[Federal Register Volume 77, Number 113 (Tuesday, June 12, 2012)]
[Proposed Rules]
[Pages 34915-34927]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2012-13651]


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ENVIRONMENTAL PROTECTION AGENCY

40 CFR Part 80

[EPA-HQ-OAR-2011-0542; FRL-9680-8]


Notice of Data Availability Concerning Renewable Fuels Produced 
From Grain Sorghum Under the RFS Program

AGENCY: Environmental Protection Agency (EPA).

ACTION: Notice of data availability (NODA).

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SUMMARY: This notice of data availability provides an opportunity to 
comment on EPA's analyses of grain sorghum used as a feedstock to 
produce ethanol under the Renewable Fuel Standard (RFS) program. EPA's 
analysis shows that ethanol from grain sorghum has estimated lifecycle 
greenhouse gas (GHG) emission reductions of 32% compared to the 
baseline petroleum fuel it would replace. This analysis indicates that 
grain sorghum ethanol qualifies as a conventional renewable fuel under 
the RFS program. Furthermore, this analysis shows that, when produced 
via certain pathways that utilize advanced process technologies (e.g., 
biogas in addition to combined heat and power), grain sorghum ethanol 
has lifecycle GHG emission reductions of over 50% compared to the 
baseline petroleum fuel it would replace, and would qualify as an 
advanced biofuel under RFS.

DATES: Comments must be received on or before July 12, 2012.

ADDRESSES: Submit your comments, identified by Docket ID No. EPA-HQ-
OAR-2011-0542, by one of the following methods:
     www.regulations.gov: Follow the on-line instructions for 
submitting comments.
     Email: [email protected].
     Mail: Air and Radiation Docket and Information Center, 
Environmental Protection Agency, Mailcode: 2822T, 1200 Pennsylvania 
Ave. NW., Washington, DC 20460.
     Hand Delivery: Air and Radiation Docket and Information 
Center, EPA/DC, EPA West, Room 3334, 1301 Constitution Ave. NW., 
Washington DC 20004. Such deliveries are only accepted during the 
Docket's normal hours of operation, and special arrangements should be 
made for deliveries of boxed information.
    Instructions: Direct your comments to Docket ID No. EPA-HQ-OAR-
2011-0542. EPA's policy is that all comments received will be included 
in the public docket without change and may be made available online at 
www.regulations.gov, including any personal information provided, 
unless the comment includes information claimed to be Confidential 
Business Information (CBI) or other information whose disclosure is 
restricted by statute. Do not submit information that you consider to 
be CBI or otherwise protected through www.regulations.gov or 
[email protected]. The www.regulations.gov Web site is an ``anonymous 
access'' system, which means EPA will not know your identity or contact 
information unless you provide it in the body of your comment. If you 
send an email comment directly to EPA without going through 
www.regulations.gov your email address will be automatically captured 
and included as part of the comment that is placed in the public docket 
and made available on the Internet. If you submit an electronic 
comment, EPA recommends that you include your name and other contact 
information in the body of your comment and with any disk or CD-ROM you 
submit. If EPA cannot read your comment due to technical difficulties 
and cannot contact you for clarification, EPA may not be able to 
consider your comment. Electronic files should avoid the use of special 
characters, any form of encryption, and be free of any defects or 
viruses. For additional information about EPA's public docket visit the 
EPA Docket Center homepage at http://www.epa.gov/epahome/dockets.htm.
    Docket: All documents in the docket are listed in the 
www.regulations.gov index. Although listed in the index, some 
information is not publicly available, e.g., CBI or other information 
whose disclosure is restricted by statute. Certain other material, such 
as copyrighted material, will be publicly available only in hard copy. 
Publicly available docket materials are available either electronically 
in www.regulations.gov or in hard copy at the Air and Radiation Docket 
and

[[Page 34916]]

Information Center, EPA/DC, EPA West, Room 3334, 1301 Constitution Ave. 
NW., Washington, DC 20004. The Public Reading Room is open from 8:30 
a.m. to 4:30 p.m., Monday through Friday, excluding legal holidays. The 
telephone number for the Public Reading Room is (202) 566-1744, and the 
telephone number for the Air Docket is (202) 566-1742.

FOR FURTHER INFORMATION CONTACT: Jefferson Cole, Office of 
Transportation and Air Quality, Transportation and Climate Division, 
Environmental Protection Agency, 1200 Pennsylvania Ave. NW., 
Washington, DC 20460 (MC: 6041A); telephone number: 202-564-1283; fax 
number: 202-564-1177; email address: [email protected].

SUPPLEMENTARY INFORMATION: 

Outline of This Preamble

I. General Information
    A. Does this action apply to me?
    B. What should I consider as I prepare my comments for EPA?
    1. Submitting CBI
    2. Tips for Preparing Your Comments
II. Analysis of Lifecycle Greenhouse Gas Emissions
    A. Methodology
    1. Scope of Analysis
    2. Models Used
    3. Scenarios Modeled for Impacts of Increased Demand for Grain 
Sorghum
    4. Model Modifications
    B. Results
    1. Agro-Economic Impacts
    2. International Land Use Change Emissions
    3. Grain Sorghum Ethanol Processing
    4. Results of Lifecycle Analysis for Ethanol From Grain Sorghum 
(Using Dry Mill Natural Gas)
    5. Results of Lifecycle Analysis for Ethanol From Grain Sorghum 
(Using Biogas and CHP)
    6. Other Advanced Technologies
    C. Consideration of Lifecycle Analysis Results
    1. Implications for Threshold Determinations
    2. Consideration of Uncertainty

I. General Information

A. Does this action apply to me?

    Entities potentially affected by this action are those involved 
with the production, distribution, and sale of transportation fuels, 
including gasoline and diesel fuel or renewable fuels such as biodiesel 
and renewable diesel. Regulated categories include:

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                                                NAICS \1\                     Examples of potentially regulated
                  Category                        codes       SIC \2\ codes                entities
----------------------------------------------------------------------------------------------------------------
Industry...................................          324110            2911  Petroleum Refineries.
Industry...................................          325193            2869  Ethyl alcohol manufacturing.
Industry...................................          325199            2869  Other basic organic chemical
                                                                              manufacturing.
Industry...................................          424690            5169  Chemical and allied products
                                                                              merchant wholesalers.
Industry...................................          424710            5171  Petroleum bulk stations and
                                                                              terminals.
Industry...................................          424720            5172  Petroleum and petroleum products
                                                                              merchant wholesalers.
Industry...................................          454319            5989  Other fuel dealers.
----------------------------------------------------------------------------------------------------------------
\1\ North American Industry Classification System (NAICS).
\2\ Standard Industrial Classification (SIC) system code.

    This table is not intended to be exhaustive, but rather provides a 
guide for readers regarding entities likely to engage in activities 
that may be affected by today's action. To determine whether your 
activities would be affected, you should carefully examine the 
applicability criteria in 40 CFR Part 80, Subpart M. If you have any 
questions regarding the applicability of this action to a particular 
entity, consult the person listed in the preceding section.

B. What should I consider as I prepare my comments for EPA?

1. Submitting CBI
    Do not submit this information to EPA through www.regulations.gov 
or email. Clearly mark the part or all of the information that you 
claim to be CBI. For CBI information in a disk or CD ROM that you mail 
to EPA, mark the outside of the disk or CD ROM as CBI and then identify 
electronically within the disk or CD ROM the specific information that 
is claimed as CBI. In addition to one complete version of the comment 
that includes information claimed as CBI, a copy of the comment that 
does not contain the information claimed as CBI must be submitted for 
inclusion in the public docket. Information so marked will not be 
disclosed except in accordance with procedures set forth in 40 CFR part 
2.
2. Tips for Preparing Your Comments
    When submitting comments, remember to:
     Identify the rulemaking by docket number and other 
identifying information (subject heading, Federal Register date and 
page number).
     Follow directions--The agency may ask you to respond to 
specific questions or organize comments by referencing a Code of 
Federal Regulations (CFR) part or section number.
     Explain why you agree or disagree; suggest alternatives 
and substitute language for your requested changes.
     Describe any assumptions and provide any technical 
information and/or data that you used.
     If you estimate potential costs or burdens, explain how 
you arrived at your estimate in sufficient detail to allow for it to be 
reproduced.
     Provide specific examples to illustrate your concerns, and 
suggest alternatives.
     Explain your views as clearly as possible, avoiding the 
use of profanity or personal threats.
     Make sure to submit your comments by the comment period 
deadline identified.

II. Analysis of Lifecycle Greenhouse Gas Emissions

A. Methodology

1. Scope of Analysis
    On March 26, 2010 (75 FR 14670), the Environmental Protection 
Agency (EPA) published changes to the Renewable Fuel Standard program 
regulations as required by 2007 amendments to CAA 211(o). This 
rulemaking is commonly referred to as the ``RFS2'' final rule. As part 
of the RFS2 final rule we analyzed various categories of biofuels to 
determine whether the complete lifecycle GHG emissions associated with 
the production, distribution, and use of those fuels meet minimum 
lifecycle greenhouse gas reduction thresholds as specified by CAA 
211(o) (i.e., 60% for cellulosic biofuel, 50% for biomass-based diesel 
and advanced biofuel, and 20% for other renewable fuels). Our final 
rule focused our lifecycle analyses on fuels that were anticipated to 
contribute relatively large volumes of renewable fuel by 2022 and thus 
did not cover all fuels that either are contributing or could 
potentially contribute to the program. In the preamble to the final 
rule EPA indicated that it had not completed the GHG emissions impact 
analysis for several specific biofuel production pathways

[[Page 34917]]

but that this work would be completed through a supplemental rulemaking 
process. Since the final rule was issued, we have continued to examine 
several additional pathways. This Notice of Data Availability 
(``NODA'') focuses on our analysis of the grain sorghum ethanol 
pathway. The modeling approach EPA used in this analysis is the same 
general approach used in the final RFS2 rule for lifecycle analyses of 
other biofuels.\1\ The RFS2 final rule preamble and Regulatory Impact 
Analysis (RIA) provides further discussion of our approach.
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    \1\ EPA. 2010. Renewable Fuel Standard Program (RFS2) Regulatory 
Impact Analysis. EPA-420-R-10-006. http://www.epa.gov/oms/renewablefuels/420r10006.pdf
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    This notice of data availability provides an opportunity to comment 
on EPA's analyses of lifecycle GHG emissions related to the production 
and use of ethanol from grain sorghum prior to EPA taking any final 
rulemaking action to add ethanol from grain sorghum as an available 
pathway in the RFS program. We intend to consider all of the relevant 
comments received. In general, comments will be considered relevant if 
they pertain to EPA's analysis of lifecycle GHG emissions of grain 
sorghum ethanol, and especially if they provide specific information 
for consideration in our modeling.
2. Models Used
    The analysis EPA has prepared for grain sorghum ethanol uses the 
same set of models that was used for the final RFS2 rule. To estimate 
the domestic agricultural impacts presented in the following sections, 
we used the Forestry and Agricultural Sector Optimization Model (FASOM) 
developed by Texas A&M University. To estimate the international 
agricultural section impacts presented below, we used the Food and 
Agricultural Policy and Research Institute international models as 
maintained by the Center for Agricultural and Rural Development (FAPRI-
CARD) at Iowa State University. For more information on the FASOM and 
FAPRI-CARD models, refer to the RFS2 final rule preamble (75 FR 14670) 
or the RFS2 Regulatory Impact Analysis (RIA).\2\ The models require a 
number of inputs that are specific to the pathway being analyzed, 
including projected yields of feedstock per acre planted, projected 
fertilizer use, and energy use in feedstock processing and fuel 
production. The docket includes detailed information on model inputs, 
assumptions, calculations, and the results of our assessment of the 
lifecycle GHG emissions performance for producing ethanol from grain 
sorghum (``grain sorghum ethanol'').
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    \2\ EPA. 2010. Renewable Fuel Standard Program (RFS2) Regulatory 
Impact Analysis. EPA-420-R-10-006. http://www.epa.gov/oms/renewablefuels/420r10006.pdf. Additional RFS2 related documents can 
be found at http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm
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3. Scenarios Modeled for Impacts of Increased Demand for Grain Sorghum
    To assess the impacts of an increase in renewable fuel volume from 
business-as-usual (what is likely to have occurred without the RFS 
biofuel mandates) to levels required by the statute, we established 
reference and control cases for a number of biofuels analyzed for the 
RFS2 final rulemaking. The reference case includes a projection of 
renewable fuel volumes without the RFS renewable fuel volume mandates. 
The control cases are projections of the volumes of renewable fuel that 
might be used in the future to comply with the volume mandates. The 
final rule reference case volumes were based on the Energy Information 
Administration's (EIA) Annual Energy Outlook (AEO) 2007 reference case 
projections. In the RFS2 rule, for each individual biofuel, we analyzed 
the incremental GHG emission impacts of increasing the volume of that 
fuel to the total mix of biofuels needed to meet the EISA requirements.
    For the analysis of grain sorghum ethanol, a new control case was 
developed to account for the current production of grain sorghum 
ethanol which is approximately 200 million gallons per year (see 
Chapter 1 of the RFS2 RIA). All other volumes for each individual 
biofuel in this new control case remain identical to the control case 
used in the RFS2 rule. For the ``grain sorghum'' case, our modeling 
assumes approximately 300 million gallons of sorghum ethanol would be 
consumed in the United States in 2022. The modeled scenario includes 
2.06 billion lbs of grain sorghum to be used to produce the additional 
100 million gallons of ethanol in 2022.
    Our volume scenario of approximately 200 million gallons of grain 
sorghum ethanol in the new control case, and 300 million gallons in the 
grain sorghum case in 2022, is based on several factors including 
historical volumes of grain sorghum ethanol production, potential 
feedstock availability and other competitive uses (e.g., animal feed or 
exports). Our assessment is described further in the inputs and 
assumptions document that is available through the docket (EPA 2011). 
Based in part on consultation with experts at the United States 
Department of Agriculture (USDA) and industry representatives, we 
believe that these volumes are reasonable for the purposes of 
evaluating the impacts of producing additional volumes of ethanol from 
grain sorghum.
    The FASOM and FAPRI-CARD models, described above, project how much 
grain sorghum will be supplied to ethanol production from a combination 
of increased production, decreases in others uses (e.g., animal feed), 
and decreases in exports, in going from the control case to the grain 
sorghum case.
4. Model Modifications
    Based on information from industry stakeholders, as well as in 
consultation with USDA, both the FASOM and FAPRI-CARD models assume 
perfect substitution in the use of grain sorghum and corn in the animal 
feed market in the U.S. Therefore, when more grain sorghum is used for 
ethanol production, grain sorghum used in feed decreases. Either 
additional corn or sorghum will be used in the feed market to make up 
for this decrease, depending upon the relative cost of additional 
production. This assumption is based on conversations with industry and 
the USDA, reflecting the primary use of sorghum in the U.S. as animal 
feed, just like corn.
    The United States is one of the largest producers and exporters of 
grain sorghum. However, two large producers of grain sorghum, India and 
Nigeria, do not actively participate in the global trade market for 
sorghum. Rather, all grain sorghum in those two countries is produced 
for domestic consumption. Therefore, as the U.S. diverts some of its 
exports of grain sorghum for the purposes of ethanol production, we 
would expect close to no reaction in the production levels of grain 
sorghum in India and Nigeria. Historical data on prices, production, 
and exports from USDA, FAOSTAT, and FAPRI support this assumption.\3\
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    \3\ See Memo to the Docket, Docket Number EPA-HQ-OAR-2011-0542, 
Dated May 18, 2012 and personal communication with USDA.
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B. Results

    As we did for our analysis of other feedstocks in the RFS2 final 
rule, we assessed what the GHG emissions impacts would be from the use 
of additional volumes of sorghum for biofuel production. The 
information provided in this section discusses the assumptions and 
outputs of the analysis using the FASOM and FAPRI-CARD agro-economic 
models to determine changes in the agricultural and livestock markets. 
These results from FASOM and FAPRI-CARD are then used to determine the 
GHG emissions impacts

[[Page 34918]]

due to land use change and other factors. Finally, we include our 
analysis of the GHG emissions associated with different processing 
pathways and how these technologies affect the lifecycle GHG emissions 
associated with grain sorghum ethanol.
    As discussed in the final RFS2 rule and the accompanying peer 
review, there are inherent challenges in reconciling the results from 
two different models. However, using two models provides a more 
complete and robust analysis than either model would be able to provide 
alone. We have attempted to align as many of the key assumptions as 
possible to get a consistent set of modeling results although there are 
structural differences in the models that account for some of the 
differences in the model results. For example, since FASOM is a long-
term dynamic optimization model, short-term spikes are smoothed out 
over the five year reporting period. In comparison, the FAPRI-CARD 
model captures annual fluctuations that may include short-term supply 
and demand responses. In addition, some of the discrepancies may be 
attributed to different underlying assumptions pertaining to 
elasticities of supply and demand for different commodities. These 
differences, in turn, affect projections of imports and exports, 
acreage shifting, and total consumption and production of various 
commodities.
1. Agro-Economic Impacts
    As biofuel production causes increased demand for a particular 
commodity, the supply generally comes from a mix of increased 
production, decreased exports, increased imports, and decreases in 
other uses of the commodity. In the case of grain sorghum, FASOM 
estimates that the majority of sorghum necessary to produce 100 million 
additional gallons of ethanol (2.06 billion lbs) by 2022 comes from a 
decrease in grain sorghum used in the animal feed market (2.05 billion 
lbs). This gap in the feed market is primarily filled by distillers 
grains (627 million lbs), a byproduct from the grain sorghum ethanol 
production process also known as DG, as well as additional corn 
production (1.6 billion lbs). This is reasonable given the close 
substitutability of corn and grain sorghum in the U.S. animal feed 
markets. When DG are produced at an ethanol facility, they contain a 
certain amount of moisture and are referred to as ``wet'' DG. If an 
ethanol facility is interested in transporting DG long distances to 
sell to distant feedlots, then the DG must be dried so they do not 
spoil. Information about the energy required for this drying process, 
as well as the different amounts of wet versus dry DG production that 
we considered can be found below in Sections II.B.3 and II.B.5. In 
those sections, we detail not only how much energy is required for 
drying DG, but show that this amount of energy is not significantly 
large enough to affect the overall threshold determinations.

           Table II-1--Summary of Projected Change in Feed Use in the U.S. in 2022 in the FASOM Model
                                                [Millions of lbs]
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                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
Sorghum.........................................................          38,998          36,947          -2,051
Corn............................................................         324,731         326,365           1,635
Distillers Grains (DG)..........................................          79,388          80,014             627
Other...........................................................          71,881          71,873              -8
                                                                 -----------------------------------------------
    Total.......................................................         514,998         515,200             202
----------------------------------------------------------------------------------------------------------------

    As demand for both grain sorghum for ethanol production and corn 
for animal feed increases, harvested crop area in the U.S. are 
predicted to increase by 92 thousand acres in 2022. The increase in 
grain sorghum area harvested is relatively modest, at an additional 4 
thousand acres, due to the fact that demand for grain sorghum for use 
in ethanol production is being met by a shift of grain sorghum from one 
existing use (in the animal feed market) to another (ethanol 
production). Meeting the subsequent gap in supply of animal feed, 
however, leads to an increase of 141 thousand corn acres in 2022. Due 
to the increased demand for corn production and harvested area, soybean 
harvested area would decrease by 105 thousand acres (corn and soybeans 
often compete for land). Other crops in the U.S., such as wheat, hay, 
and rice, are projected to have a net increase of 53 thousand acres.

      Table II-2--Summary of Projected Change in Crop Harvested Area in the U.S. in 2022 in the FASOM Model
                                              [Thousands of acres]
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                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
Sorghum.........................................................          11,108          11,111               4
Corn............................................................          77,539          77,680             141
Soybeans........................................................          69,896          69,791            -105
Other...........................................................         154,511         154,564              53
                                                                 -----------------------------------------------
    Total.......................................................         313,054         313,146              92
----------------------------------------------------------------------------------------------------------------

    As demand for grain sorghum increases for ethanol production in the 
U.S., the FAPRI-CARD model estimates that the U.S. will decrease 
exports of grain sorghum by 789 million lbs. Additionally, the U.S. 
will increase exports of corn by 106 million lbs to partially satisfy 
the gap of having less grain sorghum in the worldwide feed market. This 
combination of impacts on the world trade of grain sorghum and corn has 
effects both on major importers, as well as on other major exporters. 
For example, Mexico, one of the largest importers of grain sorghum,

[[Page 34919]]

decreases its imports of grain sorghum by 395 million lbs, and 
increases its imports of corn by 256 million lbs. Brazil also 
contributes more corn to the global market by increasing its exports by 
198 million lbs. Details for other major importers and exporters of 
grain sorghum and corn can be found in Table II-3 and Table II-4, 
respectively.

  Table II-3--Summary of Projected Change in Net Exports of Grain Sorghum by Country in 2022 in the FAPRI-CARD
                                                      Model
                                                [Millions of lbs]
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                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
U.S.............................................................          10,580           9,791            -789
Mexico..........................................................          -4,735          -4,340             395
Japan...........................................................          -3,159          -3,106              53
Argentina.......................................................           2,577           2,653              75
India...........................................................            -219            -219               0
Nigeria.........................................................             110             110               0
Rest of World...................................................          -4,655          -4,389             266
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Note: A country with negative Net Exports is a Net Importer.


    Table II-4--Summary of Projected Change in Net Exports of Corn by Country in 2022 in the FAPRI-CARD Model
                                                [Millions of lbs]
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                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
U.S.............................................................         122,688         122,795             106
Brazil..........................................................          24,661          24,859             198
China...........................................................          12,748          12,840              93
Japan...........................................................         -38,787         -38,877             -91
Mexico..........................................................         -29,008         -29,264            -256
Rest of World...................................................         -91,423         -91,474             -51
----------------------------------------------------------------------------------------------------------------
Note: A country with negative Net Exports is a Net Importer.

    The change in trade patterns directly impacts the amount of 
production and harvested crop area around the world. Harvested crop 
area for grain sorghum is not only predicted to increase in the U.S., 
but also in Mexico (7.8 thousand acres) and other parts of the world. 
Worldwide grain sorghum harvested area outside of the U.S. would 
increase by 39.3 thousand acres. Similarly, the increase in the demand 
for corn would lead to an increase of 36.8 thousand harvested acres 
outside of the U.S. While soybean harvested area would decrease in the 
U.S., Brazil would increase its soybean harvested area (18.4 thousand 
acres) to satisfy global demand. Although worldwide soybean harvested 
area decreases by 11.7 thousand acres, non-U.S. harvested area 
increases by 11.2 thousand acres.
    Overall harvested crop area in other countries also increase, 
particularly in Brazil. Brazil's total harvested area is predicted to 
increase by 32.6 thousand acres by 2022. This is mostly comprised of an 
increase in corn of 18.1 thousand acres, and an increase in soybeans of 
18.4 thousand acres, along with minor changes in other crops. More 
details on projected changes in world harvested crop area in 2022 can 
be found below in Table II-5, Table II-6, Table II-7, and Table II-8.

  Table II-5--Summary of Projected Change in International (Non-U.S.) Harvested Area by Country in 2022 in the
                                                FAPRI-CARD Model
                                              [Thousands of acres]
----------------------------------------------------------------------------------------------------------------
                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
Brazil..........................................................         137,983         138,016              33
China...........................................................         272,323         272,334              11
Africa and Middle East..........................................         315,843         315,892              48
Rest of World...................................................       1,301,417       1,301,441              24
International Total (non-U.S.)..................................       2,027,567       2,027,682             115
----------------------------------------------------------------------------------------------------------------


Table II-6--Summary of Projected Change in International (Non-U.S.) Harvested Area by Crop in 2022 in the FAPRI-
                                                   CARD Model
                                              [Thousands of acres]
----------------------------------------------------------------------------------------------------------------
                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
Sorghum.........................................................          95,108          95,148              39

[[Page 34920]]

 
Corn............................................................         307,342         307,379              37
Soybeans........................................................         202,980         202,991              11
Other...........................................................       1,422,137       1,422,165              28
                                                                 -----------------------------------------------
    International Total (non-U.S.)..............................       2,027,567       2,027,682             115
----------------------------------------------------------------------------------------------------------------


 Table II-7--Summary of Projected Change in International (Non-U.S.) Grain Sorghum Harvested Area by Country in
                                          2022 in the FAPRI-CARD Model
                                              [Thousands of acres]
----------------------------------------------------------------------------------------------------------------
                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
Mexico..........................................................           4,569           4,576               8
Argentina.......................................................           1,915           1,917               2
India...........................................................          22,261          22,261               0
Nigeria.........................................................          18,841          18,841               0
Other Africa and Middle East....................................          37,833          37,856              23
Rest of World...................................................           9,689           9,695               6
                                                                 -----------------------------------------------
    International Total (non-U.S.)..............................          95,108          95,148              39
----------------------------------------------------------------------------------------------------------------
* The change in grain sorghum harvested area in India and Nigeria is zero.


  Table II-8--Summary of Projected Change in International (Non-U.S.) Corn Harvested Area by Country in 2022 in
                                              the FAPRI-CARD Model
                                              [Thousands of acres]
----------------------------------------------------------------------------------------------------------------
                                                                                  Grain  sorghum
                                                                   Control case        case         Difference
----------------------------------------------------------------------------------------------------------------
Africa and Middle East..........................................          77,220          77,223               4
Asia............................................................         108,751         108,764              13
Brazil..........................................................          20,935          20,953              18
India...........................................................          20,176          20,180               5
Other Latin America.............................................          39,599          39,594              -5
Rest of World...................................................          40,661          40,664               2
                                                                 -----------------------------------------------
    International Total (non-U.S.)..............................         307,342         307,379              37
----------------------------------------------------------------------------------------------------------------

    More detailed information on the agro-economic modeling can be 
found in the accompanying docket. We invite comment on all aspects of 
these modeling results.\4\
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    \4\ See Memo to the Docket, Docket Number EPA-HQ-OAR-2011-0542, 
Dated May 18, 2012.
---------------------------------------------------------------------------

2. International Land Use Change Emissions
    The methodology used in today's assessment of grain sorghum as an 
ethanol feedstock is the same as was used in the final RFS2 rule for 
analyses of other biofuel pathways. However, we have updated some of 
the data underlying the GHG emissions from international land use 
changes therefore we are providing additional detail on these 
modifications in this section.
    In our analysis, GHG emissions per acre of land conversion 
internationally (i.e., outside of the United States) are determined 
using the emissions factors developed for the RFS2 final rule following 
IPCC guidelines. In addition, estimated average forest carbon stocks 
were updated based on a new study which uses a more robust and higher 
resolution analysis. For the RFS2 final rule, international forest 
carbon stocks were estimated from several data sources each derived 
using a different methodological approach. Two new peer-reviewed 
analyses on forest carbon stock estimation were completed since the 
release of the final RFS2 rule, one for three continental regions by 
Saatchi et al.\5\ and the other for the EU by Gallaun et al.\6\ We have 
updated our forest carbon stock estimates based on these new studies 
because they represent significant improvements as compared to the data 
used in the RFS2 rule. These updated forest carbon stock estimates were 
previously used in EPA's January 27, 2012, Notice of Data Availability 
Concerning Renewable Fuels Produced From Palm Oil Under the RFS Program 
(77 FR 4300). Forest carbon stocks across the tropics are important in 
our analysis of grain sorghum ethanol because a significant amount of 
the land use changes in the

[[Page 34921]]

scenarios modelled occur in tropical regions such as Brazil. In the 
scenarios modelled there are also much smaller amounts of land use 
change impacts in the EU related to grain sorghum ethanol production. 
In the interest of using the best available data we have incorporated 
the improved forest carbon stocks data in our analysis of lifecycle GHG 
emissions related to grain sorghum ethanol.
---------------------------------------------------------------------------

    \5\ Saatchi, S.S., Harris, N.L. Brown, S., Lefsky, M., Mitchard, 
E.T.A., Salas, W., Zutta, B.R., Buermann, W., Lewis, S.L., Hagen, 
S., Petrova, S., White, L., Silman, M. And Morel, A. 2011. Benchmark 
map of forest carbon stocks in tropical regions across three 
continents. PNAS doi: 10.1073/pnas.1019576108.
    \6\ Gallaun H., Zanchi, G., Nabuurs, G.J. Hengeveld, G., 
Schardt, M., Verkerk, P.J. 2010. EU-wide maps of growing stack and 
above-ground biomass in forests based on remote sensing and and 
field measurements. Forest Ecology and Mangement 260: 252-261.
---------------------------------------------------------------------------

    Preliminary results for Latin America and Africa from Saatchi et 
al. were incorporated into the final RFS2 rule, but Asia results were 
not included due to timing considerations. The Saatchi et al. analysis 
is now complete, and so the final map was used to calculate updated 
area-weighted average forest carbon stocks for the entire area covered 
by the analysis (Latin America, sub-Saharan Africa and South and 
Southeast Asia). The Saatchi et al. results represent a significant 
improvement over previous estimates because they incorporate data from 
more than 4,000 ground inventory plots, about 150,000 biomass values 
estimated from forest heights measured by space-borne light detection 
and ranging (LIDAR), and a suite of optical and radar satellite imagery 
products. Estimates are spatially refined at 1-km grid cell resolution 
and are directly comparable across countries and regions.
    In the final RFS2 rule, forest carbon stocks for the EU were 
estimated using a combination of data from three different sources. 
Issues with this `patchwork' approach were that the biomass estimates 
were not comparable across countries due to the differences in 
methodological approaches, and that estimates were not spatially 
derived (or, the spatial data were not provided to EPA). Since the 
release of the final rule, Gallaun et al. developed EU-wide maps of 
above-ground biomass in forests based on remote sensing and field 
measurements. MODIS data were used for the classification, and 
comprehensive field measurement data from national forest inventories 
for nearly 100,000 locations from 16 countries were also used to 
develop the final map. The map covers the whole European Union, the 
European Free Trade Association countries, the Balkans, Belarus, the 
Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey.
    For both data sources, Saatchi et al. and Gallaun et al., we added 
belowground biomass to reported aboveground biomass values using an 
equation in Mokany et al.\7\
---------------------------------------------------------------------------

    \7\ Mokany, K., R.J. Raison, and A.S. Prokushkin. 2006. Critical 
analysis of root: shoot ratios in terrestrial biomes. Global Change 
biology 12: 84-96.
---------------------------------------------------------------------------

    In our analysis, forest stocks are estimated for over 750 regions 
across 160 countries. For some regions the carbon stocks increased as a 
result of the updates and in others they declined. For comparison, we 
ran our grain sorghum analysis using the old forest carbon stock values 
used in the RFS2 rule and with the updated forest carbon values 
described above. Using the updated forest carbon stocks increased the 
land use change GHG emissions related to grain sorghum ethanol by 
approximately 1.2 kilograms of carbon-dioxide equivalent emissions per 
million British thermal units of grain sorghum ethanol 
(kgCO2e/mmBtu). Table II-9 includes the international land 
use change GHG emissions results for the scenarios modeled, in terms of 
kgCO2e/mmBtu. International land use change GHG emissions 
for grain sorghum is estimated at 30 kgCO2e/mmBtu.

         Table II-9--International Land Use Change GHG Emissions
                             [kgCO2e/mmBtu]
------------------------------------------------------------------------
                           Region                              Emissions
------------------------------------------------------------------------
Africa and Middle East......................................           9
Asia........................................................           5
Brazil......................................................          14
India.......................................................           1
Other Latin America.........................................           1
Rest of World...............................................           1
                                                             -----------
International Total (non-U.S.)..............................          30
------------------------------------------------------------------------

    More detailed information on the land-use change emissions can be 
found in the accompanying docket. We invite comment on all aspects of 
these modeling results.\8\
---------------------------------------------------------------------------

    \8\ See Memo to the Docket, Docket Number EPA-HQ-OAR-2011-0542, 
Dated May 18, 2012.
---------------------------------------------------------------------------

3. Grain Sorghum Ethanol Processing
    We expect the dry milling process will be the basic production 
method for producing ethanol from grain sorghum and therefore this is 
the ethanol production process considered here. In the dry milling 
process, the grain sorghum is ground and fermented to produce ethanol. 
The remaining DG are then either left wet if used in the near-term or 
dried for longer term use as animal feed.
    For this analysis the amount of grain sorghum used for ethanol 
production as modeled by the FASOM and FAPRI-CARD models was based on 
yield assumptions built into those two models. Specifically, the models 
assume sorghum ethanol yields of 2.71 gallons per bushel for dry mill 
plants (yields represents pure ethanol).
    As per the analysis done in the RFS2 final rule, the energy 
consumed and emissions generated by a renewable fuel plant must be 
allocated not only to the renewable fuel produced, but also to each of 
the by-products. For grain sorghum ethanol production, this analysis 
accounts for the DG co-product use directly in the FASOM and FAPRI-CARD 
agricultural sector modeling described above. DG are considered a 
replacement animal feed and thus reduce the need to make up for the 
grain sorghum production that went into ethanol production. Since FASOM 
takes the production and use of DG into account, no further allocation 
was needed at the ethanol plant and all plant emissions are accounted 
for there.
    In terms of the energy used at grain sorghum ethanol facilities, 
significant variation exists among plants with respect to the 
production process and type of fuel used to provide process energy 
(e.g., coal versus natural gas). Variation also exists between the same 
type of plants using the same fuel source based on the design of the 
production process such as the technology used to separate the ethanol 
from the water, the extent to which the DG are dried and whether other 
co-products are produced. Such different pathways were considered for 
ethanol made from corn. Since for the most part these same production 
processes are available for ethanol produced from sorghum, our analyses 
considered a similar set of different production pathways for grain 
sorghum ethanol production. Our focus was to differentiate among 
facilities based on key differences, namely the type of plant and the 
type of process energy fuel used. As shown in Section C, the current 
data shows that the type of RIN that different sorghum facilities will 
be able to generate will depend upon the types of process energy used 
and whether advanced technologies are included (but not on the amount 
of DG that are dried).
    Ethanol production is a relatively resource-intensive process that 
requires the use of water, electricity, and steam. In most cases, water 
and electricity are purchased from the municipality and steam is 
produced on-site using boilers fired by natural gas, coal, or in some 
cases, alternative fuels (described in more detail below).\9\
---------------------------------------------------------------------------

    \9\ Some plants pull steam directly from a nearby utility.

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

[[Page 34922]]

    Purchased process fuel and electricity use for grain sorghum 
ethanol production was based on the energy use information for corn 
ethanol production from the RFS2 final rule analysis. For the RFS2 
final rule, EPA modeled future plant energy use to represent plants 
that would be built to meet requirements of increased ethanol 
production, as opposed to current or historic data on energy used in 
ethanol production. The energy use at dry mill ethanol plants was based 
on ASPEN models developed by USDA and updated to reflect changes in 
technology out to 2022 as described in the RFS 2 final rule RIA Chapter 
1.
    The work done on grain ethanol production for the RFS2 final rule 
was based on converting corn to ethanol. Converting grain sorghum to 
ethanol will result in slightly different energy use based on 
difference in the grains and how they are processed. For example, grain 
sorghum has less oil content than corn and therefore requires less 
processing and mass transfer of the oil which results in a decrease in 
energy use compared to processing corn to ethanol. The same ASPEN USDA 
models used for corn ethanol in the final rule were also developed for 
grain sorghum ethanol. Based on the numbers from USDA, a sorghum 
ethanol plant uses 96.3% of the thermal process energy of a corn 
ethanol plant (3.7% less), and 99.3% of the electrical energy (0.7% 
less).
    The GHG emissions from production of ethanol from grain sorghum 
were calculated in the same way as other fuels analyzed as part of the 
RFS2 final rule. The GHG emissions were calculated by multiplying the 
BTUs of the different types of energy inputs at the grain sorghum 
ethanol plant by emissions factors for combustion of those fuel 
sources. The BTU of energy input was determined based on analysis of 
the industry and work done as part of the RFS2 final rule as well as 
considering the impact of different technology options on plant energy 
needs. The emission factors for the different fuel types are the same 
as those used in the RFS2 final rule and were based on assumed carbon 
contents of the different process fuels. The emissions from producing 
electricity in the U.S. were also the same as used in the RFS2 final 
rule, which were taken from the Greenhouse Gases, Regulated Emissions, 
and Energy Use in Transportation Model (GREET) and represent average 
U.S. grid electricity production emissions.
    One of the energy drivers of ethanol production is drying of the 
DG. Plants that are co-located with feedlots have the ability to 
provide the co-product without drying. This energy use has a large 
enough impact on overall results in previous analyses that we defined a 
specific category for wet versus dry co-product as part of the RFS2 
final rule. For grain sorghum ethanol production we also consider wet 
versus dry DG. For corn ethanol production, as discussed in the RFS2 
final rule, the industry average for wet DG is approximately 37%. 
Industry provided data that approximately 92% of grain sorghum DG is 
wet. However, in the case of grain sorghum ethanol production, the 
current data shows that energy used for DG drying does not change 
whether a facility meets the 20% GHG emission threshold (conventional 
renewable fuel) or the 50% GHG emission threshold (advanced renewable 
fuel). The amount of btu per gallon of ethanol produced for processes 
where DG are dried, and where they are not, can be found in Table II-10 
below. Overall lifecycle GHG emission reductions for grain sorghum 
ethanol facilities that do and do not dry DG can be found below in 
Table II-11.
    For this NODA, we analyzed several combinations of different 
advanced process technologies and fuels to determine their impacts on 
lifecycle GHG emissions from grain sorghum ethanol. As noted above, 
many of the same technologies that were considered as part of the RFS2 
final rule for corn ethanol can also be applied to grain sorghum 
ethanol production. Based on discussion with industry, we understand 
there is interest in building grain sorghum ethanol plants which 
incorporate such advanced technologies. Therefore, as was the case with 
corn ethanol in the RFS2 final rule, our intent is to provide different 
processing technology options that producers could use to meet the 
lifecycle threshold requirements required by EISA. This section 
describes the different GHG impacts associated with alternative 
processing technology and fuel options and outlines specific process 
pathways that would be needed to meet different GHG threshold 
requirements. If finalized, these pathways would allow producers to use 
the updated Table 1 in Section 80.1426 to determine whether their 
combination of technologies and process fuels would allow them to 
qualify as an advanced grain sorghum ethanol pathway.
    Several technologies and fuel choices affect emissions from process 
energy use. Fuel choice has a significant impact on process energy 
emissions; switching from natural gas to biogas,\10\ for example, will 
reduce lifecycle GHG emissions by approximately 20 percentage points. 
Another factor that influences GHG impacts from process energy use is 
the percentage of DG that is dried. If a plant is able to reduce the 
amount of DG it dries, process energy use, and therefore GHG emissions, 
decrease. The impact of going from 100% dry DG to 100% wet DG is larger 
for natural gas plants (approximately a 10% reduction in overall GHG 
emissions relative to the petroleum baseline) compared to biogas plants 
because biogas plants already have low emissions from process energy.
---------------------------------------------------------------------------

    \10\ Biogas in the context of use as a fuel source at ethanol 
plants refers to biogas from landfills, waste treatment plants, and 
waste digesters.
---------------------------------------------------------------------------

    Production facilities that utilize combined heat and power (CHP) 
systems can also reduce GHG emissions relative to less efficient system 
configurations. CHP, also known as cogeneration, is a mechanism for 
improving overall plant efficiency by using a single fuel to generate 
both power and thermal energy. The most common configuration in ethanol 
plants involves using the boiler to power a turbine generator unit that 
produces electricity, and using waste heat to produce process steam. 
While the thermal energy demand for an ethanol plant using CHP 
technology is slightly higher than that of a conventional plant, the 
additional energy used is far less than what would be required to 
produce the same amount of electricity in an offsite (central) power 
plant. The increased efficiency is due to the ability of the ethanol 
plant to effectively utilize the waste heat from the electricity 
generation process.
    In addition to CHP (or sometimes in combination), a growing number 
of ethanol producers are turning to alternative fuel sources to replace 
traditional boiler fuels (i.e., natural gas and coal), to improve their 
carbon footprint and/or become more self-sustainable. Alternative 
boiler fuels currently used or being pursued by the ethanol industry 
include biomass, co-products from the ethanol production process (bran, 
thin stillage or syrup), manure biogas (methane from nearby animal 
feedlots), and landfill gas (generated from the digestion of municipal 
solid waste). The CO2 emissions from biomass combustion as a 
process fuel source are not specifically shown in the lifecycle GHG 
inventory of the biofuel production plant; rather, CO2 
emissions from biomass use are accounted for as part of the land use 
change calculations for each feedstock.

[[Page 34923]]

    Since CHP technologies on natural gas plants reduce purchased 
electricity but increase process energy use emissions (because of 
increased natural gas use on-site), the net result is a small reduction 
in overall emissions. CHP at biogas facilities result in greater 
reductions since the increased biogas use for electricity production 
does not result in significant increases in on-site emissions.
    Although not exhaustive, Table II-10 shows the amount of process 
fuel and purchased electricity used at a grain sorghum ethanol facility 
for the different technology and fuel options in terms of Btu/gal of 
ethanol produced.

              Table II-10--Process Fuel and Electricity Options at Grain Sorghum Ethanol Facilities
                                        [Btu/gallon of ethanol produced]
----------------------------------------------------------------------------------------------------------------
                                                                    Natural gas                      Purchased
                    Fuel type and technology                            use         Biogas use      electricity
----------------------------------------------------------------------------------------------------------------
Sorghum Ethanol--Dry Mill Natural Gas
    No CHP, 100% Wet DG.........................................          16,449  ..............           2,235
    Yes CHP, 100% Wet DG........................................          18,605  ..............             508
    No CHP, 0% Wet DG...........................................          27,599  ..............           2,235
    Yes CHP, 0% Wet DG..........................................          29,755  ..............             508
Sorghum Ethanol--Dry Mill Biogas:
    No CHP, 100% Wet DG.........................................  ..............          16,449           2,235
    Yes CHP, 100% Wet DG........................................  ..............          18,605             508
    No CHP, 0% Wet DG...........................................  ..............          27,599           2,235
    Yes CHP, 0% Wet DG..........................................  ..............          29,755             508
----------------------------------------------------------------------------------------------------------------

    As discussed previously in Section II.B.3, there are a number of 
different process technologies available for grain sorghum ethanol 
production. The following Table II-11 shows the mean lifecycle GHG 
reductions compared to the baseline petroleum fuel for a number of 
different technology pathways including natural gas and biogas fired 
plants.

    Table II-11--Lifecycle GHG Emission Reductions for Dry Mill Grain
                       Sorghum Ethanol Facilities
                [% change compared to petroleum gasoline]
------------------------------------------------------------------------
                   Fuel type and technology                     % Change
------------------------------------------------------------------------
Sorghum Ethanol--Dry Mill Natural Gas:
    No CHP, 92% Wet DG.......................................       - 32
    No CHP, 100% Wet DG......................................       - 33
    Yes CHP, 100% Wet DG.....................................       - 36
    No CHP, 0% Wet DG........................................       - 22
    Yes CHP, 0% Wet DG.......................................       - 25
Sorghum Ethanol--Dry Mill Biogas:
    No CHP, 100% Wet DG......................................       - 48
    Yes CHP, 100% Wet DG.....................................       - 53
    No CHP, 0% Wet DG........................................       - 47
    Yes CHP, 0% Wet DG.......................................       - 52
------------------------------------------------------------------------

    The docket for this NODA provides more details on our key model 
inputs and assumptions (e.g., crop yields, biofuel conversion yields, 
and agricultural energy use). These inputs and assumptions are based on 
our analysis of peer-reviewed literature and consideration of 
recommendations of experts from within the grain sorghum and ethanol 
industries, USDA, and academic institutions. EPA invites comment on all 
aspects of its modeling of grain sorghum ethanol, including all 
assumptions and modeling inputs.
4. Results of Lifecycle Analysis for Ethanol from Grain Sorghum (Using 
Dry Mill Natural Gas)
    Consistent with our approach for analyzing other pathways, our 
analysis for grain sorghum ethanol includes a mid-point estimate as 
well as a range of possible lifecycle GHG emission results based on 
uncertainty analysis conducted by the Agency. The graph below (Figure 
II-1) depicts the results of our analysis (including the uncertainty in 
our land use change modeling) for grain sorghum ethanol produced in a 
plant that uses natural gas.\11\
---------------------------------------------------------------------------

    \11\ This analysis assumed 92% wet DG and 8% dry DG.
---------------------------------------------------------------------------

    Figure II-1 shows the results of our grain sorghum ethanol 
modeling. It shows the percent difference between lifecycle GHG 
emissions for 2022 grain sorghum ethanol, produced in a plant that uses 
the ``basic'' technology stated above, and those for the petroleum 
gasoline fuel 2005 baseline. Lifecycle GHG emissions equivalent to the 
statutory gasoline fuel baseline are represented on the graph by the 
zero on the X-axis. The midpoint of the range of results is a 32% 
reduction in GHG emissions compared to the 2005 gasoline baseline.\12\ 
As in the case of other biofuel pathways analyzed as part of the RFS2 
rule, the range of results shown in Figure II-1 is based on our 
assessment of uncertainty regarding the location and types of land that 
may be impacted as well as the GHG impacts associated with these land 
use changes (See Section II.B.1. for further information). These 
results and those in Table II-11, if finalized, would justify a 
determination that grain sorghum ethanol produced in plants that use 
natural gas would meet the 20% reduction threshold required for the 
generation of conventional renewable fuel RINs.
---------------------------------------------------------------------------

    \12\ The 95% confidence interval around that midpoint results in 
range of a 19% reduction to a 44% reduction compared to the 2005 
gasoline fuel baseline.

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

[[Page 34924]]

[GRAPHIC] [TIFF OMITTED] TP12JN12.018

    Table II-12 breaks down by stage the lifecycle GHG emissions for 
grain sorghum ethanol in 2022 and the statutory 2005 gasoline 
baseline.\13\ Results are included using our mid-point estimate of land 
use change emissions, as well as with the low and high end of the 95% 
confidence interval. Net agricultural emissions include impacts related 
to changes in crop inputs, such as fertilizer, energy used in 
agriculture, livestock production and other agricultural changes in the 
scenarios modeled. The fuel production stage includes emissions from 
ethanol production plants. Fuel and feedstock transport includes 
emissions from transporting bushels of harvested grain sorghum from the 
farm to ethanol production facility.
---------------------------------------------------------------------------

    \13\ Totals in the table may not sum due to rounding.

   Table II-12--Lifecycle GHG Emissions for Grain Sorghum Ethanol Produced in Plants That Use Natural Gas and
                            Produce an Industry Average of 92% Wet Distillers Grains
                                                  [gCO2e/mmBtu]
----------------------------------------------------------------------------------------------------------------
                                                                                                   2005 gasoline
                  Fuel type                                  Grain sorghum ethanol                   baseline
----------------------------------------------------------------------------------------------------------------
Net Agriculture (w/o land use change),         12,698...........................................  ..............
 Domestic and International.
Land Use Change, Mean (Low/High), Domestic     27,620 (16,196/41,903)...........................  ..............
 and International.
Fuel Production..............................  22,111...........................................          19,200
Fuel and Feedstock Transport.................  3,661............................................               *
Tailpipe Emissions...........................  880..............................................          79,004
Total Emissions, Mean (Low/High).............  66,971 (55,547/81,254)...........................          98,204
Midpoint Lifecycle GHG Percent Reduction       32%..............................................  ..............
 Compared to Petroleum Baseline.
----------------------------------------------------------------------------------------------------------------
* Emissions included in fuel production stage.


[[Page 34925]]

5. Results of Lifecycle Analysis for Ethanol From Grain Sorghum (Using 
Biogas and CHP)
    To illustrate an example where a combination of various advanced 
processing technologies can result in an overall reduction of greater 
than 50% compared to the 2005 petroleum baseline, the graph included 
below (Figure II-2) depicts the results of our analysis (including the 
uncertainty in our land use change modeling) for grain sorghum ethanol 
produced in a dry mill plant that uses biogas, 0% wet DG, and CHP 
technology.
    Figure II-2 shows the results of our grain sorghum ethanol 
modeling. It shows the percent difference between lifecycle GHG 
emissions for 2022 grain sorghum ethanol, produced in a plant that uses 
biogas as well as combined heat and power, and those for the petroleum 
gasoline fuel 2005 baseline. Lifecycle GHG emissions equivalent to the 
statutory gasoline fuel baseline are represented on the graph by the 
zero on the X-axis. The midpoint of the range of results for this 
sorghum ethanol plant configuration is a 52% reduction in GHG emissions 
compared to the 2005 gasoline baseline.\14\ As in the case of other 
biofuel pathways analyzed as part of the RFS2 rule, the range of 
results shown in Figure II-2 is based on our assessment of uncertainty 
regarding the location and types of land that may be impacted as well 
as the GHG impacts associated with these land use changes (See Section 
II.B.1 for further information). These results, if finalized, would 
justify our determination that sorghum ethanol produced in dry mill 
plants that use biogas and combined heat and power meets the 50% 
reduction threshold required for the generation of advanced renewable 
fuel RINs.
---------------------------------------------------------------------------

    \14\ The 95% confidence interval around that midpoint results in 
range of a 38% reduction to a 64% reduction compared to the 2005 
gasoline fuel baseline.
[GRAPHIC] [TIFF OMITTED] TP12JN12.019

    Table II-13 breaks down by stage the lifecycle GHG emissions for 
grain sorghum ethanol in 2022 and the statutory 2005 gasoline 
baseline.\15\ Results are included using our mid-point estimate of land 
use change emissions, as well as with the low and high end of the 95% 
confidence interval. Net agricultural emissions include impacts related 
to changes in crop inputs, such as fertilizer, energy used in 
agriculture, livestock production and other agricultural changes in the 
scenarios modeled. Emissions from fuel production include emissions 
from ethanol production plants. Fuel and feedstock transport includes 
emissions from transporting bushels of harvested grain sorghum from the 
farm to ethanol production facility.
---------------------------------------------------------------------------

    \15\ Totals in the table may not sum due to rounding.

[[Page 34926]]



  Table II-13--Lifecycle GHG Emissions for Grain Sorghum Ethanol Produced in Plants That Use Biogas as Well as
                                             Combined Heat and Power
                                                  [gCO2e/mmBtu]
----------------------------------------------------------------------------------------------------------------
                                                                                                   2005 gasoline
                  Fuel type                                  Grain sorghum ethanol                   baseline
----------------------------------------------------------------------------------------------------------------
Net Agriculture (w/o land use change),         12,698...........................................  ..............
 Domestic and International.
Land Use Change, Mean (Low/High), Domestic     27,620 (16,196/41,903)...........................  ..............
 and International.
Fuel Production..............................  1,612............................................          19,200
Fuel and Feedstock Transport.................  4,276............................................               *
Tailpipe Emissions...........................  880..............................................          79,004
Total Emissions, Mean (Low/High).............  47,086 (35,662/61,369)...........................          98,204
Midpoint Lifecycle GHG Percent Reduction       52%..............................................  ..............
 Compared to Petroleum Baseline.
----------------------------------------------------------------------------------------------------------------
* Emissions included in fuel production stage.

6. Other Ethanol Processing Technologies
    Since the promulgation of the RFS2 final rule, we have learned that 
in an effort to reduce the overall use of fossil fuels at their 
facilities, a number of renewable fuel producers are using or are 
intend to use electricity that is derived from renewable and non-carbon 
sources, such as wind power, solar power, hydropower, biogas or 
biomass, as power for process units and equipment. EPA, through a 
separate rulemaking process, is evaluating and seeking comment on the 
possibility of adding a new definition for renewable process 
electricity, and the related distribution tracking, registration, 
recordkeeping, and reporting requirements. Depending on the outcome of 
that process EPA could also evaluate the use of renewable process 
electricity as an option for reducing grain sorghum ethanol process GHG 
emissions.
    Capturing and sequestering CO2 emissions from an ethanol 
plant represents another potential technology pathway that could reduce 
lifecycle GHG emissions associated with ethanol. Carbon capture and 
sequestration (CCS) is defined by IPCC as, ``a process consisting of 
the separation of CO2 from industrial and energy-related 
sources, transport to a storage location and long-term isolation from 
the atmosphere.'' \16\ Although the analysis presented in this NODA for 
sorghum ethanol does not include a pathway for reducing GHG emissions 
reductions through CCS, EPA is interested in developing methodologies 
that would allow us to properly evaluate CCS as an emissions reduction 
technology as a part of the lifecycle analysis of fuel production for a 
variety of feedstocks under the RFS2 program. We are taking initial 
steps to that end in this NODA: We seek comment on the broad concept of 
how to properly account for CO2 emissions associated with 
CCS, including CCS in conjunction with CO2 enhanced oil and 
gas recovery (ER), in the context of our RFS lifecycle GHG 
calculations.
---------------------------------------------------------------------------

    \16\ Intergovernmental Panel on Climate Change. 2005. A Special 
Report of Working Group III: Summary for Policymakers. http://www.ipcc.ch/pdf/special-reports/srccs_summaryforpolicymakers.pdf.
---------------------------------------------------------------------------

    While some systems and technologies associated with CCS have been 
in use for many years, for purposes of evaluating lifecycle emissions 
under the RFS program CCS can still be considered an emerging field. 
Data on CCS is limited, particularly data relating to geologic 
sequestration (GS) and GS in conjunction with ER. While EPA recently 
established monitoring and reporting requirements for geologic 
sequestration under the Greenhouse Gas Reporting Program, no U.S. 
facilities have submitted data as of publication of this NODA. We 
therefore invite comment and the submission of data regarding the 
concept and practice of using CCS technologies to lower the lifecycle 
emissions of biofuels. Specifically, we seek data on the amount of 
CO2 capture that is economically and technically feasible at 
the ethanol facility and the amount of additional energy and fuel such 
capture would require. We also seek comment on emissions leakage 
throughout the process of capturing, compressing, transporting, and 
sequestering the CO2. In addition, we invite comment on the 
effectiveness and energy use of the ER CO2 recycling system, 
any fugitive emissions associated with such recycling, and energy use 
and leakage rates with respect to injecting CO2 for GS with 
and without ER. We also invite comment on the amount of CO2 
that remains sequestered and the length of time of sequestration, and 
how EPA should account for this as part of a lifecycle analysis for 
purposes of the RFS program, including how to account now for emissions 
sequestration that is planned to last for a long period of time into 
the future.
    We believe it is important for facilities that receive credit for 
GHG emissions reductions using CCS verify that these emissions 
reductions actually take place. However, we recognize that the ethanol 
facility that generates RINs is most likely not the same party that 
will be operating the GS or EOR site, therefore we invite comment on 
whether it is feasible and enforceable for the ethanol facility to 
verify that the CO2 has actually been captured and stored at 
the GS or EOR site, and how to account for a period of sequestration 
that stretches many years into the future. Furthermore, we invite 
comment on the most appropriate way for ethanol producers to validate 
and credit the GHG emissions reductions from CCS. We recognize that the 
actual GHG emission reductions from CCS can be very site specific, 
therefore we request comments on whether it would be more appropriate 
for EPA to make individual facility determinations using the 40 CFR 
80.1416 petition process rather than provide a general pathway in Table 
1 of 40 CFR 80.1426.

C. Consideration of Lifecycle Analysis Results

1. Implications for Threshold Determinations
    As discussed above, EPA's analysis shows that, based on the mid-
point of the range of results, ethanol produced from grain sorghum 
using biogas and combined heat and power at a dry mill plant would meet 
the 50 percent GHG emissions reduction threshold needed to qualify as 
an advanced biofuel (D-5 RINs). Grain sorghum ethanol meets the 20% 
lifecycle GHG emissions reduction threshold for conventional biofuels 
(D-6 RINs) when natural gas or biogas is used. If finalized, Table 1 to 
Section 80.1426 would be modified to add these three new pathways. 
Table II-14 illustrates how these new pathways would be included in the 
existing table. Data, analysis and assumptions for each

[[Page 34927]]

of these processing technologies are provided in the docket for this 
NODA. We invite comment on all aspects of this analysis.

  Table II-14--Applicable D Codes for Grain Sorghum Ethanol Produced With Different Processing Technologies for
                                             Use in Generating RINs
----------------------------------------------------------------------------------------------------------------
                Fuel type                          Feedstock            Production process requirements   D-code
----------------------------------------------------------------------------------------------------------------
Ethanol.................................  Grain Sorghum..............  Dry mill process, using Natural         6
                                                                        Gas for Process Energy.
Ethanol.................................  Grain Sorghum..............  Dry mill process, using Biogas          6
                                                                        for Process Energy, without
                                                                        Combined Heat and Power.
Ethanol.................................  Grain Sorghum..............  Dry mill process, using Biogas          5
                                                                        for Process Energy, with
                                                                        Combined Heat and Power.
----------------------------------------------------------------------------------------------------------------

2. Consideration of Uncertainty
    Because of the inherent uncertainty and the state of evolving 
science regarding lifecycle analysis of biofuels, any threshold 
determinations that EPA makes for grain sorghum ethanol will be based 
on an approach that considers the weight of evidence currently 
available. For this pathway, the evidence considered includes the mid-
point estimate as well as the range of results based on statistical 
uncertainty and sensitivity analyses conducted by the Agency. EPA will 
weigh all of the evidence available to it, while placing the greatest 
weight on the best-estimate value for the scenarios analyzed.
    As part of our assessment of the grain sorghum ethanol pathway, we 
have identified key areas of uncertainty in our analysis. Although 
there is uncertainty in all portions of the lifecycle modeling, we 
focused our analysis on the factors that are the most uncertain and 
have the biggest impact on the results. The indirect, international 
emissions are the component of our analysis with the highest level of 
uncertainty. The type of land that is converted internationally and the 
emissions associated with this land conversion are critical issues that 
have a large impact on the GHG emissions estimates.
    Our analysis of land use change GHG emissions includes an 
assessment of uncertainty that focuses on two aspects of indirect land 
use change--the types of land converted and the GHG emissions 
associates with different types of land converted. These areas of 
uncertainty were estimated statistically using the Monte Carlo analysis 
methodology developed for the RFS2 final rule.\17\ Figure II-1 and 
Figure II-2 show the results of our statistical uncertainty assessment.
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    \17\ The Monte Carlo analysis is described in EPA (2010a), 
Section 2.4.4.2.8.
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    Based on the weight of evidence considered, and putting the most 
weight on our mid-point estimate results, the results of our analysis 
indicate that grain sorghum ethanol would meet the minimum 20% GHG 
performance threshold for qualifying renewable fuel under the RFS 
program when using natural gas and average 2022 dry mill plant 
efficiencies, and would meet the minimum 50% GHG performance threshold 
for advanced biofuels under the RFS program when using biogas for 
process energy at a dry mill plant, with combined heat and power. These 
conclusions are supported by our midpoint estimates, our statistical 
assessment of land use change uncertainty, as well as our consideration 
of other areas of uncertainty.
    The docket for this NODA provides more details on all aspects of 
our analysis of grain sorghum ethanol. EPA invites comment on all 
aspects of its modeling of grain sorghum ethanol. We also invite 
comment on the consideration of uncertainty as it relates to making GHG 
threshold determinations.

    Dated: May 24, 2012.
Margo T. Oge,
Director, Office of Transportation & Air Quality.
[FR Doc. 2012-13651 Filed 6-11-12; 8:45 am]
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