year that they are produced.
Assessments are made in comparison to

the baseline transportation fuel used in 2005 in the U.S. as mandated
by EISA. The percent reduction goal is the lifecycle GHG emissions of
the biofuel compared to the baseline petroleum fuel it is replacing.

                                         Table VI.C.2-1--Payback Period
                                                   [in years]
----------------------------------------------------------------------------------------------------------------
                                                                      Payback period (years)
                                                 ---------------------------------------------------------------
                    Fuel type                        Reduction       Reduction       Reduction       Reduction
                                                     goal: 0%        goal: 20%       goal: 50%       goal: 60%
----------------------------------------------------------------------------------------------------------------
Corn Ethanol 2022 Base Dry Mill NG \315\........              33              54       \316\ N/A             N/A
Corn Ethanol 2022 Best Case Dry Mill NG \317\...              23              31             N/A             N/A
Corn Ethanol 2022 Base Dry Mill Coal \318\......              75            >100             N/A             N/A
Corn Ethanol 2022 Base Dry Mill Biomass \319\...              22              31             N/A             N/A
Soybean Biodiesel...............................              32              46             105             N/A
Waste Grease Biodiesel..........................               0               0               0             N/A
Sugarcane Ethanol...............................              18              26              61             N/A
Switchgrass Ethanol.............................               3               3               4               5
Corn Stover Ethanol.............................               0               0               0               0
----------------------------------------------------------------------------------------------------------------

---------------------------------------------------------------------------

    \315\ Dry Mill corn ethanol plant using natural gas with 2022
energy use and dry DDGS.
    \316\ Payback periods were not calculated for ethanol made from
corn starch for the advanced biofuel reduction goals of 50% and 60%
since this corn ethanol does not qualify under EISA as a potential
advanced biofuel.
    \317\ Dry Mill corn ethanol plant using natural gas with 2022
energy use and w/CHP, Fractionation, Membrane Separation, and Raw
Starch Hydrolysis (wet DGS).
    \318\ Dry Mill corn ethanol plant using coal with 2022 energy
use and dry DDGS.
    \319\ Dry Mill corn ethanol plant using biomass with 2022 energy
use and dry DDGS.
---------------------------------------------------------------------------

    As described in Section VI.B.5, we have focused our lifecycle GHG
analysis on two ways of accounting for GHG emissions over time. In one
set of results we consider lifecycle GHG emissions over 100 years and
discount future emissions with a 2% discount rate. In the other set of
results we consider 30 years of GHG emissions with no discounting of
future emissions (i.e., 0% discount rate). Whereas the discussion
immediately above focused on lifecycle GHG impacts assuming 100 years
with a 2% discount rate and 30 years with no discount rate, Table
VI.C.2-2 shows the lifecycle GHG emissions reductions estimates with a
variety of time periods and discount rates.

                       Table VI.C.2-2--Lifecycle GHG Emissions Changes of Select Biofuels Relative to the 2005 Petroleum Baseline
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                Lifecycle GHG emissions changes of select biofuels relative to the 2005 petroleum baseline
--------------------------------------------------------------------------------------------------------------------------------------------------------
          Time horizon                           30 Years                                50 Years                                100 Years
--------------------------------------------------------------------------------------------------------------------------------------------------------
          Discount rate              0%        2%        3%        7%        0%        2%        3%        7%        0%        2%        3%        7%
--------------------------------------------------------------------------------------------------------------------------------------------------------
Corn Ethanol                            5%       18%       25%       54%      -17%       -2%        7%       44%      -36%      -16%       -4%       41%
Dry Mill NG
Corn Ethanol                          -14%       -1%        6%       35%      -36%      -21%      -12%       25%      -55%      -35%      -23%       22%
Best Case Dry Mill NG
Corn Ethanol                           34%       46%       53%       83%       11%       27%       35%       72%       -8%       13%       24%       69%
Dry Mill Coal
Corn Ethanol                          -18%       -6%        1%       31%      -41%      -25%      -17%       20%      -60%      -39%      -28%       16%
Dry Mill Biomass
Soybean Biodiesel...............        4%       20%       29%       68%      -24%       -4%        7%       55%      -48%      -22%       -7%       51%
Waste Grease Biodiesel..........      -80%      -80%      -80%      -80%      -80%      -80%      -80%      -80%      -80%      -80%      -80%      -80%
Sugarcane Ethanol...............      -27%      -17%      -11%       12%      -45%      -32%      -26%        3%      -61%      -44%      -35%        1%
Switchgrass Ethanol.............     -124%     -122%     -121%     -115%     -128%     -125%     -124%     -117%     -131%     -128%     -126%     -117%
Corn Stover Ethanol.............     -116%     -117%     -117%     -118%     -115%     -116%     -116%     -117%     -114%     -115%     -115%     -117%
--------------------------------------------------------------------------------------------------------------------------------------------------------

D. Thresholds

    EISA established GHG thresholds for each category of renewable fuel
that it mandates. EISA also provided EPA with the authority to adjust
the threshold levels for each category of renewable fuels if certain
requirements are met. Renewable fuels must achieve a 20% reduction in
lifecycle greenhouse gas emissions compared to the average lifecycle
greenhouse gas emissions for gasoline or diesel sold or distributed as
transportation fuel in 2005. Due to the grandfathering provisions of
EISA as adopted in this rule, this threshold only pertains to renewable
fuel produced at plants to be constructed in the future. EPA is
permitted to adjust this threshold to as low as 10%, based on the
``maximum achievable level, taking cost into consideration, for natural
gas fired corn-based ethanol plants allowing for the use of a variety
of technologies.'' Based on our analysis, there are a number of corn
ethanol natural gas plant configurations that could meet the 20%
reduction in GHG emissions thresholds in 2022 if modeling emission over
a 100 year time frame and then discounting these emissions 2%.
Therefore, based on this assessment, we believe that an adjustment to
the 20% threshold would be unnecessary and we are proposing to maintain
it at the 20% level if we adopt the 100 year, 2% discounting
methodology.
    On the other hand, based on our current analyses, if we adopt an
assessment methodology which assesses emissions over just 30 years,
then no currently analyzed natural gas-fired corn ethanol pathway will
meet the 20% threshold. However, some of the natural gas corn ethanol
pathways do

have lifecycle GHG emission benefits in the 10% to 20% range. Corn
ethanol is expected to be the major biofuel contributing to meeting the
renewable fuel standards through at least the middle of the next
decade. Therefore, if we adopt a 30 year timeframe for emissions
assessment and do not discount the results, we may adjust the renewable
fuel thresholds to the minimum level as necessary to incorporate at
least a few of the best GHG pathways for corn ethanol. While this
adjusted threshold level could be revised based on pathway analyses
done for the final rule, at this time we would intend to allow a full
10% adjustment of the renewable fuel threshold, down to a threshold
value of 10% reduction compared to the 2005 gasoline baseline.
    Cellulosic biofuels must meet a 60% reduction in GHG emissions
relative to the petroleum baseline. EPA is permitted to adjust this
threshold to as low as 50% if it is ``not commercially feasible for
fuels made using a variety of feedstocks, technologies, and processes''
to achieve the 60% threshold. Our initial analysis indicates that
cellulosic biofuels from corn stover, switchgrass, and bagasse will all
meet the 60% threshold regardless of whether we use to 100 year, 2%
discount methodology or the 30 year analysis time frame without
discounting. Furthermore, we believe most fuels made from other
cellulosic feedstocks would as well. Therefore we do not believe it is
necessary to adjust the threshold for cellulosic biofuel at this time.
    Biomass-based diesel must achieve a 50% reduction in GHG emissions
relative to petroleum-based diesel. EPA is permitted to adjust this
threshold to as low as 40% if it is ``not commercially feasible for
fuels made using a variety of feedstocks, technologies, and processes''
to meet the 50% level. For biomass-based diesel, our analysis indicates
that biodiesel from waste oils such as yellow grease and tallow would
meet the 50% threshold, and we anticipate that biodiesel from chicken
waste and non-food grade corn oil fractionation would as well
regardless of whether we use a 100 year, 2% discount methodology or the
30 year analysis time frame without discounting. However, our current
analysis indicates that there is insufficient feedstock from waste
grease and fats to meet the one billion gallon volumetric requirement
under EISA. Biodiesel from soy oil (and we believe biodiesel from other
food grade vegetable oils) would reduce GHG emissions by no more than
22% using a 100 year, 2% discount methodology and would be estimated to
increase GHG emissions if we analyze emission impacts over 30 years
whether the emissions are discounted or not. Even if EPA adjusted the
biomass-based diesel standard to the minimum allowable level of 40%,
soybean-based biodiesel would still not meet the GHG emissions
reductions threshold for biomass based diesel fuel. One option for
meeting the volumetric requirement and the emissions reduction
threshold, assuming a 100 year timeframe and a 2% discount rate for GHG
emission impacts would be to allow biodiesel producers to average the
emissions reductions from a blend of soy oil or food grade vegetable
oil-based biodiesel with waste oil based biodiesel, as discussed in
more detail in Section VI.E. However, this approach may still be
insufficient to ensure that the required volumes of biomass-based
diesel can be produced unless other sources of biomass-based diesel
become available. Therefore, we invite comments on whether it be
appropriate to both reduce the threshold to 40% and allow biodiesel
producers to average their emissions to meet the one billion gallon
volumetric requirement as discussed below in Section VI.E.3.c.
    Advanced biofuels must achieve a 50% reduction in GHG emissions.
EPA is permitted to adjust this threshold to as low as 40% if it is
``not commercially feasible for fuels made using a variety of
feedstocks, technologies, and processes'' to achieve the 50% threshold.
Our current lifecycle analysis suggests that sugarcane based ethanol
only offers an estimated 44% reduction in GHG emissions relative to the
gasoline it replaces when assessing 100 years of emission impacts and
discounting these emissions 2%, and an estimated 27% reduction when
assessing 30 years of emission impacts with no discounting. Therefore,
it would not qualify as an advanced biofuel if we did not adjust the
50% GHG threshold. We are also unaware of other renewable fuels that
may be available in sufficient volumes over the next several years to
allow the statutory volume requirements for advanced biofuel to be met.
As a result, we are proposing that the GHG threshold for advanced
biofuels be adjusted to 44% or potentially as low as 40% depending on
the results from the analyses that will be conducted for the final
rule. Based on our current analysis of the lifecycle GHG impacts of
sugarcane ethanol, such an adjustment would help ensure that the volume
mandates for advanced biofuel can be met.
    We invite comments on these proposed thresholds and our basis for
them.

E. Assignment of Pathways to Renewable Fuel Categories

    The lifecycle analyses that we conducted for a variety of fuel
pathways formed the basis for our determination of which pathways would
be permitted to generate RINs, and to which of the four renewable fuel
categories (cellulosic biofuel, biomass-based diesel, advanced biofuel,
and renewable fuel) those RINs should be assigned. This determination
involved comparing the lifecycle GHG performance estimates to the GHG
thresholds associated with each renewable fuel category, discussed in
Section VI.D above. In addition, each of the four renewable fuel
categories is defined in EISA to include or exclude certain types of
feedstocks and production processes, and these definitions also played
a role in determining the appropriate category for each pathway. This
section describes our proposed assignments of pathways to one of the
four renewable fuel categories. The GHG lifecycle values used in this
assignment of fuel pathways to the four renewable fuel categories were
based on the lifecycle analysis results over a 100-year timeframe and
using a 2% discount rate, as described in Section VI.C. Different
assignments of pathways to the four renewable fuel categories would
occur with different lifecycle results, but we propose that the same
assignment methodology would be followed regardless.
1. Statutory Requirements
    EISA establishes requirements that are common to all four
categories of renewable fuel in addition to requirements that are
unique to each of the four categories. The common requirements
determine which fuels are valid for generating RINs under the RFS2
program. For instance, all renewable fuel must be made from renewable
biomass, which defines the types of feedstocks that can be used to
produce renewable fuel that is valid under the RFS2 program, and also
defines the types of land on which crops can be grown if those crops
are used to produce valid renewable fuel under the RFS2 program. See
Section III.B.4 for a more detailed discussion of renewable biomass.
Moreover, all renewable fuel must displace fossil fuel present in
transportation fuel, or be used as home heating oil or jet fuel.
    The requirements that are unique to each of the four categories
provide a basis for assigning each pathway to a category. For each of
the four categories of renewable fuel, EISA provides a definition,
specifies the associated GHG

thresholds, lists the allowable feedstocks and/or fuel types, and in
some cases provides exclusions. Table VI.E.1-1 summarizes these
requirements as we are applying them under the proposed RFS2 program.

                           Table VI.E.1-1--Requirements for Renewable Fuel Categories
----------------------------------------------------------------------------------------------------------------
                                                         Biomass-based
                                  Cellulosic biofuel        diesel         Advanced biofuel     Renewable fuel
----------------------------------------------------------------------------------------------------------------
GHG threshold...................  60%...............  50% \a\...........  40-44% \a\........  20% a, b.
Eligible Inclusions.............  Renewable fuel      Any renewable fuel  All cellulosic      All advanced
                                   made from           that is a diesel    biofuel and         biofuel, and any
                                   cellulose,          fuel substitute.    biomass-based       other fuel made
                                   hemicellulose, or                       diesel, as well     from renewable
                                   lignin.                                 as other            biomass that is
                                                                           renewable fuels     used to replace
                                                                           including ethanol   or reduce the
                                                                           from sugar,         quantity of
                                                                           starch, or waste    fossil fuel
                                                                           materials,          present in a
                                                                           biogas, and         transportation
                                                                           butanol and other   fuel.
                                                                           alcohols.
Exclusions......................  ..................  Any renewable fuel  Ethanol derived
                                                       made from           from corn starch.
                                                       coprocessing with
                                                       petroleum.
----------------------------------------------------------------------------------------------------------------
\a\ As discussed in Section VI.D, we are seeking comment on the need to adjust the thresholds, and are proposing
  that the GHG threshold for advanced biofuels be adjusted to as low as 40%.
\b\ 20% threshold does not apply to grandfathered volumes. See discussion in Section III.B.3.

2. Assignments for Pathways Subjected to Lifecycle Analyses
    There are a wide variety of pathways (unique combinations of
feedstock, fuel type, and fuel production process) that could result in
renewable fuel that would be valid under the RFS2 program. As described
earlier in this section, we conducted lifecycle analyses for some of
these pathways, and these analyses allowed us to determine if the GHG
thresholds shown in Table VI.E.1-1 would be met under the assumption of
a 100-year timeframe and discount rate of 2%. For other pathways that
we have not yet subjected to lifecycle analyses, there were some cases
in which we could nevertheless still make moderately confident
determinations as to the likely GHG impacts by making comparisons to
the pathways that we did analyze. A discussion of these other
determinations is provided in Section VI.E.3 below.
    For pathways that we subjected to lifecycle analysis, we were able
to assign each pathway to one of the four renewable fuel categories
defined in EISA by comparing the descriptions of each pathway and its
associated GHG performance to the requirements shown in Table VI.E.1-1.
The results are shown in Table VI.E.2-1.

   Table VI.E.2-1--Proposed Assignment of Pathways to One of the Four
 Renewable Fuel Categories for Pathways Subjected to Lifecycle Analyses
------------------------------------------------------------------------

------------------------------------------------------------------------
Cellulosic biofuel pathways.......  Ethanol produced from corn stover or
                                     switchgrass in a process that uses
                                     enzymes to hydrolyze the cellulose
                                     and hemicellulose.
Biomass-based diesel pathways.....  Biodiesel (mono alkyl esters)
                                     produced from waste grease and
                                     waste oils.
Advanced biofuel pathways.........  Ethanol produced from sugarcane
                                     sugar in a process that uses
                                     sugarcane bagasse for process heat.
                                     \a\
Renewable fuel pathways...........  Ethanol produced from corn starch in
                                     a process that uses biomass for
                                     process heat.
                                    Ethanol produced from corn starch in
                                     a process that includes:
                                       --Dry mill plant.
                                       --Process heat derived from
                                        natural gas.
                                       --Combined heat and power (CHP).
                                       --Fractionation of feedstocks.
                                       --All distillers grains are
                                        dried.
                                    Ethanol produced from corn starch in
                                     a process that includes:
                                       --Dry mill plant.
                                       --Process heat derived from
                                        natural gas.
                                       --All distillers grains are wet.
                                    Ethanol produced from corn starch in
                                     a process that includes:
                                       --Dry mill plant.
                                       --Process heat derived from coal.
                                       --Combined heat and power (CHP).
                                       --Fractionation of feedstocks.
                                       --Membrane separation of ethanol.
                                       --Raw starch hydrolysis.
                                       --All distillers grains are
                                        dried.
                                    Ethanol produced from corn starch in
                                     a process that includes:
                                       --Dry mill plant.
                                       --Process heat derived from coal.
                                       --Combined heat and power (CHP).
                                       --Fractionation of feedstocks.
                                       --Membrane separation of ethanol.
                                       --All distillers grains are wet.

                                    Biodiesel (mono alkyl esters)
                                     produced from soybean oil.
------------------------------------------------------------------------
\a\ Our current analysis concludes that ethanol from sugarcane sugar
  would have a GHG performance of 44% in comparison to gasoline under
  our assumed 100-year timeframe and 2% discount rate. Since this falls
  short of the 50% GHG threshold for advanced biofuel, we have
  categorized it as general renewable fuel. However, we request comment
  on lowering the applicable GHG threshold for advanced biofuel so that
  ethanol from sugarcane sugar could be categorized as advanced biofuel.
  See further discussion in Section VI.D.

    In addition, our lifecycle analyses also identified pathways that
did not meet the minimum 20% GHG threshold under an assumed 100-year
timeframe and 2% discount rate, and thus would be prohibited from
generating RINs unless a facility met the prerequisites for
grandfathering as described in Section III.B.3. These prohibited
pathways all involved the production of ethanol from corn starch in a
process that uses natural gas or coal for process heat, but which does
not meet any of the process technology requirements listed in Table
VI.E.2-1. Our proposal for temporary D codes in Sec.  80.1416 would
explicitly prohibit the generation of RINs for these pathways.
    The proposed assignments of individual pathways to one of the four
renewable fuel categories shown in the table above assumed a 100-year
timeframe and discount rate of 2% for lifecycle GHG emission impacts.
The assignments would be different if we had assumed a different
timeframe and discount rate. By comparing the relative GHG emission
reductions shown in Table VI.C.1-2 to the thresholds in Table VI.E.1-1,
a variety of different assignments is possible covering timeframes of
30, 50, and 100 years, and discount rates of 0%, 2%, 3%, and 7%. For
instance, under the assumption of 30 years and no discounting,
switchgrass ethanol and corn stover ethanol would continue to be
categorized as cellulosic biofuel and biodiesel made from waste grease
would continue to be categorized as biomass-based diesel. However,
sugarcane ethanol could no longer be potentially categorized as
advanced biofuel but instead would be categorized as renewable fuel.
Moreover, some pathways would not meet the minimum threshold of 20% for
renewable fuel, and so could not generate RINs if the volume was not
grandfathered. This would include soybean biodiesel and all of the corn
starch ethanol pathways shown in Table VI.E.2-1 produced from newly
constructed plants not meeting the grandfathering criteria discussed in
Section III.B.3.
3. Assignments for Additional Pathways
    We were not able to conduct lifecycle modeling for all potential
pathways in time for this proposed rulemaking. Instead, we focused the
lifecycle GHG emissions analysis on the feedstocks that, based on FASOM
predictions and other information, we anticipate could contribute the
largest volumes to the renewable fuel pool and the production processes
representing the largest shares of the market. As more information
becomes available, we anticipate that we will be updating the lifecycle
methodology and expanding the list of emission factors.
    Beyond the pathways that we explicitly subjected to lifecycle
analysis, there are additional pathways that may not currently be
significant contributors to the volume of renewable fuel produced, but
their volumes could increase in the future. Moreover, we believe it is
important that as many pathways as possible be included in the lookup
table in the regulations to help ensure that the volume requirements in
EISA can be met and to encourage the development of new fuels. To this
end, we evaluated these additional pathways to determine if they could
be deemed valid for generation of RINs, and if so which of the four
renewable fuel categories they would fall into. This section describes
our evaluation of these additional pathways and the resulting proposed
assignment to one or more of the four renewable fuel categories.
a. Ethanol From Starch
    Our lifecycle analysis focused on ethanol from corn starch.
However, there are a variety of other sources of starch that use or
could use a very similar process for conversion to ethanol. These
include wheat, barley, oats, rice, and sorghum. Some existing corn-
ethanol facilities already use small amounts of starch from these other
plants along with corn in their production of ethanol.
    Although we have not explicitly analyzed the land use or processing
impacts of these other starch plants on their lifecycle GHG
performance, we believe it would be reasonable to assume similar
impacts to corn in terms of the types of land that would be displaced
and other aspects of producing and transporting the feedstock.
Therefore, we propose that the pathways shown in Table VI.E.2-1 for
ethanol produced from corn starch also be applied to ethanol produced
from other sources of starch.
    The lifecycle analyses conducted for this proposal only examined
cases in which a corn-ethanol facility dried 100% of its distiller's
grains or left 100% of its distiller's grains wet. The treatment of the
distiller's grains for corn-ethanol facilities impacts the
determination of whether the 20% GHG threshold for renewable fuel has
been met. However, in practice some facilities may dry only a portion
of their distiller's grains and leave the remainder wet. As described
in Section III.D.3, we are proposing that a facility that dried only a
portion of its distiller's grain would be treated as if it dried 100%
of its grains, and would thus need to implement additional GHG-reducing
technologies as described in the lookup table in order to qualify to
generate RINs. However, we are also taking comment on whether a
selection of pathways should be included in the lookup table that
represent corn-ethanol facilities that dry only a portion of their
distiller's grains. We also request comment on whether RINs could be
assigned to only a portion of the facility's ethanol in cases wherein
only a portion of the distiller's grains are dried.
b. Renewable Fuels from Cellulosic Biomass
    In analyzing the lifecycle GHG impacts of cellulosic ethanol, we
determined that ethanol produced from corn stover or switchgrass
through a process using enzymatic hydrolysis followed by fermentation
of the resulting sugars met the GHG threshold of 60% for cellulosic
biofuel by a wide margin (regardless of the discount rate and the time
period over which the lifecycle GHG emissions are discounted). However,
there are many other potential sources of cellulosic biomass, and other
processing mechanisms to convert cellulosic biomass into fuel. For some
of these cases, we believe that we can make determinations regarding
whether the GHG thresholds shown in Table VI.E.1-1 are likely to be
met. In addition, as the forestry component of the FASOM model is
incorporated into the analysis,

we will analyze pathways using planted trees, tree residue, and slash
and pre-commercial thinnings from forestland, as qualify under the
renewable biomass definition, for feedstock.
    Cellulosic biomass sources include waste biomass such as corn
stover, and crops grown specifically for fuel production such as
switchgrass. While cellulosic crops grown for the purpose of fuel
production could have land use implications in a lifecycle GHG
analysis, waste materials produced during the harvesting of some other
type of crop would not. Given that the GHG impacts of a fermentation-
based fuel production process are likely to be very similar for
cellulose from a variety of feedstocks, we believe it would be
reasonable to conclude that any cellulosic feedstock from a waste
source that is subjected to enzymatic hydrolysis followed by
fermentation of the resulting sugars would be very likely to meet the
60% GHG threshold for cellulosic biofuel. Therefore, we propose that
cellulosic ethanol produced through an enzymatic hydrolysis process
followed by fermentation using any eligible waste cellulosic feedstock
would be determined to meet the 60% GHG threshold for cellulosic
biofuel. This would include such wastes as wheat straw, rice straw,
sugarcane bagasse, forest slash and thinnings, and yard waste.
    As stated earlier, cellulosic crops grown for the purpose of fuel
production could have land use implications in a lifecycle GHG
analysis. However, the only cellulosic crop that we subjected to
lifecycle analysis was switchgrass which had a relatively small impact
of land-use. Other cellulosic crops that have been considered for fuel
production include miscanthus and trees such as poplar and willow. It
is possible that the land use impacts of miscanthus and planted trees
could be different from that for switchgrass. For instance, while
switchgrass can be grown on marginal lands, planted trees may require
more arable land to thrive. However, according to our lifecycle
analysis for switchgrass, the land use impacts could significantly
increase and the 60% threshold for cellulosic biofuel would still be
met. Therefore, we propose that the pathways shown in Table VI.E.2-1
for ethanol produced from switchgrass through an enzymatic hydrolysis
process followed by fermentation also be applied to ethanol produced
from miscanthus and planted trees. We intend to examine this pathway
more closely for the final rule to determine if this categorization is
appropriate, and request comment on the land use impacts of miscanthus
and planted trees.
    Renewable fuels can also be produced from cellulosic biomass
through various thermochemical processes rather than enzymatic
hydrolysis followed by fermentation. One example of such thermochemical
processes would be biomass gasification to produce ``syngas'' (a
mixture of hydrogen and carbon monoxide) which is then catalytically
synthesized through a Fischer-Tropsch process to produce ethanol,
diesel, gasoline, or other transportation fuels. Another example would
be a catalytic depolymerization process in which the biomass is first
catalytically cracked to smaller molecules and then polymerized under
specific combinations of temperature, pressure, and residence time to
produce a transportation fuel. We have not conducted a lifecycle
analysis of these pathways, but we believe that we can nonetheless make
a reasonable determination regarding the appropriate renewable fuel
category. For instance, we would expect that the GHG emissions produced
during fuel production would be higher for a thermochemical process
than for enzymatic hydrolysis due to the need for greater process heat
produced through the combustion of fossil fuels. However, the yield of
fuel produced per ton of biomass is likely to be greater for
thermochemical processing due to the conversion of the lignin to fuel
in addition to the cellulose and hemicellulose. Thus, while the
lifecycle GHG analyses we conducted for corn stover and switchgrass
demonstrated that the 60% GHG threshold for cellulosic biofuel would be
met by a wide margin, this margin may be smaller if a thermochemical
process was used. While we intend to conduct further analyses of this
family of pathways for the final rule, we believe that a change from
enzymatic hydrolysis to a thermochemical process would be expected to
meet the 60% GHG threshold associated with cellulosic biofuel.
Therefore, we propose that the use of corn stover or other waste
cellulosic biomass, switchgrass, or planted trees in a thermochemical
process would qualify as cellulosic biofuel under the RFS2 program.
This would include pathways that produce ethanol, cellulosic diesel, or
cellulosic gasoline. Since cellulosic diesel fuel produced in this way
would also meet the requirements for biomass-based diesel, we propose
to allow it to be categorized as either cellulosic biofuel or biomass-
based diesel at the producer's discretion. See further discussion of
this issue in Section III.D.2.a. We request comment on our proposed
assignment of categories for renewable fuels produced through a
thermochemical process, as well as data and other information relating
to the various types of thermochemical fuel production processes.
c. Biodiesel
    Our lifecycle analysis of biodiesel (mono alkyl esters) produced
from waste greases/oils demonstrated that the 50% GHG threshold for
biomass-based diesel would be met. Much of the GHG benefit of these
waste greases/oils derives from the fact that they have no land use
impacts. While we did not subject corn oil that is non-food grade to
lifecycle analysis, it is likely that it would also have no land use
impacts. Moreover, such non-food grade corn oil would require nearly
the same process energy to convert it into biodiesel. Therefore, we
propose that the pathway shown in Table VI.E.2-1 for biodiesel produced
from waste greases/oils also be applied to biodiesel produced from non-
food grade corn oil. We intend to analyze this pathway in more depth
for the final rule.
    Our lifecycle analysis of biodiesel produced from soybean oil may
also be applicable to biodiesel produced from other types of virgin
(not waste) oils. This would include canola oil, rapeseed oil,
sunflower oil, and peanut oil. While we have not conducted a detailed
assessment of the land use impacts of these other virgin oils, it is
possible that they would meet the 20% threshold for generic renewable
fuel. Therefore, we propose that the pathway shown in Table VI.E.2-1
for biodiesel produced from soybean oil also be applied to biodiesel
produced from other these virgin oils. We request comment on whether
this is appropriate.
    Although our proposed list of RIN-generating pathways would allow
biodiesel made from waste greases/oils to qualify as biomass-based
diesel, it is likely that there would be insufficient quantities of
these feedstocks to reach the 1.0 billion gallon requirement by 2012.
Biodiesel produced from soybean oil would not qualify as biomass-based
diesel, but instead would be categorized as generic renewable fuel
based on our current analysis of its lifecycle GHG performance.
However, biodiesel production facilities can process either soybean oil
or waste grease with relatively minor changes in operations, and many
facilities that formerly used soybean oil have recently switched to
waste grease due to its more favorable economics. Since the GHG
performance

of biodiesel made from waste greases/oils met the 50% GHG threshold by
a wide margin, and since it is common industry practice for biodiesel
facilities to use these two feedstock sources, we believe it may be
appropriate to allow a biodiesel production facility to average the GHG
benefit generated through the use of waste grease with the lower GHG
performance of biodiesel produced from soybean oil at the same
facility.
    We recognize that an approach in which we allow a biodiesel
production facility to average the GHG benefit of waste grease with
that from soybean oil raises questions about whether similar averaging
could be allowed for other combinations of feedstocks, other types of
fuel, or across multiple facilities within the same company. While we
believe that the circumstances surrounding biodiesel production are
somewhat unique--two different feedstocks subjected to essentially the
same production process in a single facility--we nevertheless request
comment on the appropriateness of such an averaging approach for
biodiesel.
    Based on our lifecycle analyses, biodiesel produced from waste
grease has a GHG performance of 80% reduction from the conventional
diesel baseline, while biodiesel produced from soybean oil has a GHG
performance of 22% reduction. In order to meet the GHG threshold of 50%
for biomass-based diesel, a biodiesel production facility would need to
use a minimum of 48% waste grease and a maximum of 52% soybean oil.
Thus, a pathway that would allow a biodiesel production facility to
designate all of its biodiesel as biomass-based diesel would include a
requirement that the producer demonstrate that every batch has been
produced from no less than 48% waste grease and no more than 52%
soybean oil.
    Although this approach would allow the total volume of biomass-
based diesel to be larger than if waste greases/oils alone qualified,
it is still possible than the 1.0 billion gallon requirement would not
be met due to limits on the availability of waste greases and oils. For
instance, we estimate that the total volume of waste greases and oils
may be no larger than 0.3-0.4 billion gallons. As a result, we request
comment on whether it would also be appropriate to lower the GHG
threshold for biomass-based diesel. If this GHG threshold were lowered
to 40%, a biodiesel production facility would only need to use a
minimum of 31% waste greases/oils instead of 48%.
    We recognize that it may be difficult for a biodiesel production
facility to process a consistent mixture of waste grease and soybean
oil every day. Therefore, we request comment on alternative approaches.
For instance, if a biodiesel production facility processed only waste
grease for the first 175 days (48% x 365 days) of a calendar year, we
could allow it to designate any biodiesel produced from soybean oil for
the remainder of the year as biomass-based diesel. However, this may be
difficult for some producers who must contend with cold temperature
storage and blending issues in the early part of a calendar year by
processing only soybean oil. Alternatively, we could allow a company to
average the production at all of its facilities, where one facility
processed only waste grease and another processed only soybean oil.
    Finally, we request comment on an alternative approach in which an
obligated party, rather than the biodiesel production facility, would
demonstrate that a minimum number of waste grease-based biodiesel RINs
is used to meet the biomass-based diesel standard in comparison to the
number of soybean oil-based biodiesel RINs. In essence, the averaging
would be carried out by the obligated party instead of the biodiesel
producer. In this approach, biodiesel RINs would not be placed into
biomass-based diesel category shown in Table VI.E.1-1, but instead
would be placed into two separate categories as waste grease RINs or
soybean oil RINs. This designation would require that the list of
applicable D codes for use in the RIN be expanded from four to six as
shown in Table VI.E.3.c-1.

  Table VI.E.3.c-1--Alternative Approach to D Codes for Averaging Waste
           Grease and Soybean Oil Biodiesel RINs in Compliance
------------------------------------------------------------------------
                                                  Alternative approach
     D value            Proposal meaning                meaning
------------------------------------------------------------------------
1................  Cellulosic biofuel........  Cellulosic biofuel
2................  Biomass-based diesel......  Biomass-based diesel
3................  Advanced biofuel..........  Biodiesel made from
                                                soybean oil
4................  Renewable fuel............  Biodiesel made from waste
                                                grease
5................  (Not applicable)..........  Advanced biofuel
6................  (Not applicable)..........  Renewable fuel
------------------------------------------------------------------------

    Since other types of renewable fuel may still qualify as biomass-
based diesel, we would retain a separate D code for this category under
this approach. This could allow biodiesel producers who choose the
process a minimum of 48% waste greases/oils each day to continue to
assign a D code of 2 to their biodiesel.
    An obligated party could use any combination of RINs with a D code
of 2, 3, or 4 in order to comply with the biomass-based diesel
standard. However, he would also be subject to an additional
requirement that the ratio of D=3 RINs to D=4 RINs must be less than
1.08. This criterion would ensure that a minimum of 47 RINs
representing biodiesel from waste grease would be used for compliance
purposes for every 53 RINs representing biodiesel from soybean oil that
are also used for compliance.
    We request comment on these alternative approaches to the treatment
of biodiesel.
d. Renewable Diesel Through Hydrotreating
    We did not conduct a lifecycle analysis for the production of non-
ester renewable diesel through a hydrotreating process. However, we
believe that our analysis of biodiesel provides sufficient information
to allow us to designate the renewable fuel category for various
pathways leading to the production of renewable diesel.
    Renewable diesel is generally made from the same feedstocks as
biodiesel, namely soybean oil, waste greases/oils, tallow, and chicken
fat. Therefore, the GHG impacts associated with producing/collecting
the feedstock and transporting it to the production facility would be
the same regardless of whether the final product is biodiesel or
renewable diesel.
    The fossil energy requirements of the production process contribute
a relatively small amount to the overall GHG performance for biodiesel.
For example, the 50% GHG threshold would still be met for biodiesel
produced from waste grease even if the fossil energy requirements
doubled. As a result, compared to the transesterification process used
to produce biodiesel, any small variations in fossil energy
requirements for renewable diesel production in a hydrotreater would be
unlikely to change compliance with the broad categories created by the
GHG thresholds for biomass-based diesel and generic renewable fuel.
Therefore, we believe that it would be appropriate to assign applicable
renewable fuel categories to renewable diesel pathways in parallel with
the assignments we are proposing for biodiesel, including the potential
for averaging of soyoil and waste grease derived volumes. Renewable
diesel produced from waste grease, tallow, or chicken fat in a
hydrotreater that does not coprocess petroleum feedstocks would be

categorized as biomass-based diesel. Renewable diesel produced from
waste grease, tallow, or chicken fat in a hydrotreater that does
coprocess petroleum feedstocks would be categorized as advanced
biofuel. Finally, renewable diesel produced from soybean oil in a
hydrotreater would be categorized as generic renewable fuel.
4. Summary
    Based on the discussion above, we have identified 15 pathways that
we propose could be used to produce fuel that would meet the volume
requirements in EISA assuming a 100 year analysis time frame and
discounting GHG emissions over time by 2%. As noted above, these
pathways would be adjusted should we adopt other time frames or
discount rates (including a zero discount rate) for the final rule.
Each pathway would be assigned a D code for use in generating RINs that
corresponds to one of the four renewable fuel categories. Our proposed
list of allowable pathways is shown in Table VI.E.4-1.

                         Table VI.E.4-1--Applicable Categories for Each Fuel Pathway \a\
----------------------------------------------------------------------------------------------------------------
                                                                   Production process
              Fuel type                       Feedstock               requirements               Category
----------------------------------------------------------------------------------------------------------------
Ethanol..............................  Starch from corn,        --Process heat derived   Renewable fuel.
                                        wheat, barley, oats,     from biomass.
                                        rice, or sorghum.
Ethanol..............................  Starch from corn,        --Dry mill plant.......  Renewable fuel.
                                        wheat, barley, oats,
                                        rice, or sorghum.
                                                                --Process heat derived
                                                                 from natural gas.
                                                                --Combined heat and
                                                                 power (CHP).
                                                                --Fractionation of
                                                                 feedstocks.
                                                                --Some or all
                                                                 distillers grains are
                                                                 dried.
Ethanol..............................  Starch from corn,        --Dry mill plant.......  Renewable fuel.
                                        wheat, barley, oats,
                                        rice, or sorghum.
                                                                --Process heat derived
                                                                 from natural gas.
                                                                --All distillers grains
                                                                 are wet.
Ethanol..............................  Starch from corn,        --Dry mill plant.......  Renewable fuel.
                                        wheat, barley, oats,
                                        rice, or sorghum.
                                                                --Process heat derived
                                                                 from coal.
                                                                --Combined heat and
                                                                 power (CHP).
                                                                --Fractionation of
                                                                 feedstocks.
                                                                --Membrane separation
                                                                 of ethanol.
                                                                --Raw starch hydrolysis
                                                                --Some or all
                                                                 distillers grains are
                                                                 dried.
Ethanol..............................  Starch from corn,        --Dry mill plant.......  Renewable fuel.
                                        wheat, barley, oats,
                                        rice, or sorghum.
                                                                --Process heat derived
                                                                 from coal.
                                                                --Combined heat and
                                                                 power (CHP).
                                                                --Fractionation of
                                                                 feedstocks.
                                                                --Membrane separation
                                                                 of ethanol.
                                                                --All distillers grains
                                                                 are wet.
Ethanol..............................  Cellulose and            --Enzymatic hydrolysis   Cellulosic biofuel.
                                        hemicellulose from       of cellulose.
                                        corn stover,
                                        switchgrass,
                                        miscanthus, wheat
                                        straw, rice straw,
                                        sugarcane bagasse,
                                        forest waste, yard
                                        waste, or planted
                                        trees.
                                                                --Fermentation of
                                                                 sugars.
                                                                --Process heat derived
                                                                 from lignin.
Ethanol..............................  Cellulose and            --Thermochemical         Cellulosic biofuel.
                                        hemicellulose from       gasification of
                                        corn stover,             biomass.
                                        switchgrass,
                                        miscanthus, wheat
                                        straw, rice straw,
                                        sugarcane bagasse,
                                        forest waste, yard
                                        waste, or planted
                                        trees.
                                                                --Fischer-Tropsch
                                                                 process.
Ethanol..............................  Sugarcane sugar........  --Process heat derived   Advanced biofuel.
                                                                 from sugarcane bagasse.
Biodiesel (mono alkyl ester).........  Waste grease, waste      --Transesterification..  Biomass-based diesel.
                                        oils, tallow, chicken
                                        fat, or non-food grade
                                        corn oil.
Biodiesel (mono alkyl ester).........  Soybean oil and other    --Transesterification..  Renewable fuel.
                                        virgin plant oils.

Cellulosic diesel....................  Cellulose and            --Thermochemical         Cellulosic biofuel or
                                        hemicellulose from       gasification of          biomass-based diesel.
                                        corn stover,             biomass.
                                        switchgrass,
                                        miscanthus, wheat
                                        straw, rice straw,
                                        sugarcane bagasse,
                                        forest waste, yard
                                        waste, or planted
                                        trees.
                                                                --Fischer-Tropsch
                                                                 process.
                                                                --Catalytic
                                                                 depolymerization.
Non-ester renewable diesel...........  Waste grease, waste      --Hydrotreating........
                                        oils, tallow, chicken