increased production and importation of biofuels in the U.S. As
detailed below, we have included the GHG emission impacts of
international indirect land use changes. We recognize the significance
of including international land use emissions impact and in our
analysis presentation we have been transparent in breaking out the
various sources of GHG emissions so that the reader can readily see the
impact of including international land use impacts.
    In addition to the many technical issues addressed in this
proposal, this section also discusses the emissions decreases and
increases associated with the different parts of the lifecycle
emissions of various biofuels, and the timeframes in which these
emissions changes occur. Determining a single lifecycle value that best
represents this combination of emissions increases and decreases
occurring over time led EPA to consider various alternative ways to
analyze the timeframe of emissions related to biofuel production and
use as well as options for adjusting or discounting these emissions to
determine their net present value. Several variations of time period
and discount rate are discussed. The analytical time horizon and the
choice whether to discount GHG emissions and, if so, at what
appropriate rate can have a significant impact on the final assessment
of the lifecycle GHG emissions impacts of individual biofuels as well
as the overall GHG impacts of these EISA provisions and this rule.
    We believe that our lifecycle analysis is based on the best
available science, and recognize that in some aspects it represents a
cutting edge approach to addressing lifecycle GHG emissions. Because of
this, varying degrees of uncertainty are in our analysis. For this
proposal, we conducted a number of

sensitivity analyses which focus on key parameters and demonstrate how
our assessments might change under alternative assumptions. By focusing
attention on these key parameters, the comments we receive as well as
additional investigation and analysis by EPA will allow narrowing of
uncertainty concerns for the final rule. In addition to this
sensitivity analysis approach, we will also explore options for more
formal uncertainty analyses for the final rule to the extent possible.
    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 during the comment period focused specifically on our
lifecycle analysis to help ensure full understanding of the analyses
conducted, the issues addressed and options that should be considered.
We expect that this workshop will help ensure that we receive the most
thoughtful and useful comments to this proposal and that 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 explained in more detail
in the following sections, EPA is specifically seeking 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.
    The regulatory purpose of the lifecycle greenhouse gas emissions
analysis is to determine whether renewable fuels meet the GHG
thresholds for the different categories of renewable fuel.
1. Definition of Lifecycle GHG Emissions
    The GHG provisions in EISA are notable for the GHG thresholds
mandated for each category of renewable fuel and also the mandated
lifecycle approach to those thresholds. Renewable fuel must, unless
``grandfathered'' as discussed in Section II.B.3., achieve at least 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. Similarly, biomass-based
diesel and advanced biofuels must achieve a 50% reduction, and
cellulosic biofuels a 60% reduction, unless these thresholds are
adjusted according to the provisions in EISA. To EPA's knowledge, the
GHG reduction thresholds presented in EISA are the first lifecycle GHG
performance requirements included in federal law. These thresholds, in
combination with the renewable fuel volume mandates, are designed to
ensure significant GHG emission reductions from the use of renewable
fuels and encourage the use of GHG-reducing renewable fuels.
    The definition of lifecycle greenhouse gas emissions established by
Congress is also critical. Congress specified that:

    The term `lifecycle greenhouse gas emissions' means the
aggregate quantity of greenhouse gas emissions (including direct
emissions and significant indirect emissions such as significant
emissions from land use changes), as determined by the
Administrator, related to the full fuel lifecycle, including all
stages of fuel and feedstock production and distribution, from
feedstock generation or extraction through the distribution and
delivery and use of the finished fuel to the ultimate consumer,
where the mass values for all greenhouse gases are adjusted to
account for their relative global warming potential.\264\
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    \264\ Clean Air Act Section 211(o)(1).

    This definition requires EPA to look broadly at lifecycle analyses
and to develop a methodology that accounts for all the important
factors that may significantly influence this assessment, including the
secondary or indirect impacts of expanded biofuels use. EPA's analysis
described below indicates that the assessment of lifecycle GHG
emissions for biofuels is significantly affected by the secondary
agricultural sector GHG impacts from increased biofuel feedstock
production (e.g., changes in livestock emissions due to changes in
agricultural commodity prices) and also by the international impact of
land use change from increased biofuel feedstock production. Thus,
these factors must be appropriately incorporated into EPA's lifecycle
methodology to properly assess full lifecycle GHG performance of
biofuels in accordance with the EISA definition.
2. History and Evolution of GHG Lifecycle Analysis
    Traditionally, the GHG lifecycle analysis of fuels has involved
calculating the emissions associated with each individual stage in the
production and use of the fuel (e.g., growing or extracting the
feedstock, moving the feedstock to the processing plant, processing the
feedstock into fuel, moving the fuel to market, and combusting the
fuel.) EPA used this approach for the lifecycle modeling conducted for
the RFS1 program in 2005. However, it has become increasingly apparent
that this type of first order or attributional lifecycle modeling has
notable shortcomings, especially when evaluating the implications of
biofuel policies.\265\ In fact, the main criticism EPA received in
reaction to our previous RFS1 lifecycle analysis was that we did not
include important secondary, indirect, or consequential impacts of
biofuel production and use.
---------------------------------------------------------------------------

    \265\ See also, Conceptual and Methodological Issues in
Lifecycle Analysis of Transportation Fuels, Mark A. Delucchi,
Institute of Transportation Studies, University of California,
Davis, 2004, UCD-ITS-RR-04-45 for a description of issues with
traditional lifecycle analysis used to model GHG impacts of biofuels
and biofuel policies.
---------------------------------------------------------------------------

    Several studies and analyses conducted since the completion of RFS1
have contributed to our understanding of the lifecycle GHG emissions of
biofuel production. These studies, and others, have highlighted the
potential impacts of biofuel production on the agricultural sector and
have specifically identified land use change impacts as an important
consideration when determining GHG impacts of
biofuels.266 267 In the meantime, the dramatic increase in
U.S. production of biofuels has heightened the concern about the
impacts biofuels might have on land use and has increased the
importance of considering these indirect impacts in lifecycle analysis.
---------------------------------------------------------------------------

    \266\ Fargione, J., J. Hill, D. Tilman, S. Polasky, and P.
Hawthorne. 2008. Land clearing and the biofuel carbon debt. Science
319:1235-1238. See http://www.sciencemag.org/cgi/reprint/319/5867/1235.pdf.
    \267\ Searchinger, T., R. Heimlich, R.A. Houghton, F. Dong, A.
Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, and T.-H. Yu. 2008. Use of
U.S. croplands for biofuels increases greenhouse gases through
emissions from land-use change. Science 319:1238-1240. See http://www.sciencemag.org/cgi/reprint/319/5867/1238.pdf.
---------------------------------------------------------------------------

    Based on the evolution of lifecycle analysis and the new
requirements of EISA, we have developed a comprehensive methodology for
estimating the lifecycle GHG emissions associated with renewable fuels.
Through dozens of meetings with a wide range of experts and
stakeholders, EPA has shared and sought input on this methodology. We
also have relied on the expertise of the U.S. Department of Agriculture
(USDA) and the Department of Energy (DOE) to help inform many of the
key assumptions and modeling inputs for this analysis. Dialogue with
the State of California and the European Union on their parallel, on-
going efforts in GHG

lifecycle analysis has also helped inform EPA's methodology. As part of
this discussion, we have identified several of the key drivers
associated with these lifecycle GHG emissions estimates, including
assumptions about international land use change and the timing of GHG
emissions over time. The inputs we have received through these
interactions are reflected throughout this section.
    Specifically EPA has worked closely with the California Air
Resources Board (CARB) regarding their development of transportation
fuels lifecycle GHG impacts. California Executive Order S-1-07, the Low
Carbon Fuel Standard (LCFS) (issued on January 18, 2007), calls for a
reduction of at least 10 percent in the carbon intensity of
California's transportation fuels by 2020. CARB has worked to develop
lifecycle GHG impacts of different fuels for this Executive Order
rulemaking. More information about this rulemaking and the lifecycle
analysis conducted by California can be found at http://www.arb.ca.gov/fuels/lcfs/lcfs.htm. EPA will continue to coordinate with California on
this rulemaking and the biofuels lifecycle GHG analysis work.
    Because this lifecycle GHG emissions analysis is complex and
requires the use of sophisticated computer models, we have taken
several steps to increase the transparency associated with our
analysis. For example, we have updated the model documentation for the
Forest and Agricultural Sector Optimization Model (FASOM), which is
included in the docket. In addition, we have highlighted key
assumptions in FASOM and the Food and Agricultural Policy Research
Institute (FAPRI) models that impact the results of our analysis.
Finally, this NPRM provides an important opportunity for the Agency to
present our work and to receive input from stakeholders and experts in
this field. We will also continue to refine our analysis between the
proposed and final rules, and we will add or update information to the
docket as it becomes available.

B. Methodology

    This section describes EPA's methodology for assessing the
lifecycle GHG emissions associated with each biofuel evaluated as well
as the petroleum-based gasoline and diesel fuel these biofuels would
replace. Whereas lifecycle GHG emission methodologies have been well
studied and established for petroleum-based gasoline and diesel fuel,
much of EPA's work has focused on newly developing lifecycle
methodologies for biofuels. Therefore, much of the following section
describes the biofuels-related methodologies and identifies important
issues for comment. Assessing the complete lifecycle GHG impact for
each individual biofuel mandated by EISA requires that a number of key
methodological issues be addressed--from the choice of a baseline to
the selection of the most credible technique for predicting
international land use conversion due to the increase in U.S. renewable
fuels demand, to accounting for the time dimension of changes in GHG
emissions. In this section, we first describe the scenarios we have
analyzed for this proposal. Second, we discuss the scope of our
analysis and what is included in our estimates. Third, we provide
details on the tools and models we used to quantify the GHG emissions
associated with the different fuels. Fourth, we discuss the
uncertainties associated with lifecycle analysis and how we have
addressed them. Fifth, we describe the different components of the
lifecycle that we have analyzed and the key questions we have addressed
in this analysis.
1. Scenario Description
    To quantify the lifecycle GHG emissions associated with the
increase in renewable fuel mandated by EISA, we compared the
differences in total GHG emissions between two future scenarios. The
first assumed a ``business as usual'' volume of a particular renewable
fuel based on what would likely be in the fuel pool in 2022 without
EISA, as predicted by the Energy Information Agency's Annual Energy
Outlook (AEO) for 2007 (which took into account the economic and policy
factors in existence in 2007 before EISA). The second assumed the
higher volume of renewable fuels as mandated by EISA for 2022. For each
individual biofuel, we analyzed the incremental GHG emission impacts of
increasing the volume of that fuel to the total mix of biofuels needed
to meet the EISA requirements. Rather than focus on the impacts
associated with a specific gallon of fuel and tracking inputs and
outputs across different lifecycle stages, we determined the overall
aggregate impacts across sections of the economy in response to a given
volume change in the amount of biofuel produced.\268\
---------------------------------------------------------------------------

    \268\ We then normalize those impacts for each gallon of fuel
(or Btu) by dividing total impacts over the given volume change.
---------------------------------------------------------------------------

    This analysis is not a comparison of biofuel produced today versus
biofuel produced in the future. Instead, it is a comparison of two
future scenarios. Any projected changes in factors such as crop yields,
energy costs, or production plant efficiencies, both domestically and
internationally, are reflected in both scenarios. We focused our
analyses on 2022 results for three reasons. First, it would require an
extremely complex assessment and administratively difficult
implementation program to track how biofuel production might
continuously change from month to month or year to year. Instead, it
seems appropriate that each biofuel be assessed a level of GHG
performance that is constant over the implementation of this rule,
allowing fuel providers to anticipate how these GHG performance
assessments should affect their production plans. Second, it is
appropriate to focus on 2022, the final year of ramp up in the required
volumes of renewable fuel as this year. Assessment in this year allows
the complete fuel volumes specified in EISA to be incorporated. Third,
since the GHG assessment compares performance between a business as
usual case and the mandated volumes case, many of the factors that
change over time such as crop yield per acre are reflected in both
cases. Therefore the differences in these parallel assessments are
unlikely to vary significantly over time.
    EPA requests comment on its proposal to adopt fixed assessments of
fuels meeting the GHG thresholds based on a 2022 performance
assessment. Additional information on the scenarios modeled and the
supplemental analyses that will be conducted for the final rule is
included in Chapter 2 of the DRIA.
    In the existing Renewable Fuel Standard rules adopted in response
to the Energy Policy Act of 2005, biofuels and RINs associated with
them are not based on regional differences of where the feedstock was
grown or the biofuel was produced. In effect, the RINs apply to a
national average of the fuel type. Similarly, this proposal does not
distinguish biofuel on the basis of where within the country the
biofuel feedstock was grown or the biofuel produced. Thus, for example,
ethanol produced from corn starch using the same production technology
will receive the same GHG lifecycle assessment regardless of where the
corn was grown or at what facility the biofuel was produced. There are
regional differences in soil types, weather conditions, and other
factors which could affect, for example, the amount of fertilizer
applied and thus the GHG impact of corn production. Such factors could
vary somewhat across a region, within a state and even within a county.
The agricultural models used to conduct this analysis do distinguish
crop production

by region domestically and by country internationally. However, biofuel
feedstocks such as corn or soybean oil are well traded commodities
including internationally. So, for example, if corn in a certain
location in Iowa is used to produce ethanol, corn from all other
regions will be used to replace that corn for all its other potential
uses. Therefore, it is not appropriate to ascribe the indirect affects,
both domestically and internationally, to corn grown in one area
differently to corn (or other biofuel feedstock) grown in another area.
Our national treatment of biofuel feedstock also pertains to fuels
produced in other countries. Thus for example, sugarcane-based ethanol
produced in Brazil is all treated the same regardless of where the
sugarcane was grown in Brazil. Nevertheless, comments are invited on
the option of differentiating biofuels in the future based on the
location of their feedstock production within a country.
2. Scope of the Analysis
a. Legal Interpretation of Lifecycle Greenhouse Gas Emissions
    As described in VI.A.1, the definition of lifecycle greenhouse gas
emissions refers to the ``aggregate quantity of GHG emissions'' that
are ``related to the full fuel lifecycle.'' The fuel lifecycle includes
``all stages of fuel and feedstock production and distribution, from
feedstock generation or extraction through * * * use of the finished
fuel to the ultimate consumer.'' The aggregate quantity of GHG
emissions includes ``direct emissions'' and ``significant indirect
emissions such as significant emission from land use changes.'' This
provision is written in generally broad and expansive terms, such as
``aggregate quantity'', ``related to'', ``full fuel lifecycle'', and
``all stages'' of production and distribution. At the same time, these
and other terms are not themselves defined and provide discretion to
the Administrator in implementing this definition. For example, the
word ``significant,'' which is used to modify ``indirect emissions,''
is not defined.
    The definition includes both ``direct'' and ``significant
indirect'' emissions related to the full fuel lifecycle. We consider
direct emissions as those that are emitted from each stage of the full
fuel lifecycle, and indirect emissions as those from second order
effects that occur as a consequence of the full fuel lifecycle. For
example, direct emissions for a renewable fuel would include those from
the growing of renewable fuel feedstock, the distribution of the
feedstock to the renewable fuel producer, the production of renewable
fuel, the distribution of the finished fuel to the consumer, and the
use of the fuel by the consumer as transportation fuel. Similarly,
direct emissions associated with the baseline fuel would include
extraction of the crude oil, distribution of the crude oil to the
refinery, the production of gasoline and diesel from the crude oil, the
distribution of the finished fuel to the consumer, and the use of the
fuel by the consumer. Indirect emissions would include other emissions
impacts that result from fuel production or use, such as changes in
livestock emissions resulting from changes in livestock numbers, or
shifts in acreage between different crop types. The definition of
indirect emissions specifically includes ``land use changes'' which
would include changes in the kind of usage that land is put to such as
changes in forest, pasture, savannah, and crop use.\269\
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    \269\ Arguably shifts in acreage between different crops also
could be considered a land use change, but we believe there will be
less confusion if the term land use change is used with respect to
changes in land such as changing from savannah or forest to
cropland. There is no difference in result, as in both cases the
emissions need to be significant.
---------------------------------------------------------------------------

    In considering how to address land use changes in our lifecycle
analysis, two distinct questions have been raised--whether to account
for emissions that occur outside of the U.S., and under what
circumstances land use change should properly be included in the
lifecycle analysis.
    On the question of considering GHG emissions that occur outside of
the U.S., it is important to be clear that including such emissions in
the lifecycle analysis does not exercise regulatory authority over
activities that occur solely outside the U.S., and does not raise
questions of extra-territorial jurisdiction. EPA's regulatory action
involves classification of products either produced in the U.S. or
imported into the U.S. EPA is simply assessing whether the use of these
products in the U.S. satisfies requirements under the Clean Air Act for
the use of designated volumes of renewable fuel, cellulosic biofuel,
biomass-based diesel and advanced biofuel, as those terms are defined
in the Act. Considering international emissions in determining the
lifecycle GHG emissions of the domestically produced or imported fuel
does not change the fact that the actual regulation of the product
involves its use solely inside the U.S.
    When looking at the issue of international versus domestic
emissions, it is important to recognize that a large variety of
different activities outside the U.S. play a major part of the full
fuel lifecycle of baseline and renewable fuels. For example, for
baseline fuels (i.e., gasoline and diesel fuels used as transportation
fuel in 2005), GHG emissions associated with extraction and delivery of
crude oil imported to the U.S. all have occurred overseas. In addition,
for imported gasoline or diesel, all of the crude extraction and
delivery emissions, as well as the emissions associated with refining
and distribution of the finished product to the U.S., would have
occurred overseas. For imported renewable fuel all of the emissions
associated with feedstock production and distribution, processing of
the feedstock into renewable fuel, and delivery of the finished
renewable fuel to the U.S. would have occurred overseas. The definition
of lifecycle greenhouse gas emissions makes it clear that EPA is to
determine the aggregate emissions related to the ``full'' fuel
lifecycle, including ``all stages of fuel and feedstock production and
distribution.'' Thus, EPA could not, as a legal matter, ignore those
parts of a fuel lifecycle that occur overseas.
    Drawing a distinction between GHG emissions that occur inside the
U.S. as compared to emissions that occur outside the U.S. would
dramatically alter the lifecycle analysis in a way that bears no
apparent relationship to the purpose of this provision. The purpose of
including lifecycle GHG thresholds in this statutory provision is to
require the use of renewable fuels that achieve reductions in GHG
emissions compared to the baseline. Drawing a distinction between
domestic and international emissions would ignore a large part of the
GHG emission associated with the different fuels, and would result in a
GHG analysis of baseline renewable fuels that bears no relationship to
the real world emissions impact of the fuels. The baseline would be
significantly understated, given the large amount of imported crude
used to produce gasoline and diesel, and the importation of finished
gasoline and diesel, in 2005. Likewise, the emissions associated with
imported renewable fuel would be understated, as it would only consider
the emissions from distribution of the fuel to the consumer and the use
of the fuel by the consumer, and would ignore both the emissions that
occurred overseas as well as the emissions reductions from the intake
of CO2 from growing of the feedstock. While large
percentages of GHG emissions would be ignored, this would take place in
a context where the global warming impact of emissions is irrespective
of

where the emissions occur. Thus taking such an approach would
essentially undermine the provision, and would be an arbitrary
interpretation of the broadly phrased text used by Congress.
    While the emissions discussed above would more typically be
considered direct emissions related to the full fuel lifecycle, there
would also be no basis to cover just foreign direct emissions while
excluding foreign indirect emissions. The text of the statute draws no
such distinction, nor is there a distinction in achieving the purposes
of the provision. GHG emissions impact global warming wherever they
occur, and if the purpose is to achieve some reduction in GHG emissions
in order to help address global warming, then ignoring GHG emissions
because they are emitted outside our borders versus inside our borders
interferes with the ability to achieve this objective.
    For example, domestic production of a renewable fuel could lead to
indirect emissions, whether from land use changes or otherwise, some
occurring within the U.S. and some occurring in other countries.
Similarly, imported renewable fuel could have resulted in the same
indirect emissions whether occurring in the country that produced the
biofuel or in other countries. It would be arbitrary to assign the
indirect emissions to the domestic renewable fuel but not to assign the
identical indirect emissions that occur overseas to an imported
product.
    Based on the above, EPA believes that the definition of lifecycle
greenhouse gas emissions is properly interpreted as including all
direct and significant indirect GHG emissions related to the full fuel
lifecycle, whether or not they occur in the U.S. This applies to both
the baseline lifecycle greenhouse emissions as well as the lifecycle
greenhouse gas emissions for various renewable fuels.
    EPA recognizes, as discussed later, our estimates of domestic
indirect emissions are more certain than our estimate of international
indirect emissions. The issue of how to evaluate and weigh the various
elements of the lifecycle analysis, and properly account for
uncertainty in our estimates, is a different issue, however. The issue
here is whether the definition of lifecycle greenhouse gas emissions is
properly interpreted as including direct and significant indirect
emissions that occur outside the U.S. as well as those that occur
inside the U.S.
    As to the question of which land use changes should be included in
our lifecycle analyses, a central element to focus on is the
requirement that such indirect emissions be related to the full fuel
lifecycle. The term ``related to'' is generally interpreted as
providing a broad and expansive scope for a provision. It has routinely
been interpreted as meaning to have a connection to or refer to a
matter. To determine whether an indirect emission has the appropriate
connection to the full fuel lifecycle, we must look at both the
objectives of this provision as well as the nature of the relationship.
    In this case, EPA has used a global model that projects a variety
of agricultural impacts that stem from the use of feedstocks to produce
renewable fuel. We have estimated shifts in types of crops planted and
increases in crop acres planted. There is a direct relationship between
these shifts in the agricultural market as a consequence of the
increased demand for biofuels in the U.S. Increased U.S. demand for
biofuel feedstocks diverts these feedstocks from other competing uses,
and also increases the price of the feedstock, thus spurring
production. To the extent feedstocks like corn and soybeans are traded
internationally, this combined impact of lower supply from the U.S. and
higher commodity prices encourages international production to fill the
gap. Our analysis uses country specific information to determine the
amount, location, and type of land use change that would occur to meet
this change in production patterns. The linkages are generally close,
and are not extended or overly complex. While there is clearly
significant uncertainty in determining the specific degree of land use
change and the specific impact of those changes, there is considerable
overall certainty as to the existence of the land use changes in
general, the fact that GHG emissions will result, and the cause and
effect linkage of these emissions impacts to the increased use of
feedstock for production of renewable fuels.
    Overall, EPA is confident that it is appropriate to consider the
estimated emissions from land use changes as well as the other indirect
emissions as ``related to'' the full fuel lifecycle, based on the
reasonable technical basis provided by the modeling for the connection
between the full fuel lifecycle and the indirect emissions, as well as
for the determination that the emissions are significant. EPA believes
uncertainty in the resulting aggregate GHG estimates should be taken
into consideration, but that it would be inappropriate to exclude
indirect emissions estimates from this analysis. Developing a
reasonable estimate of these kinds of indirect emissions will allow for
a reasoned evaluation of total GHG impacts, which is needed to promote
the objectives of this provision, as compared to ignoring or not
accounting for these indirect emissions.
b. System Boundaries
    It is important to establish clear system boundaries in this
analysis. By determining a common set of system boundaries, different
fuel types can then be validly compared. As described in the previous
section, we have assessed the direct and indirect GHG impacts in each
stage of the full fuel lifecycle for biofuels and petroleum fuels.
    To capture the direct emissions impacts of feedstock production in
our analysis, we included the agricultural inputs (e.g., the fuel used
in the tractor, the energy used to produce and transport fertilizer to
the field) needed to grow crops directly used in biofuel production. We
also included the N2O emissions associated with agricultural
sector practices used in biofuel production (including direct and
indirect N2O emissions from synthetic fertilizer
application, N fixing crops, crop residue, and manure management), as
well as the land use change associated with converting land to grow
crops directly used in biofuel production. To capture the indirect, or
secondary, GHG emissions that result from biofuel feedstock production,
we relied on the internationally accepted lifecycle assessment
standards developed by the International Organization for
Standardization (ISO). Examples of significant secondary impacts
include the agricultural inputs associated with crops indirectly
impacted by the use of feedstock for biofuel production (domestically
and internationally), the emissions associated with land use change
that are indirectly impacted by using feedstocks for biofuel production
(e.g., to make up for lost U.S. exports), changes in livestock herd
numbers that result from higher feed costs, and changes in rice methane
emissions indirectly impacted by shifts in acres to produce feedstocks
for biofuel production. These indirect or secondary impacts would not
have occurred if it were not for the use of biomass to produce a
biofuel.
    We did not include the infrastructure related GHG emissions (e.g.,
the energy needed to manufacture the tractor used on the farm) or the
facility construction-related emissions (e.g., steel or concrete needed
to construct a refinery). As part of the GHG analysis performed for
RFS1, we performed a sensitivity analysis on expanding the corn
production system to include farm equipment production to determine the
impact it has on the overall results of our analysis. We found that
including

farm equipment production energy use and emissions increases corn
ethanol lifecycle energy use and GHG emissions and decreases the corn
ethanol lifecycle GHG benefit as compared to petroleum gasoline by
approximately 1%. Furthermore, to be consistent in the modeling if
system boundaries are expanded to include production of farming
equipment they should also be expanded to include producing other
material inputs to both the ethanol and petroleum lifecycles. The net
effect of this would be a slight increase in both the ethanol and
petroleum fuel lifecycle results and a smaller or negligible effect on
the comparison of the two.
    For this proposal, we have not yet incorporated secondary energy
sector impacts, however we plan to have this analysis complete for the
final rule. Additional details on the system boundaries are included in
the DRIA Chapter 2.
3. Modeling Framework
    Currently, no single model can capture all of the complex
interactions associated with estimating lifecycle GHG emissions for
biofuels, taking into account the ``significant indirect emissions such
as significant emissions from land use change'' required by EISA. For
example, some analysis tools used in the past focus on process
modeling--the energy and resultant emissions associated with the direct
production of a fuel at a petroleum refinery or biofuel production
facility. But this is only one component in the production of the fuel.
Clearly in the case of biofuels, impacts from and on the agricultural
sector are important, because this sector produces feedstock for
biofuel production. Commercial agricultural operations make many of
their decisions based on an economic assessment of profit maximization.
Assessment of the interactions throughout the agricultural sector
requires an analysis of the commodity markets using economic models.
However, existing economy wide general equilibrium economic models are
not detailed enough to capture the specific agricultural sector
interactions critical to our analysis (e.g., changes in acres by crop
type) and would not provide the types of outputs needed for a thorough
GHG analysis. As a result, EPA has used different tools that have
different strengths for each specific component of the analysis to
create a more comprehensive estimate of GHG emissions. Where no direct
links between the different models exist, specific components and
outputs of each are used and combined to provide an analytical
framework and the composite lifecycle assessment results. As this is a
new application of these modeling tools, EPA plans to organize peer
review of our modeling approach. The individual models are described in
the following sections and in more detail in Chapter 2 of the DRIA.
    To quantify the emissions factors associated with different steps
of the production and use of various fuels (e.g., extraction of
petroleum products, transport of feedstocks), we used the spreadsheet
analysis tool developed by Argonne National Laboratories, the
Greenhouse gases, Regulated Emissions, and Energy use in Transportation
(GREET) model. This analysis tool includes the GHG emissions associated
with the production and combustion of fossil fuels (diesel fuel,
gasoline, natural gas, coal, etc.). These fossil fuels are used both in
the production of biofuels, (e.g., diesel fuel used in farm tractors
and natural gas used at ethanol plants) and could also be displaced by
renewable fuel use in the transportation sector. GREET also estimates
the GHG emissions estimates associated with electricity production
required for biofuel and petroleum fuel production. For the
agricultural sector, we also relied upon GREET to provide GHG emissions
associated with the production and transport of agricultural inputs
such as fertilizer, herbicides, pesticides, etc. While GREET provides
direct GHG emissions estimates associated with the extraction-through-
combustion phases of fuel use, it does not capture some of the
secondary impacts associated with the fuel, such as changes in the
composition of feed used for animal production, which would be expected
due to changes in cost. EPA addresses these secondary impacts through
other models described later in this section. GREET has been under
development for several years and has undergone extensive peer review
through multiple updates. Of the available sources of information on
lifecycle GHG emissions of fossil energy consumed, we believe that
GREET offers the most comprehensive treatment of emissions from the
covered sources.
    For some steps in the production of biofuels, we used more detailed
models to capture some of the dynamic market interactions that result
from various policies. Here, we briefly describe the different models
incorporated into our analysis to provide specific details for various
lifecycle components.
    To estimate the changes in the domestic agricultural sector (e.g.,
changes in crop acres resulting from increased demand for biofuel
feedstock or changes in the number of livestock due to higher corn
prices) and their associated emissions, we used the FASOM model,
developed by Texas A&M University and others. FASOM is a partial
equilibrium economic model of the U.S. forest and agricultural sectors.
EPA selected the FASOM model for this analysis for several reasons.
FASOM is a comprehensive forestry and agricultural sector model that
tracks over 2,000 production possibilities for field crops, livestock,
and biofuels for private lands in the contiguous United States. It
accounts for changes in CO2, methane, and N2O
from most agricultural activities and tracks carbon sequestration and
carbon losses over time. Another advantage of FASOM is that it captures
the impacts of all crop production, not just biofuel feedstock. Thus,
as compared to some earlier assessments of lifecycle emissions, using
FASOM allows us to determine secondary agricultural sector impacts,
such as crop shifting and reduced demand due to higher prices. It also
captures changes in the livestock market (e.g., smaller herd sizes that
result from higher feed costs) and U.S. export changes. FASOM also has
been used by EPA to consider U.S. forest and agricultural sector GHG
mitigation options.\270\
---------------------------------------------------------------------------

    \270\ Greenhouse Gas Mitigation Potential in U.S. Forestry and
Agriculture, EPA Document 430-R-05-006. See http://www.epa.gov/sequestration/greenhouse_gas.html.
---------------------------------------------------------------------------

    To estimate the impacts of biofuels feedstock production on
international agricultural and livestock production, we used the
integrated FAPRI international models, developed by Iowa State
University and the University of Missouri. These models capture the
biological, technical, and economic relationships among key variables
within a particular commodity and across commodities. FAPRI is a
worldwide agricultural sector economic model that was run by the Center
for Agricultural and Rural Development (CARD) at Iowa State University
on behalf of EPA. The FAPRI models have been previously employed to
examine the impacts of World Trade Organization proposals and changes
in the European Union's Common Agricultural Policy, to analyze farm
bill proposals since 1984, and to evaluate the impact of biofuel
development in the United States. In addition, the FAPRI models have
been used by the USDA Office of Chief Economist, Congress, and the
World Bank to examine agricultural impacts from government policy
changes, market developments, and land use shifts.
    Although FASOM predicts land use and export changes in the U.S. due
to

greater demand for domestic biofuel feedstock, it does not assess how
international agricultural production might respond to these changes in
commodity prices and U.S. exports. The FAPRI model does predict how
much crop land will change in other countries but does not predict what
type of land such as forest or pasture will be affected. We used data
analyses provided by Winrock International to estimate what land types
will be converted into crop land in each country and the GHG emissions
associated with the land conversions. Winrock has used 2001-2004
satellite data to analyze recent land use changes around the world that
have resulted from the social, economic, and political forces that
drive land use. Winrock has then combined the recent land use change
patterns with various estimates of carbon stocks associated with
different types of land at the state level. This international land use
assessment is an important consideration in our lifecycle GHG
assessment and is explained in more detail later in this section.
    To test the robustness of the FASOM, FAPRI and Winrock results, we
are also evaluating the Global Trade Analysis Project (GTAP) model, a
multi-region, multi-sector, computable general equilibrium model that
estimates changes in world agricultural production. Maintained through
Purdue University, GTAP projects international land use change based on
the economics of land conversion, rather than using the historical data
approach applied by FAPRI/Winrock. GTAP is designed to project changes
in international land use as a result of the change in U.S. biofuel
policies, based on the relative land use values of cropland, forest,
and pastureland. The GTAP design has the advantage of explicitly
modeling the competition between different land types due to a change
in policy. As further discussed in Section VI.B.5.iv, GTAP has several
disadvantages, some of which prevented its use for the proposal. We
expect to correct several of these shortcomings between the proposed
and final rules and therefore continue to evaluate how the GTAP model
could be used as part of the final rule.
    The assessments provided in this proposal use the values provided
by the Intergovernmental Panel on Climate Change (IPCC) to estimate the
impacts of N2O emissions from fertilizer application.
However, due to concern that this may underestimate N2O
emissions from fertilizer application, \271\ we are working with the
CENTURY and DAYCENT models, developed by Colorado State University, to
update our assessments. The DAYCENT model simulates plant-soil systems
and is capable of simulating detailed daily soil water and temperature
dynamics and trace gas fluxes (CH4, N2O,
NOX and N2). The CENTURY model is a generalized
plant-soil ecosystem model that simulates plant production, soil carbon
dynamics, soil nutrient dynamics, and soil water and temperature. We
anticipate the results of this new modeling work will be reflected in
our assessments for the final rule. More description of this ongoing
work is included in the Chapter 2 of the DRIA.
---------------------------------------------------------------------------

    \271\ Crutzen, P. J., Mosier, A. R., Smith, K. A., and
Winiwarter, W.: N2O release from agro-biofuel production
negates global warming reduction by replacing fossil fuels, Atmos.
Chem. Phys., 8, 389-395, 2008. See http://www.atmos-chem-phys.net/8/389/2008/acp-8-389-2008.pdf.
---------------------------------------------------------------------------

    To estimate the GHG emissions associated with renewable fuel
production, we used detailed ASPEN-based process models developed by
USDA and DOE's National Renewable Energy Laboratory (NREL). While GREET
contains estimates for renewable fuel production, these estimates are
based on existing technology. We expect biofuel production technology
to improve over time, and we projected improvements in process
technology over time based on available information. These projections
are discussed in DRIA Chapter 4. We then utilized the ASPEN-based
process models to assess the impacts of these improvements. We also
cross-checked the ASPEN-based process model predictions by comparing
them to a number of industry sources and other modeling efforts that
estimate potential improvements in ethanol production over time,
including the Biofuel Energy Systems Simulator (BESS) model. BESS is a
software tool developed by the University of Nebraska that calculates
the energy efficiency, greenhouse gas (GHG) emissions, and natural
resource requirements of corn-to-ethanol biofuel production systems. We
used the GREET model to estimate the GHG emissions associated with
current technology as used by petroleum refineries, because we do not
expect significant changes in petroleum refinery technology.
    We used the EPA-developed Motor Vehicle Emission Simulator (MOVES)
to estimate vehicle tailpipe GHG emissions. The MOVES modeling system
estimates emissions for on-road and nonroad sources, covers a broad
range of pollutants, and allows multiple scale analysis, from fine-
scale analysis to national inventory estimation.
    Finally, for the FRM we intend to use an EPA version of the Energy
Information Administration's National Energy Modeling System (NEMS) to
estimate the secondary impacts on the energy market associated with
increased renewable fuel production. NEMS is a modeling system that
simulates the behavior of energy markets and their interactions with
the U.S. economy by explicitly representing the economic decision-
making involved in the production, conversion, and consumption of
energy products. NEMS can reflect the secondary impacts that greater
renewable fuel use may have on the prices and quantities of other
sources of energy, and the greenhouse gas emissions associated with
these changes in the energy sector. It was not possible to complete
this analysis in time for the NPRM
    While EPA is using state-of-the-art tools available today for each
of the lifecycle components considered, using multiple models
necessitates integrating these models and, where possible, applying a
common set of assumptions. As discussed later in this section, this is
particularly important for the two agricultural sector models, FASOM
and FAPRI, which are being used in combination to describe the
agricultural sector impacts domestically and internationally. As
described in more detail in the DRIA Chapter 5, we have worked with the
FAPRI and FASOM models to align key assumptions. As a result, the
projected agricultural impacts described in Section IX are relatively
consistent across both models. One outstanding issue is the differences
between the modeling results associated with increased soybean-based
biodiesel production. We intend to further refine the soybean biodiesel
scenarios for the final rule. Additional details on all of the models
used can be found in DRIA Chapter 2. Finally, as noted earlier, we are
planning to have a number of aspects of our modeling framework peer
reviewed before finalizing these regulations. In the sections below, we
have identified specific peer review plans.
4. Treatment of Uncertainty
    While EPA believes the methodology presented here represents a
robust and scientifically credible approach, we recognize that some
calculations of GHG emissions are relatively straight-forward, while
others are not. The direct, domestic emissions are relatively well
known. These estimates are based on well-established process models
that can relatively accurately capture

emissions impacts. For example, the energy and GHG emissions used by a
natural gas-fired ethanol plant to produce one gallon of ethanol can be
calculated through direct observations, though this will vary somewhat
between individual facilities. The indirect domestic emissions are also
fairly well understood; however, these results are sensitive to a
number of key assumptions (e.g., current and future corn yields). We
address uncertainty in this area by testing the impact of changing
these assumptions on our results. Finally, the indirect, international
emissions are the component of our analysis with the highest level of
uncertainty. For example, identifying what type of land is converted
internationally and the emissions associated with this land conversion
are critical issues that have a large impact on the GHG emissions
estimates. We address this uncertainty by using sensitivity analyses to
test the robustness of the results based on different assumptions. We
also identify areas of additional work that will be completed prior to
the final rulemaking. For example, while we utilized an approach using
comprehensive agricultural sector models and recent satellite data to
determine the emissions resulting from international land use impacts,
we are also considering an alternative methodology (the analyses using
GTAP) that estimates changes in land use based on the relative land use
values of cropland, forest, and pastureland. Additionally, we are
considering country-specific information which may allow us to better
predict specific trends in land use such as the degree to which
marginal or abandoned pasture land will need to be replaced if used
instead for crop production. In addition to the sensitivity analysis
approach, we will also explore options for more formal uncertainty
analyses for the final rule to the extent possible. However, formal
uncertainty analyses generally include an assumption of a statistically
based distribution of likely outcomes. In the time available for
developing this proposal, we have not developed an analytical technique
which allows us to determine the likelihood of a range of possible
outcome across the wide range of critical factors affecting lifecycle
GHG assessment. We specifically ask for recommendations on how best to
conduct a sound, statistically based uncertainty analysis for the final
rule.
    Despite the uncertainty associated with international land use
change, we would expect at least some international land use change to
occur as demand for crop land increases as a result of this rule.
Furthermore, the conversion of crop land will lead to GHG emission from
land conversion that must be accounted for in the calculation of
lifecycle GHG emissions. As discussed above, we believe that
uncertainty in the effects and extent of land use changes is not a
sufficient reason for ignoring land use change emissions. Although
uncertainties are associated with these estimates, it would be far less
scientifically credible to ignore the potentially significant effects
of land use change altogether than it is to use the best approach
available to assess these known emissions. We anticipate that comment
and information received in response to this proposal as well as
additional analyses will improve our assessment of land use impacts for
the final rule. Finally, we note that further research on key variables
will result in a more robust assessment of these impacts in the future.
5. Components of the Lifecycle GHG Emissions Analysis
    As described previously, GHG emissions from many stages of the full
fuel lifecycle are included within the system boundaries of this
analysis. Details on how these emissions were calculated are included
in the DRIA Section 2. This section highlights the key questions that
we have attempted to address in our analysis. In addition, this section
identifies some of the key assumptions that influence the GHG emissions
estimates in the following section.
a. Feedstock Production
    Our analysis addresses the lifecycle GHG emissions from feedstock
production by capturing both the direct and indirect impacts of growing
corn, soybeans, and other renewable fuel feedstocks. For both domestic
and international agricultural feedstock production, we analyzed four
main sources of GHG emissions: agricultural inputs (e.g., fertilizer
and energy use), fertilizer N2O, livestock, and rice
methane. (Emissions related to land use change are discussed in the
next section).
    As described in Section IX.A, EPA uses FASOM to model domestic
agricultural sector impacts and uses FAPRI to model international
agricultural sector impacts. However, we also recognize that these
emission estimates rely on a number of key assumptions, including crop
yields, fertilizer application rates, use of distiller grains and other
co-products, and fertilizer N2O emission rates. As described
in the following sections, we have used sensitivity analyses to test
the impact of changing these assumptions on our results.

i. Domestic Agricultural Sector Impacts

    Agricultural Sector Inputs: GHG emissions from agricultural sector
inputs (chemical and energy) are determined based on output from FASOM
combined with default factors for GHG emissions from GREET. Fuel use
emissions from GREET include both the upstream emissions associated
with production of the fuel and downstream combustion emissions. Inputs
are based on historic rates by region and include projected increases
to account for yield improvements over time. This yield increase does
not capture changes due to cropping practices such as shifts to corn-
after-corn rotations.
    N2O Emissions: FASOM estimates N2O emissions
from fertilizer application and nitrogen fixing crops based on the
amount of fertilizer used and different regional factors to represent
the percent of nitrogen (N) fertilizer applied that result in
N2O emissions. This approach is consistent with IPCC
guidelines for calculating N2O emissions from the
agricultural sector.\272\ A recent paper \273\ raised the question of
whether N2O emissions are significantly higher than
previously estimated. To better understand this issue, we are working
with Colorado State University to analyze N2O emissions.
Specifically, Colorado State University will provide several key
refinements for a re-analysis of land use and cropping trends and GHG
emissions in the FASOM assessment, including:
---------------------------------------------------------------------------

    \272\ 2006 Intergovernmental Panel on Climate Change (IPCC)
Guidelines for National Greenhouse Gas Inventories, Volume 4,
Chapter 11, N2O emissions from Managed Soils, and
CO2 Emissions from lime and Urea Application. See http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html.
    \273\ Crutzen et al., 2008.
---------------------------------------------------------------------------

     Direct N2O emissions based on DAYCENT
simulations with an accounting of all N inputs to agricultural soils,
including mineral N fertilizer, organic amendments, symbiotic N
fixation, asymbiotic N fixation, crop residue N, and mineralization of
soil organic matter. Colorado State University will provide (1) the
total emission rate on an acre basis for each simulated bioenergy crop
in the 63 FASOM regions and (2) a total emissions for each N source.
     Indirect N2O emissions on a per acre basis
using results from DAYCENT simulations of volatilization, leaching and
runoff of N from each bioenergy crop included in the analysis for the
63 FASOM regions, combined with IPCC

factors for the N2O emission associated with the simulated N
losses.
    The analyses with updated N2O estimates are not yet
complete and are not included in this proposal. We expect to complete
these analyses for the final rule.
    Livestock Emissions: GHG emissions from livestock have two main
sources: enteric fermentation and manure management. Enteric
fermentation produces methane emissions as part of the normal digestive
processes in animals. The FASOM modeling reflects changes in livestock
enteric fermentation emissions due to changes in livestock herds. As
more corn is used in producing ethanol the price of corn increases,
driving changes in livestock production costs and demand. The FASOM
model predicts reductions in livestock herds. IPCC factors for
different livestock types are applied to herd values to get GHG
emissions. The management of livestock manure can produce methane and
N2O emissions. Methane is produced by the anaerobic
decomposition of manure. N2O is produced as part of the
nitrogen cycle through the nitrification and denitrification of the
organic nitrogen in livestock manure and urine. FASOM calculates these
manure management emissions based on IPCC default factors for emissions
factors from the different types of livestock and management methods.
Manure management emissions are projected to be reduced as a result of
lower livestock animal numbers. Use of distiller grains (DGs), as
discussed in Section VI.B.5.b, has been shown to decrease methane
produced from enteric fermentation if replacing corn as animal
feed.\274\ This effect is not currently captured in the models but will
be considered for the final rule.
---------------------------------------------------------------------------

    \274\ Salil Arora, May Wu, and Michael Wang, ``Update of
Distillers Grains Displacement Ratios for Corn Ethanol Life-Cycle
Analysis,'' September 2008. See http://www.transportation.anl.gov/pdfs/AF/527.pdf.
---------------------------------------------------------------------------

    Methane from Rice: Most of the world's rice, and all rice in the
United States, is grown in flooded fields. When fields are flooded,
aerobic decomposition of organic material gradually depletes most of
the oxygen present in the soil, causing anaerobic soil conditions. Once
the environment becomes anaerobic, methane is produced through
anaerobic decomposition of soil organic matter by methanogenic
bacteria. FASOM predicts changes in rice acres resulting from the RFS2
program and calculates changes in methane emissions using IPCC factors.

ii. International Agricultural Sector GHG Impacts

    Agricultural Sector Inputs: The FAPRI model does not directly
provide an assessment of the GHG impacts of changes in international
agricultural practices (e.g., changes in fertilizer load and fuels
usage), however it does predict changes in the land area and production
by crop type and by country. We therefore determined international
fertilizer and energy use based on international data collected by the
Food and Agriculture Organization (FAO) of the United Nations and the
International Energy Agency (IEA). We used the historical trends based
on these FAO and IEA data to project chemical and energy use in 2022.
Additional details on the data used are included in DRIA Chapter 2. We
intend to review input changes required to increase yields for the
final rule and request comment on the extent to which historic trends
adequately project what could occur in 2022 or what alternative
assumptions should be made and the bases for these assumptions. For
example, will changes in farming practices or seed varieties likely
result in significantly different impacts on fertilizer use
internationally than suggested by recent trends? Additionally, we
intend to have the selection and application of this data peer reviewed
before the final rule.
    N2O Emissions: For international N2O
emissions from crops, we apply the IPCC emissions factors based on
total amount of fertilizer applied and N2O impacts of crop
residue by type of crop produced. As noted above, we are also working
with Colorado State University to update these factors as part of the
final rule analysis. Additional details on the factors used are
included in DRIA Chapter 2.
    Livestock Emissions: Similar to domestic livestock impacts
associated with an increase in biofuel production, FAPRI model predicts
international changes in livestock production due to changes in
commodity prices. The GHG impacts of these livestock changes, including
enteric fermentation and manure management GHG emissions, were included
in our analysis. Unlike FASOM, the FAPRI model does not have GHG
emissions built in and therefore livestock GHG impacts were based on
activity data provided by the FAPRI model (e.g., number and type of
livestock by country) multiplied by IPCC default factors for GHG
emissions. We seek comments on the extent to which the use of this
methodology is appropriate.
    Rice Emissions: To estimate rice emission impacts internationally,
we used the FAPRI model to predict changes in international rice
production as a result of the increase in biofuels demand in the U.S.
Since FAPRI does not have GHG emissions factors built into the model,
we applied IPCC default factors by country based on predicted changes
in rice acres. We seek comments on this methodology.
b. Land Use Change
    We are also addressing GHG emissions associated with land use
changes that occur domestically and internationally as a result of the
increase in renewable fuels demand in the U.S. Key questions we address
in this analysis include the land area converted to crop production,
where those acreage changes occur, lands types converted, and the GHG
emission impacts associated with different types of land conversion.
    EPA recognizes that analyzing international impacts of land use
change can introduce additional uncertainty to the GHG emissions
estimates. At this time, we do not have the same quality of data for
international crop production and projected future trends as we do for
the United States. For example, prediction of the economic and
geographic development of developing country agricultural systems is
far more difficult than prediction of future U.S. agricultural
development. The U.S. has a very mature agriculture system in which the
high quality agricultural lands are already under production and the
infrastructure to move crops to market is already in place. This is not
necessarily the case in other countries. Some very large countries
expected to play a significant role in future agricultural production
are still developing their agricultural system. Brazil, for example,
has vast areas of land that may be suitable for commercial agricultural
production that would allow for significant expansion in crop lands.
One of the restraints on expansion is the relative lack of
infrastructure (e.g., road and rail systems) that would allow shipment
of expanded crop production to market. Identifying what type of land is
converted internationally and the emissions associated with this land
conversion can significantly affect our assessment of GHG impacts. We
present a range of results for differences in these and other
assumptions in Section VI.C.2, and we seek comment on our approach so
that the final rule will use the best science to provide credible
estimates of lifecycle GHG emissions for each biofuel.

i. Amount of Land Converted

    The main question regarding the amount of new land needed to meet
an increasing demand for biofuels hinges on assumptions about the
intensification of existing production versus expansion of production
to other lands. This interaction is driven by the relative costs and
returns associated with each option, but there are other factors as
described below.
    Co-Products: One factor determining the amount of new crop acres
required under an increased biofuel scenario is the treatment of co-
products. For example, distillers grains (DGs) are the major co-product
of dry mill ethanol production that is also used as animal feed.
Therefore, using the DGs as an animal feed to replace the use of corn
tends to offset the loss of corn to ethanol production, and reduces the
need to grow additional corn to feed animals. As the renewable fuels
industry expands, the handling and use of co-products is also
expanding. Some uncertainty is associated with how these co-products
will be used in the future (e.g., whether it can be reformulated for
higher incorporation into pork and poultry diets, whether it will be
dried and shipped long distances, whether fractionation will become
widespread).
    Both our FASOM and FAPRI models account for the use of DGs in the
agricultural sector. The FASOM and FAPRI models both assume that a
pound of co-product would displace roughly a pound of feed. However, a
recent paper by Argonne National Laboratory \275\ estimates that 1
pound of DGs can displace more than a pound of feed due to the higher
nutritional value of DGs compared to corn.
---------------------------------------------------------------------------

    \275\ Salil et al., 2008.
---------------------------------------------------------------------------

    The Argonne replacement ratios do not take into account the dynamic
least cost feed decisions faced by livestock producers. The actual use
of DGs will depend on the maximum inclusion rates for each type of
animal (based on the digestibility of DGs), the displacement ratio for
each type of animal (based on DGs energy and protein content), and the
adoption rate (based on the feed value relative to price). Furthermore,
as world vegetable oil prices increase, dry mill ethanol producers will
have an incentive to extract the corn oil from the DGs. This step
changes the nutritional content of the DGs, which results in different
replacement rates than the ones currently used in FASOM or described by
Argonne. As we plan to evaluate and incorporate a least cost feed
rationing approach for the final rule, we invite comment on the
expected future uses of DGs and their displacement ratios.
    Crop Yields: Assumptions about yields and how they may change over
time can also influence land use change predictions. Domestic yields
were based on USDA projections, extrapolated out to 2022. In 2022, we
estimate that the U.S. average corn yield will be approximately 180
bushels/acre (a 1.6% annual increase consistent with recent trends) and
average U.S. soybean yields will be approximately 50 bushels per acre
(a 0.4% annual increase).\276\ Using the FASOM model, we conducted a
sensitivity analysis on the impact of higher and lower yields in the
U.S. Details on this scenario are included in DRIA Chapter 5.1.
International yields changes are also based on the historic trends. The
FAPRI model contains projected yields and yield growths that are
generally lower in other countries compared to the U.S. We request
comment on the projected increase in crop yields in the U.S (including
consideration of how emerging seed types might be expected to increase
average crop yields). We also request comment on the use of historical
trends to predict future agricultural production in other countries and
request information on alternative methodologies and supporting data
that would allow us to base our predictions on alternative assumptions.
---------------------------------------------------------------------------

    \276\ Note that these same assumptions apply in both the
reference case and the control cases.
---------------------------------------------------------------------------

    The FASOM and FAPRI models currently do not take into account
changes in productivity as crop production shifts to marginal acres or
the impact of price induced yield changes on land use change. We would
expect these two factors could work in opposite directions and
therefore could tend to offset each other's impacts. Marginal acres in
fully developed agricultural systems are expected to have lower yields,
because most productive acres are already under cultivation. This may
not be the case in developing systems where prime agricultural lands
are not currently in full production due to, for example, lack of
supporting infrastructure. Changes in agricultural inputs (e.g.,
fertilizer, pesticides) can also change crop yield per acre. Higher
commodity prices might provide an incentive to increase production in
existing acres. If the costs of increasing productivity on existing
land were minimal relative to the value of the increased production,
then agricultural landowners would presumably adopt these productivity-
enhancing actions under the reference case. Although it is reasonable
to assume a trend wherein some productivity-enhancing practices may
become profitable if commodity prices are high enough such as might
occur as the result of increased biofuel production, it is not clear
that farmers would find significant increases in production per acre
profitable. If crop yields either domestically or international are
significantly impacted by higher commodity prices driven by general
increase in worldwide demand, this could affect our assessment of land
use impacts and the resulting GHG emissions due to increased biofuel
demand in the U.S. However, as described in Section IX, the change in
commodity prices associated with the increase in U.S. biofuel as a
result of the EISA mandates are very small and perhaps not large enough
to induce significant increased yield changes. We invite comment on
projected yields and the potential impact of increased use of marginal
lands and price induced yield changes. For the final rule we plan to
explicitly model the impact of price induced yield changes.
    Land Conversion Costs: The assumed cost associated with different
types of land conversion can also play a key role in determining how
much land will be converted. In FASOM, the decision to convert land
from pasture or forest to cropland is based on whether the landowner
can increase the net present value of expected returns through
conversion (including any costs of conversion). Among other things, the
decision to convert land depends on regional yields, costs, and other
factors affecting profitability and on the returns to alternative land
uses. In other words, FASOM assumes that land conversion is based on
maximizing profits rather than minimizing costs. Additional details on
land conversions costs incorporated in FASOM are included in DRIA
Chapter 2.
    FAPRI does not explicitly model land conversion costs, however the
international production supply curves used by the FAPRI model
implicitly take into account conversion costs. FAPRI's supply curves
are based on historical responses to price changes, which take into
account the conversion costs of land, based on expected future returns
associated with land conversion. Thus, we believe that our assessments
of international land use changes are based on economic land-use
decisions.
ii. Where Land is Converted
    The first step in determining what domestic and international land
will be converted due to biofuels production is to estimate the extent
to which the increased demand for biofuel feedstock

will be met through increased U.S. agriculture production or reductions
in U.S. exports.
    This question has several implications. For example, U.S.
agriculture production is typically more energy and input intensive but
has higher yields than agricultural production in other parts of the
world. This implies that increased production in the U.S. has higher
input GHG emission impacts but lower land use change impacts compared
to overseas production. In addition, the types of land where
agriculture would expand would be different in the U.S. vs. other parts
of the world.
    EPA's analysis relies on FASOM predictions to represent changes in
the U.S. agricultural sector, including land use, and on FAPRI to
predict the resulting international agricultural sector impacts
including the amount of additional cropland needed under different
scenarios. The impact on the international agricultural sector is
highly dependent on the U.S. export assumptions. As the FASOM model was
used to represent domestic agricultural sector impacts with an assumed
export picture, the international agricultural sector impacts from
FAPRI needed to be based on a consistent set of export assumptions. We
worked with FASOM and FAPRI modelers to ensure this consistency. This
involved coordinating crop yields, ethanol yields and co-product use,
assumptions regarding CRP acres, a consistent export response, and a
consistent livestock demand and feed use in both models.
    As shown in Chapter 2 of the DRIA, coordination of assumptions has
generated a consistent export picture response from both the FASOM and
FAPRI model for the majority of biofuel and feedstock scenarios
considered. Differences in responses in the biodiesel scenario remain
between the two models. FASOM assumes more biodiesel will come from new
soybean acres (but total domestic acres are relatively constant as
reductions in other crops offset the increase in soybean acres). In
comparison, FAPRI contains more types of oil seed crops and has a more
elastic demand in the soybean oil market. The FAPRI model also allows
for some corn oil fractionation from DGs, which can be used as a
substitute for soybean oil. The FASOM model predicts a larger change in
net exports than the FAPRI model. Since we are using the FAPRI model as
the basis for estimating international land use changes, we may be
underestimating the international land use change emissions associated
with soybean based biodiesel. For the final rule, EPA will work, in
particular, to resolve the differences in soybean production impact
between the models. This, too, may modify our assessment of the
biodiesel lifecycle GHG emissions.
    Due to the wide range of carbon and biomass properties associated
with land in different parts of the world, the location of crop
conversion is also important to our lifecycle analysis. For example, an
average acre of forest in Southeast Asia stores a much larger quantity
of carbon than a typical acre of forest in Northern Europe. The FAPRI
model provides estimates of the acreage change by country and crop that
result from a decrease in U.S. exports due to the increase in U.S.
biofuel demand. These estimates are based on historic responsiveness to
changes in prices in other countries. Implicit in these supply curves
are the costs associated with converting new land to crop production
and the relative competitiveness of each country to increase production
based on production costs, yields, transportation costs, and currency
fluctuations. FAPRI also includes in its baseline projections of future
population growth, GDP growth, and other macroeconomic changes. FAPRI
also takes into account the fact that not all U.S. exports will need to
be made up in international production, as there is a small decreases
in demand due to shifts in crop production and higher prices.

iii. What Type of Land is Converted

    In the same way that the location of land conversion is important,
the type of land that is converted is critical to the magnitude of
impact on the GHG emissions associated with biofuel production. For
example, the conversion of rainforest results in a much larger increase
in GHG emissions than the conversion of grassland. There are several
options for determining what type of land will be converted to crop
acreage. One option is to model land rental rates for different types
of land (e.g., forest, pasture, and crop production), and allow the
model to choose the type of land that is expected to have the highest
net returns. This approach is used by FASOM on the domestic side.
Another option is to use historical land conversion trends in a given
country or region. The FAPRI/Winrock approach uses this approach for
international land use conversion.
    Domestic: The FASOM model includes competition between land types,
agriculture, pasture, and forest land. The interaction is based on
providing the highest rate of return across the different land types.
Therefore domestically we have the ability to explicitly model what
types of land would be converted to increased agriculture based on the
rates of return for different land types for the 63 regions in FASOM.
For this draft proposal we incorporated the agricultural component
(which includes both existing cropland and pasture) of the FASOM model,
but not the forestry component (see Section IX.A for explanation).
Therefore, this analysis assumes that all additional cropland predicted
by FASOM comes from pasture. As we incorporate the forestry component
for the final rule analysis we would expect to see more interaction
between the forestry and agriculture sector such that there may be
conversion of forest to agriculture based on additional cropland
needed. While we do not know if forest will be converted to cropland or
the extent that this might occur, if domestic forests were converted to
cropland, we would expect domestic GHG emissions would increase. This
work will be incorporated for our final rule.
    International: Basing land use change on the economics and rates of
return of different land uses offers advantages for capturing potential
future land use changes. However, the only model potentially capable of
fully incorporating this calculation internationally, GTAP, is still in
the process of being updated and modified for this purpose. Thus, EPA
has chosen to use historical patterns as identified by satellite images
to estimate future land conversion. This approach is referred to here
as the FAPRI/Winrock approach because it relies on the integration of
each of these tools.
    EPA believes that FAPRI/Winrock is a scientifically credible
modeling approach at this time. However, we will continue to work with
the GTAP model to help test the results generated by our primary
approach.

FAPRI/Winrock

    Since FAPRI does not contain information on what type of land is
being converted into cropland, we worked with Winrock International, a
global nonprofit organization, to address this question. A key
advantage of Winrock is that they can accurately measure and monitor
trends in forest and land use change, forest carbon content,
biodiversity, and the impact of infrastructure development.
Furthermore, several of the Winrock staff were involved in the
development of the IPCC land use change good practice guidance and are
widely recognized as the leaders in this field.
    Using satellite data from 2001-2004, Winrock provided a breakdown
of the types of land that have been converted

into cropland for a number of key agriculturally producing countries
based on the International Geosphere-Biosphere Programme (IGBP).\277\
The IGBP land cover list includes eleven classes of natural vegetation,
three classes of developed and mosaic lands, and three classes of non-
vegetated lands. The natural vegetation units distinguish evergreen and
deciduous, broadleaf and needle-leaf forests, mixed forests, where
mixtures occur; closed shrublands and open shrublands; savannas and
woody savannas; grasslands; and permanent wetlands of large areal
extent. The three classes of developed and mosaic lands distinguish
among croplands, urban and built-up lands, and cropland/natural
vegetation mosaics. Classes of non-vegetated land cover units include
snow and ice; barren land; and water bodies. Winrock aggregated these
categories into five similar classes: five classes of forest were
combined into one, two classes of savanna were combined into one, and
two classes of shrubland were combined into one. The final land cover
categories for this analysis are forest, cropland, grassland, savanna,
and shrubland. The rest of the IGBP categories not of interest to this
analysis were reclassified into the background. The satellite data
represents different types of land cover, which we are using as a proxy
for land use.
---------------------------------------------------------------------------

    \277\ U.S. Geological Survey MODIS Data Set Documentation. See
http://edcdaac.usgs.gov/modis/mod12q1v4.asp.
---------------------------------------------------------------------------

    A key strength of this approach is that satellite information is
based on empirical data instead of modeled predictions. Furthermore, it
is reasonable to assume that recent land use changes have been driven
largely by economics and recent historical patterns will continue in
the future absent major economic or land use regime shifts caused, for
example, by changes in government policies. We are using the FAPRI
model to predict where in the world, based on economic conditions, the
most likely increase in agriculture production will occur as a result
of the EISA mandates. We are then using the historical satellite data
to address the key question: If additional land is needed for crop
production in a particular country, what type of land will be used? The
Winrock analysis addresses this question by calculating the weighted
average type of land that was converted to cropland between 2001 and
2004. Essentially, we are using the Winrock data to determine the type
of land that is most likely to be converted to cropland, should
additional acres be needed as predicted by FAPRI.
    Table VI.B.5-1 shows the percentage of land converted to cropland
between 2001 and 2004 according to the Winrock satellite data analysis
for the countries currently available. We use these percentages to
calculate a weighted average of the types of land converted into
cropland. For example, if FAPRI predicts that one additional acre of
cropland will be brought into production in Argentina, we used the
Winrock data to estimate that 8% on average of that acre will come from
forest, 40% of that acre will come from grassland, 45% of that land
will come from savanna, and 8% of that land will come from shrubland.
Using GTAP might result in different percentage weights.

                         Table VI.B.5-1--Types of Land Converted to Cropland by Country
                                                  [In percent]
----------------------------------------------------------------------------------------------------------------
                     Country                          Forest         Grassland        Savanna          Shrub
----------------------------------------------------------------------------------------------------------------
Argentina.......................................               8              40              45               8
Brazil..........................................               4              18              74               4
China...........................................              17              38              23              21
EU..............................................              27              16              36              21
India...........................................               7               7              33              53
Indonesia.......................................              34               5              58               4
Malaysia........................................              74               3              19               3
Nigeria.........................................               4              56              36               4
Philippines.....................................              49               5              44               3
South Africa....................................              10              22              53              15
----------------------------------------------------------------------------------------------------------------
Source: Winrock Satellite Data (2001-2004).

    We are assuming that the weighted average, resulting from
agriculture demand as well as other possible drivers, is a reasonable
estimate of the land use change attributable to increased agricultural
demand. A shortcoming of this approach is that it assumes that when new
crop acres are needed to meet increased agricultural demand these crop
acres will follow the average pattern of recent historical land
conversion, recognizing that this pattern is based on a variety of
drivers of land use change, not all of which are associated with
agricultural demand. This approach is not able to isolate from the
historical pattern the land use changes stemming just from increased
agricultural demand. For example, it is likely that in some cases trees
are being removed from forests for the value of the wood. However,
having removed valuable wood, additional clearing may occur to allow
the land to be used for pasture or cropland. In that case the GHG
emissions associated with the removal of the trees would not occur as a
consequence of increased agricultural demand, but as a consequence of
increased demand for the wood, while the GHG emissions associated with
the additional clearing would occur as a consequence of the
agricultural demand.
    As a result, the Winrock data also does not distinguish between the
land-use impacts associated with one crop versus another. Indeed, at
least in the case of sugarcane production in Brazil, a number of
researchers argue that expanded sugarcane production is likely to occur
in significant part through the use of degraded or abandoned pasture
land without additional land use impact.\278\ These research reports
suggest that general historical trends in land use change to grow crops
in Brazil may not pertain to expected growth in sugarcane production.
Ideally, an analysis of a U.S. biofuels policy's influence on land use
change would

model the marginal impact that U.S. biofuel would have on land use and
land use change in addition to baseline land use change. Use of
historic land use change data is capturing some of this baseline land
use change. Comments are requested on our approach of assuming
historical land use changes will continue to be followed in response to
increased agricultural demand associated with our biofuel policy. We
also invite comment on what alternative methodologies and data are
available, if any, to better link the impacts of biofuels to land use
change. To the extent additional information or data may be available
for certain countries such as the example of Brazil, we also ask how
this country-specific data and similar information might best be
integrated with the modeling results otherwise available. Furthermore,
to the extent different government policies can shift land use patterns
(e.g., through regulations, financial supports), these weighted
averages could change in the future. We request comment on whether
these government policies and regulations should be incorporated into
the future land use change calculations and the best methodology for
taking into account these changes.
---------------------------------------------------------------------------

    \278\ See for example ``Mitigation of GHG emissions using
sugarcane bio-ethanol--Working Paper'' by Isaias C. Macedo and
Joaquim E. A. Seabra, and ``Prospects of the Sugarcane Expansion in
Brazil: Impacts on Direct and Indirect Land Use Changes--Working
Paper'' by Andre Nassar et al., both received by EPA October 13,
2008.
---------------------------------------------------------------------------

    The Winrock data and analyses present an aggregate picture of land
use changes; they cannot predict the nature of the land use change that
will result due to an additional acre of corn planted in a country
versus an additional acre of sugarcane or soybeans. In reality,
sugarcane may be more suitable for planting in different regions with
different soil types compared to corn or soybeans. However, because we
are using weighted averages to characterize the type of land that is
converted to crop acres, all additional crop acres in a particular
country are treated identically.
    Winrock also provides information on land conversions between other
categories (e.g., forest to savanna). For one set of GHG analyses, we
assumed that land taken out of actively managed use \279\ (e.g.,
pasture used for livestock production) would have to be replaced with
new pasture acreage, thereby capturing some of the domino effect
associated with converting previously productive land into cropland.
Therefore, in addition to land conversion shown in Table VI.B.5-1, we
also include land conversion to replace some of the grassland and
savanna that is used as pasture. An alternative approach would be to
assume that no additional land is necessary, since there is a
significant amount of pastureland that could be used more intensively
for grazing purposes. For example, as noted above, in Brazil almost all
of the direct land conversion associated with expanding sugarcane
production is coming out of existing pasture land, in some cases,
depleted, low value pasture land, that may have relatively low levels
of stored carbon compared to other land. Also in Brazil there is a
trend toward more intensive use of existing pasture land by grazing
higher numbers of cattle per unit of pasture, decreasing the need to
replace pasture converted to cropland. This more intensive use of
pasture is encouraged by two factors: improved grasses which can
sustain more intensive grazing and lack of transportation
infrastructure which tends to constrain geographic expansion of pasture
lands. However, we also note that depleted cropland in Brazil might
also be suitable for other crop production. To extend sugarcane limits
to production of these other crops on this land, the indirect effect
could be that these crops move into other areas of Brazil and resulting
in increased emissions due to land use change. We invite comment on
alternative methodologies for predicting land use changes in particular
in other countries. Some alternative methodologies are described in
more detail in Chapter 2 of the DRIA.
---------------------------------------------------------------------------

    \279\ GTAP Land Cover Data (2000-2001).
---------------------------------------------------------------------------

    The FAPRI model results have been used in peer reviewed literature
in conjunction with satellite data to assess land use changes \280\ and
we also believe it is an appropriate method for projecting biofuel
induced land use changes. However, we recognize the uncertainty
associated with this approach and, in addition to seeking public
comment, we plan to conduct an expert peer review of the data and
methods used, including the appropriateness of using historic satellite
data to project future land use changes.
---------------------------------------------------------------------------

    \280\ Searchinger et al., 2008.
---------------------------------------------------------------------------

iv. What Are the GHG Emissions Associated With Different Types of Land
Conversion?
    Our estimates of domestic land use change GHG emissions are based
on outputs of the FASOM model. As we are just using the agricultural
portion of the FASOM model for this analysis the land use change GHG
emissions are limited to changes in land use for existing crop and
pasture land. Some of that crop land could currently be fallow and some
of the pasture land could currently be unused. However, no new crop or
pasture land (beyond some Conservation Reserve Program (CRP) land due
to legislative changes in the program) is added compared to current
levels. Thus FASOM only models shifts in the use of this land.
    Changes in the agricultural sector due to increased corn used for
ethanol have impacts on land use change in a number of ways. FASOM
explicitly models change in soil carbon from increased crop production
acres and from different types of crop production. FASOM also models
changes in soil carbon from converting non crop land into crop
production. Land converted to crop land could include pasture land. As
recommended by USDA, we are assuming that 32 million acres of CRP land
will remain in that program even if crop prices increase and thus
increase land values. This assumption is consistent with the 2008 Farm
Bill, which limits CRP acres to 32 million. A sensitivity analysis on
this assumption is included in Chapter 5 of the DRIA.
    For the international impacts, we used the 2006 IPCC Agriculture,
Forestry, and Other Land Use (AFOLU) Guidelines \281\ and the Winrock
provided GHG emissions factors for each country based on the weighted
average type of land converted. GHG emissions estimates were based on
immediate releases (e.g., changes in biomass carbon stocks, soil carbon
stocks, and non-CO2 emissions assuming the land is cleared
with fire) and foregone forest sequestration (the future growth in
vegetation and soil carbon). Additional details on these calculations
are included in Chapter 2 of the DRIA. For the emissions factors
presented, we assume forests cleared would have continued to sequester
carbon for another 80 years, based on the amount of time it takes for
forests to reach a general equilibrium stage. We request comment on
whether it is appropriate to include foregone sequestration in the GHG
emissions estimates. Carbon soil calculations \282\ take into account
the annual changes in carbon content in the top 30 centimeters of soil
over the first 20 years, based on IPCC recommendations.\283\ We also
request comment on whether soil carbon calculations should be based on
the top 30 centimeters of soil. These emission factors do not include
credits for harvested wood products, based on the expectation that they
would have a

very small impact on our estimates of land use change emissions.
However, we intend to analyze the impact of wood product credits for
the final rule. We invite comment on whether it is appropriate to
include wood product credits in the GHG emissions estimates.
---------------------------------------------------------------------------

    \281\ 2006 IPCC Guidelines for National Greenhouse Gas
Inventories, Volume 4, Agriculture, Forestry and Other Land Use
(AFOLU). See http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html.
    \282\ See ftp://www.daac.ornl.gov/data/global_soil/IsricWiseGrids.
    \283\ 2006 IPCC Guidelines for National Greenhouse Gas
Inventories, Volume 4, Section <IP_ADDRESS>.
---------------------------------------------------------------------------

    GHG emissions associated with land use changes vary significantly
based on the type of land and the geographic region. For example, the
GHG emissions associated with converting an acre of grassland to
cropland in China are lower than the emissions associated with
converting an acre of shrubland to cropland in China. Similarly, the
GHG emissions associated with converting an acre of forest to cropland
in Malaysia are larger than the emissions associated with converting an
acre of forest in Nigeria to cropland. Where country specific emission
factors were not available in time for the proposal, we used world
average. For the proposal, we focused on the countries with the largest
projected changes in crop acreage. The Winrock data currently covers
63% of total land use change acres associated with corn ethanol, 53% of
the acres associated with biodiesel, 57% of the acres associated with
switchgrass, and 87% of the acres associated sugarcane ethanol. We will
continue to add additional countries for our analysis for the final
rule. Two changes that may impact these results for the final rule
include the addition of perennial crops and the conversion on land with
peat soils. We request comment on our calculation of emission factors
due to land use change; improved data and assumptions are specifically
requested. Additionally, we plan to have the calculation of these
emissions factors reviewed by experts in this field. Details on the
Winrock estimates are included in the DRIA Chapter 2.

GTAP Approach:

    GTAP is an economy-wide general equilibrium model that was
originally developed for addressing agricultural trade issues among
countries. The databases and versions of the model are widely used
internationally.\284\ Since its inception in 1993, GTAP has rapidly
become a common ``language'' for many of those conducting global
economic analysis. For example, the WTO and the World Bank co-sponsored
two conferences on the so-called Millennium Round of Multilateral Trade
talks in Geneva. Here, virtually all of the quantitative, global
economic analyses were based on the GTAP framework. Over the past few
years, a version of the model was developed to explicitly model global
competition among different land types (e.g., forest, agricultural
land, pasture) and different qualities of land based on the relative
value of the alternative land-uses. More recently, it was modified to
include biofuel substitutes for gasoline and diesel. In simulating land
use changes due to biofuels production, GTAP explicitly models land-use
conversion decisions, as well as land management intensification. For
example, it allows for price-induced yield changes (e.g., farmers can
reallocate inputs to increase yields when commodity prices are high)
and considers the marginal productivity of additional land (e.g.,
expansion of crop land onto lower quality land as a result of the
increased use of biofuels). Most importantly, in contrast to other
models, GTAP is designed with the framework of predicting the amount
and types of land needed in a region to meet demands for both food and
fuel production. The GTAP framework also allows predictions to be made
about the types of land available in the region to meet the needed
demands, since it explicitly represents different land types within the
model.
---------------------------------------------------------------------------

    \284\ https://www.gtap.agecon.purdue.edu.
---------------------------------------------------------------------------

    The global modeling of land-use competition and land management
decisions is relatively new, and evolving.\285\ GTAP does not yet
contain cellulosic feedstocks in the model. In addition, GTAP does not
currently contain unmanaged land, which could be a major factor driving
current GTAP land use projections and is a significant potential source
of GHG emissions. We expect to update GTAP with cellulosic feedstocks
and unmanaged land in time for the final rule.
---------------------------------------------------------------------------

    \285\ See Hertel, Thomas, Steven Rose, Richard Tol (eds.), (in
press). Economic Analysis of Land Use in Global Climate Change
Policy, Routledge Publishing.
---------------------------------------------------------------------------

    Our proposal is therefore based on the FAPRI/Winrock estimates.
There are advantages and disadvantages associated with any model choice
and we have chosen the FAPRI/Winrock combination as the best approach
available for preparing the proposal. Although we have not relied on
the current version of GTAP for the principal analyses in this
proposal, others have used versions of the current model to assess land
use changes which could result from expanded biofuel demand. The
California Air Resources Board as part of the analysis for their low
carbon fuel standard used GTAP to model indirect land use change for
biofuels. More information on their program and GTAP analysis can be
found at http://www.arb.ca.gov/fuels/lcfs/lcfs.htm. Furthermore,
researchers from Purdue University have released a report on work using
GTAP to look at land use change associated with corn ethanol production
scenarios.\286\ This work was partially funded by Argonne National Lab
for possible inclusion in the GREET model. We anticipate additional
refinements will be made to the GTAP model between the proposal and
final rule and we will include this information and results in the
docket as they become available. We invite comments in this NPRM on the
use of the GTAP model in helping to establish the GHG emissions
estimates for the final rule.
---------------------------------------------------------------------------

    \286\ Land Use Change Carbon Emissions due to US Ethanol
Production, Wallace E. Tyner, Farzad Taheripour, Uris Baldos,
January 2009. Available at http://www.agecon.purdue.edu/papers/biofuels/Argonne-GTAP_Revision%204a.pdf.
---------------------------------------------------------------------------

v. Assessing GHG Emissions Impacts Over Time and Potential Application
of a GHG Discount Rate
    When comparing the lifecycle GHG emissions associated with biofuels
to those associated with gasoline or diesel emissions, it is critical
to take into consideration the time profile associated with each fuel's
GHG emissions stream. With gasoline, a majority of the lifecycle GHG
emissions associated with extraction, conversion, and combustion are
likely to be released over a short period of time (i.e., annually) as
crude oil is converted into gasoline or diesel fuel which quickly pass
to market. This means that the lifecycle GHG emissions of a gallon of
gasoline produced one year are unlikely to vary much from the lifecycle
GHG emissions of a similar gallon of gasoline produced in a subsequent
year.
    In contrast, the lifecycle GHG emissions from the production of a
typical biofuel may continue to occur over a long period of time. As
with petroleum based fuels, renewable fuel lifecycle GHG emissions are
associated with the conversion and combustion of biofuels in every year
they are produced. In addition, GHG emissions could be released through
time if new acres are needed to produce corn, soybeans or other crops
as a replacement for crops that are directly used for biofuel
production or displaced due to biofuels production. The GHG emissions
associated with converting land into crop production would accumulate
over time with the largest release occurring in the first few years due
to clearing with fire or biomass decay. After the land is converted,
moderate amounts of soil carbon would continue to be released for

approximately 20 years.\287\ Furthermore, there would be foregone
sequestration associated with forest clearing as this forest would have
continued to sequester carbon had it not been cleared for approximately
80 years.
---------------------------------------------------------------------------

    \287\ Following Section <IP_ADDRESS> of the IPCC AFOLU guidelines,
the total difference in soil carbon stocks before and after
conversion was averaged over 20 years.
---------------------------------------------------------------------------

    Therefore, we have included an analysis which considers GHG
emissions from land use change that may continue for up to 80 years,
based on our estimate of the average length of foregone sequestration
after a forest is cleared. Annual foregone sequestration rates were
estimated by ecological region using growth rates for forests greater
then 20 years old from the 2006 IPCC guidelines for Agriculture,
Forestry and Other Land Use.\288\ Studies have estimated that new
forests grow for 90 years to over 120 years.\289\ More recent estimates
suggest that old growth forests accumulate carbon for up to 800
years.\290\ The foregone sequestration methods used in this proposal
are within the range supported by the scientific literature and the
2006 IPCC guidelines. Details of the foregone sequestration estimates
are included in DRIA Chapter 2. We seek comment on our estimate of the
average length of annual foregone forest sequestration for
consideration in biofuel lifecycle GHG analysis.
---------------------------------------------------------------------------

    \288\ Table 4.9 from the 2006 GL AFOLU was used to estimate the
lost C sequestration of forests that were converted to another land
use.
    \289\ See Greenhouse Gas Mitigation Potential in U.S. Forestry
and Agriculture, EPA Document 430-R-05-006 for a discussion of the
time required for forests to reach carbon saturation.
    \290\ Luyassert, S et al., 2008. Old-growth forests as global
carbon sinks. Nature 455: 213-215. Link: http://www.nature.com/nature/journal/v455/n7210/abs/nature07276.html.
---------------------------------------------------------------------------

    Figure VI.B.5-1 shows how lifecycle GHG emissions vary over time
for a natural gas fired dry mill corn ethanol plant assuming that all
land use change occurs in 2022. While biomass feedstocks grown each
year on new cropland can be converted to biofuels that offer an annual
GHG benefit relative to the petroleum product they replace, these
benefits may be small compared to the upfront release of GHG emissions
from land use change. Depending on the specific biofuel in question, it
can take many years for the benefits of the biofuel to make up for the
large initial releases of carbon that result from land conversion
(e.g., the payback period). As shown in Figure VI.B.5-1, the payback
period for a natural gas-fired dry mill corn ethanol plant which begins
operation in 2022 would be approximately 33 years. We present a similar
payback period calculation for the full range of biofuels analyzed in
Section VI.C.
[GRAPHIC] [TIFF OMITTED] TP26MY09.008

    As required by EISA, our analysis must demonstrate whether biofuels
reduce GHG emissions by the required percentage relative to the 2005
petroleum baseline. A payback period alone cannot answer that question.
Since the payback period alone is not sufficient for our analysis, we
have considered accounting methods for capturing the full stream of
emissions and benefits over time. There are at least two necessary
criteria for the accounting methods we have considered. First, they
must provide an estimate of renewable fuel lifecycle GHG emissions that
is consistent over time. Otherwise, for example, all of the upfront
emissions due to land clearing would be assigned to corn ethanol
produced in the first year, and none of those emissions to corn ethanol
produced the following years even though this land use change is
central to the production over these following years. Second, the
accounting method must also provide a common metric that allows for a
direct comparison of biofuels to gasoline or

diesel. When accounting for the time profile of lifecycle GHG
emissions, the two most important assumptions in the determination of
whether a biofuel meets the specified emissions reduction thresholds
include: (1) The time period considered and (2) the discount rate
(which could be zero) applied to future emissions streams.

Time Periods Considered

    The illustration of the payback period in Figure VI.B.5-1
demonstrates the importance of the time period over which to consider
both the lifecycle GHG emissions increases associated with the
production of a biofuel as well as the benefits from using the biofuel.
As mentioned above, based on our lifecycle GHG analysis for this
proposed rule we estimate that the payback period for corn ethanol
produced in a natural gas-fired dry mill is approximately 33 years. In
this case, if we measure GHG impacts over a time period of less than 33
years we will determine that the total corn ethanol produced over this
time period increases lifecycle GHG emissions. Conversely, total corn
ethanol production will reduce net lifecycle GHG emissions if we look
beyond 33 years, with net emissions reductions increasing the further
into the future we extend our analysis. To inform our decision of which
time period for analysis is most appropriate, we must consider a number
of factors including but not limited to the length of time over which
we expect a particular biofuel to be produced, the time over which
biofuel production continues to impact GHG emissions into the future,
the importance of achieving near-term GHG emissions reductions, and the
increasing uncertainty of projecting GHG emissions impacts into the
future. Based on these considerations, our discussion of lifecycle
analyses prepared for this proposed rule focuses on time periods of 100
years and 30 years.
    There are advantages and disadvantages to using the 100 and 30 year
time frames to represent both emissions impacts as well as emissions
benefits of use of biofuels over time. There are several principal
reasons for using the 100 year time frame. First, greenhouse gases are
chemically stable compounds and persist in the atmosphere over long
time scales that span two or more generations. Second, the 100 year
time frame captures the emissions associated with land use change that
may continue for a long period of time after biofuel-induced land
conversion first takes place.\291\ For example, physical changes in
carbon stocks on unmanaged lands may not slow until after 100 years,
and optimal forest rotation ages can influence greenhouse gas emissions
for 100 years on managed lands. Similarly, a 100 year time frame would
allow estimating the future changes in the land should the need for
these changes due to biofuel production cease. For example, as
discussed in more detail below, if production of a biofuel ended, then
the land use impacts associated with that biofuel would also tend to go
away in a process known as land use reversion. A longer time frame
would allow assessment of the impacts of that land use reversion.
---------------------------------------------------------------------------

    \291\ Luyassert, S et al., 2008. Old-growth forests as global
carbon sinks. Nature 455: 213-215. Link: http://www.nature.com/nature/journal/v455/n7210/abs/nature07276.html.
---------------------------------------------------------------------------

    For a number of reasons we believe that biofuel production could
continue for a long time into the future. As biofuel technologies
advance and production costs are decreased, it is likely that renewable
fuels will become increasingly competitive with petroleum-based fuels.
Another reason for expecting long term biofuel production is that,
unlike a specific facility that has an expected lifetime, the RFS
program does not have a specified expiration date. The expectation that
renewable fuel production will continue for a long time provides
justification for using a longer time frame for analysis, such as 100
years. Another reason for considering an inter-generational time period
such as 100 years for lifecycle GHG analysis is that climate change is
a long-term environmental problem that may require GHG emissions
reductions for many decades.
    The 100 year time frame also has drawbacks. A general concern with
projecting impacts over a very long time period is that uncertainty
increases the further the analysis is extended into the future. For
example, a 100 year analysis presumes that production of a particular
biofuel will continue for at least 100 years. Although we expect
renewable fuel production as a whole to continue for a long time, it is
possible that due to changing market conditions or other factors, the
use of first generation biofuels (e.g., corn ethanol) could see a
decline in use over a shorter period of time.
    For this proposal, we are also showing the results of analyzing
both GHG emissions impacts of producing a biofuel as well as benefits
from using the biofuel over 30 years, a time frame which has been used
in the literature to estimate the greenhouse gas impacts of
biofuels.292 293 Since a time period such as 30 years would
truncate the potential GHG benefits that accumulate over time, this
second option would reduce the GHG benefits of biofuels relative to
gasoline or diesel compared to assuming a longer time frame for biofuel
production such as 100 years.
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    \292\ Searchinger et al., 2008.
    \293\ M. Delucchi, ``A multi-country analysis of lifecycle
emissions from transportation fuels and motor vehicles'' (UCD-ITS-
RR-05-10, University of California at Davis, Davis, CA 2005). See
also http://www.its.ucdavis.edu/people/faculty/delucchi/.
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    One advantage of using a shorter time period is that it is more
``conservative'' from a climate change policy perspective. In general,
the further out into the future an analysis projects, the more
uncertainty is introduced into the results. For example, with a longer
time period for analysis, it is more likely that significant changes in
market factors or policies will change the incentives for producing
biofuels. If a biofuel only has greenhouse benefits when considered in
an extended future time frame, it is not clear that these benefits will
be realized due to the inherent uncertainty of the future. Also,
potential irreversible climate change impacts or future actions in
other sectors of the economy, such as reductions from stationary
sources, could influence the relative importance of renewable fuel GHG
impacts. The timing and severity of these potential irreversible
climate change impacts are clearly uncertain as is the degree to which
near-term lifecycle emissions related to biofuel production influences
these climate change impacts. Given these uncertainties, it may be
appropriate to limit our analysis horizon to a much shorter time period
such as 30 years.
    Several disadvantages are also associated with choosing the 30 year
time frame to represent both emissions impacts as well as emissions
benefits. One key disadvantage is that it ignores the potential sources
of GHG emissions impacts of producing biofuel after 30 years such as
foregone sequestration from forests that may have been removed which
could have continuing impacts even after production of a biofuel has
ended. Thus, it doesn't account for the full land use emissions
``signature'' of biofuels. In addition, even if second generation fuels
start to dominate new construction, building a first generation fuel
production facility such as a corn ethanol refinery represents a
significant capital investment. Once the facility is built and
financed, it may continue

producing biofuel as long as it is covering its operating costs. This
suggests that, once a plant is built, if the variable cost of corn
ethanol production is less than the cost to produce gasoline, then corn
ethanol production at that facility may continue. This economic
advantage may contribute to the longevity of first generation biofuel
production and usage far into the future.
    An appropriate time frame for analysis could also be different for
different biofuels. While we could assume that corn ethanol would be
phased out after a shorter time period such as 30 years, it might be
more appropriate to use a longer time period over which to analyze the
benefits of other advanced biofuels such as cellulosic biofuels. It
could be reasonably assumed that cellulosic biofuels will be produced
for more than 30 years, perhaps for 100 years or longer. However, even
if cellulosic biofuels are expected to be produced for 100 years or
longer, a shorter time period, such as 30 years, may still be the most
relevant period over which to assess GHG emissions, given the
importance of near-term emissions reductions and the increasing
uncertainty of future events. We specifically seek comments on the 100
year and 30 year time frames discussed in this proposal. We also seek
general comments on the most appropriate time periods for analysis of
biofuels, and whether we should use different time periods for
different types of renewable fuels.
    Another way to look at the time period issue, which we have not
specifically analyzed for this proposed rule, would separate the time
period into two parts. The first part would consider how long we expect
production of a particular biofuel to continue into the future. We
refer to this concept, which is similar to the project lifetime often
considered in traditional cost benefit analysis, as the ``project''
time horizon. The second part would address the length over which to
account for the changes in GHG emissions due to land use changes which
result from biofuel production. We call this the ``impact'' time
horizon.
    Our analysis for this proposed rule has not considered a scenario
where the project time horizon is shorter than impact time horizon.
However, we are considering this option for the final rule. For
example, we could look at a scenario where corn ethanol production
continues for 30 years and land use related GHG emissions are estimated
for 100 years. Specifically, we are considering whether to use 30 years
after 2015 (as an approximation of when ethanol production from corn
starch reaches 15 billion gallons) as a reasonable estimate of when
corn will no longer be used for ethanol production due to advances in
other biofuels and the competing demand to use corn for food rather
than biofuel feedstock. We specifically ask whether a 30 year estimate
of continued corn starch ethanol production (i.e., through 2045) is a
reasonable estimate for assessing the stream of GHG benefits from corn
ethanol use while 100 years would be appropriate for assessing impacts
of the land use change. Under such an assumption a 100 time horizon
would capture the longer term emission impacts of corn ethanol
production (including indirect land use impacts) while the benefits
from 31 through 100 years would be zero since corn ethanol would be
modeled as no longer in use.
    In that scenario, we would have to consider the lifecycle GHG
impacts after the production of corn ethanol ends. This would include
the issue of land reversion, or what happens to the land used to
produce a biofuel feedstock after its use for biofuel production has
ceased. A full accounting of land reversion would involve economic
modeling to determine how long we expect production of a particular
biofuel to last, and to determine the land use changes after that
biofuel production ends. Ideally this modeling would extend well beyond
2022 to the point where land reversion is complete, and it would
include projections for global crop yield improvements, population
trends, food demand, and other key factors. For this proposal, we have
not projected the GHG emissions associated with land reversion, but we
plan to consider land reversion in our final rule analysis and we seek
comments on methodologies and approaches for doing this. We also seek
comment on the related issue of how best to estimate how long each type
of biofuel is most likely to continue to be produced, and whether
production of these biofuels is likely to end abruptly or phase out
gradually.
    Agricultural and economic models that look beyond 2022 would not
only help to estimate the impacts of land reversion after biofuel
production ends, they would also help to project how evolving
agricultural conditions could influence the lifecycle GHG emissions of
biofuels beyond 2022. For example, corn yields per acre are expected to
continue increasing after 2022; this increase in yields per acre will
decrease the amount of land required to produce a bushel of corn. At
higher yields, fewer acres are required to grow the corn used for the
15 billion gallons of corn starch ethanol modeled for the rule. The
indirect impacts of maintaining 15 billion gallons of corn ethanol
production would similarly be reduced. EPA intends to more carefully
model these transitions in particular to better account for future land
use impacts and we invite comments on methodology, sources of data,
factors that should be considered in assessing whether and when a
particular biofuel such as ethanol from corn starch, for example, will
no longer be produced and recommendations on how to improve on our
assessment of the likely stream of GHG emissions after 2022 that will
result from the EISA mandates.
    A complicating consideration in this analysis arises in determining
future use of the land (post-biofuel production) is the fact that
perhaps significant land use change occurred as a result of biofuel
production and that land is now more easily suited for alternative uses
compared to its pre-biofuel state. For example, the demand created by
biofuel production may have justified clearing forested lands and
turning them into productive cropland. Even if the need for the land to
produce crops in response to biofuel demand ceases when the biofuel
production ends, the land will still be in an altered form making it,
for example, more economically available for other crop production than
when it had been forested. How this land is subsequently used can
affect its impact on GHG emissions. If it is used for intensive crop
production, the land will have a much different carbon sequestration
profile, for example, than if it returned to its pre-biofuel forested
state. EPA asks for suggestions on how to best treat these lingering
effects of land use change when attributing the effects of biofuel
demand to uses of land even after biofuel production ends.
    For the determination of whether biofuels meet the GHG emissions
reduction required by EISA, we present the results for a range of time
periods, including both 100 years and 30 years in Section VI.C and
specifically invite comment on whether use of a 100 year time frame, a
30 year time frame, or some other time frame, would be most
appropriate.
    In addition to this general issue of the appropriate time frames
for analysis, several more specific issues exist. Since it would be
likely that corn starch ethanol production will phase out gradually
rather than stopping all of a sudden in 2045, we also are evaluating
options for estimating the phase out of corn starch ethanol production.
One simplifying assumption would have corn ethanol production phase out

linearly between 2022 and 2045 as production of other biofuels such as
cellulosic biofuels continue to expand. Comments are requested on the
option of linearly phasing out corn ethanol production from 2022
through 2045 and other approaches for estimating this transition in
corn ethanol production. Finally, its not only corn starch ethanol that
might be replaced in future years. For example, the use of soy oil for
biodiesel fuel production might be replaced by other non-food oils such
as oil from algae. Comments are requested on whether other biofuels
will similarly phase out of use and therefore the land use change
impacts need to be similarly considered.
    In addition to seeking comments on all of the issues related to the
time periods for lifecycle analysis, EPA plans to convene a peer review
of the range of time periods considered in this proposed rule. This
peer review will also seek expert feedback on all of the issues raised
above in this section, including how to determine the most appropriate
time periods for consideration in the final rule.

Discounting of Lifecycle GHG Emissions

    Economic theory suggests that in general consumers have a time
preference for benefits received today versus receiving them in the
future. Therefore, future benefits are often valued at a discounted
rate. Although discount rates are most often applied to the monetary
valuation of future versus present benefits, a discounting approach can
also be used to compare physical quantities (i.e., total GHG emissions
per gallon of fuel used).
    The concept of weighting physical units accruing at different times
has been used in the environmental and resource economics
literature,\294\ and is analogous to valuing the monetary cost and
benefits of a policy, only that in this case the metric that we `value'
is the reduction in GHG emissions. \295\ An important part of the
economic theory of time is the idea that benefits expected to accrue in
the long term are less certain than benefits in the near term. This is
true in the case of GHG emissions changes from biofuel production which
are dependent upon how long biofuel production will continue, how
technologies will develop over time, and other factors. Another reason
to give more weight to near-term emissions changes is that the risks
associated with climate change in the future include the possibility of
extreme climate change and threshold impacts (e.g., species and
ecosystem thresholds, catastrophic events). Increased GHG emissions in
the near-term may be more important in terms of physical damage to the
world's environment. Some scientists, for example, believe that effects
on factors such as arctic summer ice, Himalayan-Tibetan Glaciers, and
the Greenland ice sheet are more likely to be effected by near-term GHG
emissions, causing non-linearities in the effects attributable to GHG
emissions.\296\ Long-term GHG reductions may be too late to mitigate
these irreversible impacts, providing further justification for
discounting GHG emissions changes that are expected in the distant
future. Under this perspective, it would be appropriate to discount the
physical quantities of future emissions, and especially in a long term
analysis of lifecycle GHG emissions. Thus in our analysis with a 100
year time frame, or impact horizon, we discount the value of future GHG
emissions changes.
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    \294\ Herzog et al. 2003 (See http://sequestration.mit.edu/pdf/climatic_change.pdf), Richards 1997, Stavins and Richards 2005 (See
http://www.pewclimate.org/docUploads/Sequest_Final.pdf).
    \295\ Sunstein and Rowell, 2007, On Discounting Regulatory
Benefits: Risk, Money, and Intergenerational Equity, Chicago Law
Review.
    \296\ Ramanathan and Feng, 2008. On avoiding dangerous
anthropogenic interference with the climate system: Formidable
challenges ahead. Proceedings of the National Academy of Sciences
105:143245-14250.
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    Despite the rationale for discounting future GHG emissions changes
discussed above, there are reasons to be cautious about the application
of discounting in lifecycle GHG analysis. One argument is that it may
only be appropriate to discount monetized values. Our lifecycle
analysis estimates GHG emission impacts, not their monetary value, and
under this argument emissions should not be directly discounted.
Rather, the physical GHG emissions should be converted into monetary
impacts, where these monetary impacts are also a function of climate
science. The resulting climate impacts would then have to be translated