Capital Costs ($MM).............................................          -5,878          -7,311          -1,433
Amortized Capital Costs ($MM/yr)................................            -647            -804            -158
Fixed Operating Cost ($MM/yr)...................................            -178            -222             -43
Variable Operating Cost ($MM/yr)................................            -201            -491            -290
Fuel Economy Cost ($MM/yr)......................................           1,848           3,255            1407
    Total Cost ($MM/yr).........................................             823            1739             915
Capital Costs (c/gal of gasoline)...............................           -0.40           -0.49           -0.10
Fixed Operating Cost (c/gal of gasoline)........................           -0.11           -0.14           -0.03
Variable Operating Cost (c/gal of gasoline).....................           -0.12           -0.30           -0.18
Fuel Economy Cost (c/gal of gasoline)...........................            1.13            1.98            0.86
    Total Cost Excluding Subsidies (c/gal of gasoline)..........            0.50            1.06            0.56
----------------------------------------------------------------------------------------------------------------

    Our analysis shows that when considering all the costs associated
with these fuel changes resulting from the expanded use of subsidized
ethanol that these various possible gasoline use scenarios will
increase fuel costs by $820 million or $1,740 million in the year 2012
for the RFS and EIA cases, respectively. Expressed as per-gallon costs,
these fuel changes would increase fuel costs by 0.50 to 1.1 cents per
gallon of gasoline.
b. Gasoline Costs Including Ethanol Consumption Tax Subsidies
    Table VII.C.3.b-1 expresses the total and per-gallon gasoline costs
for the two control scenarios with the federal and state ethanol
subsidies included. The federal tax subsidy is 51 cents per gallon for
each gallon of new ethanol blended into gasoline. The state tax
subsidies apply in 5 states and range from 1.6 to 29 cents per gallon.
The cost reduction to the fuel industry and consumers is estimated by
multiplying the subsidy times the volume of new ethanol estimated to be
used in the state. The per-gallon costs are derived by dividing the
total costs over all U.S. gasoline projected to be consumed in 2012.

                   Table VII.C.3.B-1.--Estimated Cost Including Ethanol Consumption Subsidies
                         [Million dollars and cents per gallon; 7% ROI and 2006 dollars]
----------------------------------------------------------------------------------------------------------------
                                                                   RFS case 6.8    EIA case 9.6    EIA case 9.6
                                                                   billion gals    billion gals    billion gals
                                                                  incremental to  incremental to  incremental to
                                                                  reference case  reference case     RFS case
----------------------------------------------------------------------------------------------------------------
Total Cost ($MM/yr).............................................             823            1739             915
Federal Subsidy ($MM/yr)........................................           -1376           -2865           -1489
State Subsidies ($MM/yr)........................................              -5             -31             -26
    Revised Total Cost ($MM/yr).................................            -558           -1158            -600
Per-Gallon Cost Excluding Subsidies (c/gal of gasoline).........            0.50            1.06            0.56
Federal Subsidy (c/gal of gasoline).............................           -0.84           -1.74           -0.90
State Subsidies (c/gal of gasoline).............................          -0.003           -0.02           -0.02
    Total Cost Including Subsidies (c/gal of gasoline)..........           -0.34           -0.71           -0.37
----------------------------------------------------------------------------------------------------------------

    The cost including subsidies better represents gasoline's
production cost as reflected to the fuel industry as a whole and to
consumers ``at the pump'' because the federal and state subsidies tend
to hide a portion of the actual costs. Our analysis estimates that the
fuel industry and consumers will see a 0.34 and 0.71 cent per gallon
decrease in the apparent cost of producing gasoline for the RFS and EIA
cases, respectively.

VIII. What Are the Impacts of Increased Ethanol Use on Emissions and
Air Quality?

    In this section, we evaluate the impact of increased production and
use of renewable fuels on emissions and air quality in the U.S.,
particularly ethanol and biodiesel. In performing these analyses, we
compare the emissions which would have occurred in the future if fuel
quality had remained unchanged from pre-Act levels to those which will
be either required under the Energy Policy Act of 2005 (Energy Act or
the Act) or exist due to market forces.
    This approach differs from that traditionally taken in EPA
regulatory impact analyses. Traditionally, we would have compared
future emissions with and without the requirement of the Energy Act.
However, as described in Section II, we expect that total renewable
fuel use in the U.S. in 2012 to exceed the Act's requirements even in
the absence of the RFS program. Thus, a traditional regulatory impact
analysis would have shown no impact on emissions or air quality. This
is because, strictly speaking, if the same volume and types of
renewable fuels are produced and used with and without the RFS program,
the RFS program has no impact on fuel quality and thus, no impact on
emissions or air quality. However, levels of renewable fuel use are
increasing dramatically relative to both today and the recent past,
with corresponding impacts on emissions and air quality. We believe
that it is appropriate to evaluate these changes here, regardless of
whether they are occurring due to economic forces or Energy Act
requirements.
    In the process of estimating the impact of increased renewable fuel
use, we also include the impact of reduced use of MTBE in gasoline. It
is the

increased production and use of ethanol which is facilitating the
continued production of RFG which meets both commercial and EPA
regulatory specifications without the use of MTBE. Because of this
connection, we found it impractical to isolate the impact of increased
ethanol use from the removal of MTBE.

A. Effect of Renewable Fuel Use on Emissions

1. Emissions From Gasoline Fueled Motor Vehicles and Equipment
    Several models of the impact of gasoline quality on motor vehicle
emissions have been developed since the early 1990's. We evaluated
these models and selected those which were based on the most
comprehensive set of emissions data and developed using the most
advanced statistical tools for this analysis. Still, as will be
described below, significant uncertainty exists as to the effect of
these gasoline components on emissions from both motor vehicle and
nonroad equipment, particularly from the latest vehicle and engine
models equipped with the most advanced emission controls. Pending
adequate funding, we plan to conduct significant vehicle and equipment
testing over the next several years to improve our estimates of the
impact of these additives and other gasoline properties on emissions.
We hope that the results from these test programs will be available for
reference in the future evaluations of the emission and air quality
impacts of U.S. fuel programs required by the Act.\101\
---------------------------------------------------------------------------

    \101\ Subject to funding.
---------------------------------------------------------------------------

    The remainder of this sub-section is divided into three parts. The
first evaluates the impact of increased ethanol use and decreased MTBE
use on gasoline quality. The second evaluates the impact of increased
ethanol use and decreased MTBE use on motor vehicle emissions. The
third evaluates the impact of increased ethanol use and decreased MTBE
use on nonroad equipment emissions.
a. Gasoline Fuel Quality
    For the final rulemaking, we estimate the impact of increased
ethanol use and decreased MTBE use on gasoline quality using refinery
modeling conducted specifically for the RFS rulemaking.\102\ In
general, adding ethanol to gasoline reduces the aromatic content of
conventional gasoline and the mid- and high-distillation temperatures
(e.g., T50 and T90). RVP increases except in areas where ethanol blends
are not provided a 1.0 RVP waiver of the applicable RVP standards in
the summer. With the exception of RVP, adding MTBE directionally
produces the same impacts. Thus, the effect of removing MTBE results in
essentially the opposite impacts. Neither oxygenate is expected to
affect sulfur levels, as refiners control sulfur independently in order
to meet the Tier 2 sulfur standards.
---------------------------------------------------------------------------

    \102\ Refinery modeling performed in support of the original RFG
rulemaking is also used to help separate the effects of the two
oxygenates.
---------------------------------------------------------------------------

    The impacts of oxygenate use are smaller with respect to RFG. This
is due to RFG's VOC and toxics emission performance specifications,
which limit the range of feasible fuel quality values. Thus, oxygenate
type or level does not consistently affect the RVP level and aromatic
and benzene contents of RFG.
    Table VIII.A.1.a-1 shows the fuel quality of a typical summertime,
non-oxygenated conventional gasoline and how these qualities change
with the addition of 10 volume percent ethanol. Similarly, the table
shows the fuel quality of a typical MTBE RFG blend and how fuel quality
might change with either ethanol use or simply MTBE removal. All of
these fuels are based, in whole or in part, on projections made by
Jacobs in their recent refinery modeling performed for EPA and
therefore, represent improvements over the projections made for the
NPRM. Please see Chapter 2 of the RIA for a detailed description of the
methodologies used to determine the specific changes in projected fuel
quality. As discussed there, we use the Jacobs model projections of RFG
fuel quality directly in our emission modeling. For conventional
gasoline, we use the Jacobs modeling described in Section VII to
determine the change in fuel quality due to ethanol use and apply this
change to base fuel quality estimates contained in EPA's NMIM emission
inventory model. Sulfur is not shown in Table VIII.A.1.a-1, as it is
held constant at 30 ppm, which is the average Tier 2 sulfur standard
applicable to all gasoline sold in the U.S. in the timeframe of our
emission analyses.

                              Table VIII.A.1.A-1.--Typical Summertime Fuel Quality
----------------------------------------------------------------------------------------------------------------
                                                   Conventional gasoline         Reformulated gasoline \a\
                                                ----------------------------------------------------------------
                 Fuel parameter                                                                          Non-
                                                  Typical 9     Ethanol     MTBE blend    Ethanol     oxygenated
                                                     RVP         blend                     blend        blend
----------------------------------------------------------------------------------------------------------------
RVP (psi)......................................          8.7          9.7          7.0          7.0          7.0
T50............................................          218          205          179          184          175
T90............................................          332          329          303          335          309
E200...........................................           41           50           60           58           52
E300...........................................           82           82           89           82           88
Aromatics (vol%)...............................           32           27           20           20           20
Olefins (vol%).................................          7.7          7.7            4           14           15
Oxygen (wt%)...................................            0          3.5          2.1          3.5            0
Benzene (vol%).................................          1.0          1.0         0.74         0.70        0.72
----------------------------------------------------------------------------------------------------------------
\a\ MTBE blend--Reference Case PADD 1 South, Ethanol blend--RFS Case PADD 1 North, Non-oxy blend. -RFS Case PADD
  1 South.

b. Emissions From Motor Vehicles
    We use the EPA Predictive Models to estimate the impact of gasoline
fuel quality on exhaust VOC and NOX emissions from motor
vehicles. These models were developed in 2000, in support of EPA's
response to California's request for a waiver of the RFG oxygen
mandate. These models represent a significant update of the EPA Complex
Model. However, they are still based on emission data from Tier 0
vehicles (roughly equivalent to 1990 model year vehicles). We based our
estimates of the impact of fuel quality on CO emissions on the EPA
MOBILE6.2 model. We base our estimates of the impact of fuel quality

on exhaust toxic emissions (benzene, formaldehyde, acetaldehyde, and
1,3-butadiene) primarily on the MOBILE6.2 model, updated to reflect the
effect of fuel quality on exhaust VOC emissions per the EPA Predictive
Models. Very limited data are available on the effect of gasoline
quality on PM emissions. Therefore, the effect of increased ethanol use
on PM emissions can only be qualitatively discussed.
    In responding to California's request for a waiver of the RFG
oxygen mandate in 2000, we found that both very limited and conflicting
data were available on the effect of fuel quality on exhaust emissions
from Tier 1 and later vehicles.\103\ Thus, we assumed at the time that
changes to gasoline quality would not affect VOC, CO and NOX
exhaust emissions from these vehicles.\104\ Very little additional data
have been collected since that time on which to modify this assumption.
Consequently, for our primary analysis for today's final rule we have
maintained the assumption that changes to gasoline do not affect
exhaust emissions from Tier 1 and later technology vehicles.
---------------------------------------------------------------------------

    \103\ The one exception was the impact of sulfur on emissions
from these later vehicles, which is not an issue here due to the
fact that renewable fuel use is not expected to change sulfur levels
significantly.
    \104\ An exception is that MOBILE6.2 applies the effect of
oxygenate on CO emissions to Tier 1 and later vehicles which are
expected to be high emitters based on their age and mileage.
---------------------------------------------------------------------------

    For the NPRM, we evaluated one recent study by the Coordinating
Research Council (CRC) which assessed the impact of ethanol and two
other fuel properties on emissions from twelve 2000-2004 model year
vehicles (CRC study E-67). Based on comments received on the NPRM, we
evaluated four additional studies of the fuel-emission effects of
recent model year vehicles. The results of these test programs indicate
that emissions from these late model year vehicles are likely sensitive
to changes in fuel properties. However, both the size and direction of
the effects are not consistent between the various studies. More
testing is still needed before confident predictions of the effect of
fuel quality on emissions from these vehicles can be made.
    In the NPRM, we developed two sets of assumptions regarding the
effect of fuel quality on emissions from Tier 1 and later vehicles to
reflect this uncertainty. A primary analysis assumed that exhaust
emissions from Tier 1 and later vehicles are not sensitive to fuel
quality. This is consistent with our analysis of California's request
for a waiver of the RFG oxygen mandate. A sensitivity analysis assumed
that the NMHC and NOX emissions from Tier 1 and later
vehicles were as sensitive to fuel quality as Tier 0 vehicles. Only one
effect of fuel quality on CO emissions was assumed, that contained in
EPA's MOBILE6.2 emission inventory model.
    The five available studies of Tier 1 and later vehicles support
continuing this approach for exhaust NMHC and NOX emissions.
The assumptions supporting both our primary and sensitivity analyses
reasonably bracket the results of the five studies. However, we have
decided to perform a sensitivity analysis for CO emissions, as well. In
this case, we apply the fuel-emission effects from MOBILE6.2 for Tier 0
vehicles to Tier 1 and later vehicles. This is analogous to the
approach taken for exhaust NMHC and NOX emissions.
    We base our estimates of fuel quality on non-exhaust VOC and
benzene emissions on the EPA MOBILE6.2 model. The one exception to this
is the effect of ethanol on permeation emissions through plastic fuel
tanks and elastomers used in fuel line connections. Recent testing has
shown that ethanol increases permeation emissions, both by permeating
itself and increasing the permeation of other gasoline components. This
effect was included in EPA's analysis of California's most recent
request for a waiver of the RFG oxygen requirement, but is not in
MOBILE6.2.\105\ Therefore, we have added the effect of ethanol on
permeation emissions to MOBILE6.2's estimate of non-exhaust VOC
emissions in assessing the impact of gasoline quality on these
emissions.
---------------------------------------------------------------------------

    \105\ For more information on California's request for a waiver
of the RFG oxygen mandate and the Decision Document for EPA's
response, see http://www.epa.gov/otaq/rfg_regs.htm#waiver.

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

    No models are available which address the impact of gasoline
quality on PM emissions. Very limited data indicate that ethanol
blending might reduce exhaust PM emissions under very cold weather
conditions (e.g., -20 [deg]F to 0 [deg]F). Very limited testing at
warmer temperatures (e.g., 20 [deg]F to 75 [deg]F) shows no definite
trend in PM emissions with oxygen content. Thus, for now, no
quantitative estimates can be made regarding the effect of ethanol use
on direct PM emissions.
    Table VIII.A.1.b-1 presents the average per vehicle (2012 fleet)
emission impacts of three types of RFG: Non-oxygenated, a typical MTBE
RFG as has been marketed in the Gulf Coast, and a typical ethanol RFG
which has been marketed in the Midwest.

  Table VIII.A.1.B-1.--Effect of RFG on Per Mile Emissions From Tier 0 Vehicles Relative to a Typical 9psi RVP
                                             Conventional Gasoline a
----------------------------------------------------------------------------------------------------------------
                                                                                11 Volume    10 Volume
                 Pollutant                              Source             Non-Oxy RFG    percent      percent
                                                                            (percent)       MTBE       ethanol
----------------------------------------------------------------------------------------------------------------
                                                Exhaust Emissions
----------------------------------------------------------------------------------------------------------------
VOC.......................................  EPA Predictive Models........        -13.4        -15.3         -9.7
NOX.......................................                                        -2.4         -1.7          7.3
CO........................................  MOBILE6.2....................          -22          -31          -36
Exhaust Benzene...........................  EPA Predictive and Complex           -21.2        -29.7        -38.9
                                             Models.
Formaldehyde..............................                                        -5.9         19.4          2.3
Acetaldehyde..............................                                        -0.2         -9.5        173.7
1,3-Butadiene.............................                                        20.9        -29.2          6.1
----------------------------------------------------------------------------------------------------------------
                                              Non-Exhaust Emissions
----------------------------------------------------------------------------------------------------------------
VOC.......................................  MOBILE6.2 & CRC E-65.........          -30          -30          -18
Benzene...................................  MOBILE6.2 & Complex Models...          -40          -43         -32
----------------------------------------------------------------------------------------------------------------
\a\ Average per vehicle effects for the 2012 fleet during summer conditions.

    As can be seen, all three types of RFG produce significantly lower
emissions of VOC, CO and benzene than conventional gasoline. The impact
of ethanol RFG on non-exhaust VOC emissions is lower than the other two
types of RFG due to the impact of ethanol on permeation emissions. The
impact of RFG on emissions of NOX and the other air toxics
depends on the type of RFG blend. The most notable effect on toxic
emissions in percentage terms is the 173 percent increase in
acetaldehyde with the use of ethanol. However, as will be seen below,
base acetaldehyde emissions are low relative to the other toxics. While
not shown, the total mass emissions of the four toxic pollutants always
decreases, as benzene is by far the largest constituent.
    It should be noted that these comparisons assume that all gasoline
blends meet EPA's Tier 2 gasoline sulfur standard of 30 ppm. Prior to
the Tier 2 program, RFG contained less sulfur than conventional
gasoline and reduced NOX emissions to a greater degree
compared to conventional gasoline.
    Historically, no non-oxygenated RFG was sold, due to the
requirement that RFG contain at least 2.0 weight percent oxygen.
However, with the Energy Act's removal of this requirement, all three
types of RFG blends can be sold today. Increased use of ethanol in RFG
would therefore either replace MTBE RFG or non-oxygenated RFG. The
former has already occurred in many areas, as MTBE was essentially
removed from the U.S. gasoline market by the end of 2006. The impact of
using ethanol in RFG in lieu of MTBE or no oxygenate can be seen from
comparing the relative impacts of the various RFG blends shown in Table
VIII.A.1.b-1.
    Blending RFG with ethanol instead of MTBE or no oxygenate will
increase VOC and NOX emissions and decrease CO emissions.
Exhaust benzene and formaldehyde emissions will decrease, but non-
exhaust benzene, acetaldehyde, and 1,3-butadiene emissions will
increase. All of these impacts are on a per vehicle basis and apply to
Tier 0 vehicles only. The overall impact of increased ethanol use on
total emissions of these various pollutants is described below.
    Table VIII.A.1.b-2 presents the effect of blending either MTBE or
ethanol into conventional gasoline while matching octane.

Table VIII.A.1.B-2.--Effect of MTBE and Ethanol in Conventional Gasoline
    on Tier 0 Vehicle Emissions Relative to a Typical Non-Oxygenated
                         Conventional Gasoline a
------------------------------------------------------------------------
                                                 11 Volume    10 Volume
          Pollutant                 Source        percent      percent
                                                    MTBE     ethanol \b\
------------------------------------------------------------------------
Exhaust VOC..................  EPA Predictive          -9.2         -7.4
                                Models.
NOX..........................                          -2.6          7.7
CO \c\.......................  MOBILE6.2......       -6/-11      -11/-19
Exhaust Benzene..............  EPA Predictive         -22.8        -24.9
                                and Complex
                                Models.
Formaldehyde.................                         +21.3         +6.7
Acetaldehyde.................                          +0.8       +156.8
1,3-Butadiene................                          -3.7        -13.2
Non-Exhaust VOC..............  MOBILE6.2......         Zero          +30
Non-Exhaust Benzene..........  MOBILE6.2 &             -9.5        +15.8
                                Complex Models.
------------------------------------------------------------------------
a Average per vehicle effects for the 2012 fleet during summer
  conditions.
b Assumes a 1.0 psi RVP waiver for ethanol blends.
c The first figure shown applies to normal emitters; the second applies
  to high emitters.

    Use of either oxygenate reduces exhaust VOC and CO emissions, but
increases NOX emissions. The ethanol blend increases non-
exhaust VOC emissions due to the commonly granted 1.0 psi waiver of the
RVP standard, as well as increased permeation emissions. Both
oxygenated blends reduce exhaust benzene and 1,3-butadiene emissions.
As above, ethanol increases non-exhaust benzene and acetaldehyde
emissions. While small amounts of MTBE may have still been used in CG
in 2004, for our reference case we have assumed that all MTBE use was
in RFG. Therefore, we are not predicting any emissions impact related
to removing MTBE from conventional gasoline. Increased use of
conventional ethanol blends will be in lieu of non-oxygenated
conventional gasoline. Thus, the more relevant column in Table
VII.A.1.b-2 for our modeling is the last column, which shows the
emission impact of a 10 volume percent ethanol blend relative to non-
oxygenated gasoline.
    The exhaust emission effects shown above for VOC and NOX
emissions only apply to Tier 0 vehicles in our primary analysis. For
example, MOBILE6.2 estimates that 34 of exhaust VOC emissions and 16 of
NOX emissions from gasoline vehicles in 2012 come from Tier
0 vehicles. In the sensitivity analysis, these effects are extended to
all gasoline vehicles. The effect of RVP and permeation on non-exhaust
VOC emissions is temperature dependent. The figures shown above are
based on the distribution of temperatures occurring across the U.S. in
July.
    We received several comments related to the effect of ethanol on
emissions from onroad vehicles. None of the comments led us to change
the basic approach taken to estimating the impact of changing fuel
quality described above. Several comments suggested that we expand our
discussion of the uncertainty in these fuel effects (as well as the
effects of fuel quality on emissions from nonroad equipment and diesels
described below). While such an expanded discussion might be generally
desirable, the lack of relevant emission data from late model vehicles
and equipment prevents this. We believe that we have adequately
described the uncertainty in the emission estimates presented below and
our plans to obtain more data in order to improve these estimates in
the near future.
c. Nonroad Equipment
    To estimate the effect of gasoline quality on emissions from
nonroad equipment, we used EPA's NONROAD emission model. We used the
2005 version of this model, NONROAD2005, which includes the effect of
ethanol on permeation emissions from most nonroad equipment.
    Only sulfur and oxygen content affect exhaust VOC, CO and
NOX emissions in NONROAD. Since sulfur level is assumed to
remain constant, the only difference in exhaust emissions between
conventional and reformulated gasoline is due to oxygen content. Table
VIII.A.1.c-1 shows the effect of adding

11 volume percent MTBE or 10 volume percent ethanol to non-oxygenated
gasoline on these emissions.

 Table VIII.A.1.C-1.--Effect MTBE and Ethanol on Nonroad Exhaust Emissions Relative to a Typical Non-Oxygenated
                                                    Gasoline
----------------------------------------------------------------------------------------------------------------
                                                                  4-Stroke engines          2-Stroke engines
                                                             ---------------------------------------------------
                          Base fuel                            11 Volume    10 Volume    11 Volume    10 Volume
                                                                percent      percent      percent      percent
                                                                  MTBE       ethanol        MTBE       ethanol
----------------------------------------------------------------------------------------------------------------
Exhaust VOC.................................................           -9          -16           -1           -2
Non-Exhaust VOC.............................................            0           26            0           26
CO..........................................................          -13          -22          -13          -23
NOX.........................................................          +23          +40          +37          +65
----------------------------------------------------------------------------------------------------------------

    As can be seen, higher oxygen content reduces exhaust VOC and CO
emissions significantly, but also increases NOX emissions.
However, NOX emissions from these engines tend to be fairly
low to start with, given the fact that these engines run much richer
than stoichiometric. Thus, a large percentage increase of a relative
low base value can be a relatively small increase in absolute terms.
    Evaporative emissions from nonroad equipment are impacted by only
RVP, and permeation by ethanol content. Both the RVP increase due to
blending of ethanol and its permeation effect cause non-exhaust VOC
emissions to increase with the use of ethanol in nonroad equipment. The
26 percent effect represents the average impact across the U.S. in July
for both 2-stroke and 4-stroke equipment. We updated the NONROAD2005
hose permeation emission factors for small spark-ignition engines and
recreational marine watercraft to reflect the use of ethanol.
    For nonroad toxics emissions, we base our estimates of the impact
of fuel quality on the fraction of exhaust VOC emissions represented by
each toxic on MOBILE6.2 (i.e., the same effects predicted for onroad
vehicles). The National Mobile Inventory Model (NMIM) contains
estimates of the fraction of VOC emissions represented by the various
air toxics based on oxygenate type (none, MTBE or ethanol). However,
estimates for nonroad gasoline engines running on different fuel types
are limited, making it difficult to accurately model the impacts of
changes in fuel quality. In the recent final rule addressing mobile air
toxic emissions, EPA replaced the toxic-related fuel effects contained
in NMIM with those from MOBILE6.2 for onroad vehicles.\106\ We follow
the same methodology here. Future testing could significantly alter
these emission impact estimates.
---------------------------------------------------------------------------

    \106\ 71, Federal Register, 15804, March 29, 2006.
---------------------------------------------------------------------------

2. Diesel Fuel Quality: Biodiesel
    EPA assessed the impact of biodiesel fuel on emissions in 2002 and
published a draft report summarizing the results.\107\ This report
included a technical analysis of biodiesel effects on regulated and
unregulated pollutants from diesel powered vehicles and concluded that
biodiesel fuels improved PM, HC and CO emissions of diesel engines
while slightly increasing their NOX emissions.
---------------------------------------------------------------------------

    \107\ ``A Comprehensive Analysis of Biodiesel Impacts on Exhaust
Emissions,'' Draft Technical Report, U.S. EPA, EPA420-P-02-001,
October 2002. http://www.epa.gov/otaq/models/biodsl.htm.

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

    While the conclusions reached in the 2002 EPA report relative to
biodiesel effects on VOC, CO and PM emissions have been generally
accepted, the magnitude of the B20 effect on NOX remains
controversial due to conflicting results from different studies.
Significant new testing is being planned with broad stakeholder
participation and support in order to better estimate the impact of
biodiesel on NOX and other exhaust emissions from the in-use
fleet of diesel engines. We hope to incorporate the data from such
additional testing into the analyses for other studies required by the
Energy Act in 2008 and 2009, and into a subsequent rule to set the RFS
program standard for 2013 and later.
3. Renewable Fuel Production and Distribution
    Areas experiencing increased renewable production will experience
the corresponding emission increases associated with their production.
The primary impact of renewable fuel production and distribution
regards ethanol, since it is expected to be the predominant renewable
fuel used in the foreseeable future. We approximate the impact of
increased ethanol and biodiesel production, including corn and soy
farming, on emissions based on DOE's GREET model, version 1.7. In
addition, we develop a second estimate of emissions from ethanol
production facilities using estimates of emissions from current ethanol
plants obtained from the States. We also include emissions effects
resulting from the transport of increased volumes of renewable fuels
and decreased volumes of gasoline and diesel fuel. These emissions are
summarized in Table VIII.A.3-1.

            Table VIII.A.3-1.--Well-to-Pump Emissions for Producing and Distributing Renewable Fuels
                                   [Grams per gallon ethanol or biodiesel] \a\
----------------------------------------------------------------------------------------------------------------
                                                 GREET1.7            GREET1.7 + state data
                                        ----------------------------------------------------
               Pollutant                   Current       Future      Current       Future    Biodiesel--GREET1.7
                                           ethanol      ethanol      ethanol      ethanol
                                            plants       plants       plants       plants
----------------------------------------------------------------------------------------------------------------
VOC....................................          1.8          1.8          3.6          3.2             37.6
CO.....................................          4.0          4.1          4.4          4.3             12.7
NOX....................................         11.4         11.4         10.8         13.0             25.1
PM10...................................          4.9          4.9          6.1          2.8              4.8

SOX....................................          6.4          6.4          7.2          9.7             21.8
----------------------------------------------------------------------------------------------------------------
\a\ Includes credit for reduced distribution of gasoline and diesel fuel.

    At the same time, areas with refineries might experience reduced
emissions, not necessarily relative to current emission levels, but
relative to those which would have occurred in the future had renewable
fuel use not risen. However, to the degree that increased renewable
fuel use reduces imports of gasoline and diesel fuel, as opposed to the
domestic production of these fuels, these reduced refinery emissions
will occur overseas and not in the U.S.
    Similarly, areas with MTBE production facilities might experience
reduced emissions from these plants as they cease producing MTBE.
However, many of these plants may be converted to produce other
gasoline blendstocks, such as iso-octane or alkylate. In this case,
their emissions are not likely to change substantially.

B. Impact on Emission Inventories

    We use the NMIM to estimate emissions under the various ethanol
scenarios on a county by county basis. NMIM basically runs MOBILE6.2
and NONROAD2005 with county-specific inputs pertaining to fuel quality,
ambient conditions, levels of onroad vehicle VMT and nonroad equipment
usage, etc. We ran NMIM for two months, July and January. We estimate
annual emission inventories by summing the two monthly inventories and
multiplying by six.
    As described above, we removed the effect of gasoline fuel quality
on exhaust VOC and NOX emissions from the onroad motor
vehicle inventories which are embedded in MOBILE6.2. We then applied
the exhaust emission effects from the EPA Predictive Models. In our
primary analysis, we only applied these EPA Predictive Model effects to
exhaust VOC and NOX emissions from Tier 0 vehicles. In a
sensitivity case, we applied them to exhaust VOC and NOX
emissions from all vehicles. Regarding the effect of fuel quality on
emissions of four air toxics from nonroad equipment (in terms of their
fraction of VOC emissions), in all cases we replaced the fuel effects
contained in NMIM with those for motor vehicles contained in MOBILE6.2.
The projected emission inventories for the primary analysis are
presented first, followed by those for the sensitivity analysis.
1. Primary Analysis
    The national emission inventories for VOC, CO and NOX in
2012 with current fuels (i.e., ``reference fuel'') are summarized in
Table VIII.B.1-1. Also shown are the changes in emissions projected for
the two levels of ethanol use (i.e., ``control cases'') described in
Section VI.

 Table VIII.B.1-1.--2012 Emissions Nationwide From Gasoline Vehicles and
     Equipment Under Several Ethanol Use Scenarios--Primary Analysis
                          [Tons per year] \108\
------------------------------------------------------------------------
                                    Inventory    Change in inventory in
                                  -------------       control cases
            Pollutant               Reference  -------------------------
                                       case       RFS case     EIA case
------------------------------------------------------------------------
VOC..............................    5,882,000       18,000       43,000
NOX..............................    2,487,000       23,000       40,000
CO...............................   55,022,000     -483,000   -1,366,000
Benzene..........................      178,000       -3,200       -7,200
Formaldehyde.....................       40,400         -600         -200
Acetaldehyde.....................       19,900        3,400        7,100
1,3-Butadiene....................       18,900         -200         -300
------------------------------------------------------------------------

    Both VOC and NOX emissions are projected to increase
with increased use of ethanol. However, the increases are small,
generally less than 2 percent. CO emissions are projected to decrease
by about 0.9 to 2.5 percent. Benzene emissions are projected to
decrease by 1.8 to 4.0 percent. Formaldehyde emissions are projected to
decrease slightly, on the order of 0.5 to 1.5 percent. 1,3-butadiene
emissions are projected to decrease by about 1.1 to 1.6 percent. The
largest change is in acetaldehyde emissions, an increase of 17.1 to
35.7 percent, as acetaldehyde is a partial combustion product of
ethanol.
---------------------------------------------------------------------------

    \108\ These emission estimates do not include the impact of the
recent mobile source air toxic standards (72 FR 8428, February 26,
2007).
---------------------------------------------------------------------------

    CO also participates in forming ozone, much like VOCs. Generally,
CO is 15-50 times less reactive than typical VOC. Still, the reduction
in CO emissions is roughly 27-32 times the increase in VOC emissions in
the two scenarios. Thus, the projected reduction in CO emissions is
important from an ozone perspective. However, as described above, the
methodology for projecting the effect of ethanol use on CO emissions is
inconsistent with that for exhaust VOC and NOX emissions.
Thus, comparisons between changes in VOC and CO emissions are
particularly uncertain.
    There will also be some increases in emissions due to ethanol and
biodiesel production. Table VIII.B.1-2 shows estimates of annual
emissions expected to occur nationwide due to increased production of
ethanol. These estimates include a reduction in emissions related to
the distribution of the displaced gasoline. The table reflects the use
of

emissions factors from DOE's GREET model, version 1.7, as well as
estimates of ethanol plant emissions obtained from the States. It
should be noted that emissions in the base case assume an 80/20 mix of
dry mill and wet mill facilities. New plants (and thus, the emission
increases) assume 100% dry mill facilities.

         Table VIII.B.1-2.--Annual Emissions Nationwide From Ethanol Production and Transportation: 2012
                                                 [Tons per year]
----------------------------------------------------------------------------------------------------------------
                                                   GREET1.7                        GREET1.7 + State data
                                   -----------------------------------------------------------------------------
                                     Base case     RFS case     EIA case    Base case     RFS case     EIA case
----------------------------------------------------------------------------------------------------------------
                                      Emissions    Increase in emissions     Emissions    Increase in emissions
----------------------------------------------------------------------------------------------------------------
VOC...............................        8,000        5,000       11,000       14,000       10,000       20,000
NOX...............................       17,000       13,000       26,000       18,000       14,000       27,000
CO................................       49,000       35,000       72,000       56,000       40,000       81,000
PM10..............................       21,000       15,000       30,000       12,000        9,000       18,000
SOX...............................       27,000       20,000       41,000       42,000       30,000       61,000
----------------------------------------------------------------------------------------------------------------

    As can be seen, the potential increases in emissions from ethanol
production and transportation are of the same order of magnitude as
those from ethanol use, with the exception of CO emissions. The vast
majority of these emissions are related to farming and ethanol
production. Both farms and ethanol plants are generally located in
ozone attainment areas.
    Where counties are constructing new ethanol plants, expanding
existing plants, or planning construction for future plants, the
average increase in VOC and NOX emissions from plants alone
are about 26 tons/month VOC and 35 tons/month NOX using
state data (about 17 tons/month VOC and 25 tons/month NOX
using GREET 1.7 emission factors). Average VOC and NOX
emissions increase to about 61 tons/month and 83 tons/month,
respectively, in the 10% of counties expecting largest increases in
ethanol production. For both VOC and NOX, emissions
estimates are about 35% less when using the GREET 1.7 emission factors.
    Table VIII.B.1-3 shows estimates of annual emissions expected to
occur nationwide due to increased production of biodiesel. These
estimates include a reduction in emissions related to the distribution
of the displaced diesel fuel. Again, these emissions are generally
expected to be in ozone attainment areas.

Table VIII.B.1-3.--Annual Emissions Nationwide From Biodiesel Production
                           and Transportation
                             [Tons per year]
------------------------------------------------------------------------
                                                                 2012
                                                 Reference    Emissions
                                                 inventory:   inventory:
                   Pollutant                    30 mill gal    300 mill
                                                 biodiesel       gal
                                                  per year    biodiesel
                                                               per year
------------------------------------------------------------------------
VOC...........................................        1,400       14,000
NOX...........................................        1,500       15,000
CO............................................          800        8,000
PM10..........................................           50          500
SOX...........................................          250        2,500
------------------------------------------------------------------------

2. Sensitivity Analysis
    The national emission inventories for VOC and NOX in
2012 with current fuels are summarized in Table VIII.B.2-1. Here, the
emission effects contained in the EPA Predictive Models are assumed to
apply to all vehicles, not just Tier 0 vehicles. Also shown are the
changes in emissions projected for the two cases for future ethanol
volume.

 Table VIII.B.2-1.--2012 Emissions Nationwide From Gasoline Vehicles and
 Equipment Under Two Future Ethanol Use Scenarios--Sensitivity Analysis
                             [Tons per year]
------------------------------------------------------------------------
                                    Inventory    Change in inventory in
                                  -------------       control cases
            Pollutant               Reference  -------------------------
                                       case       RFS case     EIA case
------------------------------------------------------------------------
VOC..............................    5,834,000      -20,000       -4,000
NOX..............................    2,519,000       68,000      106,000
CO...............................   54,315,000     -692,000   -1,975,000
Benzene..........................      175,700       -5,000       -9,400
Formaldehyde.....................       39,600       -1,100         -700
Acetaldehyde.....................       19,500        3,000        6,600
1,3-Butadiene....................       18,600         -400         -600
------------------------------------------------------------------------

    The overall VOC and NOX emission impacts of the various
ethanol use scenarios change to some degree when all motor vehicles are
assumed to be sensitive to fuel ethanol content. The increase in VOC
emissions turns into a net decrease due to a greater reduction in
exhaust VOC emissions from onroad vehicles. However, the increase in
NOX emissions gets larger, as more vehicles are assumed to
be affected by ethanol. Emissions of the four air toxics generally
decrease slightly, due to the greater reduction in exhaust VOC
emissions.

3. Local and Regional VOC and NOX Emission Impacts in July
    We also estimate the percentage change in VOC, NOX, and
CO emissions from gasoline fueled motor vehicles and equipment in those
areas which actually experienced a significant change in ethanol use.
Specifically, we focused on areas where the market share of ethanol
blends was projected to change by 50 percent or more. We also focused
on summertime emissions, as these are most relevant to ozone formation.
Finally, we developed separately estimates for: (1) RFG areas,
including the state of California and the portions of Arizona where
their CBG fuel programs apply, (2) low RVP areas (i.e., RVP standards
less than 9.0 RVP, and (3) areas with a 9.0 RVP standard. This set of
groupings helps to highlight the emissions impact of increased ethanol
use in those areas where emission control is most important.
    Table VIII.B.3-1 presents our primary estimates of the percentage
change in VOC, NOX, and CO emission inventories for these
three types of areas. Note that the analyses here are very similar to
those described in Section 5.1 of the RIA, with the exception that
Table VIII.B.3-1 below reflects 50 states (instead of 37 eastern
states) and excludes diesel emissions.

 Table VIII.B.3-1.--July 2015 Change in Emissions from Gasoline Vehicles
   and Equipment in Counties Where Ethanol Use Changed Significantly--
                            Primary Analysis
------------------------------------------------------------------------
           Ethanol use                 RFS case            EIA case
------------------------------------------------------------------------
                                RFG Areas
------------------------------------------------------------------------
Ethanol Use.....................  Down..............  Up.
VOC.............................  0.8%..............  2.3%.
NOX.............................  -3.4%.............  1.6%.
CO..............................  6.1%..............  -2.6%.
------------------------------------------------------------------------
                              Low RVP Areas
------------------------------------------------------------------------
Ethanol Use.....................  Up................  Up.
VOC.............................  4.2%..............  4.6%.
NOX.............................  6.2%..............  5.7%.
CO..............................  -12.5%............  -13.7%.
------------------------------------------------------------------------
                          Other Areas (9.0 RVP)
------------------------------------------------------------------------
Ethanol Use.....................  Up................  Up.
VOC.............................  3.6%..............  4.6%.
NOX.............................  7.3%..............  7.0%.
CO..............................  -6.4%.............  -6.0%.
------------------------------------------------------------------------

    As expected, increased ethanol use tends to increase NOX
emissions. The increase in low RVP and other areas is greater than in
RFG areas, since the RFG in the RFG areas included in this analysis all
contained MTBE. Also, increased ethanol use tends to increase VOC
emissions, indicating that the increase in non-exhaust VOC emissions
exceeds the reduction in exhaust VOC emissions. This effect is muted
with RFG due to the absence of an RVP waiver for ethanol blends. We
would expect very similar results for 2012. The reader is referred to
Chapter 2 of the RIA for discussion of how ethanol levels will change
at the state-level.
    Table VIII.B.3-2 presents the percentage change in VOC,
NOX, and CO emission inventories under our sensitivity case
(i.e., when we apply the emission effects of the EPA Predictive Models
to all motor vehicles).

 Table VIII.B.3-2.--July 2015 Change in Emissions From Gasoline Vehicles
   and Equipment in Counties Where Ethanol Use Changed Significantly--
                          Sensitivity Analysis
------------------------------------------------------------------------
           Ethanol use                 RFS case            EIA case
------------------------------------------------------------------------
                                RFG Areas
------------------------------------------------------------------------
Ethanol Use.....................  Down..............  Up.
VOC.............................  -1.0%.............  1.0%.
NOX.............................  -0.9%.............  5.6%.
CO..............................  7.3%..............  -3.0%.
------------------------------------------------------------------------
                              Low RVP Areas
------------------------------------------------------------------------
Ethanol Use.....................  Up................  Up.
VOC.............................  3.4%..............  3.7%.
NOX.............................  10.4%.............  10.8%.
CO..............................  -15.0%............  -16.4%.
------------------------------------------------------------------------
                          Other Areas (9.0 RVP)
------------------------------------------------------------------------
Ethanol Use.....................  Up................  Up.
VOC.............................  3.0%..............  3.9%.
NOX.............................  10.8%.............  11.0%.

CO..............................  -9.0%.............  -8.9%.
------------------------------------------------------------------------

    Directionally, the changes in VOC and NOX emissions in
the various areas are consistent with those from our primary analysis.
The main difference is that the increases in VOC emissions are smaller,
due to more vehicles experiencing a reduction in exhaust VOC emissions,
and the increases in NOX emissions are larger.

C. Impact on Air Quality

    We estimate the impact of increased ethanol use on the ambient
concentrations of two pollutants: Ozone and PM. Quantitative estimates
are made for ozone, while only qualitative estimates can be made
currently for ambient PM. These impacts are described below.
1. Impact of Increased Ethanol Use on Ozone
    We use a metamodeling tool developed at EPA, the ozone response
surface metamodel (Ozone RSM), to estimate the effects of the projected
changes in emissions from gasoline vehicles and equipment for the RFS
and EIA cases. We included the estimated changes in emissions from
renewable fuel production and distribution. Because of limitations in
the Ozone RSM, we could not easily assign these emissions to the
specific counties where the plants are or are expected to be located.
Instead, we assigned all of the emissions related to renewable fuel
production and distribution to the set of states expected to contain
most of the production facilities.
    The Ozone RSM was created using multiple runs of the Comprehensive
Air Quality Model with Extensions (CAMX). Base and proposed
control CAMX metamodeling was completed for the year 2015
over a modeling domain that includes all or part of 37 Eastern U.S.
states, plus the District of Columbia. For more information on the
Ozone RSM, please see Chapter 5 of the RIA for this final rule.
    The Ozone RSM limits the number of geographically distinct changes
in VOC and NOX emissions which can be simulated. As a
result, we could not apply distinct changes in emissions for each
county. Therefore, two separate runs were made with different VOC and
NOX emissions reductions. We then selected the ozone impacts
from the various runs which best matched the VOC and NOX
emission reductions for that county. This models the impact of local
emissions reasonably well, but loses some accuracy with respect to
ozone transport. No ozone impact was assumed for areas which did not
experience a significant change in ethanol use. The predicted ozone
impacts of increased ethanol use for those areas where ethanol use is
projected to change by more than a 50% market share are summarized in
Table VIII.C.1-1. As shown in the Table 5.1-2 of the RIA, national
average impacts (based on the 37-state area modeled) which include
those areas where no change in ethanol use is occurring are
considerably smaller.

                Table VIII.C.1-1.--Impact on 8-Hour Design Value Equivalent Ozone Levels (ppb) a
----------------------------------------------------------------------------------------------------------------
                                                                  Primary analysis        Sensitivity analysis
                                                             ---------------------------------------------------
                                                                RFS case     EIA case     RFS case     EIA case
----------------------------------------------------------------------------------------------------------------
Minimum Change..............................................       -0.015        0.000       -0.115        0.028
Maximum Change..............................................        0.329        0.337        0.624        0.549
Average Change \b\..........................................        0.153        0.181        0.300        0.325
Population-Weighted Change \b\..............................        0.154        0.183        0.272       0.315
----------------------------------------------------------------------------------------------------------------
\a\ In comparison to the 80 ppb 8-hour ozone standards.
\b\ Only for those areas experiencing a change in ethanol blend market share of at least 50 percent.

    As can be seen, ozone levels generally increase to a small degree
with increased ethanol use. This is likely due to the projected
increases in both VOC and NOX emissions. Some areas do see a
small decrease in ozone levels. In our primary analysis, where exhaust
emissions from Tier 1 and later onroad vehicles are assumed to be
unaffected by ethanol use, the population-weighted increase in ambient
ozone levels in those areas where ethanol use changed significantly is
0.154-0.183 ppb. Since the 8-hour ambient ozone standard is 85 ppb,
this increase represents about 0.2 percent of the standard, a very
small percentage.
    In our sensitivity analysis, where exhaust emissions from Tier 1
and later onroad vehicles are assumed to respond to ethanol like Tier 0
vehicles, the population-weighted increase in ambient ozone levels is
slightly less than twice as high, or 0.272-0.315 ppb. This increase
represents about 0.35 percent of the standard.
    There are a number of important caveats concerning these estimates.
First, the emission effects of adding ethanol to gasoline are based on
extremely limited data for recent vehicles and equipment. Second, the
Ozone RSM does not account for changes in CO emissions. As shown above,
ethanol use should reduce CO emissions significantly, directionally
reducing ambient ozone levels in those areas where ozone formation is
VOC-limited. (Ozone levels in areas which are NOX-limited
are less likely to be affected by a change in CO emissions.) The Ozone
RSM also does not account for changes in VOC reactivity. With
additional ethanol use, the ethanol content of VOC should increase.
Ethanol is less reactive than the average VOC. Therefore, this change
should also reduce ambient ozone levels in a way not addressed by the
Ozone RSM, again in those areas where ozone formation is predominantly
VOC-limited. Because of these limitations, anyone interested in the
impact of increased ethanol use on ozone in any particular area should
utilize more comprehensive dispersion modeling which accounts for these
and other important factors.
    We received several requests in comments on the proposal to
quantify the impact of the reduced CO emissions

and VOC reactivity on ozone. As discussed in the S&A document, this is
not possible without running more sophisticated ambient dispersion
models. The impact of CO emissions and VOC reactivity on ozone vary
significantly depending on ambient conditions and the relative amount
of VOC and NOX in the atmosphere. Therefore, general rules
of thumb cannot be applied.
    Moving to health effects, exposure to ozone has been linked to lung
function decrements, respiratory symptoms, aggravation of asthma,
increased hospital and emergency room visits, increased asthma
medication usage, inflammation of the lungs, and a variety of other
respiratory effects and cardiovascular effects including premature
mortality. Ozone can also adversely affect the agricultural and
forestry sectors by decreasing yields of crops and forests. Although
the health and welfare impacts of changes in ambient ozone levels are
typically quantified in regulatory impact analyses, we do not evaluate
them for this analysis. On average, the changes in ambient ozone levels
shown above are small and would be even smaller if changes in CO
emissions and VOC reactivity were taken into account. The increase in
ozone would likely lead to negligible monetized impacts. We therefore
do not estimate and monetize ozone health impacts for the changes in
renewable use due to the small magnitude of this change, and the
uncertainty present in the air quality modeling conducted here, as well
as the uncertainty in the underlying emission effects themselves
discussed earlier.
2. Particulate Matter
    Ambient PM can come from two distinct sources. First, PM can be
directly emitted into the atmosphere. Second, PM can be formed in the
atmosphere from gaseous pollutants. Gasoline-fueled vehicles and
equipment contribute to ambient PM concentrations in both ways.
    As described above, we are not currently able to predict the impact
of fuel quality on direct PM emissions from gasoline-fueled vehicles or
equipment. Therefore, we are unable at this time to project the effect
that increased ethanol use will have on levels of directly emitted PM
in the atmosphere.
    PM can also be formed in the atmosphere (termed secondary PM here)
from several gaseous pollutants emitted by gasoline-fueled vehicles and
equipment. Sulfur dioxide emissions contribute to ambient sulfate PM.
NOX emissions contribute to ambient nitrate PM. VOC
emissions contribute to ambient organic PM. Increased ethanol use is
not expected to change gasoline sulfur levels, so emissions of sulfur
dioxide and any resultant ambient concentrations of sulfate PM are not
expected to change. Increased ethanol use is expected to increase
NOX emissions, so the possibility exists that ambient
nitrate PM levels could increase. Increased ethanol is generally
expected to increase total VOC emissions, which could also impact the
formation of secondary organic PM. However, while non-exhaust VOC
emissions are expected to increase, exhaust VOC emissions are expected
to decrease. Generally, the higher the molecular weight of the specific
VOC emitted, the greater the likelihood it will form PM in the
atmosphere. Non-exhaust VOC is predominantly low in molecular weight,
as much of it is due to fuel evaporating. Thus, emissions of VOCs
likely to form PM in the atmosphere are likely decreasing with ethanol
use.
    The formation of secondary organic PM is very complex, due in part
to the wide variety of VOCs emitted into the atmosphere. The degree to
which a specific gaseous VOC reacts to form PM in the atmosphere
depends on the types of reactions that specific VOC undergoes and the
products of those reactions. Both of these factors depend on other
pollutants present, such as the hydroxyl radical, ozone, NOX
and other reactive compounds. The relative mass of secondary PM formed
per mass of gaseous VOC emitted can also depend on the total
concentration of gaseous VOC and organic PM in the atmosphere. Most of
the secondary organic PM exists in a continually changing equilibrium
between the gaseous and PM phases. Both the rates of these reactions
and the gaseous-PM equilibria depend on temperature, so seasonal
differences can be expected.
    Recent smog chamber studies have indicated that gaseous aromatic
VOCs can form secondary PM under certain conditions. These compounds
comprise a greater fraction of exhaust VOC emissions than non-exhaust
VOC emissions, as non-exhaust VOC emissions are dominated by VOCs with
relatively high vapor pressures. Aromatic VOCs tend to have lower vapor
pressures. As increased ethanol use is expected to reduce exhaust VOC
emissions, emissions of aromatic VOCs should also decrease. In
addition, refiners are expected to reduce the aromatic content of
gasoline by 5 volume percentage points as ethanol is blended into
gasoline. Emissions of aromatic VOCs should decrease with lower
concentrations of aromatics in gasoline. Thus, emissions of gaseous
aromatic VOCs could decrease for both reasons.
    Overall, we expect that the decrease in secondary organic PM is
likely to exceed the increase in secondary nitrate PM. In 1999,
NOX emissions from gasoline-fueled vehicles and equipment
comprised about 20% of national NOX emissions from all
sources. In contrast, gasoline-fueled vehicles and equipment comprised
over 60% of all national gaseous aromatic VOC emissions. The percentage
increase in national NOX emissions due to increased ethanol
use should be smaller than the percentage decrease in national
emissions of gaseous aromatics. Finally, in most urban areas, ambient
levels of secondary organic PM exceed those of secondary nitrate PM.
Thus, directionally, we expect a net reduction in ambient PM levels due
to increased ethanol use. However, we are unable to quantify this
reduction at this time.
    EPA currently utilizes the CMAQ model to predict ambient levels of
PM as a function of gaseous and PM emissions. This model includes
mechanisms to predict the formation of nitrate PM from NOX
emissions. However, it does not currently include any mechanisms
addressing the formation of secondary organic PM. EPA is currently
developing a model of secondary organic PM from gaseous toluene
emissions. We plan to incorporate this mechanism into the CMAQ model in
2007. The impact of other aromatic compounds will be added as further
research clarifies their role in secondary organic PM formation.
Therefore, we expect to be able to quantitatively estimate the impact
of decreased toluene emissions and increased NOX emissions
due to increased ethanol use as part of future analyses of U.S. fuel
requirements required by the Act.

IX. Impacts on Fossil Fuel Consumption and Related Implications

    Renewable fuels have been of significant interest for many years
due to their potential to displace fossil fuels, which have often been
targeted as primary contributors to emissions of greenhouse gases such
as carbon dioxide, and national energy concerns primarily due to an
increasing dependence on foreign sources of petroleum. In the Notice of
Proposed Rulemaking, we provided a preliminary assessment of the
greenhouse gas emission and energy impacts of renewable fuel and an
initial assessment of the economic value of renewable fuel displacing
petroleum-based fuels. We

also indicated that we would be updating an analysis of energy security
impacts that had been prepared by analysts at the Oak Ridge National
Laboratory (ORNL) of the Department of Energy. We present some
discussion of that analysis here.
    We also performed a full lifecycle or well-to-wheel analysis for
this final rule to estimate the GHG and fossil energy reductions from
replacing petroleum based fuels with renewable fuels. Argonne National
Laboratory's (ANL) GREET \109\ model was utilized for this lifecycle
analysis. Table IX-1 summarizes this model's estimated impact that
increases in the use of renewable fuels are projected to have on GHG
emissions and fossil fuel consumption for the two renewable fuel volume
scenarios considered in this final rulemaking relative to the reference
case. As described later in this section, the results in Table IX-1 are
based on a number of input assumptions including coal being used as
process fuel in 14% of ethanol facilities.
---------------------------------------------------------------------------

    \109\ Greenhouse gases, Regulated Emissions, and Energy use in
Transportation.
---------------------------------------------------------------------------

    As noted in Section III, although we have chosen to base our
lifecycle analyses on Argonne National Laboratory's GREET model there
are a variety of other lifecycle models and analyses available. The
choice of model inputs and assumptions all have a bearing on the
results of lifecycle analyses, and many of these assumptions remain the
subject of debate among researchers. Lifecycle analyses must also
contend with the fact that the inputs and assumptions generally
represent industry-wide averages even though energy consumed and
emissions generated can vary widely from one facility or process to
another.
    There currently exists no organized, comprehensive dialogue among
stakeholders about the appropriate tools and assumptions behind any
lifecycle analyses. We will be initiating more comprehensive
discussions about lifecycle analyses with stakeholders in the near
future.

 Table IX-1.--GREET Model Lifecycle Reductions From Increased Renewable Fuel Use Relative to the 2012 Reference
                                                      Case
----------------------------------------------------------------------------------------------------------------
                                                                     RFS case                   EIA case
                                                           -----------------------------------------------------
                                                                          % of trans.                % of trans.
                                                              Reduction      sector      Reduction      sector
----------------------------------------------------------------------------------------------------------------
Fossil Energy (QBtu)......................................         0.15          0.48         0.27          0.85
Petroleum Energy (Bgal)...................................         2.0           0.82         3.9           1.60
GHG Emissions (MMT CO2-eq.)...............................         8.0           0.36        13.1           0.59
CO2 Emissions (MMT CO2)...................................        11.0           0.52        19.5           0.93
----------------------------------------------------------------------------------------------------------------

    We used the petroleum energy reductions shown in Table IX-1 to
determine implications on imports of petroleum products. Our analysis
found that calculated petroleum energy reductions come almost entirely
from imports of finished products in this 2012 case and amount to the
equivalent of 123,000 barrels of transportation fuel under the RFS case
and 240,000 barrels of transportation fuel for the EIA case.
    Another effect of increased use of renewable fuels in the U.S. is
that it diversifies the energy sources in making transportation fuel.
Diverse sources of fuel energy reduce both financial and strategic
risks associated with a potential disruption in supply or a spike in
cost of a particular energy source. This reduction in risks is a
measure of improved energy security. The ORNL report used an ``oil
premium'' approach to identify those energy-security related impacts
which are not reflected in the market price of oil, and which are
expected to change in response to an incremental change in the level of
U.S. oil imports.
    The following sections provide a more complete description of our
analyses of the GHG emissions, fossil fuel, oil imports, and energy
security impacts of this final rule.

A. Impacts on Lifecycle GHG Emissions and Fossil Energy Use

    Although the use of renewable fuels in the transportation sector
directly displaces some petroleum consumed as motor vehicle fuel, this
displacement of petroleum is in fact only one aspect of the overall
impact of renewable fuels on fossil fuel use. Fossil fuels are also
used in producing and transporting renewable feedstocks such as plants
or animal byproducts, in converting the renewable feedstocks into
renewable fuel, and in transporting and blending the renewable fuels
for consumption as motor vehicle fuel. To estimate the true impacts of
increases in renewable fuels on fossil fuel use, modelers attempt to
take many or all these steps into account.
    Similarly, energy is used and GHGs emitted in the pumping of oil,
transporting the oil to the refinery, refining the crude oil into
finished transportation fuel, transporting the refined gasoline or
diesel fuel to the consumer and then burning the fuel in the vehicle.
Such analyses are termed lifecycle or well-to-wheels analyses. We
performed a full lifecycle analysis as part of this final rulemaking to
determine the GHG and fossil energy reductions from the increased use
of renewable fuels.
    This lifecycle assessment approach and rationale were highlighted
in the proposal. Comments received focused mainly on improving the
process, for example the choice of lifecycle model used and initiating
a stakeholder dialogue to build consensus around the assumptions and
approach. In general comments were supportive of using a full lifecycle
assessment approach, but differed on the appropriate model and
associated assumptions EPA should use in its analysis.
1. Time Frame and Volumes Considered
    The results presented in this analysis represent a snapshot in
time. They represent annual GHG and fossil fuel savings in the year
considered, in this case 2012. Consistent with the emissions modeling
described in Section VII, our analysis of the GHG and fossil fuel
consumption impacts of renewable fuel use was conducted using three
volume scenarios. The first scenario was the same reference case used
elsewhere in this final rulemaking. The reference case scenario
provided the point of comparison for the other two scenarios. The other
two renewable fuel scenarios for 2012 represented the

RFS program requirements and the volume projected by EIA.
    In both the RFS and EIA scenarios, we assumed that the biodiesel
production volume would be 0.303 billion gallons based on EIA AEO2006
projections. Furthermore, for both scenarios we assume that 250 million
gallons of ethanol that qualify for cellulosic biomass ethanol credit
will be produced in 2012 from corn using biomass as the process energy
source. The remaining renewable fuel volumes in each scenario would be
ethanol made from corn and imports. The import volume is based on EIA's
projections for the percent of total ethanol volume supplied by imports
in 2012. The total volumes for all three scenarios are shown in Table
II.A.1-1.
    For the purposes of calculating this difference or the amount of
conventional fuel no longer consumed--that is, displaced--as a result
of the use of the replacement renewable fuel, we assumed the ethanol
volumes shown in Table II.A.1-1 are 5% denatured. The ethanol volumes
were adjusted down to represent pure (100%) ethanol, biodiesel volumes
were not adjusted. The adjusted volumes were then converted to total
Btu using the appropriate volumetric energy content values (76,000 Btu/
gal for ethanol, 115,000 Btu/gal for gasoline, 118,000 Btu/gal for
biodiesel, and 130,000 Btu/gal for diesel fuel). We make the assumption
that vehicle energy efficiency will not be affected by the presence of
renewable fuels (i.e., efficiency of combusting one Btu of ethanol is
equal to the efficiency of combusting one Btu of gasoline).
    This lifecycle analysis is conducted without any regard to the
geographic attributes of where emissions or energy use occurs; the
model represents global reductions in GHG emissions and energy use, not
just those occurring in the U.S. For example, under a full lifecycle
assessment approach, the savings associated with reducing overseas
crude oil extraction and refining are included, as are the
international emissions associated with producing imported ethanol.
There were two exceptions to this, both dealing with secondary impacts
that may result internationally due to the expanded use of renewable
fuels within the United States.
    The first exception is the emissions associated with international
land use change. Due to decreasing corn exports some changes to
international land use may occur, for example, as more crops are
planted in other regions to compensate for the decrease in crop exports
from the U.S. While the emissions associated with domestic land use
change are well understood and are included in our lifecycle analysis,
we did not include the potential impact on international land use and
any emissions that might directly result. Our currently modeling
capability does not allow us to assess what international land use
changes would occur or how these changes would affect greenhouse gas
emissions. For example, we would need to know how international
cropping patterns would change as well as farming inputs and practices
that might affect emissions assessment.
    The second case where we have not quantified the international
impacts results from any reduction in world oil prices would tend to
result from decreased demand in the U.S. as renewable fuels replace
oil. It is commonly presumed in economic analyses that all else being
equal quantity demanded of a valuable good (i.e., oil) will increase as
price decreases. A world wide reduction of oil price would tend to
reduce the cost of producing transportation fuel which in turn would
tend to reduce the price consumers internationally would have to pay
for this fuel.
    To the extent fuel prices are decreased, demand and consumption
would tend to increase; this impact of reduced cost of driving is
sometimes referred to as a ``rebound effect.'' Such a greater
consumption internationally would presumably result in an increase in
greenhouse gas emissions as consumers in the rest of the world drive
more. These increased emissions would in part offset the emission
impacts otherwise described in this preamble. While such international
impacts of U.S. actions are important to understand, we have not have
fully considered and quantified the international rebound effects of
this renewable fuel standard. Nevertheless, such impacts remain an
important consideration for future analysis.
2. GREET Model
    As in the analyses for the proposal, for this final rulemaking we
used the GREET fuel-cycle model. 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 analyses
of energy consumed and emissions generated, we believe that GREET
offers the most comprehensive treatment of the transportation sector.
For this final rule, we used an updated version of the GREET model
\110\, with a few modifications to its input assumptions. These changes
since the NPRM are described below.
---------------------------------------------------------------------------

    \110\ GREET version 1.7, released November 10, 2006.
---------------------------------------------------------------------------

    The two main comments we received on our lifecycle modeling were
that we should initiate a public dialogue on lifecycle analyses, models
and assumptions, and that our sole reliance on the GREET model should
be avoided, given other models are available. We have begun a public
dialogue in that we identify the assumptions in the GREET model that
were examined and modified for this final rulemaking. Furthermore, we
will be initiating more comprehensive discussions about lifecycle
analysis with stakeholders which could lead to an increased use of
lifecycle analysis in future actions.
    In terms of our sole reliance on the GREET model, several other
models have been developed for conducting renewable fuels lifecycle
analysis. For example, researchers at the Energy and Resources Group
(ERG) of the University of California Berkeley have developed the ERG
Biofuel Analysis Meta-Model (EBAMM) and Mark Delucchi at the Institute
of Transportation Studies of the University of California Davis has
developed the Lifecycle Emissions Model (LEM). Other non-fuel specific
lifecycle modeling tools could also be used to perform renewable fuel
lifecycle analysis.
    Several studies have been released recently making use of these
other models and showing different results than we find in the analysis
done for this rule. For example, whereas GREET estimates a net GHG
reduction of about 22% for corn ethanol compared to gasoline, the
previously cited works by Farrell et al. utilizing the EBAMM show
around a 13% reduction. The main difference in results is not due to
the model used but assumptions on scope and input data.
    For example, most studies focus on average or current ethanol
production which uses a current mix of wet and dry mill ethanol
production and use of coal and natural gas as process energy. In
contrast, for this rulemaking we consider future increases in renewable
fuel production so we focus on new production capacity which will rely
more heavily on more efficient dry mill production than the current mix
of wet and dry mill capacities. Other studies also typically base
ethanol and farm energy use on historic data while we are assuming
future capacity increases will use a state of the art dry milling plant
and most current farming energy use

data. Varying assumptions concerning how land use change impact
CO2 emissions and agriculture related GHG emissions could
also have an impact on overall results. Other studies also differ in
the environmental flows considered. For example, GREET uses the
internationally accepted set of greenhouse gases while Delucchi uses
additional types of greenhouse gases.
    We have not had an opportunity to develop comparable analyses of
the GHG and energy impacts of this rule using these other models.
However, as discussed in chapters 6.1.1 and 6.2.3 of the RIA, we
believe the scope of the GREET model and the assumptions we have used
in running the model tend toward the middle of the range. Therefore we
believe these results provide a reasonable assessment of the energy and
GHG impacts of the expanded use of renewable fuels.
a. Renewable Fuel Pathways Considered
    The feedstocks and processes used to model renewable fuel
production were those which our analysis in Chapter 1 of the RIA shows
will primarily be used through 2012. However, other pathways for
producing renewable fuels may become popular such as producing
cellulosic biomass ethanol from municipal solid waste as well as
different process for the feedstocks considered, like gasification of
switchgrass and production of ``renewable'' diesel fuel through
hydrotreating vegetable oils.
    Furthermore, the lifecycle analysis used for this rulemaking is
based on averages of the different renewable fuels modeled. For
example, the GHG emission and fossil energy savings associated with
increased use of corn ethanol are calculated based on a mix of corn wet
and dry milling, assuming a certain projected mix of each process.
While this method may not exactly represent the reductions associated
with a given gallon of renewable fuel, it is accurate for the purpose
of this analysis which is to determine the impact of the total
increased volume of renewable fuels used.
    We recognize that different feedstocks and processes will each have
unique characteristics when it comes to lifecycle GHG emissions and
energy use. However, we understand that other feedstocks and processes
as well as differences in other parts of the renewable fuel lifecycle
will impact the savings associated with their use and this is the focus
of ongoing work at the agency.
b. Modifications to GREET
    Since the analysis done for the NPRM, we have updated the GREET
model with the following changes:

--Included CO2 emissions from corn farming lime use.
--Updated the corn farming fertilizer use inputs.
--Added cellulosic biomass ethanol production from corn stover and
forest waste.
--Modeled biomass as a process fuel source in corn ethanol dry milling.

    In addition to the changes listed above we also examined and
updated other GREET input assumptions for corn ethanol and biodiesel
production.
    We also examined several other GREET input values, but determined
that the default GREET values should not be changed for a variety of
reasons. These included, corn and ethanol transport distances and modes
and byproduct allocation methods. Our investigation of these other
GREET input values are discussed more fully in Chapter 6 of the RIA.
The current GREET default factors for these other inputs were included
in the analysis for this final rule.
    We did not investigate the input values associated with the
production of petroleum-based gasoline or diesel fuel in the GREET
model for this final rule. However, the refinery modeling discussed in
Section VII provides some additional information on the process energy
requirements associated with the production of gasoline and diesel
under a renewable fuels mandate. We will use information from this
refinery modeling in future analysis to determine if any GREET input
values should be changed.
    A summary of the GREET input values we investigated and modified
for the final rule analysis is given below.
    Corn Farming Energy Use: Corn farming energy use was updated based
on the most recent USDA Agricultural Resource Management Survey (ARMS)
data.
    CO2 from Land Use Change: The GREET model has a default factor for
CO2 from land use change that was included in the NPRM
analysis. This factor was updated based on the results of the
agricultural sector modeling outlined in Section X. The CO2
emissions from land use change used in the final rulemaking represents
approximately 1% of total corn ethanol lifecycle GHG emissions.
However, this value could be more significant if increased amounts of
renewable fuels are used in the transportation sector. The issue of
CO2 emissions from land use change associated with
converting forest or Conservation Reserve Program (CRP) land into crop
production for use in producing renewable fuels is an important factor
to consider when determining the overall sustainability of renewable
fuel use. While the analysis described above is indicating that the
volumes of renewable fuel analyzed in this rulemaking will not cause a
significant change in land use, this is an area we will continue to
research for any future analysis.
    Corn Ethanol Wet-Mill Versus Dry Mill Plants: For this analysis, we
expect most new ethanol plants will be dry mill operations. That has
been the trend in the last few years as the demand for ethanol has
grown, and our analysis of ethanol plants under construction and
planned for the near future has verified this. Our analysis of
production plans, as outlined in Section VI, indicates that essentially
all new ethanol production will be from dry mill plants (99%).
    Corn Ethanol Dry Mill Plant Energy Use and Fuel Mix: Our review of
plants under construction and those planned for the near future as
outlined in Section VI, indicates that coal will be used as process
fuel for approximately 14% of the new under construction and planned
ethanol production volume capacity. The energy use at a dry mill plant
using natural gas was based on the model developed by USDA and modified
by EPA for use in the cost analysis of this rulemaking described in
Section VII. For this analysis, we assumed that a coal plant would
require 15% \111\ more electricity demand due to coal handling and have
a 13% increase in thermal demand for steam dryers as compared to the
natural gas fueled plant. We also considered a case where a corn
ethanol plant utilized biomass as a fuel source. For this case we
assumed the same amount of fuel and purchased electricity energy per
gallon as a coal powered plant. This assumption is based on the biomass
plant having more fuel handling than a natural gas plant and producing
steam for DDGS drying.
---------------------------------------------------------------------------

    \111\ Baseline Energy Consumption Estimates for Natural Gas and
Coal-based Ethanol Plants--The Potential Impact of Combined Heat and
Power (CHP), Prepared for: U.S. Environmental Protection Agency
Combined Heat & Power Partnership, Prepared by: Energy and
Environmental Analysis, Inc., July 2006.
---------------------------------------------------------------------------

    Corn Ethanol Dry Mill Plant Production Yield: Modern ethanol plants
are now able to produce more than 2.7 gallons of ethanol per bushel of
corn compared with less than 2.4 gallons of ethanol per bushel of corn
in 1980. The development of new enzymes continues to increase the
potential ethanol yield. We used a value of

2.71 \112\ gal/bu in our analysis, which may underestimate actual
future yields. For additional information on our yield analysis, see
the cost modeling of corn ethanol discussed in Section VII.
---------------------------------------------------------------------------

    \112\ All yield values presented represent pure ethanol
production (i.e. no denaturant).
---------------------------------------------------------------------------

    Corn Ethanol Co-Products: We based the amount of DDGS produced by
an ethanol dry mill plant on the USDA model used in the cost analysis
work of this rulemaking, described in Section VII. Based on the
agricultural sector modeling outlined in Section X, we assumed that one
ton of DDGS displaces 0.5 tons of corn and 0.5 tons of soybean meal. We
also assume for corn ethanol wet milling that one ton of corn gluten
meal substitutes for one ton of soybean meal, one ton of corn gluten
feed substitutes for 0.5 tons of corn, and one ton of corn oil
substitutes for one ton of soybean oil.
    Biodiesel Production: Two scenarios for biodiesel production were
considered, one utilizing soybean oil as a feedstock and one using
yellow grease. For the soybean oil scenario, the energy use and inputs
for the biodiesel production process were based on a model developed by
NREL and used by EPA in the cost modeling of soybean oil biodiesel, as
discussed in Section VII. The GREET model does not have a specific case
of biodiesel production from yellow grease. Therefore, as a surrogate
we used the soybean oil based model with several adjustments. For the
yellow grease case, we did not include soybean agriculture emissions or
energy use. Soybean crushing was still included as a surrogate for
yellow grease processing (purification, water removal, etc.). Also, due
to additional processing requirements, the energy use associated with
producing biodiesel from yellow grease is higher than for soybean oil
biodiesel production. As per the cost modeling of yellow grease
biodiesel discussed in Section VII, the energy use for yellow grease
biodiesel production was assumed to be 1.72 times the energy used for
soybean oil biodiesel.
    Biodiesel Transportation: Biodiesel transportation was based on the
distribution infrastructure modeling for this rulemaking which
indicates pipelines are not currently used to transport biodiesel and
are not projected to play a role in biodiesel transport in the future
time frame considered. Therefore, GREET default factors for biodiesel
transportation from plant to terminal were modified to remove pipeline
transport.
c. Sensitivity Analysis
    As mentioned above, the results of lifecycle analysis are highly
dependent on the input data assumptions used. Section IX.A.1.b outlined
changes made to the GREET model inputs to better represent the scope
and purpose of our analysis for this rulemaking. However, we also
performed several sensitivity analyses on some key assumptions to see
how varying them would impact overall results.
    We performed a sensitivity analysis on expanding the lifecycle fuel
production system boundaries to include farm equipment production
(e.g., emissions and energy use associated with producing steel,
rubber, etc. used to make farming equipment). It was found that
including farm equipment production energy use and emissions increases
ethanol lifecycle energy use and GHG emissions by approximately 1
percent. Therefore, the lifecycle results are not changed significantly
due to this expansion of system boundaries.
    We also performed a sensitivity analysis on the allocation method
used in ethanol production. A number of by-products are made during the
production of ethanol. In lifecycle analyses, the energy consumed and
emissions generated by an ethanol plant must be allocated not only to
ethanol, but also to each of the by-products. There are a number of
methods that can be used to estimate by-product allocations. The
displacement method for by-product allocation, described in Section
<IP_ADDRESS> of the RIA, is the default for the GREET model and is the
method used by EPA. However, we evaluated another method, the process
energy approach, to determine the impact this assumption has on the
overall results of the analysis.
    Use of the process energy based allocation method reduces ethanol
lifecycle energy use and GHG emissions by approximately 30 percent
compared to the displacement allocation approach. This indicates that
ethanol lifecycle analysis results are extremely sensitive to the
choice of allocation method used. (See the RIA, Chapter 6 for more
information on these two by-product allocation methods) The
displacement allocation method is the method supported by international
lifecycle assessment standards \113\ and therefore EPA feels that it is
the most accurate and preferred method to use. This does however
highlight the sensitivity of lifecycle analysis results to choice of
input parameters and assumptions.
---------------------------------------------------------------------------

    \113\ ISO 14044:2006(E), ``Environmental Management--Life Cycle
Assessment--Requirements and Guidelines'', International
Organization for Standardization (ISO), First edition, 2006-07-01,
Switzerland.
---------------------------------------------------------------------------

3. Displacement Indexes (DI)
    The displacement index (DI) represents the percent reduction in GHG
emissions or fossil fuel energy brought about by the use of a renewable
fuel in comparison to the conventional gasoline or diesel that the
renewable fuel replaces. The formula for calculating the displacement
index depends on which fuel is being displaced (i.e. gasoline or
diesel), and which endpoint is of interest (e.g. petroleum energy,
GHG). For instance, when investigating the CO2 impacts of
ethanol used in gasoline, the displacement index is calculated as
follows:
[GRAPHIC] [TIFF OMITTED] TR01MY07.058

    The units of g/Btu ensure that the comparison between the renewable
fuel and the conventional fuel is made on a common basis, and that
differences in the volumetric energy content of the fuels is taken into
account. The denominator includes the CO2 emitted through
combustion of the gasoline itself in addition to all the CO2
emitted during its manufacturer and distribution. The numerator, in
contrast, includes only the CO2 emitted during the
manufacturer and distribution of ethanol, not the CO2
emitted during combustion of the ethanol.
    The combustion of biomass-based fuels, such as ethanol from corn
and woody crops, generates CO2. However, in the long run the
CO2 emitted from biomass-based fuels combustion does not
increase atmospheric CO2 concentrations, assuming the
biogenic carbon emitted is offset by the uptake of CO2
resulting from the growth of new biomass. Thus ethanol's carbon can be
thought of as cycling from the environment into the plant material

used to make ethanol and, upon combustion of the ethanol, back into the
environment from which it came. As a result, CO2 emissions
from biomass-based fuels combustion are not included in their lifecycle
emissions results and are not used in the CO2 displacement
index calculations shown above. Net carbon fluxes from changes in
biogenic carbon reservoirs in wooded or crop lands are accounted for
separately in the GREET model.
    Using GREET, we calculated the lifecycle values for energy consumed
and GHGs produced for corn-ethanol, cellulosic ethanol, and soybean-
based biodiesel. These values were in turn used to calculate the
displacement indexes. The results are shown in Table IX.A.3-1. Details
of these calculations can be found in Chapter 6 of the RIA.

                                                Table IX.A.3-1.--Displacement Indexes Derived From GREET
                                                                      [In percent]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                Corn ethanol       Cellulosic
                                                                  Corn ethanol     (biomass fuel)        ethanol      Imported ethanol      Biodiesel
--------------------------------------------------------------------------------------------------------------------------------------------------------
DIFossil Fuel.................................................         39.4              76.3              92.7              69.0              61.5
DIPetroleum...................................................         91.8              92.0              91.7              92.0              91.2
DIGHG.........................................................         21.8              54.1              90.9              56.0              67.7
DICO2.........................................................         40.3              72.3             100.1              71.0              69.8
--------------------------------------------------------------------------------------------------------------------------------------------------------

    The displacement indexes in this table represent the impact of
replacing a Btu of gasoline or diesel with a Btu of renewable fuel.
Thus, for instance, for every Btu of gasoline which is replaced by corn
ethanol, the total lifecycle GHG emissions that would have been
produced from that Btu of gasoline would be reduced by 21.8 percent.
For every Btu of diesel which is replaced by biodiesel, the total
lifecycle petroleum energy that would have been consumed as a result of
burning that Btu of diesel fuel would be reduced by 91.2 percent.
    Consistent with the cost modeling done for this rule, for the 2012
cases we assume the ``cellulosic'' ethanol volume is actually produced
from corn utilizing a biomass fuel source at the ethanol production
plant. The displacement index for that fuel as shown in Table IX.A.3-1
is used in the calculation of reductions. We have included the column
for cellulosic ethanol for comparison, indicating that a move toward
cellulosic ethanol will not displace petroleum much differently than
other renewable fuels but will have a positive impact on GHG emissions
reductions.
    For imported ethanol, it is more difficult to estimate the
lifecycle energy and GHG displacement indexes since we know much less
about how the crops used to make the ethanol are grown and what energy
is used in the ethanol production facility. While not exclusively, we
anticipate much imported ethanol to be primarily sugarcane based
ethanol.
    The GHG emissions when producing sugarcane ethanol differs from
corn ethanol in that the GHG emissions from growing sugarcane is likely
different than for growing a equivalent amount of corn to make a gallon
of ethanol. Also, the process of turning sugar into ethanol is easier
than when starting with starch and therefore less energy intensive
(which typically translates into lower GHG). Importantly, we understand
that at least some of the ethanol produced in Brazil uses the bagasse
from the sugarcane itself as a process fuel source. We know from our
analysis that using a biomass source for process energy greatly
improves the GHG benefit of the renewable fuel. These factors would
result in sugarcane ethanol having a greater GHG benefit per gallon
than corn ethanol, certainly where natural gas or coal is the typical
process fuel source used.
    Conversely, sugarcane ethanol production does not result in a co-
product such as distillers grain as in the case of corn ethanol. In our
analyses, accounting for co-products significantly improved the GHG
displacement index for corn ethanol. Furthermore, there would be
additional transportation emissions associated with transporting the
imported ethanol to the U.S. as compared to domestically produced
ethanol. Developing a technically rigorous lifecycle estimate for
energy needs and GHG impacts for imported ethanol is not a simple task
and was not available in the timeframe of this rulemaking.
    Considering all of the differences between imported and domestic
ethanol, for this rulemaking, we assumed imported ethanol would be
predominately from sugarcane and have estimated DI's approximately mid-
way between the DI's for corn ethanol and DI's for cellulosic ethanol.
We are continuing to develop a better understanding of the lifecycle
energy and GHG impacts of producing ethanol from sugarcane and other
likely feedstock sources of imported ethanol for any future analysis.
4. Impacts of Increased Renewable Fuel Use
    We used the methodology described above to evaluate impacts of
increased use of renewable fuels on consumption of petroleum and fossil
fuels and also on emissions of CO2 and GHGs. This section
describes our results.
a. Greenhouse Gases and Carbon Dioxide
    We estimated the reduction associated with the increased use of
renewable fuels on lifecycle emissions of CO2 and total GHG.
Since total GHG emission reductions are lower than CO2
reductions, this indicates that lifecycle emissions of CH4
and N2O are higher for renewable fuels than for the
conventional fuels replaced. These values are then compared to the U.S.
transportation sector emissions to get a percent reduction. The
estimates for the 2012 cases are presented in Table IX.A.4.a-1.

 Table IX.A.4.A-1.--Estimated CO2 and GHG Emission Impacts of Increased
Use of Renewable Fuels in the Transportation Sector in 2012, Relative to
                         the 2012 Reference Case
------------------------------------------------------------------------
                                               RFS case       EIA case
------------------------------------------------------------------------
CO2 Reduction (million metric tons CO2)...         11.0           19.5
Percent reduction in Transportation Sector          0.52           0.93
 CO Emissions.............................
GHG Reduction (million metric tons CO2-             8.0           13.1
 eq.).....................................
Percent reduction in Transportation Sector          0.36           0.59
 GHG Emissions............................
------------------------------------------------------------------------

b. Fossil Fuel and Petroleum
    We estimated the reduction associated with the increased use of
renewable fuels on lifecycle fossil fuels and petroleum. These values
are then compared to the U.S. transportation sector emissions to get a
percent reduction. The estimates for the 2012 cases are presented in
Table IX.A.4.b-1.

    Table IX.A.4.B-1.--Estimated Fossil Fuel and Petroleum Impacts of
 Increased Use of Renewable Fuels in the Transportation Sector in 2012,
                   Relative to the 2012 Reference Case
------------------------------------------------------------------------
                                               RFS case       EIA case
------------------------------------------------------------------------
Fossil Fuel Reduction (quadrillion Btu)...          0.15           0.27
Percent reduction in Transportation Sector          0.48           0.85
 Fossil Fuel Use..........................
Petroleum Energy Reduction (billion gal.).          2.0            3.9
Percent reduction in Transportation Sector          0.82           1.60
 Petroleum Use............................
------------------------------------------------------------------------

B. Implications of Reduced Imports of Petroleum Products

    In the proposal, we estimated the impact of expanded renewable fuel
use on the importation of oil and finished transportation fuel. No
comments were received suggesting alternative methodologies should be
used. Therefore, we have incorporated that calculation in this final
rule without change.
    In 2005, the United States imported almost 60 percent of the oil it
consumed. This compares to just over 35 percent of oil from imports in
1975.\114\ Transportation accounts for 70 percent of the U.S. oil
consumption. It is clear that oil imports have a significant impact on
the U.S. economy. Expanded production of renewable fuel is expected to
contribute to energy diversification and the development of domestic
sources of energy. We consider whether the RFS will reduce U.S.
dependence on imported oil by calculating avoided expenditures on
petroleum imports. Note that we do not calculate whether this reduction
is on the net, socially beneficial, which would depend on the scarcity
value of domestically produced ethanol versus that of imported
petroleum products. However, the next section does discuss some of the
energy security implications unique to petroleum imports.
---------------------------------------------------------------------------

    \114\ Davis, Stacy C.; Diegel, Susan W., Transportation Energy
Data Book: 25th Edition, Oak Ridge National Laboratory, U.S.
Department of Energy, ORNL-6974, 2006.
---------------------------------------------------------------------------

    To assess the impact of the RFS program on petroleum imports, we
estimate the fraction of domestic consumption derived from foreign
sources using results from the AEO 2006. We compared the levels and mix
of imports in the AEO reference case with those in the low
macroeconomic growth case and high oil price case. In Section 6.4.1 of
the RIA we describe in greater detail how fuel producers may change
their levels and mix of imports in response to a decrease in fuel
demand. For the purposes of this rulemaking, we show values for the low
macroeconomic growth comparison, where import reductions come almost
entirely from imports of finished products as shown below in Table
IX.B-1. The reductions in imports are compared to the AEO projected
levels of net petroleum imports. The range of reductions in net
petroleum imports are estimated to be between 0.9 to 1.7 percent, as
shown in Table IX.B-1.

            Table IX.B-1.--Net Reductions in Imports in 2012
------------------------------------------------------------------------
                                                     RFS case   EIA case
------------------------------------------------------------------------
Reduction in finished products* (barrels per day).    123,000    240,000
Percent reduction**...............................      0.89%      1.73%
------------------------------------------------------------------------
* Net reductions relative to 2012 reference case.
** Compared to AEO 2006 projections for 2012 reference case.

    We also calculate the change in expenditures in both petroleum and
ethanol imports and compare these with the U.S. trade position measured
as U.S. net exports of all goods and services economy-wide. The
decreased expenditures were calculated by multiplying the changes in
gasoline, diesel, and ethanol imports by the respective AEO 2006
wholesale gasoline, distillate, and ethanol price forecasts for the
specific analysis years. In Table IX.B-2, the net expenditures in
reduced petroleum imports, increased ethanol imports, and decreased
corn exports are compared to the total value of U.S. net exports of
goods and services for the whole economy for 2012. Relative to the 2012
projection, the avoided expenditures due to the RFS would represent 0.4
to 0.7% of economy-wide net exports.

                                               Table IX.B-2.--Avoided Import Expenditures ($2004 Billion)
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                Expenditures
                                                                                on       Expenditures   Decreased        Net       Percent of
                      Cases                              AEO total net exports          petroleum    on ethanol       corn     expenditures   total net
                                                                                imports       imports      exports     on  imports    exports
--------------------------------------------------------------------------------------------------------------------------------------------------------
RFS Case........................................  -$383 (year 2012).................         -$2.6         +$0.7        +$0.6         -$1.4         0.4%
EIA Case........................................  ..................................         -$5.1         +$1.0        +$1.3         -$2.8         0.7%
--------------------------------------------------------------------------------------------------------------------------------------------------------

C. Energy Security Implications of Increases in Renewable Fuels

    One of the effects of increased use of renewable fuels in the U.S.
from the RFS is that it diversifies the energy sources in making
transportation fuel. A potential disruption in supply reflected in the
price volatility of a particular energy source carries with it both
financial as well as strategic risks. These risks can be reduced to the
extent that diverse sources of fuel energy reduce the dependence on any
one source. This reduction in risks is a measure of improved energy
security.
    At the time of the proposal, EPA stated that an analysis would be
completed and estimates provided in support of this rule. In order to
understand the energy security implications of the RFS, EPA has worked
with Oak Ridge National Laboratory (ORNL), which has developed
approaches for evaluating the social costs and energy security
implications of oil use. In a new study produced for the RFS, entitled
``The Energy Security Benefits of Reduced Oil Use, 2006-2015,'' ORNL
has updated and applied the method used in the 1997 report ``Oil
Imports: An Assessment of Benefits and Costs'', by Leiby, Jones, Curlee
and Lee.115 116 While the 1997 report including a
description of methodology and results at that time has been used or
cited on a number of occasions, this updated analysis and results have
not been available for full public consideration. Since energy security
will be a key consideration in future actions aimed at reducing our
dependence on oil, it is important to assure estimates of energy
security impacts have been thoroughly examined in a full and open
public forum. Since the updated analysis was only recently available,
such a thorough analysis has not been possible. Therefore, EPA has
decided to consider this update as a draft report, include it as part
of the record of this rulemaking and invite further public analysis and
consideration of both this particular draft report but also other
perspectives on how to best quantify energy security benefits. To
facilitate that additional consideration, we highlight below some of
the key aspects of this particular draft analysis.
---------------------------------------------------------------------------

    \115\ Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and
Russell Lee, Oil Imports: An Assessment of Benefits and Costs, ORNL-
6851, Oak Ridge National Laboratory, November, 1997.
    \116\ The 1997 ORNL paper was cited and its results used in DOT/
NHTSA's rules establishing CAFE standards for 2008 through 2011
model year light trucks. See DOT/NHTSA, Final Regulatory Impacts
Analysis: Corporate Average Fuel Economy and CAFE Reform MY 2008-
2011, March 2006.
---------------------------------------------------------------------------

    The approach developed by ORNL estimates the incremental benefits
to society, in dollars per barrel, of reducing U.S. oil imports, called
``oil premium.'' Since the 1997 publication of this report, changes in
oil market conditions, both current and projected, suggest that the
magnitude of the oil premium has changed. Significant driving factors
that have been revised include: Oil prices, current and anticipated
levels of OPEC production, U.S. import levels, the estimated
responsiveness of regional oil supplies and demands to price, and the
likelihood of oil supply disruptions. For this analysis, oil prices
from the EIA's AEO 2006 were used. Using the ``oil premium'' approach,
estimates of benefits of improved energy security from reduced U.S. oil
imports from increased use of renewable fuels are calculated.
    In conducting this analysis, ORNL considered the full economic cost
of importing petroleum into the U.S. The full economic cost of
importing petroleum into the U.S. is defined for this analysis to
include two components in addition to the purchase price of petroleum
itself. These are: (1) The higher costs for oil imports resulting from
the effect of U.S. import demand on the world oil price and OPEC market
power (i.e., the so called ``demand'' or ``monoposony'' costs); and (2)
the risk of reductions in U.S. economic output and disruption of the
U.S. economy caused by sudden disruptions in the supply of imported oil
to the U.S. (i.e., macroeconomic disruption/adjustment costs).
1. Effect of Oil Use on Long-Run Oil Price, U.S. Import Costs, and
Economic Output
    The first component of the full economic costs of importing
petroleum into the U.S. follows from the effect of U.S. import demand
on the world oil price over the long-run. Because the U.S. is a
sufficiently large purchaser of foreign oil supplies, its purchases can
affect the world oil price. This monopsony power means that increases
in U.S. petroleum demand can cause the world price of crude oil to
rise, and conversely, that reduced U.S. petroleum demand can reduce the
world price of crude oil. Thus, one consequence of decreasing U.S. oil
purchases due to increased use of renewable fuel is the potential
decrease in the crude oil price paid for all crude oil purchased.
2. Short-Run Disruption Premium From Expected Costs of Sudden Supply
Disruptions
    The second component of the external economic costs resulting from
U.S. oil imports arises from the vulnerability of the U.S. economy to
oil shocks. The cost of shocks depends on their likelihood, size, and
length, the capabilities of the market and U.S. Strategic Petroleum
Reserve (SPR), the largest stockpile of government-owned emergency
crude oil in the world, to respond, and the sensitivity of the U.S.
economy to sudden price increases. While the total vulnerability of the
U.S. economy to oil price shocks depends on the levels of both U.S.
petroleum consumption and imports, variation in import levels or demand
flexibility can affect the magnitude of potential increases in oil
price due to supply disruptions. Disruptions are uncertain events, so
the costs of alternative possible disruptions are weighted by
disruption probabilities. The probabilities used by the ORNL study are
based on a 2005 Energy Modeling Forum\117\ synthesis of expert judgment
and are used to determine an expected value of disruption costs, and
the change in those expected costs given reduced U.S. oil imports.
3. Costs of Existing U.S. Energy Security Policies
    The last often-identified component of the full economic costs of
U.S. oil

imports is the costs to the U.S. taxpayers of existing U.S. energy
security policies. The two primary examples are maintaining a military
presence to help secure stable oil supply from potentially vulnerable
regions of the world and maintaining the SPR to provide buffer supplies
and help protect the U.S. economy from the consequences of global oil
supply disruptions.
    U.S. military costs are excluded from the analysis performed by
ORNL because their attribution to particular missions or activities is
difficult. Most military forces serve a broad range of security and
foreign policy objectives. Attempts to attribute some share of U.S.
military costs to oil imports are further challenged by the need to
estimate how those costs might vary with incremental variations in U.S.
oil imports. Similarly, while the costs for building and maintaining
the SPR are more clearly related to U.S. oil use and imports,
historically these costs have not varied in response to changes in U.S.
oil import levels. Thus, while SPR is factored into the ORNL analysis,
the cost of maintaining the SPR is excluded.
    As stated earlier, we have placed the draft report in the docket of
this rulemaking for the purposes of inviting further consideration.
However, the draft results of that report have not been used in
quantifying the impacts of this rule.

X. Agricultural Sector Economic Impacts

    As described in the Notice of Proposed Rulemaking (NPRM), we used
the Forest and Agricultural Sector Optimization Model (FASOM) developed
by Professor Bruce McCarl of Texas A&M University and others, to
estimate the agricultural sector impacts of increasing renewable fuel
volumes required by the RFS and for those volumes anticipated by EIA
for 2012. Although current renewable fuel volume predictions are higher
than the scenarios described in this rulemaking, we based our analysis
on assumptions developed during the NPRM process. Our agricultural
sector analysis considered the impacts of the domestic production of
renewable fuels. Therefore, when we refer to either the RFS Case or the
EIA Case, we include only renewable fuels produced from feedstocks
grown in the U.S.\118\
    At the time the NPRM was published, we had not yet finished our
analysis of the agricultural impacts associated with the RFS. In the
NPRM, we stated our intent to have the analysis completed in time for
the Final Rulemaking (FRM). In the proposal we described our plan to
evaluate the effect of increasing renewable fuels volumes on U.S.
commodity prices, renewable fuel byproduct prices, livestock feed
sources, land use, exports, and farm income. The results of this
analysis are summarized in this section. Additional details are
included in the Regulatory Impact Analysis (RIA).
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    \117\ Stanford Energy Modeling Forum, Phillip C. Beccue and
Hillard G. Huntington, ``An Assessment of Oil Market Disruption
Risks,'' Final Report, EMF SR 8, October, 2005.
    \118\ The RIA contains additional information on the renewable
fuels volumes analyzed for this rulemaking.
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    FASOM is a long-term economic model of the U.S. agriculture sector
that attempts to maximize total revenues for producers while meeting
the demands of consumers. Using a number of inputs, FASOM estimates
which crops, livestock, and processed agricultural products will be
produced in the U.S. The cost of these and other inputs are used to
determine the price and level of production of commodities (e.g., field
crops, livestock, and biofuel products). FASOM does not capture short-
term fluctuations (i.e., month-to-month, annual) in prices and
production, however, as it is designed to identify long-term trends
(i.e., five to ten years).
    FASOM predicts that as renewable fuel volumes increase, corn prices
will rise by about 18 cents (RFS Case) and 39 cents (EIA Case) above
the Reference Case price of $2.32 per bushel. For consistency, all of
the dollar estimates are presented in 2004 dollars. Soybean prices will
rise by about 18 cents (RFS Case) and 21 cents (EIA Case) above the
Reference Case price of $5.26 per bushel by 2012. Since biodiesel
volumes will not increase significantly in either the RFS or EIA
scenarios, FASOM does not predict significant changes in the soybean
related markets with respect to usage changes, or most other variables
of interest for this rulemaking. The one exception is U.S. soybean
exports, which are affected modestly.
    Changes in corn use can be seen by the changing percentage of corn
used for ethanol. In 2005, approximately 12 percent of the corn supply
was used for ethanol production, however we estimate the amount of corn
used for ethanol in 2012 will increase to 20 percent (RFS Case) and 26
percent (EIA Case).
    The rising price of corn and soybeans has a direct impact on how
corn is used. Higher domestic corn prices lead to lower U.S. exports as
the world markets shift to other sources of these products or expand
the use of substitute grains. FASOM estimates that U.S. corn exports
will drop from about 2 billion bushels in our Reference Case, to 1.6
billion bushels (RFS Case) and 1.3 billion bushels (EIA Case) by 2012.
U.S. exports of corn are estimated to drop by about 19 percent by 2012
for the RFS Case and by roughly 38 percent in the EIA Case. In value
terms, U.S. exports of corn fall by $573 million in the RFS Case and by
$1.29 billion in the EIA Case in 2012.
    The impact on domestic livestock feed due to higher corn prices and
higher U.S. demand for corn in ethanol is also partially offset by
decreasing the use of corn for U.S. livestock feed. Substitutes are
available for corn as a feedstock, and this market is price sensitive.
One alternate feedstock is distillers dried grains with solubles
(DDGS), a byproduct associated with the dry milling of ethanol
production. Since FASOM predicts relatively flat prices for DDGS across
all ethanol volume scenarios, the result is a significant increase in
the use of DDGS as a feed source. We estimate DDGS in feed for the RFS
case will almost double by 2012, increasing from 8.5 million tons to
15.2 million tons. Under the EIA Case, we expect DDGS to increase to
22.2 million tons by 2012.
    The increase in soybean prices is estimated to cause a decline in
U.S. soybean exports. In terms of export earnings, U.S. exports of
soybeans fall by $220 million in the RFS Case and by $194 million in
the EIA Case in 2012.
    The increase in renewable fuel production provides a significant
increase in net farm income to the U.S. agricultural sector. FASOM
predicts that in 2012, net U.S. farm income will increase by $2.6
billion dollars in the RFS renewable fuel volumes case (RFS Case) and
$5.4 billion in the EIA renewable fuel volumes case (EIA Case). The RFS
and EIA farm revenue increases represent roughly a 5 and 10 percent
increase, respectively, in U.S. net farm income from the sale of farm
commodities over the Reference Case of roughly $53 billion.
    Higher corn prices will have a direct impact on the value of U.S.
agricultural land. As demand for corn and farm products increases, the
price of U.S. farm land will also increase. Our analysis shows that in
2012, higher renewable fuel volumes increase land prices by about 8
percent (RFS Case) and 17 percent (EIA Case). Much of the high quality,
suitable land in the U.S. is already being used to produce corn. FASOM
estimates an increase of 1.6 million acres (RFS Case) and 2.6 million
acres (EIA Case) above the 78.5 million corn acres harvested in the
Reference Case in 2012. Due to this higher value of land, we are
predicting that farms will withdraw a portion of the land currently in
the Conservation Reserve Program (CRP), about 2.3 million acres (RFS
Case) and 2.5 million acres (EIA

Case) out of the approximately 40 million acres in CRP.\119\
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    \119\ Since much of the CRP land is ill suited for corn or
soybean production, it is unlikely this land will go directly into
corn or soybean production but instead will more likely be used to
replace other agricultural land uses displaced by expanded corn and
soybean production.
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    FASOM estimates U.S. annual wholesale food costs will increase by
approximately $2.2 billion with the RFS renewable volumes and $3.7
billion with the EIA renewable volumes by 2012. These costs translate
to approximately $7 per person per year (RFS case) and $12 per person
per year (EIA case).
    In the proposal, we noted that expansion in the use of renewable
fuels also raises the issue of whether water quality and rural
ecosystems in general could be affected due to increased production of
agricultural feedstocks used to produce greater volumes of renewable
fuels. We received one comment from Marathon asserting that our
environmental assessment was incomplete and did not address water
quality issues. In the time frame to complete this rulemaking, we were
not able to conduct a comprehensive assessment of the environmental
impacts in the agricultural sector of the wider use of renewable fuels.
However, we have considered two indicators--fertilizer use on
agricultural crops and Conservation Resource Program (CRP) lands--that
may relate to environmental quality and water quality from the
production of renewable fuels. The CRP is a voluntary program
administered by the U.S. Department of Agriculture that helps defray
the costs to farmers of taking agricultural lands out of production and
placing them in CRP to provide environmental protection.
    As discussed in Section X, FASOM predicts the total amount of
nitrogen applied on all farms will increase by 1.2 percent in the RFS
Case and by 2 percent in the EIA Case, relative to the Reference Case
in 2012. The total amount of phosphorous applied on all farms increases
by 0.7 percent in the RFS Case and 1.2 percent in the EIA Case,
relative to the Reference Case in 2012. Currently, there are
approximately 40 million acres in the CRP. FASOM predicts 2.3 million
acres (RFS Case) and 2.5 million acres (EIA Case) of land would be