Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules 34915 

April 19, 2012, submission addressing 

section 110(a)(2)(E)(ii). 

VI. Statutory and Executive Order 

Reviews 

Under the CAA, the Administrator is 

required to approve a SIP submission 

that complies with the provisions of the 

Act and applicable Federal regulations. 

See 42 U.S.C. 7410(k); 40 CFR 52.02(a). 

Thus, in reviewing SIP submissions, 

EPA’s role is to approve state choices, 

provided that they meet the criteria of 

the CAA. Accordingly, this proposed 

action merely approves state law as 

meeting Federal requirements and does 

not impose additional requirements 

beyond those imposed by state law. For 

that reason, this proposed action: 

• Is not a ‘‘significant regulatory 

action’’ subject to review by the Office 

of Management and Budget under 

Executive Order 12866 (58 FR 51735, 

October 4, 1993); 

• Does not impose an information 

collection burden under the provisions 

of the Paperwork Reduction Act (44 

U.S.C. 3501 et seq.); 

• Is certified as not having a 

significant economic impact on a 

substantial number of small entities 

under the Regulatory Flexibility Act (5 

U.S.C. 601 et seq.); 

• Does not contain any unfunded 

mandate or significantly or uniquely 

affect small governments, as described 

in the Unfunded Mandates Reform Act 

of 1995 (Pub. L. 104–4); 

• Does not have Federalism 

implications as specified in Executive 

Order 13132 (64 FR 43255, August 10, 

1999); 

• Is not an economically significant 

regulatory action based on health or 

safety risks subject to Executive Order 

13045 (62 FR 19885, April 23, 1997); 

• Is not a significant regulatory action 

subject to Executive Order 13211 (66 FR 

28355, May 22, 2001); 

• Is not subject to requirements of 

Section 12(d) of the National 

Technology Transfer and Advancement 

Act of 1995 (15 U.S.C. 272 note) because 

application of those requirements would 

be inconsistent with the CAA; and 

• Does not provide EPA with the 

discretionary authority to address, as 

appropriate, disproportionate human 

health or environmental effects, using 

practicable and legally permissible 

methods, under Executive Order 12898 

(59 FR 7629, February 16, 1994). 

In addition, this rule does not have 

tribal implications as specified by 

Executive Order 13175 (65 FR 67249, 

November 9, 2000), because the SIP is 

not approved to apply in Indian country 

located in the state, and EPA notes that 

it will not impose substantial direct 

costs on tribal governments or preempt 

tribal law. 

List of Subjects in 40 CFR Part 52 

Air pollution control, Environmental 

protection, Intergovernmental relations, 

Nitrogen dioxide, Particulate matter, 

Reporting and recordkeeping 

requirements, Volatile organic 

compounds. 

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 

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 http:// 

www.epa.gov/epahome/dockets.htm. 

Docket: All documents in the docket 

are listed in the www.regulations.gov 

index. Although listed in the index, 

some information is not publicly 

available, e.g., CBI or other information 

whose disclosure is restricted by statute. 

Certain other material, such as 

copyrighted material, will be publicly 

available only in hard copy. Publicly 

available docket materials are available 

either electronically in 

www.regulations.gov or in hard copy at 

the Air and Radiation Docket and 

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34916 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: 

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: 

Category NAICS1 codes SIC 2 codes Examples of potentially regulated 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). 

2Standard 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 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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Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules 34917 

1EPA. 2010. Renewable Fuel Standard Program 

(RFS2) Regulatory Impact Analysis. EPA–420–R– 

10–006. http://www.epa.gov/oms/renewablefuels/ 

420r10006.pdf 2EPA. 2010. Renewable Fuel Standard Program 

(RFS2) Regulatory Impact Analysis. EPA–420–R– 

10–006. http://www.epa.gov/oms/renewablefuels/ 

420r10006.pdf. Additional RFS2 related documents 

can be found at http://www.epa.gov/otaq/fuels/ 

renewablefuels/regulations.htm 

3See Memo to the Docket, Docket Number EPA– 

HQ–OAR–2011–0542, Dated May 18, 2012 and 

personal communication with USDA. 

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. 

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’’). 

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

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 

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34918 Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules 

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 ...................................................................................................................................... 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] 

Control case Grain 

sorghum 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, 

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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 

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 Grain 

sorghum 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] 

Control case Grain 

sorghum 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] 

Control case Grain 

sorghum 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] 

Control case Grain 

sorghum case Difference 

Sorghum ...................................................................................................................................... 95,108 95,148 39 

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4See Memo to the Docket, Docket Number EPA– 

HQ–OAR–2011–0542, Dated May 18, 2012. 

5Saatchi, 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. 

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 ............................................................................................................................................. 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] 

Control case Grain 

sorghum 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] 

Control case Grain 

sorghum 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

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 

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7Mokany, K., R.J. Raison, and A.S. Prokushkin. 

2006. Critical analysis of root: shoot ratios in 

terrestrial biomes. Global Change biology 12: 84–96. 

8See Memo to the Docket, Docket Number EPA– 

HQ–OAR–2011–0542, Dated May 18, 2012. 

9Some plants pull steam directly from a nearby 

utility. 

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 

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. 

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

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 nearterm 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 coproducts 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

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10Biogas in the context of use as a fuel source at 

ethanol plants refers to biogas from landfills, waste 

treatment plants, and waste digesters. 

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 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 

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. 

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11This analysis assumed 92% wet DG and 8% 

dry DG. 

12The 95% confidence interval around that 

midpoint results in range of a 19% reduction to a 

44% reduction compared to the 2005 gasoline fuel 

baseline. 

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. 

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] 

Fuel type and technology Natural gas 

use Biogas use Purchased 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

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. 

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13Totals in the table may not sum due to 

rounding. 

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] 

Fuel type Grain sorghum ethanol 

2005 

gasoline 

baseline 

Net Agriculture (w/o land use change), Domestic and International ................................................. 12,698 ................................ ........................

Land Use Change, Mean (Low/High), Domestic and International ................................................... 27,620 (16,196/41,903) ...... ........................

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 Compared to Petroleum Baseline ................................ 32% .................................... ........................

* Emissions included in fuel production stage. 


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14The 95% confidence interval around that 

midpoint results in range of a 38% reduction to a 

64% reduction compared to the 2005 gasoline fuel 

baseline. 

15Totals in the table may not sum due to 

rounding. 

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. 

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. 


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34926 Federal Register / Vol. 77, No. 113 / Tuesday, June 12, 2012 / Proposed Rules 

16 Intergovernmental Panel on Climate Change. 

2005. A Special Report of Working Group III: 

Summary for Policymakers. http://www.ipcc.ch/

pdf/special-reports/srccs_summaryforpolicy

makers.pdf. 

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 ................................................. 12,698 ................................ ........................

Land Use Change, Mean (Low/High), Domestic and International ................................................... 27,620 (16,196/41,903) ...... ........................

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 Compared to Petroleum Baseline ................................ 52% .................................... ........................

* 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 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 

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. 

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 34927 

17The Monte Carlo analysis is described in EPA 

(2010a), Section 2.4.4.2.8. 

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 Gas for Process Energy .............. 6 

Ethanol ....................................... Grain Sorghum ........................... Dry mill process, using Biogas for Process Energy, without Combined Heat and Power.6 

Ethanol ....................................... Grain Sorghum ........................... Dry mill process, using Biogas for Process Energy, with Combined Heat and Power.5 

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 

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 

AGENCY: Environmental Protection 

Agency (EPA). 

ACTION: Notice of data availability. 

SUMMARY: On April 20, 2011, EPA 

published proposed standards for 

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: 

• http://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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