Notice of Data Availability Concerning Renewable Fuels Produced From Grain Sorghum Under the RFS Program, 34915-34927 [2012-13651]
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Authority: 42 U.S.C. 7401 et seq.
Dated: June 1, 2012.
A. Stanley Meiburg,
Acting Regional Administrator, Region 4.
[FR Doc. 2012–14244 Filed 6–11–12; 8:45 am]
BILLING CODE 6560–50–P
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
Environmental Protection
Agency (EPA).
ACTION: Notice of data availability
(NODA).
AGENCY:
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: asdinfo@epa.gov.
• Mail: Air and Radiation Docket and
Information Center, Environmental
SUMMARY:
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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 asdinfo@epa.gov. 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 https://
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
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either electronically in
www.regulations.gov or in hard copy at
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Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules
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: cole.jefferson@epa.gov.
SUPPLEMENTARY INFORMATION:
1 North
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)
NAICS 1 codes
Category
Industry
Industry
Industry
Industry
Industry
Industry
Industry
Outline of This Preamble
............................................
............................................
............................................
............................................
............................................
............................................
............................................
SIC 2 codes
324110
325193
325199
424690
424710
424720
454319
2911
2869
2869
5169
5171
5172
5989
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:
Examples of potentially regulated entities
Petroleum Refineries.
Ethyl alcohol manufacturing.
Other basic organic chemical manufacturing.
Chemical and allied products merchant wholesalers.
Petroleum bulk stations and terminals.
Petroleum and petroleum products merchant wholesalers.
Other fuel dealers.
American Industry Classification System (NAICS).
Industrial Classification (SIC) system code.
2 Standard
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?
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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
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disclosed except in accordance with
procedures set forth in 40 CFR part 2.
II. Analysis of Lifecycle Greenhouse
Gas Emissions
2. Tips for Preparing Your Comments
A. Methodology
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.
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 biomassbased 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
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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.
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.
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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
1 EPA. 2010. Renewable Fuel Standard Program
(RFS2) Regulatory Impact Analysis. EPA–420–R–
10–006. https://www.epa.gov/oms/renewablefuels/
420r10006.pdf
2 EPA. 2010. Renewable Fuel Standard Program
(RFS2) Regulatory Impact Analysis. EPA–420–R–
10–006. https://www.epa.gov/oms/renewablefuels/
420r10006.pdf. Additional RFS2 related documents
can be found at https://www.epa.gov/otaq/fuels/
renewablefuels/regulations.htm
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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’’).
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
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34917
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
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
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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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]
Control case
Grain
sorghum case
Difference
Sorghum ......................................................................................................................................
Corn .............................................................................................................................................
Distillers Grains (DG) ...................................................................................................................
Other ............................................................................................................................................
38,998
324,731
79,388
71,881
36,947
326,365
80,014
71,873
¥2,051
1,635
627
¥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]
Control case
Grain
sorghum case
Difference
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Sorghum ......................................................................................................................................
Corn .............................................................................................................................................
Soybeans .....................................................................................................................................
Other ............................................................................................................................................
11,108
77,539
69,896
154,511
11,111
77,680
69,791
154,564
4
141
¥105
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.
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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
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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,
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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
34919
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]
Control case
U.S. ..............................................................................................................................................
Mexico ..........................................................................................................................................
Japan ...........................................................................................................................................
Argentina ......................................................................................................................................
India .............................................................................................................................................
Nigeria ..........................................................................................................................................
Rest of World ...............................................................................................................................
10,580
¥4,735
¥3,159
2,577
¥219
110
¥4,655
Grain
sorghum case
9,791
¥4,340
¥3,106
2,653
¥219
110
¥4,389
Difference
¥789
395
53
75
0
0
266
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]
Control case
U.S. ..............................................................................................................................................
Brazil ............................................................................................................................................
China ............................................................................................................................................
Japan ...........................................................................................................................................
Mexico ..........................................................................................................................................
Rest of World ...............................................................................................................................
122,688
24,661
12,748
¥38,787
¥29,008
¥91,423
Grain
sorghum case
122,795
24,859
12,840
¥38,877
¥29,264
¥91,474
Difference
106
198
93
¥91
¥256
¥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]
Control case
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Brazil ............................................................................................................................................
China ............................................................................................................................................
Africa and Middle East ................................................................................................................
Rest of World ...............................................................................................................................
International Total (non-U.S.) ......................................................................................................
137,983
272,323
315,843
1,301,417
2,027,567
Grain
sorghum case
138,016
272,334
315,892
1,301,441
2,027,682
Difference
33
11
48
24
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]
Control case
Sorghum ......................................................................................................................................
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95,108
12JNP1
Grain
sorghum case
95,148
Difference
39
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TABLE II–6—SUMMARY OF PROJECTED CHANGE IN INTERNATIONAL (NON-U.S.) HARVESTED AREA BY CROP IN 2022 IN
THE FAPRI–CARD MODEL—Continued
[Thousands of acres]
Control case
Grain
sorghum case
Difference
Corn .............................................................................................................................................
Soybeans .....................................................................................................................................
Other ............................................................................................................................................
307,342
202,980
1,422,137
307,379
202,991
1,422,165
37
11
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]
Control case
Grain
sorghum case
Difference
Mexico ..........................................................................................................................................
Argentina ......................................................................................................................................
India .............................................................................................................................................
Nigeria ..........................................................................................................................................
Other Africa and Middle East ......................................................................................................
Rest of World ...............................................................................................................................
4,569
1,915
22,261
18,841
37,833
9,689
4,576
1,917
22,261
18,841
37,856
9,695
8
2
0
0
23
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]
Control case
Grain
sorghum case
Difference
Africa and Middle East ................................................................................................................
Asia ..............................................................................................................................................
Brazil ............................................................................................................................................
India .............................................................................................................................................
Other Latin America .....................................................................................................................
Rest of World ...............................................................................................................................
77,220
108,751
20,935
20,176
39,599
40,661
77,223
108,764
20,953
20,180
39,594
40,664
4
13
18
5
¥5
2
International Total (non-U.S.) ...............................................................................................
307,342
307,379
37
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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
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
4 See Memo to the Docket, Docket Number EPA–
HQ–OAR–2011–0542, Dated May 18, 2012.
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(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
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
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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
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.
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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.
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
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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
TABLE II–9—INTERNATIONAL LAND
respect to the production process and
USE CHANGE GHG EMISSIONS
type of fuel used to provide process
energy (e.g., coal versus natural gas).
[kgCO2e/mmBtu]
Variation also exists between the same
Region
Emissions type of plants using the same fuel
source based on the design of the
Africa and Middle East ...............
9 production process such as the
Asia .............................................
5
technology used to separate the ethanol
Brazil ...........................................
14
India ............................................
1 from the water, the extent to which the
Other Latin America ...................
1 DG are dried and whether other coRest of World ..............................
1 products are produced. Such different
pathways were considered for ethanol
International Total (non-U.S.) .....
30 made from corn. Since for the most part
these same production processes are
More detailed information on the
available for ethanol produced from
land-use change emissions can be found sorghum, our analyses considered a
in the accompanying docket. We invite
similar set of different production
comment on all aspects of these
pathways for grain sorghum ethanol
modeling results.8
production. Our focus was to
differentiate among facilities based on
3. Grain Sorghum Ethanol Processing
key differences, namely the type of
We expect the dry milling process
plant and the type of process energy fuel
will be the basic production method for used. As shown in Section C, the
producing ethanol from grain sorghum
current data shows that the type of RIN
and therefore this is the ethanol
that different sorghum facilities will be
production process considered here. In
able to generate will depend upon the
the dry milling process, the grain
types of process energy used and
sorghum is ground and fermented to
whether advanced technologies are
produce ethanol. The remaining DG are
included (but not on the amount of DG
then either left wet if used in the nearthat are dried).
term or dried for longer term use as
Ethanol production is a relatively
animal feed.
resource-intensive process that requires
For this analysis the amount of grain
the use of water, electricity, and steam.
sorghum used for ethanol production as In most cases, water and electricity are
modeled by the FASOM and FAPRI–
purchased from the municipality and
CARD models was based on yield
steam is produced on-site using boilers
assumptions built into those two
fired by natural gas, coal, or in some
cases, alternative fuels (described in
7 Mokany, K., R.J. Raison, and A.S. Prokushkin.
more detail below).9
2006. Critical analysis of root: shoot ratios in
aboveground biomass values using an
equation in Mokany et al.7
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.
terrestrial biomes. Global Change biology 12: 84–96.
8 See Memo to the Docket, Docket Number EPA–
HQ–OAR–2011–0542, Dated May 18, 2012.
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9 Some plants pull steam directly from a nearby
utility.
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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
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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 coproduct 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
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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.
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 selfsustainable. Alternative boiler fuels
currently used or being pursued by the
ethanol industry include biomass, coproducts 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.
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.
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Since CHP technologies on natural gas
plants reduce purchased electricity but
increase process energy use emissions
(because of increased natural gas use onsite), 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.
34923
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
use
Fuel type and technology
Sorghum Ethanol—Dry Mill Natural Gas
No CHP, 100% Wet DG .......................................................................................................
Yes CHP, 100% Wet DG .....................................................................................................
No CHP, 0% Wet DG ...........................................................................................................
Yes CHP, 0% Wet DG .........................................................................................................
Sorghum Ethanol—Dry Mill Biogas:
No CHP, 100% Wet DG .......................................................................................................
Yes CHP, 100% Wet DG .....................................................................................................
No CHP, 0% Wet DG ...........................................................................................................
Yes CHP, 0% Wet DG .........................................................................................................
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.
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
TABLE II–11—LIFECYCLE GHG EMIS- comment on all aspects of its modeling
SION REDUCTIONS FOR DRY MILL of grain sorghum ethanol, including all
GRAIN SORGHUM ETHANOL FACILI- assumptions and modeling inputs.
TIES
[% change compared to petroleum gasoline]
%
Change
Fuel type and technology
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Sorghum Ethanol—Dry Mill Natural Gas:
No CHP, 92% Wet DG ..........
No CHP, 100% Wet DG ........
Yes CHP, 100% Wet DG ......
No CHP, 0% Wet DG ............
Yes CHP, 0% Wet DG ..........
Sorghum
Ethanol—Dry
Mill
Biogas:
No CHP, 100% Wet DG ........
Yes CHP, 100% Wet DG ......
No CHP, 0% Wet DG ............
Yes CHP, 0% Wet DG ..........
¥
¥
¥
¥
¥
32
33
36
22
25
¥
¥
¥
¥
48
53
47
52
11 This analysis assumed 92% wet DG and 8%
dry DG.
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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
Figure II–1 shows the results of our
grain sorghum ethanol modeling. It
12 The 95% confidence interval around that
midpoint results in range of a 19% reduction to a
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Biogas use
Purchased
electricity
16,449
18,605
27,599
29,755
........................
........................
........................
........................
2,235
508
2,235
508
........................
........................
........................
........................
16,449
18,605
27,599
29,755
2,235
508
2,235
508
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.
44% reduction compared to the 2005 gasoline fuel
baseline.
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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 midpoint 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.
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]
Grain sorghum ethanol
2005
gasoline
baseline
Net Agriculture (w/o land use change), Domestic and International .................................................
Land Use Change, Mean (Low/High), Domestic and International ...................................................
Fuel Production ...................................................................................................................................
Fuel and Feedstock Transport ...........................................................................................................
Tailpipe Emissions ..............................................................................................................................
Total Emissions, Mean (Low/High) .....................................................................................................
Midpoint Lifecycle GHG Percent Reduction Compared to Petroleum Baseline ................................
12,698 ................................
27,620 (16,196/41,903) ......
22,111 ................................
3,661 ..................................
880 .....................................
66,971 (55,547/81,254) ......
32% ....................................
........................
........................
19,200
*
79,004
98,204
........................
* Emissions included in fuel production stage.
13 Totals in the table may not sum due to
rounding.
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Fuel type
Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules
34925
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.
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 midpoint 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.
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.
15 Totals in the table may not sum due to
rounding.
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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
5. Results of Lifecycle Analysis for
Ethanol From Grain Sorghum (Using
Biogas and CHP)
34926
Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules
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]
Fuel type
Grain sorghum ethanol
2005 gasoline
baseline
Net Agriculture (w/o land use change), Domestic and International .................................................
Land Use Change, Mean (Low/High), Domestic and International ...................................................
Fuel Production ...................................................................................................................................
Fuel and Feedstock Transport ...........................................................................................................
Tailpipe Emissions ..............................................................................................................................
Total Emissions, Mean (Low/High) .....................................................................................................
Midpoint Lifecycle GHG Percent Reduction Compared to Petroleum Baseline ................................
12,698 ................................
27,620 (16,196/41,903) ......
1,612 ..................................
4,276 ..................................
880 .....................................
47,086 (35,662/61,369) ......
52% ....................................
........................
........................
19,200
*
79,004
98,204
........................
* Emissions included in fuel production stage.
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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 longterm 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
16 Intergovernmental Panel on Climate Change.
2005. A Special Report of Working Group III:
Summary for Policymakers. https://www.ipcc.ch/
pdf/special-reports/srccs_summaryforpolicy
makers.pdf.
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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.
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.
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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
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Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules
of these processing technologies are
provided in the docket for this NODA.
34927
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
Feedstock
Production process requirements
Ethanol .......................................
Ethanol .......................................
Grain Sorghum ...........................
Grain Sorghum ...........................
Ethanol .......................................
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Fuel type
Grain Sorghum ...........................
Dry mill process, using Natural Gas for Process Energy ..............
Dry mill process, using Biogas for Process Energy, without Combined Heat and Power.
Dry mill process, using Biogas 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.
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
17 The Monte Carlo analysis is described in EPA
(2010a), Section 2.4.4.2.8.
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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]
BILLING CODE 6560–50–P
ENVIRONMENTAL PROTECTION
AGENCY
40 CFR Parts 122, 123, 124, and 125
[EPA–HQ–OW–2008–0667, FRL–9681–5]
RIN 2040–AE95
National Pollutant Discharge
Elimination System—Proposed
Regulations To Establish
Requirements for Cooling Water Intake
Structures at Existing Facilities; Notice
of Data Availability Related to EPA’s
Stated Preference Survey
Environmental Protection
Agency (EPA).
ACTION: Notice of data availability.
AGENCY:
On April 20, 2011, EPA
published proposed standards for
SUMMARY:
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D-code
6
6
5
cooling water intake structures at all
existing power generating,
manufacturing, and industrial facilities
as part of implementing section 316(b)
of the Clean Water Act (CWA). This
notice presents a summary of new
information EPA has developed since
the rule proposal. The information
results from a stated preference survey
that EPA conducted after the proposed
rule was published. Stated preference
surveys are an attempt to determine the
economic value of goods or services by
means other than by assessing the
effects of changes in the market for the
goods and services. In this notice EPA
solicits comment on the information
presented in this notice and on what
role, if any, it should play in EPA’s
assessment of the benefits of regulatory
options for the final rule, pending
completion of the survey and external
peer review.
DATES: Comments must be received on
or before July 12, 2012.
ADDRESSES: Submit your comments,
identified by Docket No. EPA–HQ–OW–
2008–0667 by one of the following
methods:
• https://www.regulations.gov: Follow
the on-line instructions for submitting
comments.
• Email: OW-Docket@epa.gov,
Attention Docket ID No. EPA–HQ–OW–
2008–0667.
• Mail: Water Docket, U.S.
Environmental Protection Agency, Mail
Code: 4203M, 1200 Pennsylvania Ave.
NW., Washington, DC 20460. Attention
Docket ID No. EPA–HQ–OW–2008–
0667. Please include a total of 3 copies.
In addition, please mail a copy of your
comments on information collection
provisions to the Office of Information
and Regulatory Affairs, Office of
Management and Budget (OMB), Attn:
Desk Officer for EPA, 725 17th St. NW.,
Washington, DC 20503.
• Hand Delivery: Water Docket, EPA
Docket Center, EPA West Building
Room 3334, 1301 Constitution Ave.
NW., Washington, DC, Attention Docket
ID No. EPA–HQ–OW–2008–0667. Such
deliveries are only accepted during the
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]]>Agencies
[Federal Register Volume 77, Number 113 (Tuesday, June 12, 2012)]
[Proposed Rules]
[Pages 34915-34927]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: 2012-13651]
-----------------------------------------------------------------------
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).
-----------------------------------------------------------------------
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: asdinfo@epa.gov.
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
asdinfo@epa.gov. 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 https://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: cole.jefferson@epa.gov.
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:
----------------------------------------------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\1\ EPA. 2010. Renewable Fuel Standard Program (RFS2) Regulatory
Impact Analysis. EPA-420-R-10-006. https://www.epa.gov/oms/renewablefuels/420r10006.pdf
---------------------------------------------------------------------------
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'').
---------------------------------------------------------------------------
\2\ EPA. 2010. Renewable Fuel Standard Program (RFS2) Regulatory
Impact Analysis. EPA-420-R-10-006. https://www.epa.gov/oms/renewablefuels/420r10006.pdf. Additional RFS2 related documents can
be found at https://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\3\ See Memo to the Docket, Docket Number EPA-HQ-OAR-2011-0542,
Dated May 18, 2012 and personal communication with USDA.
---------------------------------------------------------------------------
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]
----------------------------------------------------------------------------------------------------------------
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]
----------------------------------------------------------------------------------------------------------------
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]
----------------------------------------------------------------------------------------------------------------
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
----------------------------------------------------------------------------------------------------------------
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]
----------------------------------------------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\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. https://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.
---------------------------------------------------------------------------
\17\ The Monte Carlo analysis is described in EPA (2010a),
Section 2.4.4.2.8.
---------------------------------------------------------------------------
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]
BILLING CODE 6560-50-P
