[Federal Register Volume 87, Number 74 (Monday, April 18, 2022)]
[Proposed Rules]
[Pages 22823-22843]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-07598]


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ENVIRONMENTAL PROTECTION AGENCY

40 CFR Part 80

[EPA-HQ-OAR-2021-0845; FRL-9075-03-OAR]
RIN 2060-AV55


Renewable Fuel Standard Program: Canola Oil Pathways to Renewable 
Diesel, Jet Fuel, Naphtha, Liquefied Petroleum Gas and Heating Oil

AGENCY: Environmental Protection Agency (EPA).

ACTION: Proposed rule.

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SUMMARY: In this proposed rule, the Environmental Protection Agency 
(EPA) is providing an opportunity for comment on a proposed analysis of 
the lifecycle greenhouse gas (GHG) emissions associated with certain 
biofuels that are produced from canola/rapeseed oil. This assessment 
considers diesel, jet fuel, heating oil, naphtha, and liquefied 
petroleum gas (LPG) produced from canola/rapeseed oil via a 
hydrotreating process, and proposes to find that these pathways would 
meet the lifecycle GHG emissions reduction threshold of 50 percent 
required for advanced biofuels and biomass-based diesel under the 
Renewable Fuel Standard (RFS) program. Based on these analyses, EPA is 
proposing to approve these fuel pathways, making them eligible to 
generate Renewable Identification Numbers (RINs), provided they satisfy 
the other definitional and RIN generation criteria for renewable fuel 
specified in the RFS regulations.

DATES: 
    Comments. Comments must be received on or before May 18, 2022.

[[Page 22824]]

    Public hearing. EPA will not hold a public hearing on this matter 
unless a request is received by the person identified in the FOR 
FURTHER INFORMATION CONTACT section of this preamble by May 3, 2022. If 
EPA receives such a request, we will publish information related to the 
timing and location of the hearing and a new deadline for submission of 
public comments.

ADDRESSES: You may send comments, identified by Docket ID No. EPA-HQ-
OAR-2021-0845, by any of the following methods:
    <bullet> Federal eRulemaking Portal: https://www.regulations.gov 
(our preferred method). Follow the online instructions for submitting 
comments.
    <bullet> Email: <a href="/cdn-cgi/l/email-protection#c6a7eba7a8a2ebb4eb82a9a5ada3b286a3b6a7e8a1a9b0"><span class="__cf_email__" data-cfemail="76175b1718125b045b3219151d13023613061758111900">[email protected]</span></a>. Include Docket ID No. EPA-
HQ-OAR-2021-0845 in the subject line of the message.
    <bullet> Mail: U.S. Environmental Protection Agency, EPA Docket 
Center, OAR, Docket EPA-HQ-OAR-2021-0845, Mail Code 28221T, 1200 
Pennsylvania Avenue NW, Washington, DC 20460.
    <bullet> Hand Delivery or Courier (by scheduled appointment only): 
EPA Docket Center, WJC West Building, Room 3334, 1301 Constitution 
Avenue NW, Washington, DC 20004. The Docket Center's hours of 
operations are 8:30 a.m.-4:30 p.m., Monday-Friday (except Federal 
Holidays).
    Instructions: All submissions received must include the Docket ID 
No. for this rulemaking. Comments received may be posted without change 
to https://www.regulations.gov, including any personal information 
provided. For the full EPA public comment policy, information about CBI 
or multimedia submissions, and general guidance on making effective 
comments, please visit http://www.epa.gov/dockets/commenting-epa-dockets.
    Out of an abundance of caution for members of the public and our 
staff, the EPA Docket Center and Reading Room are closed to the public, 
with limited exceptions, to reduce the risk of transmitting COVID-19. 
Our Docket Center staff will continue to provide remote customer 
service via email, phone, and webform. We encourage the public to 
submit comments via https://www.regulations.gov or email, as there may 
be a delay in processing mail and faxes. Hand deliveries and couriers 
may be received by scheduled appointment only. For further information 
on EPA Docket Center services and the current status, please visit us 
online at https://www.epa.gov/dockets.
    EPA continues to monitor information carefully and continuously 
from the Centers for Disease Control and Prevention (CDC), local area 
health departments, and our Federal partners so that we can respond 
rapidly as conditions change regarding COVID-19.

FOR FURTHER INFORMATION CONTACT: Christopher Ramig, Office of Air and 
Radiation, Office of Transportation and Air Quality, Mail Code: 6401A, 
U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue NW, 
Washington, DC 20460; telephone number: 202-564-1372; email address: 
<a href="/cdn-cgi/l/email-protection#6c1e0d01050b420f041e051f18031c04091e2c091c0d420b031a"><span class="__cf_email__" data-cfemail="82f0e3efebe5ace1eaf0ebf1f6edf2eae7f0c2e7f2e3ace5edf4">[email protected]</span></a>.

SUPPLEMENTARY INFORMATION:

Does this action apply to me?

    Entities potentially affected by this proposed rule are those 
involved with the production, distribution, and sale of transportation 
fuels, including gasoline and diesel fuel or renewable fuels such as 
ethanol, biodiesel, heating oil, renewable diesel, naphtha and 
liquified petroleum gas. Potentially regulated categories include:

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                                                         Examples of
             Category               NAICS \1\ code  potentially affected
                                                          entities
------------------------------------------------------------------------
Industry..........................          111120  Oilseed (except
                                                     Soybean) Farming.
Industry..........................          324110  Petroleum refineries
                                                     (including
                                                     importers).
Industry..........................          325193  Ethyl alcohol
                                                     manufacturing.
Industry..........................          325199  Other basic organic
                                                     chemical
                                                     manufacturing.
Industry..........................          424690  Chemical and allied
                                                     products merchant
                                                     wholesalers.
Industry..........................          424710  Petroleum Bulk
                                                     Stations and
                                                     Terminals.
Industry..........................          424720  Petroleum and
                                                     Petroleum Products
                                                     Merchant
                                                     Wholesalers.
Industry..........................          454310  Other fuel dealers.
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\1\ North American Industry Classification System (NAICS).

    This table is not intended to be exhaustive, but rather provides a 
guide for readers regarding entities likely to be regulated or 
otherwise affected by this action. This table lists the types of 
entities that EPA is now aware could potentially be affected by this 
action. Other types of entities not listed in the table could also be 
affected. To determine whether your entity is regulated by this action, 
you should carefully examine the applicability criteria in the 
referenced regulations. If you have any questions regarding the 
applicability of this action to a particular entity, consult the person 
listed in the FOR FURTHER INFORMATION CONTACT section.

Table of Contents

I. Introduction
II. Analysis of GHG Emissions Associated With Production of Biofuels 
From Canola Oil
    A. Overview of Canola Oil
    B. Petition Overview
    C. Analysis of Lifecycle GHG Emissions
III. Consideration of Lifecycle Analysis Results
IV. Summary
V. Statutory & Executive Order Reviews
    A. Executive Order 12866: Regulatory Planning and Review and 
Executive Order 13563: Improving Regulation and Regulatory Review
    B. Paperwork Reduction Act (PRA)
    C. Regulatory Flexibility Act (RFA)
    D. Unfunded Mandates Reform Act (UMRA)
    E. Executive Order 13132: Federalism
    F. Executive Order 13175: Consultation and Coordination With 
Indian Tribal Governments
    G. Executive Order 13045: Protection of Children From 
Environmental Health and Safety Risks
    H. Executive Order 13211: Actions Concerning Regulations That 
Significantly Affect Energy Supply, Distribution, or Use
    I. National Technology Transfer and Advancement Act (NTTAA)
    J. Executive Order 12898: Federal Actions To Address 
Environmental Justice in Minority Populations and Low-Income 
Populations
VI. Statutory Authority

I. Introduction

    Section 211(o) of the Clean Air Act (CAA) establishes the Renewable 
Fuel Standard (RFS) program, under which EPA sets annual percentage 
standards specifying the total amount of renewable fuel, as well as 
three subcategories of renewable fuel, that must be used to reduce or 
replace fossil fuel present in transportation fuel, heating oil, or jet 
fuel. Non-exempt renewable fuels must achieve at least a

[[Page 22825]]

20-percent reduction in lifecycle greenhouse gas (GHG) emissions as 
compared to a 2005 petroleum baseline. Advanced biofuel and biomass-
based diesel must achieve at least a 50 percent reduction, and 
cellulosic biofuel must achieve at least a 60 percent reduction.\1\
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    \1\ See generally 42 U.S.C. 7545(o)(1).
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    In addition to meeting the applicable lifecycle GHG reduction 
requirements, RINs may only be generated if the fuel meets the 
definitional and other criteria for renewable fuel (e.g., produced from 
renewable biomass as defined in the regulations and used to reduce or 
replace the quantity of fossil fuel present in transportation fuel, 
heating oil, or jet fuel) in CAA 211(o) and the RFS regulations at 40 
CFR part 80, subpart M.
    Only fuels produced using pathways that EPA has approved as meeting 
all applicable requirements are eligible to generate RINs. There are 
three critical components of fuel pathways under the RFS program: (1) 
Fuel type; (2) feedstock; and (3) production process. Each approved 
pathway is associated with a specific ``D code'' corresponding to 
whether the fuel meets the requirements for renewable fuel, advanced 
fuel, cellulosic fuel, or biomass-based diesel.\2\ Since the formation 
of the RFS program, EPA has periodically promulgated rules to add new 
pathways to the regulations.\3\ In addition, EPA has approved facility-
specific pathways through the petition process in 40 CFR 80.1416.
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    \2\ For additional information see: https://www.epa.gov/renewable-fuel-standard-program/fuel-pathways-under-renewable-fuel-standard.
    \3\ See, e.g., 83 FR 37735 (August 2, 2018) approving grain 
sorghum oil pathways and 78 FR 41703 (July 11, 2013) approving giant 
reed and Napier grass pathways.
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    EPA's lifecycle analyses are used to assess the overall GHG impacts 
of a fuel throughout each stage of its production and use. The results 
of these analyses, considering uncertainty and the weight of available 
evidence, are used to determine whether a fuel meets the necessary GHG 
reductions required under the CAA. Lifecycle analysis includes an 
assessment of emissions related to the full fuel lifecycle, including 
feedstock production, feedstock transportation, fuel production, fuel 
transportation and distribution, and tailpipe emissions. Per the CAA 
definition of lifecycle GHG emissions,\4\ EPA's lifecycle analyses also 
include an assessment of significant indirect emissions, such as those 
from land use changes and agricultural sector impacts.
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    \4\ 42 U.S.C. 7545(o)(1)(H).
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    EPA conducted lifecycle GHG analyses for several combinations of 
biofuel feedstocks, production processes, and fuels and promulgated 
several fuel pathways as part of its March 26, 2010 RFS final rule (75 
FR 14670) (the ``March 2010 RFS2 rule''). In the preamble to that final 
rule, EPA indicated that it intended to add fuel pathways to the 
regulations via further notice-and-comment rulemakings. EPA 
subsequently completed a proposed assessment for canola oil biodiesel; 
this proposed assessment was published in the Federal Register for 
notice and comment on July 26, 2010 (75 FR 43522). This proposed 
assessment evaluated the GHG emissions associated with biodiesel 
produced from canola oil through a transesterification process. On 
September 28, 2010, EPA published a rule finalizing our determination 
that canola oil biodiesel meets the lifecycle GHG emissions reduction 
threshold of 50 percent required by the CAA, and added row G to table 1 
to 40 CFR 80.1426, making canola oil biodiesel produced through a 
transesterification process eligible for biomass-based diesel (D-code 
4) RINs (75 FR 59622) (September 2010 Canola Oil rule). This final rule 
did not include determinations for renewable diesel, jet fuel, naphtha, 
LPG, or heating oil produced from canola oil via a hydrotreating 
process.\5\ In the 2013 Pathways I final rule (78 FR 14190, March 5, 
2013) (``2013 Pathways I rule''), EPA added ``rapeseed'' to the 
existing pathway in row G for renewable fuel made from canola oil 
because ``we had not intended the supplemental determination to cover 
just those varieties or sources of rapeseed that are identified as 
canola'' (78 FR 14214). In that same rule, for clarity EPA also added 
``heating oil'' to the rows in Table 1 that already included renewable 
diesel or biodiesel (78 FR 14201). As in the 2013 Pathways I rule, in 
this action we are similarly proposing to add new pathways to table 1 
for biofuels produced from ``Canola/Rapeseed oil'' but for simplicity 
we refer to both canola and rapeseed as ``canola.''
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    \5\ Hydrotreating, the process used to produce the vast majority 
of renewable diesel, consists of catalytic reactions in the presence 
of hydrogen. This process produces a ``drop-in'' fuel with 
properties virtually identical to petroleum diesel and distinct from 
biodiesel.
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    In 2020, the United States Canola Association (USCA) submitted a 
petition to EPA requesting an evaluation of the GHG emissions 
associated with renewable diesel, jet fuel, naphtha, LPG and heating 
oil produced from canola oil via a hydrotreating process, and a 
determination of the renewable fuel categories, if any, for which such 
biofuels may be eligible.\6\ This preamble describes EPA's analysis of 
the lifecycle GHG emissions associated with these fuel pathways and 
provides a brief overview of its results.\7\
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    \6\ U.S. Canola Association. (2020). Petition for Pathways for 
Renewable Diesel from Canola Oil as ``Advanced Biofuel'' Under the 
Renewable Fuel Standard Program.
    \7\ The full set of modeling results, post-processing 
spreadsheets and other technical documents describing this analysis 
are available in the docket for this action.
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    As described in Section II.C.12 of this preamble, we estimate that 
the lifecycle GHG emissions associated with the production of renewable 
diesel via a hydrotreating process are approximately 63 to 69 percent 
less than the applicable diesel baseline. We estimate that the naphtha 
and LPG co-produced with the renewable diesel has similar reductions of 
64 to 69 percent and 63 to 69 percent compared to baseline GHG 
emissions, respectively. We estimate that jet fuel produced from canola 
oil through a hydrotreating process configured to maximize jet fuel 
output has lifecycle GHG emissions approximately 59 to 67 percent lower 
than baseline emissions. These ranges of GHG emissions estimates are 
based on differences in hydrotreating process configurations. Section 
II.C.9 of this preamble discusses these estimates and our consideration 
of uncertainty in the analysis.
    Based on these estimates, we propose to find that these biofuels 
meet the 50 percent GHG reduction threshold required for advanced 
biofuel and biomass-based diesel. In this action, based on our analysis 
of available data and other input, EPA is proposing to add to table 1 
of 40 CFR 80.1426 pathways for the production of renewable diesel, jet 
fuel, naphtha, LPG and heating oil produced from canola oil via a 
hydrotreating process. Specifically, we propose to add ``Canola oil'' 
to the Feedstock column in rows G, H, and I of table 1 to 40 CFR 
80.1426. If finalized, these fuel pathways would be eligible for either 
biomass-based diesel (D-code 4) or advanced biofuel (D-code 5) RINs, 
depending on the fuel type and whether they are produced through a 
hydrotreating process that co-processes renewable biomass with 
petroleum. EPA requests public comment on these proposed pathway 
approvals.
    EPA is also seeking comment on its proposal to add these fuel 
pathways to rows G, H, and I of table 1 to 40 CFR 80.1426. We note that 
in addition to approving generally-applicable pathways by adding them 
to table 1, EPA has also approved fuel pathways

[[Page 22826]]

on a facility-specific basis in cases where the evaluation involved a 
straightforward application of prior modeling and analysis established 
through a notice and comment process. Consistent with this practice, 
EPA may also consider the analysis in this proposed rule and any 
comments it receives in evaluating facility-specific pathway petitions 
submitted pursuant to 40 CFR 80.1416 that propose using canola oil as a 
biofuel feedstock or hydrotreating as a production process.

II. Analysis of GHG Emissions Associated With Production of Biofuels 
From Canola Oil

A. Overview of Canola Oil

    Canola oil is a vegetable oil that contains low concentrations of 
erucic acid (less than 2 percent), originally bred from cultivars of 
the Brassica and Sinapis genera.\8\ In addition to use as a renewable 
fuel feedstock, canola oil is a common vegetable oil for food use. In 
many instances, canola oil is used synonymously with rapeseed oil, or 
is considered a varietal of it. We propose definitions of canola/
rapeseed oil to be included in 40 CFR 80.1401. We request comment on 
this definition.
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    \8\ See 21 CFR 184.1555 Rapeseed oil.
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    In September 2010, EPA evaluated a pathway for biodiesel produced 
from canola oil using a transesterification process to generate 
biomass-based diesel (D-code 4) RINs.\9\ For that analysis, EPA 
performed lifecycle analysis using the methodology first described in 
the March 2010 RFS2 rule.\10\ This methodology included the Forest and 
Agricultural Sector Optimization Model with Greenhouse Gases model 
(hereafter referred to as ``FASOM'') and the FAPRI-CARD model (Food and 
Agricultural Policy Research Institute international model; hereafter 
referred to as ``FAPRI'') developed at the Center for Agriculture and 
Rural Development at Iowa State University. These frameworks were used 
to estimate upstream GHG emissions associated with the production and 
transport of the canola oil feedstock.\11\ These upstream emissions 
were evaluated in concert with a transesterification biodiesel 
production process using natural gas and electricity for process energy 
and glycerin as a co-product. Based on that analysis, EPA determined 
that canola oil biodiesel produced via transesterification meets the 50 
percent GHG reduction threshold and added this fuel pathway to row G in 
table 1 to 40 CFR 80.1426, making this fuel eligible for biomass-based 
diesel (D-code 4) RINs. The September 2010 Canola Oil rule did not 
address pathways for renewable diesel, naphtha, LPG, jet fuel or 
heating oil produced from canola oil through a hydrotreating process.
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    \9\ 75 FR 59622 (September 28, 2010).
    \10\ For documentation of this methodology, see Docket Item No. 
EPA[hyphen]HQ[hyphen]OAR[hyphen]2005-0161-3173.
    \11\ For further discussion of the scientific reasoning behind 
the use of these two specific models of this methodology, see 
Chapter 2 of the Final Regulatory Impact Analysis associated with 
the March 2010 RFS2 rule (EPA-420-R-10-006).
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    In addition to the lifecycle GHG analysis, another factor EPA has 
analyzed in pathway determinations is the invasiveness properties of 
the feedstock and the appropriateness of requiring associated risk 
management measures. EPA began evaluating invasiveness concerns in the 
context of fuel pathway evaluation under the RFS program in the July 
11, 2013 rule approving renewable fuel pathways for giant reed (Arundo 
Donax) and Napier grass (Pennisetum Purpureum) after receiving comments 
that these feedstocks present a risk of invasiveness.\12\ Commenters 
stated that EPA should conduct an invasiveness species analysis, citing 
requirements of Executive Order (E.O.) 13112.\13\ E.O. 13112, signed in 
February 1999, defines ``invasive species'' as ``an alien species whose 
introduction does or is likely to cause economic or environmental harm 
or harm to human health.'' In the July 2013 rule (78 FR 41703), we 
established requirements that producers of renewable fuel using giant 
reed or napier grass include a Risk Mitigation Plan (RMP) demonstrating 
measures taken to prevent the spread of these species, or demonstrate 
that an RMP is not needed because the species do not pose a significant 
likelihood of spread beyond the planted area. We are not proposing any 
risk management measures related to potential invasiveness of canola in 
this rule. Canola is an established feedstock with 89 million acres 
planted in over 30 countries in 2020.\14\ We do not believe canola is 
an invasive species as defined in E.O. 13112, and we do not believe the 
approval of additional canola oil-based fuels would have implications 
for invasiveness. We request comment on this decision and the 
appropriateness of risk mitigation practices.
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    \12\ 78 FR 41703 (July 11, 2013).
    \13\ 64 FR 6183 (February 3, 1999).
    \14\ United States Department of Agriculture, Foreign 
Agricultural Service. PSD Only Query tool. https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery. Data 
queried November 5, 2021.
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B. Petition Overview

    The USCA submitted a petition in March 2020, pursuant to the 
petition process described at 40 CFR 80.1416, requesting EPA's 
evaluation of the lifecycle GHG emissions associated with producing 
renewable diesel, jet fuel, naphtha, LPG and heating oil from canola 
oil feedstock through a hydrotreating process. The petition requested 
that EPA evaluate these pathways using the same lifecycle analysis 
modeling approach used to evaluate canola-oil based biodiesel in the 
September 2010 Canola Oil Rule (75 FR 59622). However, USCA stated in 
their petition that, in our 2010 analysis of canola oil-based 
biodiesel, we overestimated the lifecycle GHG emissions associated with 
canola oil production in four categories: Domestic land use change, 
domestic crop inputs, international land use change and international 
crop inputs. USCA supported their statements by comparing data sources 
underlying parts of our 2010 assessment of canola oil with more recent 
data. Specifically, the petition referenced more recent data on canola 
production, yields, trade, and oil extraction. Based on these 
comparisons, the USCA petition requested that we adjust our 2010 canola 
oil estimates without conducting new agricultural sector modeling.
    The USCA petition requests that we simply adjust the results of our 
previously completed agricultural sector modeling based on new 
information. We believe such adjustments would be inappropriate because 
they would create inconsistencies between the agricultural sector 
modeling and the results. For example, it would be inappropriate to 
reduce planted area of canola based on new yield data and simply assume 
that the rest of the agricultural model results would remain unchanged. 
Thus, while we are not adjusting or otherwise reopening our 2010 canola 
oil-based biodiesel analysis or estimates, we do believe that the USCA 
petition highlights appropriate and significant areas where the data 
and information considered in the 2010 canola modeling should be 
updated for purposes of evaluating new fuel pathways that use canola 
oil feedstock. The petition includes detailed information showing that 
more recent data on canola oil production and trade patterns differed 
significantly from the data considered in the 2010 analysis. Based on 
these significant differences, and since we have not previously 
published lifecycle GHG emissions estimates for canola oil-based fuels 
produced through a hydrotreating process, we believe it is important to 
consider the more recent data highlighted in the USCA petition in a new 
lifecycle GHG analysis for these

[[Page 22827]]

fuel pathways. This analysis uses the same modeling frameworks and 
methodology as we have used previously to evaluate agricultural 
feedstocks but includes updated data inputs as discussed later in this 
proposal.\15\
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    \15\ For documentation of the LCA frameworks and methodology, 
see Docket Item No. EPA[hyphen]HQ[hyphen]OAR[hyphen]2005-0161-3173.
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C. Analysis of Lifecycle GHG Emissions

1. Overview of Lifecycle Analysis Methodology
    For this proposed rule, we evaluated the lifecycle GHG emissions of 
producing renewable diesel and other biofuels from canola oil. In this 
section, we describe our methodology for conducting this evaluation, 
the assumptions and scenarios evaluated using this methodology, and the 
results of our analysis. We used the same biofuel lifecycle analysis 
methodology and modeling framework developed for the March 2010 RFS2 
rule and that was subsequently used for the September 2010 Canola Oil 
Rule.\16\ The components of this methodology are described further 
later in this proposal, but generally involve the use of agricultural 
modeling to estimate emissions from land use change, crop production, 
livestock, and rice methane, as well as application of coefficients and 
assumptions from the Greenhouse Gases, Regulated Emissions, and Energy 
use in Technologies (GREET) model \17\ and other sources to evaluate 
emissions associated with feedstock and fuel transport, processing, and 
use. This methodology was developed to estimate ``lifecycle greenhouse 
gas emissions'' as defined at section 211(o)(1)(H) of the Clean Air 
Act. It was used for the March 2010 RFS2 rule after an extensive peer 
review and public comment process.
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    \16\ For information about our 2010 methodology and analysis see 
Section 2 of the regulatory impact analysis (RIA) for the March 2010 
RFS2 rule and the associated lifecycle results (Docket Item No. 
EPA[hyphen]HQ[hyphen]OAR[hyphen]2005-0161-3173).
    \17\ See documentation and description available from Argonne 
National Lab at https://greet.es.anl.gov.
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    In general, this methodology involves using two agricultural sector 
models, FASOM and the FAPRI-CARD model, to estimate U.S. and non-U.S. 
GHG emissions impacts respectively. In this methodology, we model and 
evaluate a hypothetical canola oil demand shock scenario to estimate 
changes in agricultural production and land use and associated GHG 
emissions associated with the biofuel pathway under consideration. In 
this demand shock scenario, U.S. domestic consumption of a specific 
biofuel pathway is assumed to increase by some amount relative to the 
volume of U.S. domestic consumption in a reference scenario.
    Following the lifecycle GHG analysis methodology developed for the 
March 2010 RFS2 rule, the modeling scenarios used in this analysis are 
designed to isolate the GHG impacts associated with the biofuel pathway 
being considered. They are not meant to project or forecast future 
market conditions, or to otherwise predict what will happen in the 
future if a given biofuel pathway is approved. Some of our assumptions, 
which are necessary to construct a scenario which appropriately 
isolates the impacts of a single fuel pathway, intentionally simplify 
what we would expect to occur in the real world. For example, in these 
scenarios, we hold U.S. consumption of all biofuels constant throughout 
the entire modeled period, except for the biofuel being evaluated. In 
reality, an increase in domestic consumption of one biofuel product 
would be expected to have some impact on consumption of other biofuel 
products. However, allowing for such market-balancing behavior would 
confound our ability to estimate the GHG impacts of one biofuel in 
isolation. Therefore, such simplifying assumptions are necessary for 
the purposes of our analysis. For these same reasons, it would be 
inappropriate to characterize the scenario results presented later in 
this proposal as a projection or forecast; these results should be 
interpreted as hypothetical scenarios.
    This methodology also includes estimating GHG emissions associated 
with fuel production, distribution and use based on data from GREET and 
other sources. All of these GHG emissions estimates are added together 
and divided by the change in the amount of biofuel produced in the 
scenarios evaluated to estimate the lifecycle GHG emissions associated 
with fuel produced through the evaluated pathway, in terms of carbon 
dioxide-equivalent emissions per megajoule (MJ) of fuel produced. We 
are not reopening this overall lifecycle analysis methodology and 
modeling framework in this proposed rule; thus, any comments on the 
overall methodology and modeling framework are outside the scope of 
this rulemaking action.
    Although we are using the same overall methodology and modeling 
framework as developed for the March 2010 RFS2 rule, we have updated 
the data inputs into this analysis in the following areas: (1) Canola/
rapeseed oil production, crushing, yields and trade based on historical 
data from USDA and other sources, (2) GHG emissions factors and 
transportation and distribution assumptions based on the latest version 
of the GREET model,\18\ (3) the most recent global warming potentials 
from the Intergovernmental Panel on Climate Change (IPCC), (4) 
international crop production energy inputs based on historical FAO 
data, and (5) hydrotreating process assumptions based on literature 
review and information submitted through new pathway petitions. We 
request comment on these data input updates. As discussed in Section 
II.C.9 of this preamble, we also request comment on our use of the 
energy allocation method to account for co-products from the 
hydrotreating process, given that prior RFS rules used a displacement 
approach for some of these co-products. The rest of this section 
describes the updated data inputs used in our analysis and the 
scenarios modeled.
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    \18\ Argonne National Laboratory. (2021). Greenhouse gases, 
Regulated Emissions, and Energy use in Transportation (GREET) Model. 
https://greet.es.anl.gov/.
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    The lifecycle analysis for the March 2010 RFS2 rule relied to a 
relatively large extent on data and GHG emissions factors from the 
GREET model developed and maintained by Argonne National Laboratory. 
Version 1.8b of GREET was the most recent version available at the time 
of the March 2010 RFS2 rule.\19\ For the analysis for this proposed 
rule, we have updated GHG emissions factors based on more recent data 
in GREET-2020. Some of the emissions factors have not changed 
substantially, while others have. For example, the carbon dioxide-
equivalent emissions factor for natural gas consumed in the U.S. in 
medium-size industrial boiler increased by only 1% from GREET 1.8b to 
GREET-2020. Whereas, the emissions factor for U.S. average electricity 
has decreased by 41% reflecting significant changes to the U.S. 
grid.\20\
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    \19\ As noted previously, we are not reopening the 2010 
lifecycle GHG analysis for canola oil biodiesel.
    \20\ Both the natural gas and electricity emissions factor 
comparisons are weighted with the same 100-year GWP values from the 
IPCC Fifth Assessment Report.
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    The latest version of GREET was released in October 2021. While the 
analysis for this proposed rule was almost entirely complete using data 
and emissions factors from GREET-2020 prior to the release of GREET-
2021, we do consider the updated hydrotreating input-output data from 
GREET-2021 in this proposed rule. A brief review shows that the other 
relevant changes to emissions factors from GREET-2020 to GREET-2021 are 
relatively small--for

[[Page 22828]]

example, in the latest version of GREET the GHG emissions factors per 
energy unit for average natural gas did not change, the emissions 
factor for gaseous hydrogen increased by one percent, and U.S. average 
grid electricity decreased by two percent. We intend to update these 
data to GREET-2021 for the final rule, but we do not expect these 
updates to change our estimates enough to affect our overall finding 
that the pathways evaluated satisfy the statutory 50 percent GHG 
reduction threshold for qualification as biomass-based diesel or 
advanced biofuel.
    Another update is that the analysis for the March 2010 RFS2 rule 
used 100-year global warming potential (GWP) values from the IPCC 
Second Assessment Report. The analysis for this proposed rule uses 100-
year GWP values from the most recent IPCC Fifth Assessment Report.\21\ 
Based on these updates, the GWP for methane increased from 21 to 30, 
and the GWP for nitrous oxide decreased from 310 to 265.
---------------------------------------------------------------------------

    \21\ IPCC, 2014: Climate Change 2014: Synthesis Report. 
Contribution of Working Groups I, II and III to the Fifth Assessment 
Report of the Intergovernmental Panel on Climate Change [Core 
Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, 
Switzerland, 151 pp.
---------------------------------------------------------------------------

    Our analysis for this proposed rule considers updated data based on 
information submitted as part of the USCA petition. Global canola 
acreage has increased over the last decade, from 83 million acres 
globally in 2010 to 89 million acres in 2020.\22\ U.S. canola acreage 
increased over this time from 1.43 million acres in 2010 to 1.80 
million acres in 2020, representing 1.7 percent and 2 percent of global 
totals respectively. Yields have increased over the same period in 
several producing regions. Average U.S. yields grew from 1,713 pounds 
per acre in 2010 to 1,927 pounds per acre in 2020 (12.5 percent 
increase) while yields improved more substantially in Canada and China 
over the same period (25 percent and 18 percent increases 
respectively). Global production of canola oil increased 24 percent 
between 2010 and 2020 to meet growing demand. This increase in demand 
was led by China. China's consumption of canola oil grew from 13 
billion pounds in 2010 to 18 billion pounds in 2020. The U.S. canola 
oil consumption grew by 1.9 billion pounds over this timeframe, from 
3.7 billion pounds to 5.6 billion pounds, representing a 54 percent 
increase.\23\
---------------------------------------------------------------------------

    \22\ In most of the world, canola is referred to as 
``rapeseed''. For consistency, we use ``canola'' throughout to refer 
to both canola and rapeseed.
    \23\ United States Department of Agriculture, Foreign 
Agricultural Service. PSD Only Query tool. https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery. Last 
accessed March 16, 2022.
---------------------------------------------------------------------------

    Specifically, for the purpose of this rulemaking we have updated 
our FASOM and FAPRI input assumptions to include more recent USDA 
historical data on global canola oil production, yields and trade.\24\ 
Updates were made consistently between the two frameworks, using common 
data sources and assumption values where applicable (i.e., where both 
models require the same input assumption). These assumption updates are 
described in more detail in Sections II.C.2 and 3 later in this 
proposal. We have also updated the data source for estimating GHG 
emissions associated with farming energy use for canola oil and other 
crop production outside of the U.S. For more details, see Section 
II.C.5 of this preamble. We also consider new data on canola crushing 
from the USCA petition, feedstock and fuel transport from GREET-2020 
and hydrotreating from GREET-2021, as well as data from review of the 
literature and information provided through RFS new pathway petitions. 
All these updates taken together decrease our estimates of the 
lifecycle GHG emission associated with using canola oil as a biofuel 
feedstock compared to compared to our analysis for the September 2010 
Canola Oil Rule. EPA previously determined that biodiesel produced from 
canola oil via transesterification meets the 50 percent threshold to 
generate D4 RINs. EPA is not revisiting, revising, or requesting 
comment on canola oil-based biodiesel or any other existing pathways. 
Given that most of the updates for this proposed rule pertain 
specifically to canola oil, we note that it would be inappropriate to 
draw any conclusions about the lifecycle GHG emissions associated with 
biofuel pathways that use feedstocks other than canola oil from our 
estimates for this proposed rule. EPA is therefore not requesting 
comment on pathways using any other feedstock besides canola oil.
---------------------------------------------------------------------------

    \24\ These are taken from the USDA PSD data cited above and from 
the USDA National Agricultural Statistical Service QuickStats 
database (USDA NASS QuickStats). https://quickstats.nass.usda.gov. 
Last accessed March 16, 2022.
---------------------------------------------------------------------------

    EPA conducted two modeling scenarios in both FASOM and FAPRI for 
this analysis.\25\ The difference in GHG emissions between these two 
scenarios represents our estimate of the emissions from land use 
change, agricultural input, livestock, and rice methane associated with 
using canola oil as a biofuel feedstock (our emissions estimates are 
described in Table II.C.8-1). First, we ran an updated Control Case 
that reflected the updated assumptions for global canola oil 
production, yields, and trade.\26\ In this Control Case, we assumed no 
canola oil-based biofuels were consumed in the U.S. over the period of 
analysis (2012-2052 in FASOM, 2012-2022 in FAPRI), consistent with our 
Control Case assumptions for previous analyses. Second, we conducted a 
shock scenario that assumed a 1.53 billion pound increase in canola oil 
production for use as feedstock to produce approximately 200 million 
gallons of canola oil-based renewable diesel, jet fuel, naphtha, LPG 
and heating oil for U.S. consumption of in 2022 (hereafter the ``Canola 
Case''), which was assumed to ramp up linearly from 2012 to 2022 (see 
Table II.C.1-1).\27\ According to USDA historical data, annual U.S. 
consumption of canola oil ranged from about 5.3 to 6.4 billion pounds 
over the period between 2015 and 2020.\28\ In addition, global canola/
rapeseed seed annual exports ranged from approximately 32 to 38 billion 
pounds between 2015 and 2020 and canola/rapeseed oil exports ranged 
from about 9 to 13 billion pounds over the same period; this suggests 
substantial quantities of additional feedstock may be available for 
import to the U.S. market.\29\ Based on data from the EPA Moderated 
Transaction System (EMTS), the U.S. produced approximately 160 million 
gallons of canola oil biodiesel in 2020, and another 123 million 
gallons of biodiesel produced from a mix of feedstocks were imported 
from Canada, which likely included a portion from canola oil. Thus, the 
volume of hydrotreated canola oil-based fuels in the modeled shock is a 
similar order of magnitude as the volume of biodiesel currently 
produced from canola oil. Finally, according to EPA's administrative 
data from the RFS program, about 1.5 billion RINs were generated for 
renewable diesel in 2019, equivalent to about 900 million

[[Page 22829]]

gallons.\30\ Based on these data, we believe the magnitude of the 
assumed shock in the Canola Case is reasonable and appropriate.
---------------------------------------------------------------------------

    \25\ Complete sets of results for these FASOM and FAPRI modeling 
scenarios are available on the docket.
    \26\ A memorandum describing these updates and referencing their 
sources is available on the docket.
    \27\ Depending on the source of hydrotreating process data used, 
the size of the shock ranges from 187 million gallons of 
hydrotreated renewable fuel (based on GREET-2021) to 220 million 
gallons (based on data in petitions submitted pursuant to 40 CFR 
80.1416 claimed as confidential business information).
    \28\ See for reference the USDA Oil Crop Yearbook at https://www.ers.usda.gov/data-products/oil-crops-yearbook. Last accessed 
March 16, 2022.
    \29\ United States Department of Agriculture, Foreign 
Agricultural Service. PSD Only Query tool. https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery. Data 
queried March 16, 2022
    \30\ See public data from the RFS program at https://www.epa.gov/fuels-registration-reporting-and-compliance-help/rins-generated-transactions.
---------------------------------------------------------------------------

    All other assumptions were held constant between the Control Case 
and the Canola Case. The structure of this shock was designed to be 
consistent with the shock methodology approach used for EPA's previous 
lifecycle GHG analyses of agricultural feedstocks under the RFS 
program.

             Table II.C.1-1--Canola Oil Shock Scenario \31\
------------------------------------------------------------------------
                                                        Assumed increase
                                                       in USA canola oil
                                                        consumption for
                         Year                              biodiesel
                                                           production
                                                        (billion pounds
                                                         of canola oil)
------------------------------------------------------------------------
2012.................................................               0.25
2017.................................................                0.9
2022 through 2057....................................               1.53
------------------------------------------------------------------------

2. FASOM Analysis
---------------------------------------------------------------------------

    \31\ Note that, consistent with our existing methodology, the 
volume shock is implemented slightly differently in FASOM and FAPRI. 
For FASOM, which operates in 5-year time steps, the values in this 
table fully represent the assumptions used to implement the shock. 
For FAPRI, which operates in annual time steps, interim year 
assumption values are interpolated linearly to create a smooth 
``ramp-up'' path for the volume shock. Further description of this 
methodology can be found in Chapter 2 of the Final Regulatory Impact 
Analysis associated with the March 2010 RFS2 rule (EPA-420-R-10-
006).
---------------------------------------------------------------------------

    EPA used FASOM to estimate the GHG emissions from domestic land use 
change, farm inputs, livestock, and rice methane associated with using 
canola oil as a biofuel feedstock. This is the same methodology EPA 
previously used to estimate these GHG emissions sources for soybean 
oil-based biodiesel and other agricultural feedstocks.\32\ EPA updated 
several aspects of its analysis of the domestic U.S. emissions 
associated with production of fuels from canola oil for this analysis, 
building on the version of FASOM used for the analysis of the GHG 
emissions attributable to the production and transport of sugar beets 
for use as a biofuel feedstock.\33\ In this section, we first review 
the updates made to model inputs and other assumptions for this 
analysis. Following this, we present a summary of the FASOM modeling 
results.\34\
---------------------------------------------------------------------------

    \32\ See Docket Item No. EPA-HQ-OAR-2005-0161-3173 for details 
on the version of FASOM used to analyze emissions associated with 
soybean oil-based biodiesel. See Docket No. EPA-HQ-OAR-2010-0133 for 
details on the version of FASOM used to analyze emissions associated 
with canola oil-based biodiesel. See Docket No. EPA-HQ-OAR-2016-0771 
for details on the version of FASOM used to analyze emissions 
associated with sugar beet-based ethanol.
    \33\ See Docket No. EPA-HQ-OAR-2016-0771 for details on the 
version of FASOM used to analyze emissions associated with sugar 
beets.
    \34\ Further information about our assumptions and the modeling 
results are available in the docket for this action.
---------------------------------------------------------------------------

i. Modifications to Model Inputs and Assumptions
    For this analysis, EPA updated FASOM assumptions related to market 
conditions for canola seed, canola meal, and canola oil. This included 
assumptions about historical U.S. prices; quantities of seed, meal, and 
oil consumed; planted area; seed yields; and trade quantities and 
elasticities. Updated assumptions for prices, planted area, and seed 
yields were primarily taken from USDA National Agricultural Statistical 
Service (NASS) historical data sets.\35\ In some cases, these NASS data 
were supplemented with additional data taken from the USDA Oil Crop 
Yearbook and the USCA. These updates replaced previous assumptions in 
FASOM for the years 2011 through 2020. In the case of canola seed 
yields, FASOM's baseline trend of future yields was also reprojected 
using the updated NASS data.\36\
---------------------------------------------------------------------------

    \35\ See USDA NASS QuickStats. https://quickstats.nass.usda.gov. 
Last accessed March 16, 2022.
    \36\ Further information regarding these updated assumptions is 
detailed in the memorandum, ``Memo on FASOM Assumptions,'' available 
in the docket for this action.
---------------------------------------------------------------------------

    EPA also updated FASOM to reflect differences in historical pricing 
between U.S. domestically-produced canola seed, oil, and meal and 
imported canola seed, oil, and meal. Imported canola seed and oil from 
Canada are important components of the U.S. market, generally 
representing well over 90 percent of the canola products consumed in 
the U.S. in any given year.\37\ Reflective of this market dynamic, 
historical data show that Canadian producers exporting to the U.S. were 
systematically paid less for their canola oil than domestic U.S. 
producers.\38\ In previous modeling analyses, FASOM assumed a single 
price for both domestic and imported canola oil. This led to a 
consumption mix that included a greater percentage share of 
domestically-produced canola products, especially oil, than actually 
occurred historically. In the updated modeling conducted for this 
assessment, EPA differentiated the prices at which domestic and 
imported canola seed and oil could be supplied to the U.S. market and 
then recalibrated canola trade elasticities to better reproduce 
historical market shares of domestically-produced canola products and 
Canadian imported canola products more accurately in FASOM.\39\ EPA 
requests comment on these updates to our modeling assumptions. We are 
not seeking comment on the overall lifecycle analysis methodology and 
modeling framework used to conduct this analysis, which were subject to 
notice and comment in the March 2010 RFS2 rule.\40\
---------------------------------------------------------------------------

    \37\ For detailed data on US imports of canola seed, meal, and 
oil by trade partner, see the UN Comtrade database at https://comtrade.un.org/data.
    \38\ For U.S. price data see USDA ERS--Oil Crops Yearbook. 
Canola Seed and Canola Seed Products. https://www.ers.usda.gov/data-products/oil-crops-yearbook. Last accessed March 16, 2022. For 
Canadian price data, see Canola Council of Canada. Canadian canola 
export statistics. https://www.canolacouncil.org/markets-stats/exports/#export-values. Last accessed March 16, 2022.
    \39\ Further information regarding the assumptions made to 
conduct the FASOM modeling in support of this analysis is available 
in the memorandum, ``Memo on FASOM Assumptions,'' available in the 
docket for this action.
    \40\ EPA (2010). Renewable fuel standard program (RFS2) 
regulatory impact analysis. Washington, DC, US Environmental 
Protection Agency Office of Transportation Air Quality. EPA-420-R-
10-006.
---------------------------------------------------------------------------

ii. Summary of Results
    This section describes the differences in FASOM results between 
modeled outcomes from the Control Case and the Canola Case (described 
in Table II.C.1-1). Unless otherwise stated, the data presented in this 
section are the calculated differences between the Control Case and the 
Canola Case (i.e., the model output value for a variable reported in 
the Canola Case minus the output value for that same variable reported 
in the Control Case). In this summary, we first describe the ways in 
which FASOM estimates the canola oil feedstock used to supply the 
biofuel shock would be sourced. We then describe the market adjustments 
in canola oil prices, supply, demand, and trade which FASOM estimates 
would be necessary to facilitate this sourcing of canola oil for fuel 
use. Following this, we describe the shifts in production of other 
crops, cropland use, and land use which FASOM estimates would occur as 
a result of the sourcing of canola oil for fuel use.
    The total quantity of canola oil required to produce the assumed 
marginal volume shock in the Canola Case was assumed to be 
approximately 1.53 billion pounds. To supply this quantity of canola 
oil to the biofuel production sector, FASOM made several market 
adjustments. Of the total 1.53 billion pounds required, FASOM estimated 
approximately 1.28 billion pounds would be supplied by increasing the 
total U.S. supply of

[[Page 22830]]

canola oil via a combination of increased imports and increased 
domestic production. These 1.28 billion pounds would represent an 
approximately 28 percent increase in total domestic supplies of canola 
oil. FASOM estimates canola oil imports would increase by about 1.18 
billion pounds. Domestic crushing of canola seed into meal and oil 
would produce about 0.1 billion pounds of additional canola oil. 
Domestic demand for non-fuel uses of canola oil, inclusive of all food 
uses (e.g., cooking, baking, salad dressings) and non-fuel industrial 
uses (e.g., industrial lubricants, cleaning products, cosmetics), would 
decrease by approximately 0.25 billion pounds to provide the remaining 
canola oil required to meet the 1.53-billion-pound shock. These shares 
of biofuel feedstock are summarized in Table II.C.2.ii-1.

  Table II.C.2.ii-1--Sources of Canola Oil for Biofuel Feedstock in the
                               Canola Case
------------------------------------------------------------------------
                                             Quantity       Percent  of
            Feedstock source                 (billion      total volume
                                              pounds)          shock
------------------------------------------------------------------------
Increased Imports.......................            1.18              77
Reduced Domestic Demand for Non-Fuel                0.25              16
 Uses...................................
Increased Domestic Production...........             0.1               7
                                         -------------------------------
    Total Volume Shock..................            1.53             100
------------------------------------------------------------------------

    As stated earlier in this proposal, most of the additional supply 
of biofuel feedstock is expected to come from imported canola oil.\41\ 
FASOM estimates these imports would increase by approximately 40 
percent in 2022 in response to the shock. Because modeled non-fuel uses 
of canola oil are not drawn on as significantly to provide feedstock 
for this shock, FASOM does not estimate there would be a significant 
need to backfill the domestic U.S. vegetable oil market. Domestic 
consumption of other vegetable oils therefore does not change 
significantly in these results. Following this, FASOM estimates 
virtually no changes in imports of other vegetable oils in these 
results. Increased demand for canola oil in response to the volume 
shock is estimated to cause the average price of canola oil for all 
uses to increase by approximately 24 percent in the Canola Case. This 
price increase would put downward pressure on other uses of canola oil, 
and non-biofuel domestic demand for canola oil is estimated to decrease 
by approximately 5.6 percent. FASOM estimates these higher prices would 
also induce domestic U.S. production of canola oil to increase by about 
7 percent. Table II.C.2.ii-2 reports changes in supply, demand, and 
prices for canola oil in the Canola Case relative to the Control case. 
Changes for other modeled vegetable oils, specifically soybean oil and 
corn oil, are estimated to be in the range of 0.03 percent or less and 
are not presented here, though these results are available in the 
docket.\42\
---------------------------------------------------------------------------

    \41\ FASOM is a U.S.-only model and does not disaggregate 
imports and exports to and from the U.S. by country of origin.
    \42\ Further information is available in the documents, 
``Canola_FASOM results'' and ``FASOM HTML (full results)'' available 
in the docket for this action.

         Table II.C.2.ii-2--Canola Oil Market Responses in 2022
                         [In percentage changes]
------------------------------------------------------------------------
                                                               Percent
                                                             change from
                                                               control
                                                                 case
------------------------------------------------------------------------
Total Domestic Demand......................................         -5.6
U.S. Imports...............................................         38.9
U.S. Production............................................          7.0
U.S. Price.................................................         24.1
------------------------------------------------------------------------

    FASOM estimates the increase in canola oil production would result 
in an increase in canola seed crushing of approximately 253.5 million 
pounds, an increase in domestic canola oil production of about 7 
percent compared to the Control Case. Most of this increase in canola 
crushing would be supplied through increased imports of whole canola 
seed. Of the total increase in canola seed supply to the crushing 
market, 87 percent is estimated to come from increased imports and 13 
percent is estimated to come from increased domestic U.S. production. 
As observed above, the U.S. canola product markets are historically 
import-dependent. Based on this, we believe the response in FASOM is 
consistent with historical market patterns. However, FASOM estimates 
the increase in domestic crushing would also induce a response from 
domestic canola seed demands. FASOM estimates direct domestic uses of 
canola seed other than crushing would decrease by approximately 16 
percent. Domestic canola seed production also responds, and FASOM 
estimates domestic production would increase by approximately 1 
percent. These impacts are summarized in Table II.C.2.ii-3. This 
increase in U.S. canola seed production would be facilitated in part by 
a modeled expansion in canola harvested crop area of about 17,600 
acres, or about 1.2 percent, in the U.S. in 2022 (see Table II.C.2.ii-
4).

         Table II.C.2.ii-3--Canola Seed Market Responses in 2022
                           [In million pounds]
------------------------------------------------------------------------
                                                          Change from
                                                          control case
------------------------------------------------------------------------
Total Domestic Demand................................        -5.8 (-16%)
U.S. Imports.........................................        216.5 (20%)
U.S. Production......................................          31.3 (1%)
U.S. Canola Seed Crushing............................         253.5 (7%)
------------------------------------------------------------------------

    These shifts in canola supply, demand, and trade would also have 
implications for production and consumption of other crops. The modeled 
increase in canola crushing also produces an additional 156 million 
pounds of canola meal, all of which FASOM estimates would be supplied 
to the domestic livestock market. This influx of meal would primarily 
displace corn in livestock diets. Corn consumption in the domestic feed 
market is estimated to decrease by about 306 million pounds (about 0.08 
percent). This same dynamic can be observed in the FASOM results for 
commodity trade. As international trade partners increase exports of 
canola oil to the U.S., these exporters crush additional canola seed. 
This creates additional supplies of meal for these canola-producing 
nations, reducing their demands for corn as well. As a result, corn 
exports from U.S. are estimated to decrease by about 271 million pounds 
(about 0.28 percent). On net, FASOM estimates that U.S. corn production 
would decline by about 589 million pounds and that corn harvested area 
would decline by about 49,100 acres, or about 0.06 percent (see Table 
II.C.2.ii-4).
    Canola and wheat can be produced on the same type of land in high 
latitude

[[Page 22831]]

agricultural systems like Canada and North Dakota, and many farmers 
rotate the two crops. In response to an increase in production of 
canola, farmers are likely to respond in one of two ways. One option is 
that total acres in wheat/canola rotation could increase. The other 
option is for canola to displace wheat area to some extent as farmers 
tilt rotations more heavily towards the former (e.g., canola-canola 
rotations rather than canola-wheat rotations). We observe these complex 
dynamics in the FASOM results for the Canola Case. To increase canola 
exports to the U.S. market, FASOM estimates the international market 
would decrease production of wheat, creating an opportunity for U.S. 
wheat producers to increase their exports. This impact is relatively 
marginal in comparison to the shock. However, FASOM estimates U.S. 
wheat exports would increase by about 174 million pounds, or about 0.18 
percent. Domestic wheat production would increase by about 169 million 
pounds and the harvested area in wheat production (excluding wheat used 
for grazing) would expand by about 63,000 acres, or about 0.02 percent 
(see Table II.C.2.ii-4).
    The modeling results also show some minor net shifts in other 
cropland as markets re-equilibrate in response to the shock, totaling 
about 28,100 harvested acres, or about 0.01 percent. Harvested crop 
area impacts are summarized in Table II.C.2.ii-4. The shock results in 
modeled net increase in total domestic harvested crop area of 
approximately 60,600 acres. This increase would require some shifting 
of land use from other uses to cropland; as discussed later in this 
section this land is shifted into cropland from pasture and cropland 
pasture on net.

        Table II.C.2.ii-4--Harvested Crop Area Responses in 2022
                           [In thousand acres]
------------------------------------------------------------------------
                                                          Change from
                                                            control
------------------------------------------------------------------------
Canola...............................................        17.6 (1.2%)
Wheat................................................         63 (0.02%)
Corn.................................................     -49.1 (-0.06%)
All Else.............................................       28.1 (0.01%)
                                                      ------------------
    Total............................................       60.6 (0.02%)
------------------------------------------------------------------------

    Our FASOM results estimate these small shifts in agricultural 
production volumes would have some modest impact on agricultural 
prices. In our scenario, canola meal and wheat prices are estimated to 
decline as production increases, by 0.02 percent and 0.51 percent 
respectively, while corn prices would rise by 0.44 percent as 
production decreases. FASOM estimates the livestock market would 
respond to the increase in corn prices by consuming slightly less corn 
(0.08 percent compared to baseline consumption). This would be made up 
in part by a modeled increase in canola meal consumption. However, the 
modeled increase in corn prices is estimated to create some upward 
pressure on overall feed prices as well, raising the estimated cost of 
livestock production. On net in these results, beef slaughter is 
estimated to decrease by 0.04 percent in response to higher costs and 
chicken (broiler) slaughter would decrease by 0.05 percent.
    Geographically, the modeled domestic response to the shock is 
concentrated in North Dakota. Canola production is estimated to 
increase in North Dakota by about 28.9 million pounds (about 1.4 
percent) and canola crop area is estimated to expand by 16,300 acres 
(as discussed later in this section, this acreage comes from a mix of 
existing and new agricultural land). This accounts for about 92 percent 
of the total estimated increase in U.S. domestic canola production in 
the Canola Case. As North Dakota is the dominant producer of canola in 
the U.S., this modeled impact appears to be consistent with historical 
agricultural patterns. North Dakota is also a significant producer of 
wheat. As canola production is estimated to expand in North Dakota, 
FASOM estimated wheat production would shift to North Dakota region by 
about 218 million pounds, decreasing on net in all other regions by 
about 50 million pounds.
    Canola is generally crushed near areas of cultivation and a 
majority of U.S. facilities that process canola seed are located in 
North Dakota.\43\ Following this, as North Dakota canola production is 
estimated to expand to supply the canola shock, FASOM estimates the 
additional seed would be crushed into oil and meal in this region as 
well. This would expand regional supply of livestock feed and would 
decrease regional feed prices, relative to other regions of the U.S. 
FASOM estimates that this, in turn, would create incentives to shift 
livestock production to North Dakota and nearby states. Since livestock 
feed mixes require several different components, FASOM estimates this 
shift in livestock production towards North Dakota would also shift 
production of other feed crops (e.g., corn, soybeans, hay) into North 
Dakota. Production of these feed crops are estimated to increase by a 
total of 115,000 acres in 2022. The modeled changes in North Dakota 
crop area are summarized in Table II.C.2.ii-5. FASOM estimates net 
cropland in North Dakota would increase by 218,300 acres.\44\
---------------------------------------------------------------------------

    \43\ National Oilseed Processors Association, ``NOPA Plant 
Locations'', https://www.nopa.org/oilseed-processing/nopa-plant-locations/. Last accessed March 16, 2022.
    \44\ Note that FASOM does not track conversion of other land 
types to cropland by crop. This modeled expansion in North Dakota 
cropland is best understood as an increase in total cropland at the 
expense of other land uses rather than an expansion cropland for 
canola, wheat, or any other specific crop into previously uncropped 
area.

      Table II.C.2.ii-5--Changes in North Dakota Crop Area in 2022
                           [In thousand acres]
------------------------------------------------------------------------
                                                          Change from
                                                          control case
------------------------------------------------------------------------
Canola...............................................       16.3 (1.39%)
Wheat................................................       86.8 (1.42%)
All Else.............................................      115.2 (1.38%)
                                                      ------------------
    Total............................................      218.3 (1.39%)
------------------------------------------------------------------------

    Within North Dakota, FASOM estimates that most this additional 
cropland (212,000 acres) would be taken from Conservation Reserve 
Program (CRP) land and a smaller amount (7,000 acres) would be taken 
from cropland pasture. However, as discussed later in this section, the 
nationwide net effect on land use from the shock would affect other 
land types as well.
    As crop area expands in North Dakota in response to the shock and 
livestock production shifts to this region, FASOM estimates total crop 
area would decrease in the rest of the U.S. FASOM estimates this 
dynamic would primarily shift production from Iowa and Kansas to North 
Dakota, suggesting a relatively modest northwesterly shift overall. On 
net, national crop area is estimated to expand by 60,600 acres in 2022. 
The modeled state-level changes in total harvested crop area are 
summarized in Table II.C.2.ii-6.

   Table II.C.2.ii-6--Changes in Regional Harvested Crop Area in 2022
                           [In thousand acres]
------------------------------------------------------------------------
                                                          Change from
                                                          control case
------------------------------------------------------------------------
North Dakota.........................................       218.3 (1.4%)
Iowa.................................................      -82.7 (-0.3%)
Kansas...............................................      -60.5 (-0.5%)
All Other Regions....................................     -14.5 (-0.01%)
                                                      ------------------
  Total..............................................       60.6 (0.02%)
------------------------------------------------------------------------

    As FASOM estimates cropland would expand in North Dakota, the 
majority, about 212,000 acres, is estimated to shift into cropland 
status from land that is placed in CRP in the Control Case. The

[[Page 22832]]

remaining area shifting into cropland status is estimated to shift from 
cropland pasture. As modeled crop production shifts on the margin out 
of Iowa and Kansas, FASOM estimates CRP area would increase in these 
regions to compensate for the decrease in North Dakota CRP area; 
nationwide CRP area does not change on net in our results. FASOM 
estimates pasture area would decrease nationwide as greater 
availability of livestock feed would slightly reduce demand for 
grazing. In some regions, FASOM estimates this previously grazed 
pastureland would be forested instead, leading to a modeled increase in 
forestland. The changes in total regional crop area are summarized in 
Table II.C.2.ii-7.

        Table II.C.2.ii-7--Changes in National Land Area in 2022
                           [In thousand acres]
------------------------------------------------------------------------
                                                          Change from
                                                          control case
------------------------------------------------------------------------
Cropland \45\........................................         61 (0.02%)
Cropland Pasture.....................................       -57 (-0.07%)
Pasture..............................................       -36 (-0.04%)
Forest...............................................         32 (0.01%)
------------------------------------------------------------------------

3. FAPRI Analysis
---------------------------------------------------------------------------

    \45\ Note that cropland reported in national land area includes 
land that is planted but intentionally not harvested, e.g., crops 
grown for grazing. Land area totals will therefore differ slightly 
from the harvested crop area data discussed above.
---------------------------------------------------------------------------

    Like the assessment of domestic impacts using the FASOM model 
described in Section II.C.2, EPA used FAPRI to estimate the GHG 
emissions associated with producing canola oil biofuel from 
international land use change and livestock. This is the same 
methodology EPA previously used to estimate these emissions sources for 
soybean oil-based biodiesel and other agricultural feedstocks (e.g., in 
the March 2010 RFS2 rule, but also in several subsequent pathway 
determinations). EPA updated several aspects of its analysis of the 
international GHG emissions associated with canola oil biofuel 
feedstock production this analysis, building on the FAPRI model used 
for EPA's analysis of the GHG emissions attributable to the production 
and transport of sugar beets for use as a biofuel feedstock.\46\ In 
this section, we first review the updates made for this analysis. 
Following this, we present a summary of the FAPRI modeling results.\47\
---------------------------------------------------------------------------

    \46\ See 82 FR 34656, July 26, 2017 for details on the version 
of FAPRI used to analyze emissions associated with sugar beets.
    \47\ Further information about our assumptions and the modeling 
results are available in the document, ``FAPRI Outputs,'' available 
in the docket for this action.
---------------------------------------------------------------------------

i. Modifications to Model Inputs and Assumptions
    For this analysis, EPA updated FAPRI assumptions related to market 
conditions for canola seed, canola meal, and canola oil. This included 
assumptions about historical U.S. consumption, planted area, seed 
yields, and trade quantities. Updated assumptions for prices, planted 
area, and seed yields were primarily taken from NASS historical data 
sets. In some cases, these NASS data were supplemented with additional 
data taken from the USDA Oil Crop Yearbook and the USCA. In addition to 
updated canola yields in the U.S., USDA Foreign Agricultural Service 
(FAS) Production, Supply and Distribution (PSD) data \48\ was used to 
update the FAPRI baseline trend of future yields in the EU, China, and 
Canada, regions where real-world yields had diverged most from previous 
FAPRI baseline assumptions.
---------------------------------------------------------------------------

    \48\ USDA, Foreign Agricultural Service. PSD Only Query tool. 
https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery. 
Last accessed March 16, 2022.
---------------------------------------------------------------------------

    Additionally, three elasticities were adjusted to better align the 
projected international canola market conditions from FAPRI with recent 
historical data. Notably, the previous FAPRI baseline did not reflect 
the emergence of Canada as an important producer and exporter of canola 
and canola oil. Changes were made to align production and trade 
patterns in Canada, China, and the European (EU) using historical data 
for the 2009/2010-2021/2022 model periods obtained from the USDA PSD 
database. The first adjustment made was to increase the crush demand 
elasticity of canola in Canada from 0.22 to 0.4 to reflect Canada's 
greater canola oil production and export relative to the previous FAPRI 
baseline. Increasing this elasticity estimate results in more canola 
crushed in Canada if the price increases. If Canada produces more 
canola oil, all else equal, Canadian exports would increase because of 
this assumption of increased elasticity. Second, we reduced the Chinese 
canola crush elasticity from 0.26 to 0.18 to reduce the higher-than-
observed Chinese canola oil production and export in the FAPRI baseline 
relative to historical data.\49\ As a results of this change, Chinese 
canola crushing is less responsive to a change in the price of canola. 
If China crushes less canola, all else equal, Chinese canola exports 
would decrease. Last, the own-price demand elasticity for rapeseed oil 
in China was reduced from -0.25 to -0.15. This adjustment was made to 
further reduce the strong Chinese canola oil export position estimated 
by the previous FAPRI baseline. Making the Chinese own-price elasticity 
of demand for canola oil more inelastic has the effect of making 
Chinese domestic consumption of canola oil less responsive 
(``stickier'') to changes in price.
---------------------------------------------------------------------------

    \49\ USDA, Foreign Agricultural Service. PSD Only Query tool. 
https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery. 
Last accessed March 16, 2022.
---------------------------------------------------------------------------

    EPA also updated the representation of canola and canola oil 
production in the India region to further align FAPRI with historical 
data. Indian trade of canola and canola oil are fixed in the FAPRI 
model at historical levels given very low levels of trade activity of 
these commodities historically.\50\ Similarly, the FAPRI modeling for 
this proposed rule does not allow for any changes in Indian canola or 
canola oil production in response to increased demand for canola oil-
based biofuels. In 2020, global exports of canola oil were 14 billion 
pounds. Of this total, India exported 11 million pounds, or 0.08 
percent. India does not export any canola seed.\51\ Therefore, we 
believe these adjustments are reasonable based on consideration of 
recent data and generally consistent with observed agricultural trade 
patterns.\52\
---------------------------------------------------------------------------

    \50\ USDA, Foreign Agricultural Service. Oilseeds and Products 
Annual. March 31, 2021. Available at https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportByFileName?fileName=Oilseeds%20and%20Products%20Annual_New%20Delhi_India_04-01-2021. Last accessed March 16, 2022.
    \51\ USDA, Foreign Agricultural Service. PSD Only Query tool. 
https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery. 
Last accessed March 16, 2022.
    \52\ Further information regarding these updated assumptions is 
detailed in the memorandum, ``TITLE,'' available in the docket for 
this action.
---------------------------------------------------------------------------

ii. Summary of Results
    To meet the 200 million gallons per year shock of canola oil 
biofuel, FAPRI estimates that the U.S. will import 100 percent of the 
feedstock required to meet the canola oil biodiesel shock in 2022. The 
FAPRI modeling results estimate that 48 percent of this canola oil 
feedstock would come from new production, with the remainder coming 
from shifts in other end uses. FAPRI estimates that global agricultural 
markets would provide the U.S. this feedstock in several ways. EU and 
Canadian net exports are estimated to increase by 750 and 278 million 
pounds, equivalent to 49 percent and 18 percent of the increase in U.S. 
net imports respectively. China's net imports of canola oil would be 
reduced

[[Page 22833]]

by 362 million pounds relative to the baseline, equivalent to 23 
percent of the increase in U.S. net imports. The remaining increase in 
U.S. net imports are modeled to be supplied through increased net 
exports from other countries.
    FAPRI estimates that all of the canola oil to satisfy the shock 
would be supplied through increased net imports to the U.S. Since we 
use the FASOM results to estimate U.S. GHG emissions and the FAPRI 
results for non-U.S. GHG emissions, the effect of this discrepancy 
likely increases our GHG emissions estimates relative to a case where 
both models are perfectly aligned on the share of canola oil supplied 
through increased U.S. canola production. This is because we include 
the GHG emissions in the U.S. associated with producing 7 percent of 
the needed canola oil as estimated with FASOM and also the GHG 
emissions associated with producing 100 percent of the needed canola 
oil outside of the U.S. as estimated with FAPRI. For this reason, our 
estimates may be viewed as conservative (i.e., resulting in greater GHG 
emissions).\53\ In the March 2010 RFS2 rule, we considered comments 
that questioned the benefit of using both FASOM and FAPRI given the 
inconsistencies in the results and decided that the benefits of FASOM's 
more detailed representation of the U.S. agricultural and forestry 
sectors and associated GHG emissions outweighed the inevitable 
inconsistencies associated with using both models (75 FR 14768). We 
took steps in the March 2010 RFS2 rule and in the analysis for this 
proposed rule to reconcile the two model results to the extent possible 
by applying the same set of scenarios and key input assumptions in both 
models.\54\ Overall, we believe the 7 percent difference in sourcing of 
U.S. canola oil supplies provides a reasonably aligned and conservative 
estimate of the lifecycle GHG emissions associated with scenario 
modeled.
---------------------------------------------------------------------------

    \53\ The purpose of lifecycle assessment for RFS pathway 
assessments is not to precisely estimate lifecycle GHG emissions 
associated with particular biofuels, but instead to determine 
whether or not the fuels satisfy specified lifecycle GHG emissions 
thresholds to qualify as one or more of the four types of renewable 
fuel specified in the statute (March 26, 2010, 75 FR 14785). Where 
there are a range of possible outcomes and the fuel satisfies the 
GHG reduction requirements when ``conservative'' assumptions are 
used, then a more precise quantification of the matter is not 
required for purposes of a pathway determination.
    \54\ As explained earlier in this section, we are not reopening 
the overall modeling framework or approach established in 2010 in 
this rulemaking.
---------------------------------------------------------------------------

    FAPRI results show that canola seed production would increase by 
1,743 million pounds and canola oil production by 733 million pounds 
globally in 2022 in response to the shock. Table II.C.3.ii-1 
illustrates the source and amounts of additional canola and canola oil 
production in 2022.

  Table II.C.3.ii-1--FAPRI 2022 Canola and Canola Oil Production Response by Region in 2022 Relative to Control
                                                      Case
----------------------------------------------------------------------------------------------------------------
                                                                        Canola                     Canola oil
                                                        --------------------------------------------------------
                                                         Acreage (thousand      Production         Production
                                                               acres)        (million pounds)   (million pounds)
----------------------------------------------------------------------------------------------------------------
Australia..............................................                 60                 70                  4
Canada.................................................                207                453                263
China..................................................                285                536                173
EU.....................................................                223                629                234
All Other..............................................                 43                 56                 60
                                                        --------------------------------------------------------
    Total..............................................                819              1,743                733
----------------------------------------------------------------------------------------------------------------

    While FAPRI estimates that the EU will produce the most additional 
canola (629 million pounds), Canada is estimated to produce the most 
additional canola oil (263 million pounds). This is because, in 
addition to increasing of domestic production of canola seed, Canada is 
also estimated to reduce net exports of canola seed by 146 million 
pounds, and to crush that additional amount of seed.
    The amount and composition of land use change associated with these 
canola expansions varies by region. While FAPRI estimates that China 
would experience the largest expansion of canola acres in 2022 (285,000 
acres), there would be a relatively small amount of net cropland 
expansion (12,000 acres) as there would also be reductions in wheat and 
corn acres. Similarly, is the results show a net reduction of 12,000 
acres of cropland in Canada as wheat, corn, and barley production would 
be reduced due to a change in relative prices stemming from the canola 
oil shock. In the EU, there would be a net expansion of cropland of 
103,000 acres, and in Brazil there would be an increase of 58,000 acres 
of cropland, led by corn and soybean expansion. FAPRI also estimates a 
reduction of 232,000 acres of pasture in Brazil, as the infusion of 
canola meal as a byproduct of additional canola crushing alleviates 
demand for grazing. In total, FAPRI estimates that cropland would 
expand by 372,000 acres outside of the U.S. in response to the shock.

       Table II.C.3.ii-2--Non-U.S. Changes in Agricultural Land by Region in 2022 Relative to Control Case
                                               [In thousand acres]
----------------------------------------------------------------------------------------------------------------
                                                           Change in area   Change in pasture   Total change in
                                                             harvested          acres \55\           acres
----------------------------------------------------------------------------------------------------------------
EU.....................................................                103                 NR                103
Brazil.................................................                 58               -232               -175
Rest of Non-USA........................................                211                 NR                211
                                                        --------------------------------------------------------
    Total Non-USA......................................                372               -232                140
----------------------------------------------------------------------------------------------------------------


[[Page 22834]]

4. Domestic Agricultural and Land Use Change GHG Emissions
---------------------------------------------------------------------------

    \55\ NR stands for ``not reported''. Pasture acreage is only 
reported for Brazil in the FAPRI model.
---------------------------------------------------------------------------

    We used the results from the FASOM analysis to estimate domestic 
agricultural GHG emissions following the methodology developed for the 
March 2010 RFS2 rule. As noted above, for this proposed rule we used 
emissions factors from GREET-2020 for energy inputs and feedstock and 
co-product transportation. Domestic agricultural GHG emissions include 
GHG emissions associated with changes in crop and livestock production. 
Overall, we estimate that increasing the consumption of hydrotreated 
canola oil biofuels in the U.S. would result in a net reduction in 
domestic agricultural GHG emissions of 40 grams of carbon dioxide-
equivalent emissions (gCO<INF>2</INF>e) per pound of canola oil used as 
feedstock relative to scenario absent this hydrotreated canola oil 
biofuel production (``gCO<INF>2</INF>e per pound of canola oil'').\56\
---------------------------------------------------------------------------

    \56\ Consistent with the methodology developed for the March 
2010 RFS2 rule, for purposes of this lifecycle GHG analysis we use 
100-year global warming potential (GWP) weighed emissions of carbon 
dioxide, methane, and nitrous oxide to calculated CO<INF>2</INF>e 
emissions.
---------------------------------------------------------------------------

    The 40 gCO<INF>2</INF>e per pound of canola oil reduction in 
domestic agricultural GHG emissions has a handful of components. As 
discussed in Section II.C.2.ii, the FASOM results estimate a small 
shift away from corn production towards canola and wheat. This leads to 
a small net decline in farm input usage, resulting in a small estimated 
reduction in GHG emissions of about 1 gCO<INF>2</INF>e per pound of 
canola oil. The estimated net decrease in beef and chicken slaughter 
discussed in Section II.C.2.ii of this preamble is associated with a 
GHG emissions decrease of about 40 gCO<INF>2</INF>e per pound of canola 
oil. There is also a small increase in rice production in the U.S. 
(about 0.02 percent), leading to an increase of about 1 
gCO<INF>2</INF>e per pound of canola oil from rice methane. As 
discussed above, our FASOM modeling results estimate that almost all 
the canola oil feedstock would be sourced outside of the U.S., and the 
relatively small effects on the domestic agricultural sector reflect 
this result.
    Domestic land use change GHG emissions are reported separately from 
domestic agricultural emissions. Based on the FASOM modeling discussed 
in Section IV.C.2 of this preamble, we estimate a net reduction in 
domestic land use change emissions of 77 gCO<INF>2</INF>e per pound of 
canola oil. It is based on the same methodology used for the March 2010 
RFS rule whereby the land use change GHG emissions estimates from FASOM 
are considered over a 30-year period and then annualized (i.e., divided 
by 30 years). For a detailed description of how FASOM estimates land 
use change GHG emissions see Section 2.4.4.1 (``Evaluation of Domestic 
Land Conversion GHG Emissions Impacts'') of the Regulatory Impact 
Analysis for the March 2010 RFS2 rule.\57\ FASOM estimates land 
conversions and associated changes in the biomass and soil carbon 
stocks. Given the many interactions simulated in FASOM it is difficult 
to summarize why domestic land use change GHG emissions are estimated 
to decline as a result of the modeled scenario. However, the reduction 
in emissions is consistent with the overall land use changes summarized 
in Table II.C.2.ii-7. Cropland area increases by 61 thousand acres, 
which is usually associated with increased land use change GHG 
emissions, but this is offset by an increase of 32 thousand acres of 
forest area, which is associated with a net reduction in GHG emissions.
---------------------------------------------------------------------------

    \57\ EPA (2010). Renewable fuel standard program (RFS2) 
regulatory impact analysis. Washington, DC, US Environmental 
Protection Agency Office of Transportation Air Quality. EPA-420-R-
10-006.
---------------------------------------------------------------------------

5. International Agricultural and Land Use Change GHG Emissions
    We used the results from the FAPRI analysis to estimate 
international (i.e., non-U.S.) agricultural and land use change GHG 
emissions following the methodology developed for the March 2010 RFS2 
rule, except that, as described in this section, we updated our 
estimates of the GHG emissions associated with changes in international 
on-farm energy use. International agricultural sector GHG emissions are 
associated with estimated changes in crop and livestock production 
outside of the U.S. International land use change emissions are 
primarily changes in biomass and soil carbon associated with land use 
changes, but they also include non-CO<INF>2</INF> emissions some cases 
(e.g., when land is cleared with fire). Overall, we estimate a small 
reduction of 5 gCO<INF>2</INF>e per pound of canola oil associated with 
changes in international agriculture.
    The small reduction in GHG emission associated with international 
agriculture is the result of counterbalancing effects. We estimate that 
the modeled canola oil shock increases GHG emissions associated with 
international farm inputs (e.g., fertilizer, pesticide, energy) by 70 
gCO<INF>2</INF>e per pound of canola oil. The canola shock is 
associated with changes in livestock production that reduce GHG 
emissions by 72 gCO<INF>2</INF>e per pound of canola oil. Changes in 
rice production results in a small decreased of 3 gCO<INF>2</INF>e per 
pound of canola oil. These changes largely balance each other out and 
result in an overall reduction in international agricultural emissions, 
not including land use change, of 5 gCO<INF>2</INF>e per pound of 
canola oil. These estimates are summarized along with the domestic 
estimates in Table II.C.8-1. The rest of this section describes our 
updates to estimate GHG emissions associated with changes in 
international on-farm energy use and then discusses the estimated 
international land use change GHG emissions.
    Based on our assessment of the information provided in the USCA 
petition, we updated the data sources used to estimate the changes in 
energy inputs and associated GHG emissions corresponding with changes 
in international crop production as estimated with the FAPRI model. The 
USCA petition stated, ``For countries except Canada, EPA used 
International Energy Agency (IEA) data for energy use for the forest 
and agriculture sector and then divided that by the crop area. The 
energy use, based on this data, is overstated because it includes 
forestry energy use.'' We confirmed that the IEA data used in our 2010 
analysis to estimate changes in non-U.S. on-farm energy use included 
forestry energy use along with crop production energy use, and these 
data were then rolled into our estimates of energy use per acre of crop 
production for each region. We also found that the IEA data are 
aggregated so that forestry could not be excluded.
    We reviewed other available sources on energy use and found that 
the Food and Agriculture Organization of the United Nations (FAO) 
reports emissions data on the amount of energy used within the farm 
gate to operate machinery.\58\ The FAO also reports GHG emissions from 
aquaculture and fishing, but we exclude these data in order to 
exclusively estimate emissions from on-farm energy use energy use. The 
FAO data are available annually from 1970-2019 for over 200 countries. 
FAO reports emissions of carbon dioxide, methane, and nitrous oxide for 
seven different energy products (i.e., coal, electricity, fuel oil, 
gas-diesel oil, LPG, motor gasoline, and natural gas including LNG). 
After reviewing the

[[Page 22835]]

FAO farm energy use GHG emissions data, we believe they are an 
improvement compared to the IEA data used previously for the purposes 
of this analysis because they are more recent and exclude forestry 
energy use. For these reasons, we have updated our assumptions to use 
the FAO data for this analysis of canola oil renewable diesel.
---------------------------------------------------------------------------

    \58\ FAO, 2021. FAOSTAT Energy Use domain, FAO, Rome, Italy. 
Available at: http://www.fao.org/faostat/en/#data/GN. Last accessed 
March 16, 2022. FAOSTAT Analytical Briefs can be found at: http://www.fao.org/food-agriculture-statistics/data-release/environment/en. 
Last accessed March 16, 2022.
---------------------------------------------------------------------------

    The FAO data report energy GHG emissions within the farm gate, 
including off-farm GHG emissions associated with generating 
electricity. Although the FAO estimates include off-farm GHG emissions 
associated with electricity generation, they exclude GHG emissions 
associated with producing the energy products and feedstocks for this 
electricity generation. For example, they exclude GHG emissions 
associated with natural gas production and distribution. In prior 
analyses, we adjusted the IEA estimates to include these upstream GHG 
emissions based on estimates from the GREET Model (version 1.8b) on the 
ratio of total lifecycle emissions to fuel use (or generation for 
electricity) emissions for each production. For this analysis of canola 
oil, we used the same approach but updated these ratios based on data 
from GREET-2020.\59\
---------------------------------------------------------------------------

    \59\ For more information on these estimates see the memo to the 
docket titled, ``Memo on Hydrotreated Canola Lifecycle GHG 
Calculation Workbooks.''
---------------------------------------------------------------------------

    The rest of this section discusses the international land use 
change GHG estimates. We estimate international land use change GHG 
emissions of 316 gCO<INF>2</INF>e per pound of canola oil. We consider 
the uncertainty in the types of land converted and the emissions 
associated with those conversions and estimate a 95% confidence 
interval for international land use change emissions ranging from 131 
to 529 gCO<INF>2</INF>e per pound of canola oil.
    International land use change GHG emissions were estimated 
following the methodology developed for the March 2010 RFS2 rule. The 
FAPRI model estimates changes in harvested crop area by region as a 
result of the modeled canola oil biofuel scenarios. FAPRI also 
estimates changes in pasture area for five sub-regions of Brazil. For 
other regions, changes in pasture area are estimated based on FAPRI's 
estimated changes in livestock production and FAO data on stocking 
rates (i.e., grazing animals per acre of pasture). In regions where the 
sum of changes in cropland or pasture are non-zero, we estimate changes 
in the areas of other land types based on land use change patterns in 
each region as estimated with satellite data. The estimated land use 
changes are then converted to GHG emissions based on land use change 
emissions factors estimated from a number of data sources following 
IPCC guidelines. International land use changes are estimated over 30 
years and then annualized (i.e., divided by 30 years). For details on 
this methodology see Section 2.4.4.2 (``International Land Conversion 
GHG Emissions Impacts'') of the Regulatory Impact Analysis for the 
March 2010 RFS2 rule.
    Following the approach developed for the March 2010 RFS2 rule, we 
consider the uncertainty in the international land use change GHG 
estimates to produce a 95% confidence interval. This uncertainty 
analysis considers two major components: (1) Uncertainty in the 
classification of land transitions with satellite data to determine the 
types of land affected by changes in cropland and pasture area in each 
region, and (2) uncertainty in the emissions factors used to translate 
the land conversions to GHG emissions. For more information about our 
evaluation of the uncertainty in international land use change GHG 
emissions see Section 2.4.4.2.8 (``Uncertainty Assessment for 
International Land Conversion GHG Emissions Impacts'') of the RIA for 
the March 2010 RFS2 rule.
    We recognize that there are other uncertainties that could 
theoretically be estimated, for example uncertainties in the areas of 
cropland estimated by the FAPRI model. However, quantifying additional 
sources of uncertainty was not part of the modeling framework or 
methodology developed for the March 2010 RFS2 rule, and would require 
the development of new methodologies and modeling approaches. Running 
multiple scenarios with the FAPRI model in order to systematically 
quantify parameter uncertainty would take a very long time and be 
impractical for this rule. As discussed in Section III., we consider 
the weight of available evidence when proposing RIN D-code eligibility 
for the evaluated pathways. In weighing the available evidence, we put 
the most weight on the quantified range of lifecycle GHG estimates but 
also recognize qualitatively that there are unquantified sources of 
uncertainty.
6. Feedstock Processing
    After the canola seeds are harvested, they are transported to a 
crushing facility to separate the canola oil and meal. The most common 
process uses the solvent hexane. The canola seeds are first cleaned, 
heated, and flaked. The seeds are then cooked and screw-pressed to 
remove most of the oil. To remove the remaining oil, the meal is 
saturated with hexane solvent, which is removed and then recycled back 
into the process. The oil is further refined to remove free fatty acids 
and other impurities.
    We estimate canola crushing GHG emissions following the methodology 
developed for the March 2010 RFS2 rule. We estimate the total GHG 
emissions associated with canola crushing with no allocation to the 
canola meal co-product that is primarily used as livestock feed. The 
effects of using canola meal as feed are considered in the FASOM and 
FAPRI modeling described above. In lifecycle analysis terminology, this 
would be described as a system expansion approach as opposed to 
allocating emissions to the meal.
    The USCA petition included annual canola crushing input-output data 
from Canada that we used in our analysis. We believe these data are 
appropriate for our analysis because a large share of canola oil 
feedstock for the U.S. is likely to be sourced in Canada, and the 
Canadian extraction process is representative of extraction processes 
in other regions that are likely to crush canola to supply canola oil 
biofuel feedstock to the U.S. For example, data compiled by the United 
Nations International Civil Aviation Organization (ICAO) for canola 
crushing in Canada, Europe and the U.S. shows similar but smaller 
amounts of natural gas and electricity use per pound of canola oil 
extracted. \60\ The USCA data reports average energy use of 1,310 Btu 
of per pound of canola oil extracted in Canada. For comparison the ICAO 
reports energy use of 790 to 1,220 Btu per pound of canola oil 
extracted. Based on this comparison, we believe that using the USCA 
data for canola crushing energy use is reasonable and somewhat 
conservative.
---------------------------------------------------------------------------

    \60\ ICAO (2021). CORSIA Eligible Fuels--Lifecycle Assessment 
Methodology. CORSIA Supporting Document. March 2021. Version 3. 
Table 43. Page 65.
---------------------------------------------------------------------------

    Based on the USCA crushing data, we assume approximately 40 percent 
yield of canola oil per seed on a mass basis, and that natural gas and 
electricity are used for heat and power. We estimated the GHG emissions 
associated with the natural gas based on GREET-2020 estimates for 
average North American natural gas production and use. For electricity, 
we used the GREET-2020 emissions factor for average Canadian 
electricity. GREET includes 2012 data for the Canadian grid mix, which 
we updated based on 2018 data from Natural Resources Canada.\61\ Based 
on

[[Page 22836]]

these assumptions, we estimate GHG emissions from canola oil extraction 
of 87 gCO2e per pound of canola oil.
---------------------------------------------------------------------------

    \61\ Natural Resources Canada. Last updated October 6, 2020. 
``Electricity Facts.'' https://www.cer-rec.gc.ca/en/data-analysis/
energy-markets/provincial-territorial-energy-profiles/provincial-
territorial-energy-profiles-
canada.html#:~:text=More%20than%20half%20of%20the,and%20petroleum%20(
Figure%202). Last Accessed March 16, 2022.
---------------------------------------------------------------------------

    Recognizing that canola may be crushed in other regions, we 
considered the effects of canola crushing in the U.S., Europe and China 
to determine if crushing in other regions would affect our proposed 
determination that hydrotreated canola oil meets the 50% GHG reduction 
threshold. To evaluate this question, we used the same crushing input-
output data from the USCA petition and considered regional differences 
in grid average electricity GHG emissions factors and GHG emissions 
associated with additional canola oil shipping. Although the U.S. grid 
is more GHG intensive than the Canadian grid, the effect of crushing in 
the U.S. compared to Canada is less than one gram CO<INF>2</INF>e per 
pound canola oil and we assume there would be no significant change in 
GHG emissions associated with canola oil transport. The average 
European grid is less GHG intensive than the Canadian grid but the 
effect on crushing in Europe compared to Canada is also less than on 
gram CO<INF>2</INF>e per pound of canola oil. If we consider canola oil 
shipping from Europe of 4,000 nautical miles (e.g., Rotterdam to 
Houston) via ocean tanker fueled with bunker fuel, that adds 
approximately 13 gCO<INF>2</INF>e per pound of canola oil, equivalent 
to approximately a one percent increase in GHG emissions relative to 
the petroleum baseline. Crushing in China and shipping 5,500 nautical 
miles (e.g., Beijing to Los Angeles) would add approximately 18 
gCO<INF>2</INF>e per pound of canola oil, which is still equivalent to 
approximately a one percent increase in GHG emissions relative to the 
baseline. As an extremely conservative scenario, if we assume crushing 
in China with coal instead of natural gas for process energy and 5,500 
nautical miles of shipping, this adds approximately 139 
gCO<INF>2</INF>e per pound of canola oil, or approximately 9% relative 
to the petroleum baseline. Even with these extremely conservative 
assumptions, renewable diesel and jet fuel still satisfy the 50% GHG 
reduction threshold when we use our mean estimate of international land 
use change GHG emissions (i.e., 55% to 61% reduction for renewable 
diesel and 51% to 59% reduction for renewable jet fuel). Overall, this 
shows that our proposed determinations are not sensitive to our 
assumption about where canola is crushed, and we believe that assuming 
canola crushing occurs in Canada is a reasonable approach for this 
analysis.
7. Feedstock Transport
    There are three stages of feedstock transport considered in our 
lifecycle analysis. The transportation modes and distances for canola 
seed and oil in our analysis are from the GREET-2020 model. First 
canola seeds are assumed to be transported 10 miles from the farm field 
to a collection point by medium-duty truck. The model then assumes 
seeds are then transported 40 miles to the crushing facility by heavy 
duty truck. After crushing, the oil is transported 80 miles by tanker 
truck to a hydrotreating facility. The trucks in this transportation 
chain are assumed to consume diesel fuel and we estimated the 
associated GHG emissions based on the GREET-2020 emissions factor for 
conventional diesel. Overall, we estimate GHG emissions of 15 
gCO<INF>2</INF>e per pound of canola oil for seed transport and 13 
gCO<INF>2</INF>e per pound of canola oil for canola oil transport. As 
discussed in Section IV.C.7, importing canola oil from Europe or China 
would increase oil shipping emissions but not to a large enough extent 
to change our proposed determinations that biofuels produced from 
hydrotreated canola oil meet the 50 percent GHG reduction requirement.
8. Summary of Upstream GHG Emissions
    Based on all of the modeled effects discussed above associated with 
producing canola oil feedstock including effects on domestic and 
international crop production, livestock production and land use, we 
can summarize the estimated lifecycle GHG emissions per pound of canola 
oil delivered to a hydrotreating production facility. These upstream 
GHG emissions (i.e., upstream of feedstock conversion to fuel) are 
summarized in Table II.C.8-1. A range of GHG emissions is presented 
based on our evaluation of the uncertainty associated with 
international land use change GHG emissions, as discussed in Section 
IV.C.5 of this preamble.

     Table II.C.8-1--Estimated Upstream GHG Emissions Associated With Producing Canola Oil Used for Biofuel
                                                   Production
                                [In grams of CO2-equivalent per pound canola oil]
----------------------------------------------------------------------------------------------------------------
 
----------------------------------------------------------------------------------------------------------------
Emissions category                                                                   Estimate
----------------------------------------------------------------------------------------------------------------
Domestic farm inputs............................................                         -1
Domestic Livestock..............................................                        -40
Domestic Rice Methane...........................................                         1
Domestic Land Use Change........................................                        -77
International Farm Inputs.......................................                         70
International Livestock.........................................                        -72
International Rice Methane......................................                         -3
Seed transport..................................................                         15
Crushing........................................................                         87
Oil Transport...................................................                         13
                                                                 -----------------------------------------------
International Land Use Change Estimate..........................       Mean             Low            High
                                                                 -----------------------------------------------
International Land Use Change...................................             316             131             529
                                                                 -----------------------------------------------
    Total.......................................................             305             118             517
----------------------------------------------------------------------------------------------------------------
Note: The ``Low'' international land use change estimate represents the low-end of the 95% confidence interval
  and the ``High'' estimate represents the high-end of the 95% confidence interval.


[[Page 22837]]

9. Fuel Production
    Canola oil is converted to renewable diesel, jet fuel, naphtha, and 
LPG through a hydrotreating process, also sometimes referred to as 
hydroprocessing. The renewable diesel may also be used as heating oil, 
although this is unlikely based on recent market conditions such as 
strong demand for renewable diesel to satisfy low carbon fuel standards 
in California, Oregon and Washington.\62\ The process consists of 
catalytic reactions in the presence of hydrogen. The steps in a typical 
hydrotreating process often include a combination of hydrogenation, 
hydro-deoxygenation, decarboxylation and decarbonylation. The primary 
output of hydrotreating is renewable diesel, with estimates ranging 
from approximately 75 to 100 percent of the output based on the data 
sources discussed later in this proposal. Other outputs include jet 
fuel, naphtha, LPG, and propane. Hydrotreating facilities can process a 
wide range of vegetable oil feedstocks without significant operational 
changes.
---------------------------------------------------------------------------

    \62\ U.S. Energy Information Administration. (2021). ``U.S. 
renewable diesel capacity could increase due to announced and 
developing projects.'' July 29, 2021; U.S. Energy Information 
Administration. (2018). ``Renewable diesel is increasingly used to 
meet California's Low Carbon Fuel Standard.'' November 13, 2021.
---------------------------------------------------------------------------

    The hydrotreating process can be configured to maximize renewable 
jet fuel output instead of renewable diesel, but this requires 
additional hydrogen and other energy inputs. To maximize jet fuel 
output, the renewable diesel is subjected to additional refining, 
namely hydro-isomerization and hydrocracking. These processes involve 
the addition of more hydrogen to crack the longer carbon chain length 
diesel to shorter length jet fuel. Essentially, the diesel is cracked 
to produce jet fuel and naphtha. Overall, maximizing hydrotreating 
processes for jet fuel output results in higher production costs and 
GHG emissions per gallon relative to processes that are maximized for 
diesel output.\63\ As described later in this proposal, these effects 
are considered in our analysis.
---------------------------------------------------------------------------

    \63\ Wang, W.C., Tao, L., Markham, J., Zhang, Y., Tan, E., 
Batan, L., Warner, E., & Biddy, M. (2016). Review of Biojet Fuel 
Conversion Technologies. Report prepared by National Renewable 
Energy Laboratory.
---------------------------------------------------------------------------

    Several hydrotreating pathways have been evaluated and approved 
under the RFS program. In the March 2010 RFS2 rule, we approved 
multiple pathways for renewable diesel produced from hydrotreated 
vegetable oils and biogenic waste fats, oils, and greases (FOG) as 
meeting the 50 percent GHG reduction requirement to qualify as biomass-
based diesel and advanced biofuel. In the 2013 Pathways I rule (78 FR 
14190), we evaluated renewable diesel from camelina oil and reported 
the GHG emissions associated with the hydrotreating process used to 
convert the camelina oil to renewable diesel. That analysis relied on 
data published in Pearlson et al. (2013), a study that modeled the 
emissions and fuel production costs associated with of a commercial 
scale hydrotreating process.\64\ We also used the Pearlson et al. 
(2013) data in our analysis of hydrotreating for the 2018 distillers 
sorghum oil rule (83 FR 37735).
---------------------------------------------------------------------------

    \64\ Pearlson, M., et al. (2013). ``A techno-economic review of 
hydroprocessed renewable esters and fatty acids for jet fuel 
production.'' Biofuels, Bioproducts and Biorefining 7(1): 89-96.
---------------------------------------------------------------------------

    In addition to evaluating generally applicable hydrotreating 
pathways, we have approved several facility-specific pathways for 
hydrotreating facilities. For the facility-specific analyses, we relied 
on data from the individual facilities, submitted under claims of CBI 
on their energy use and fuel yields. In October 2013, we approved a 
facility-specific petition for renewable LPG and naphtha co-products 
produced from distillers' corn oil at Diamond Green Diesel's 
hydrotreating facility in Louisiana.\65\ In 2017 and 2018, we also 
approved pathways for LPG and naphtha produced from distillers' corn 
oil and waste FOG at Renewable Energy Group's hydrotreating facility in 
Louisiana.\66\ In July 2021, we approved a facility specific pathway 
for jointly filed petition from Koole and Neste for renewable diesel 
and jet fuel produced from waste FOG.\67\ We have also received 
additional facility-specific petitions for hydrotreating processes that 
are currently under review. In total, we have received hydrotreating 
data, claimed as CBI, from five different facilities through the 
petition process for new RFS pathways at 40 CFR 80.1416.
---------------------------------------------------------------------------

    \65\ EPA. (2013). ``Diamond Green Diesel Request for Fuel 
Pathway Determination under the RFS Program.'' Office of 
Transportation and Air Quality. October 28, 2013. https://www.epa.gov/renewable-fuel-standard-program/diamond-green-diesel-llc-approval. Last Accessed March 16, 2022.
    \66\ EPA (2017). ``Evaluation of Renewable Energy Group, Inc. 
Request for Fuel Pathway Determination under the RFS Program'' April 
13, 2017. https://www.epa.gov/renewable-fuel-standard-program/reg-geismar-approval. Last Accessed March 16, 2022. EPA (2018). 
``Renewable Energy Group, Inc. Fuel Pathway Determination under the 
RFS Program'' February 23, 2018. https://www.epa.gov/renewable-fuel-standard-program/reg-geismar-approval-0. Last Accessed March 16, 
2022.
    \67\ EPA. (2021). ``Koole-Neste Fuel Pathway Determination under 
the RFS Program.'' Office of Transportation and Air Quality. July 
12, 2021. https://www.epa.gov/system/files/documents/2021-08/koole-neste-deter-ltr-2021-07-12.pdf. Last Accessed March 16, 2022.
---------------------------------------------------------------------------

    We estimated hydrotreating GHG emissions based on 12 sources of 
vegetable oil hydrotreating input-output data. Eight of the modeled 
processes primarily produce renewable diesel with co-products, varying 
by process, of naphtha, LPG, and jet fuel. Four of the modeled 
processes are configured to maximize jet fuel output with co-products, 
varying by process, of renewable diesel, naphtha, and LPG.
    The eight data sources for hydrotreating processes that primarily 
produce renewable diesel include Pearlson et al. (2013), GREET-2021, 
aggregated data provided by the California Air Resources Board (CARB), 
and five facilities that submitted data under claims of CBI pursuant to 
the petition process. As mentioned above, Pearlson et al. (2013) is a 
peer-reviewed study that modeled a commercial scale hydrotreating 
process. The renewable diesel production data have been updated in the 
GREET-2021 model with operational data from 2018 and 2019 from a survey 
of domestic renewable diesel producers conducted by Argonne National 
Laboratory and the National Biodiesel Board.\68\ The CARB provided data 
are the average inputs and outputs associated with the hydrotreating 
processes used to produce renewable diesel for use under the California 
Low Carbon Fuel Standard Program, as of June 2021. The data for five 
hydrotreating facilities submitted through new pathway petitions and 
claimed as CBI were submitted between 2018 and 2020.
---------------------------------------------------------------------------

    \68\ Wang et al. 2021. ``Summary of Expansions and Updates in 
GREET 2021.'' October 2021. ANL/ESD-21/16.
---------------------------------------------------------------------------

    The four data sources used to model hydrotreating processes 
configured to maximize jet fuel output are Pearlson et al. (2013), 
GREET-2021 and two from an analysis published by the International 
Civil Aviation Organization (ICAO) in 2021. The first data source is 
the ``maximum jet fuel'' scenario from Pearlson et al. (2013). The data 
in GREET-2021 for renewable jet fuel production through hydrotreating 
is unchanged from previous versions of GREET. We also evaluated two 
scenarios from ICAO (2021): One that is representative of U.S. 
hydrotreating and one that is representative of European hydrotreating.
    To estimate the GHG emissions associated with these hydrotreating 
processes, we used energy allocation to account for the fuel coproducts 
from the hydrotreating process. We estimated the total GHG emissions 
from the

[[Page 22838]]

hydrotreating process and allocated them to the renewable diesel, jet 
fuel, naphtha, LPG, and propane co-products on an energy basis. The 
propane is treated as a co-product in these calculations but is unlike 
the other co-products because we do not expect it to be exported from 
the facility. For data sources that reported propane as an output, we 
assume that this propane is used at the facility as process fuel, and 
that this propane use is reflected in the input data reducing the 
amount of purchased natural gas. As a result of this energy allocation 
approach, all the co-products are assigned equivalent emissions from 
the fuel production stage on a gCO<INF>2</INF>e per MJ basis. To 
translate energy use into GHG emissions, we used emissions factors for 
natural gas, electricity, and hydrogen from the GREET-2020 model 
representing the GHG emissions associated with the supply of these 
energy inputs in the U.S.\69\
---------------------------------------------------------------------------

    \69\ As discussed above, although we have evaluated the updated 
hydrotreating data from the GREET-2021 model, the rest of our 
analysis had already been conducted using emissions factors from the 
GREET-2020 model. We will update these emissions factors for the 
final rule, but we do not expect this to have a large enough impact 
on our estimates to affect the pathway approvals proposed in this 
rule.
---------------------------------------------------------------------------

    In previous GHG analyses of hydrotreating processes, we assumed 
that some of the co-products (propane and in some cases LPG and 
naphtha) would not be used as RIN-generating fuels, and we included GHG 
reductions associated with these renewable co-products displacing the 
use of equivalent conventional fuels.\70\ In contrast, the analysis for 
this proposed rule does not include GHG reductions associated with 
hydrotreating co-products displacing other fuels. Instead, we use 
energy allocation for all the co-products. We are taking this approach 
for four reasons. One, the USCA petition requests RIN eligibility for 
all of the co-products except propane, so propane is the only co-
product for which a displacement approach would be considered. Second, 
we believe that using energy allocation for all of the co-products, 
including propane, provides a reasonably conservative estimate (i.e., 
tends to result in higher GHG estimates). Third, using energy 
allocation for co-products the estimates do not depend on which co-
products generate RINs, which is subject to change based on market and 
regulatory conditions. Fourth, we also note that the energy allocation 
approach results in GHG estimates that are more consistent across 
facilities compared to the displacement approach due to the variation 
in co-product outputs across facilities. As an illustrative example of 
how much this assumption influences the estimates, if we assumed the 
propane co-product displaces natural gas the fuel production emissions 
for renewable diesel would decrease by an average of 2.1 
gCO<INF>2</INF>e per MJ, and up to 5.9 gCO<INF>2</INF>e per MJ, 
relative to the estimates in Table II.C.9-1 that are based on energy 
allocation for propane. For renewable jet fuel, the same displacement 
approach for propane co-product would reduce fuel production emissions 
by an average of 3 gCO<INF>2</INF>e per MJ, and up to 4.7 
gCO<INF>2</INF>e per MJ, relative to the estimates in Table II.C.9-2 
that are based on energy allocation for propane. We request comment on 
the use of energy allocation to evaluate co-products from hydrotreating 
processes.
---------------------------------------------------------------------------

    \70\ See for example the March 2013 Pathways I rule (78 FR 
14190) and the August 2018 sorghum oil rule (83 FR 37735).
---------------------------------------------------------------------------

    Hydrogen is major energy input to hydrotreating processes. We used 
the GREET-2020 emissions factor representing hydrogen produced from 
natural gas through a stream methane reforming process at central 
plants. Central plants are large hydrogen production facilities that 
produce greater than 50,000 kilograms of hydrogen per day.\71\ This is 
a conservative choice as GREET has lower GHG estimates for other 
sources of hydrogen. We believe this choice is reasonable and 
appropriate for this analysis as the proposed pathway would be 
available to renewable diesel plants irrespective of their hydrogen 
sources.
---------------------------------------------------------------------------

    \71\ U.S. Department of Energy. ``The Hydrogen Analysis (H2A) 
Project.'' https://www.hydrogen.energy.gov/h2a_analysis.html. Last 
accessed March 16, 2022.
---------------------------------------------------------------------------

    The estimated lifecycle GHG emissions associated with hydrotreating 
processes that primarily produce renewable diesel are summarized in 
Table II.C.9-1. As shown in the table, the highest and lowest estimates 
are based on data from two of the facility-specific petitions. The 
estimates based on data from Pearlson et al. (2013), GREET-2021 and 
CARB are within 1.2 gCO<INF>2</INF>e/MJ of each other and between the 
estimates for individual facilities.

     Table II.C.9-1--GHG Emissions Associated With Renewable Diesel
                      Production via Hydrotreating
                   [In grams of CO2 equivalent per MJ]
------------------------------------------------------------------------
                  Hydrotreating data source                    Estimate
------------------------------------------------------------------------
Pearlson et al. (2013)......................................        10.8
GREET-2021..................................................        11.8
CARB (2021).................................................        12.0
Facility 1..................................................        15.0
Facility 2..................................................        10.4
Facility 3..................................................        13.7
Facility 4..................................................        10.9
Facility 5..................................................        14.4
Range.......................................................   10.4-15.0
------------------------------------------------------------------------

    The estimated lifecycle GHG emissions associated with hydrotreating 
processes configured to maximize jet fuel output are summarized in 
Table II.C.9-2. The estimate based on GREET-2021 is significantly 
greater than the other sources because it includes greater natural gas 
and hydrogen use per unit of jet fuel output.

    Table II.C.9-2--GHG Emissions Associated With Renewable Jet Fuel
                      Production via Hydrotreating
                   [In grams of CO2 equivalent per MJ]
------------------------------------------------------------------------
                  Hydrotreating data source                    Estimate
------------------------------------------------------------------------
Pearlson et al. (2013) Maximized Jet........................        12.9
ICAO (2021) EU Jet..........................................        14.7
ICAO (2021) U.S. Jet........................................        12.7
GREET-2021 Jet..............................................        20.7
Range.......................................................   12.7-20.7
------------------------------------------------------------------------

    Based on the analysis and data sources discussed above, we estimate 
the GHG emissions associated with the hydrotreating stage range from 
10.4 to 15.0 gCO<INF>2</INF>e/MJ for renewable diesel and 12.7 to 20.7 
gCO<INF>2</INF>e/MJ for jet fuel. As discussed in Section III, we 
consider the full range of hydrotreating GHG estimates in this proposal 
to approve these canola oil-based biofuel pathways.
10. Fuel Distribution
    We estimated the GHG emissions associated with transporting the 
renewable diesel, jet fuel, naphtha, and LPG products to end users 
based on transportation and distribution data in GREET-2020. The 
renewable diesel and jet fuel are assumed to be transported by truck, 
rail, and barge. The naphtha and LPG are assumed to be transported 
primarily by pipeline and rail. The fuel distribution GHG estimates are 
0.4 gCO<INF>2</INF>e/MJ for renewable diesel and jet fuel and 0.6 
gCO<INF>2</INF>e/MJ for renewable naphtha and LPG.
11. Fuel Use
    For this analysis, we applied non-CO<INF>2</INF> fuel use GHG 
emissions factors from

[[Page 22839]]

GREET-2020.\72\ For renewable diesel, we used the factors for renewable 
diesel used in a compression ignition direct injection vehicle. For 
renewable jet fuel, we used the factors for hydrotreated renewable jet 
fuel consumed in a single aisle passenger aircraft. For renewable 
naphtha, we used the factors for renewable gasoline consumed in a 
spark-ignition vehicle and for LPG we used factors for a dedicated LPG 
vehicle. The fuel use GHG estimates are 0.9 gCO<INF>2</INF>e/MJ for 
renewable diesel, 0.1 gCO<INF>2</INF>e/MJ for renewable jet fuel, and 
0.5 gCO<INF>2</INF>e/MJ for renewable naphtha and LPG.
---------------------------------------------------------------------------

    \72\ Following the methodology developed for the March 2010 RFS2 
rule after notice, public comment, and peer review, the carbon in 
the finished fuel derived from renewable biomass is treated as 
biologically derived carbon originating from the atmosphere. In the 
context of a full lifecycle analysis, the uptake of this carbon from 
the atmosphere by the renewable biomass and the CO<INF>2</INF> 
emissions from combusting it cancel each other out. Therefore, 
instead of presenting both the carbon uptake and tailpipe 
CO<INF>2</INF> emissions, we leave both out of the results. Note 
that our analysis also accounts for all significant indirect 
emissions, such as from land use changes, meaning we do not simply 
assume that biofuels are ``carbon neutral.''
---------------------------------------------------------------------------

12. Results of GHG Lifecycle Analysis
    Table II.C.12-1 reports our estimates of the lifecycle GHG 
emissions associated with renewable diesel produced from canola oil 
through a hydrotreating process, and the corresponding percent 
reduction relative to the petroleum baseline. Three sets of estimates 
are presented for canola oil renewable diesel. The emissions categories 
are aggregated to simplify the presentation of the table. Domestic and 
international agricultural emissions include emissions associated with 
changes in crop and livestock production. Feedstock processing (i.e., 
canola seed crushing) and feedstock seed and oil transport emissions 
are reported together. Downstream and use includes emissions from fuel 
distribution and fuel use. Land use change emissions include emissions 
from domestic and international land use changes.
    Our evaluation considers uncertainty in international land use 
change emissions based on the methodology used for the March 2010 RFS2 
rule. The table includes a range of land use change estimates based on 
our analysis of this uncertainty. The first column includes results 
based on our average estimate of international land use change GHG 
emissions. We also report results for the low and high ends of our 95 
percent confidence interval for international land use change 
emissions. Ranges for domestic agriculture, international agriculture, 
feedstock transport and crushing, and fuel production are based on 
estimated ranges in the yield of finished fuel (in MJ of fuel produced 
per pound of canola oil feedstock).

  Table II.C.12-1--Lifecycle GHG Emissions Associated With Renewable Diesel Produced From Canola Oil Through a
                                              Hydrotreating Process
                                       [In grams of CO2 equivalent per MJ]
----------------------------------------------------------------------------------------------------------------
 
----------------------------------------------------------------------------------------------------------------
Emissions category                                   2005 Diesel
                                                        baseline            Canola oil renewable diesel
----------------------------------------------------------------------------------------------------------------
Domestic Agriculture............................              18                   -2.5 to -2.2
International Agriculture.......................                                  -0.33 to -0.28
Feedstock Transport & Crushing..................                                    6.2 to 7.3
Fuel Production.................................                                   10.4 to 15.0
Downstream & Use................................              75                        1.3
                                                                 -----------------------------------------------
Land Use Change Estimate........................  ..............       Mean             Low            High
                                                                 -----------------------------------------------
Land Use Change.................................  ..............    13.0 to 15.2      3.0 to 3.5    24.6 to 28.7
Net Emissions...................................              93      28.9 to 34    18.6 to 23.4    40.7 to 46.4
% GHG Reduction Relative to Baseline............  ..............      63% to 69%      75% to 80%      50% to 56%
----------------------------------------------------------------------------------------------------------------

    In many cases, when vegetable oils are hydrotreated to produce 
renewable diesel, there are co-product outputs of naphtha, LPG, and jet 
fuel. The GHG estimates for these co-product fuels differ slightly from 
the renewable diesel estimates presented in the table above based on 
differences in how they are transported to end users and end use 
emissions. The results for naphtha and LPG, based on the mean 
international land use change estimates, are summarized in Table 
II.C.12-2.

Table II.C.12-2--Lifecycle GHG Emissions Associated With Naphtha and LPG
        Produced From Canola Oil Through a Hydrotreating Process
                   [In grams of CO2 equivalent per MJ]
------------------------------------------------------------------------
                                              Naphtha           LPG
------------------------------------------------------------------------
Lifecycle GHG Emissions.................    28.7 to 33.9    28.7 to 33.9
Percent Reduction Relative to Baseline..      64% to 69%      63% to 69%
------------------------------------------------------------------------

    We do not present separate results of heating oil as it is not 
reported as an output for any of the hydrotreating processes evaluated. 
However, renewable diesel could be used as heating oil if market 
conditions change substantially. The GHG emissions associated with 
heating oil are therefore very similar to renewable diesel, although 
there may be small differences in GHG emissions associated with fuel 
distribution and use.
    As discussed above, canola oil hydrotreating processes that are set 
up to maximize jet fuel output require more processing and hydrogen, 
resulting in greater lifecycle GHG emissions. For example, our 
lifecycle GHG estimates

[[Page 22840]]

using hydrotreating input-output data from GREET-2021 are 31.0 
gCO<INF>2</INF>e/MJ for renewable diesel and 38.2 gCO<INF>2</INF>e/MJ 
for renewable jet fuel, and our estimates based on hydrotreating data 
from Pearlson et al. (2013) are 29.5 gCO<INF>2</INF>e/MJ for renewable 
diesel and 30.5 gCO<INF>2</INF>e/MJ for renewable jet fuel. The range 
of lifecycle GHG estimates for canola oil renewable jet fuel are 
reported in Table II.C.12-3.

 Table II.C.12-3--Lifecycle GHG Emissions Associated With Renewable Jet Fuel Produced From Canola Oil Through a
                                              Hydrotreating Process
                                       [In grams of CO2 equivalent per MJ]
----------------------------------------------------------------------------------------------------------------
 
----------------------------------------------------------------------------------------------------------------
Emissions category                                   2005 Diesel
                                                        baseline           Canola oil renewable jet fuel
----------------------------------------------------------------------------------------------------------------
Domestic Agriculture............................              18                   -2.4 to -2.2
International Agriculture.......................                                  -0.31 to -0.28
Feedstock Transport & Crushing..................                                    6.3 to 7.0
Fuel Production.................................                                   12.7 to 20.7
Downstream & Use................................              75                        0.5
                                                                 -----------------------------------------------
Land Use Change Estimate........................  ..............       Mean             Low            High
                                                                 -----------------------------------------------
Land Use Change (LUC)...........................  ..............    13.2 to 14.5      3.0 to 3.3    24.9 to 27.5
Net Emissions...................................              93    30.5 to 38.2      20.2 to 28    42.2 to 49.9
% GHG Reduction Relative to Baseline............  ..............      59% to 67%      70% to 78%      46% to 54%
----------------------------------------------------------------------------------------------------------------

III. Consideration of Lifecycle Analysis Results

    We evaluated the lifecycle GHG emission associated with renewable 
diesel, jet fuel, naphtha and LPG produced from canola oil through a 
hydrotreating process. The purpose of this analysis was to determine 
whether these fuel pathways satisfy the statutory 50 percent GHG 
reduction threshold under the RFS program for advanced biofuel and 
biomass-based diesel. Our approach to considering the lifecycle GHG 
estimates for purposes of threshold determinations is consistent with 
the ``weight of evidence'' approach that we used for the March 2010 
RFS2 rule. In the preamble to the March 2010 RFS2 rule we said, 
``because of the inherent uncertainty and the state of the evolving 
science on this issue, EPA is basing its GHG threshold compliance 
determinations for this rule on an approach that considers the weight 
of evidence currently available.'' 75 FR 14785. In this section we 
consider the weight of the evidence and propose to make threshold 
determinations on this basis.
    Based on the range of lifecycle GHG emissions estimates presented 
above, the weight of available evidence, and our technical judgments, 
we propose to find that all the pathways evaluated would meet the 50 
percent GHG reduction threshold required for advanced biofuel and 
biomass-based diesel. Our evaluation considers variability in 
hydrotreating processes and uncertainty in land use change emissions.
    When we consider the mean land use change GHG estimates, the entire 
range of GHG reduction results exceeds the 50 percent GHG reduction 
requirement for all of the pathways evaluated. When we consider the 
high-end of the 95-percent confidence interval for international land 
use change GHG emissions and the hydrotreating process data with the 
highest GHG emissions, all the pathways evaluated except for jet fuel 
still exceed the 50 percent GHG reduction threshold. Thus, based on the 
range of estimated GHG reduction results and the weight of available 
evidence, we judge that there is a reasonable basis to be confident 
that the 50% GHG reduction threshold will be achieved for renewable 
diesel, naphtha and LPG produced from canola oil through a 
hydrotreating process.
    When we consider the high-end of the 95-percent confidence interval 
for international land use change GHG emissions and the hydrotreating 
process data with the highest GHG emissions, we estimate that jet fuel 
produced from canola oil results in a 46 percent reduction relative to 
the petroleum baseline. That is, the entire range of lifecycle GHG 
estimates for jet fuel does not exceed the 50 percent threshold. We 
follow the approach taken in the March 2010 RFS2 rule for considering 
such information for purposes of proposing a threshold determination 
for jet fuel produced from canola oil. In that rule we said, ``In 
making the threshold determinations for this rule, EPA weighed all of 
the evidence available to it, while placing the greatest weight on the 
best estimate value for the base yield scenario. In those cases where 
the best estimate for the potentially conservative base yield scenario 
exceeds the reduction threshold, EPA judges that there is a good basis 
to be confident that the threshold will be achieved and is determining 
that the bio-fuel pathway complies with the applicable threshold. To 
the extent the midpoint of the scenarios analyzed lies further above a 
threshold for a particular biofuel pathway, we have increasingly 
greater confidence that the biofuel exceeds the threshold.'' 75 FR 
14785.
    When we consider our mean estimates of international land use 
change GHG emissions, the estimated range of GHG reductions for canola 
oil-based jet fuel produced through hydrotreating is a 59% to 67% GHG 
reduction relative to the petroleum baseline. Given that this range, 
which is already based on reasonably conservative assumptions, exceeds 
the 50% GHG reduction threshold, and considering the weight of evidence 
across all the available results, we judge that there is a reasonable 
basis to be confident that the 50% GHG reduction threshold will be 
achieved for canola oil jet fuel produced through a hydrotreating 
process.
    Based on the evaluation and results described above, we propose to 
add ``Canola/Rapeseed oil'' to the Feedstock columns in rows G and I of 
table 1 to 40 CFR 80.1426. This addition to row G would make renewable 
diesel, jet fuel, and heating oil produced through a hydrotreating 
process eligible for biomass-based diesel (D-code 4) RINs if the 
hydrotreating process does not co-process renewable biomass and 
petroleum. This addition to row I would make naphtha and LPG produced 
from canola oil through a hydrotreating process eligible for advanced 
biofuel (D-code 5) RINs. The RFS regulatory definition of biomass-based 
diesel at 40

[[Page 22841]]

CFR 80.1401 excludes naphtha and LPG.
    The GHG estimates reported in Section II.C.12 of this preamble are 
based on our evaluation of standalone hydrotreating processes that 
process only vegetable oil. While there is substantial hydrotreating 
capacity at refineries that is potentially suitable for co-processing 
canola oil or other vegetable oils with petroleum, there is currently 
relatively little production or detailed input-output data for co-
processing vegetable oil and petroleum in hydrotreating units.\73\ For 
example, a co-processing module was added to GREET for the first time 
with the release of GREET-2021, but it currently contains ``placeholder 
parametric assumptions'' that Argonne National Laboratory is planning 
to replace after additional research.\74\ The information that is 
available suggests that co-processing vegetable oil in hydrotreating 
units will require relatively minor adjustments compared to 
hydrotreating units that do not co-process with petroleum. There are 
also very few lifecycle GHG estimates of this process in peer-reviewed 
journals. The one study we found in the literature evaluated a 
hydrotreating unit of a Colombian refinery with four different feed 
rates of soybean oil (8.1 to 12.5 percent by mass) and reported similar 
input-output ratios as the standalone processes evaluated above in 
terms of hydrogen input, natural gas input, and fuel outputs per pound 
of feed.\75\ Given that the large majority of our GHG reduction 
estimates significantly exceed the 50 percent reduction threshold for 
biofuels produced from canola oil hydrotreated without co-processing 
(see Section II.C.12 of this preamble), we believe our estimates 
support a finding that canola oil-based fuels from hydrotreating 
processes that co-process canola oil with petroleum also meet the 50 
percent threshold. Thus, we propose to add ``Canola/Rapeseed oil to the 
feedstock column of row H in table 1 to 40 CFR 80.1426, which would 
make, if finalized, renewable diesel, jet fuel, naphtha, LPG and 
heating oil produced from canola oil through a hydrotreating process 
that includes co-processing with petroleum eligible for advanced 
biofuel (D-code 5) RINs. Note that based on the definition of biomass-
based diesel at CAA 211(o), fuels produced through co-processing 
renewable biomass and petroleum do not qualify as biomass-based diesel, 
but these fuels may qualify as advanced biofuels if they meet the GHG 
reduction and other statutory requirements. We request data and 
information on producing renewable fuel through hydrotreating processes 
that co-process canola oil and petroleum. We request comments on our 
proposal to make these co-processed fuels eligible for advanced biofuel 
(D-code 5) RINs.
---------------------------------------------------------------------------

    \73\ Freeman, C.J., et al. (2013). Initial assessment of US 
refineries for purposes of potential bio-based oil insertions, 
Pacific Northwest National Lab. (PNNL), Richland, WA; van Dyk, S., 
et al. (2019). ``Potential synergies of drop-in biofuel production 
with further co-processing at oil refineries.'' Biofuels, 
Bioproducts and Biorefining 13(3): 760-775; Bezergianni, S., et al. 
(2018). ``Refinery co-processing of renewable feeds.'' Progress in 
Energy and Combustion Science 68: 29-64.
    \74\ ANL (2021). Summary of Expansions and Updates in GREET 
2021, Energy Systems Division: 58.
    \75\ Garra[iacute]n, D., et al. (2014). ``Well-to-Tank 
environmental analysis of a renewable diesel fuel from vegetable oil 
through co-processing in a hydrotreatment unit.'' Biomass and 
Bioenergy 63: 239-249.
---------------------------------------------------------------------------

IV. Summary

    Based on our GHG lifecycle evaluation described above, we propose 
to find that renewable diesel, jet fuel, naphtha, LPG, and heating oil 
produced from canola oil via a hydrotreating process meet the 50 
percent GHG reduction threshold. This finding would support a 
determination that renewable diesel, jet fuel and heating oil produced 
from canola oil are eligible for biomass-based diesel (D-code 4) RINs 
if they are produced through a hydrotreating process that does not co-
process renewable biomass and petroleum, and for advanced biofuel (D-
code 5) RINs if they are produced through a process that does co-
process renewable biomass and petroleum. This finding would also 
support a determination that naphtha and LPG production from canola oil 
through a hydrotreating process are eligible for advanced biofuel (D-
code 5) RINs. EPA requests comment on these proposed pathways.

V. Statutory & Executive Order Reviews

    Additional information about these statutes and Executive Orders 
can be found at https://www.epa.gov/laws-regulations/laws-and-executive-orders.

A. Executive Order 12866: Regulatory Planning and Review and Executive 
Order 13563: Improving Regulation and Regulatory Review

    This proposed action is a significant regulatory action that was 
submitted to the Office of Management and Budget (OMB) for review. Any 
changes made in response to OMB recommendations have been documented in 
the docket. The GHG lifecycle analysis conducted for this proposed 
determination, ``Renewable Fuel Standard Program: Canola Oil Pathways 
to Renewable Diesel, Jet Fuel, Naphtha, Liquefied Petroleum Gas and 
Heating Oil,'' is available in the docket.

B. Paperwork Reduction Act (PRA)

    This proposed action would not impose any new information 
collection burden under the PRA. OMB has previously approved the 
information collection activities contained in the existing regulations 
and has assigned OMB control number 2060-0725. This proposed action 
would create new pathways by which to generate RINs for renewable fuels 
under the RFS program but creates no new information collection 
requirements for these additional pathways.

C. Regulatory Flexibility Act (RFA)

    I certify that this proposed action would not have a significant 
economic impact on a substantial number of small entities under the 
RFA. In making this determination, EPA concludes that the impact of 
concern for this proposed rule is any significant adverse economic 
impact on small entities and that the agency is certifying that this 
proposed rule would not have a significant economic impact on a 
substantial number of small entities if the proposed rule would have no 
net burden. This proposed rule would enable canola oil producers and 
producers of biofuels from canola oil to participate in the RFS 
program, see CAA section 211(o), if they choose to do so to obtain 
economic benefits. We have therefore concluded that this proposed 
action would have no net regulatory burden for all directly regulated 
small entities.

D. Unfunded Mandates Reform Act (UMRA)

    This proposed action does not contain an unfunded mandate of $100 
million or more as described in UMRA, 2 U.S.C. 1531-1538, and would not 
significantly or uniquely affect small governments. The proposed action 
would impose no enforceable duty on any state, local or tribal 
governments or the private sector.

E. Executive Order 13132: Federalism

    This proposed action does not have federalism implications. It 
would not have substantial direct effects on the states, on the 
relationship between the national government and the states, or on the 
distribution of power and responsibilities among the various levels of 
government.

F. Executive Order 13175: Consultation and Coordination With Indian 
Tribal Governments

    This proposed action does not have tribal implications as specified 
in Executive Order 13175. This proposed

[[Page 22842]]

rule would affect only producers of canola oil and producers of 
biofuels made from canola oil. Thus, Executive Order 13175 does not 
apply to this proposed action.

G. Executive Order 13045: Protection of Children From Environmental 
Health and Safety Risks

    The EPA interprets Executive Order 13045 as applying only to those 
regulatory actions that concern environmental health or safety risks 
that the EPA has reason to believe may disproportionately affect 
children, per the definition of ``covered regulatory action'' in 
section 2-202 of the Executive order. This proposed action is not 
subject to Executive Order 13045 because it does not concern an 
environmental health risk or safety risk.

H. Executive Order 13211: Actions Concerning Regulations That 
Significantly Affect Energy Supply, Distribution, or Use

    This proposed action is not a ``significant energy action'' because 
it is not likely to have a significant adverse effect on the supply, 
distribution or use of energy. This proposed rule would enable canola 
oil producers and producers of biofuels from canola oil to participate 
in the RFS program, see CAA section 211(o), if they choose to do so. 
This may create additional supplies of energy, potentially leading to 
positive impacts on the energy system. This proposed rule would create 
no new burdens on the distribution or use of energy.

I. National Technology Transfer and Advancement Act (NTTAA)

    This rulemaking does not involve technical standards.

J. Executive Order 12898: Federal Actions To Address Environmental 
Justice in Minority Populations and Low-Income Populations

    The EPA believes that this proposed action is not subject to 
Executive Order 12898 (59 FR 7629, February 16, 1994) because it does 
not establish an environmental health or safety standard. This proposed 
rule would give renewable fuel producers the ability to generate 
credits under the RFS program for the production of specified biofuels 
from canola oil. This proposed rule does not affect the level of 
protection provided to human health or the environment by applicable 
air quality standards. Future actions to set biofuel volume 
requirements may take into consideration the availability of this 
renewable fuel pathway for the production of biofuel from canola oil 
and thus may affect GHG emissions, air quality, water or soil quality, 
or fuel and food prices.\76\ However, this proposed action does not 
modify biofuel volume requirements and thus the EPA believes that the 
proposed rule to approve a new pathway, in and of itself, will not 
affect human health or the environment.
---------------------------------------------------------------------------

    \76\ For a recent discussion of such potential impacts, see 
Chapter 8 of the Draft Regulatory Impact Analysis for the RFS 
``Proposed Volume Standards for 2020, 2021, and 2022''. EPA-HQ-OAR-
2021-0324.
---------------------------------------------------------------------------

VI. Statutory Authority

    Statutory authority for this action comes from CAA sections 114, 
208, 211, and 301.

List of Subjects in 40 CFR Part 80

    Environmental protection, Administrative practice and procedure, 
Air pollution control, Diesel fuel, Fuel additives, Gasoline, Imports, 
Oil imports, Petroleum, Renewable fuel.

Michael S. Regan,
Administrator.
    For the reasons set forth in the preamble, the EPA proposes to 
amend 40 CFR part 80 as follows:

PART 80--REGULATION OF FUELS AND FUEL ADDITIVES

0
1. The authority citation for part 80 continues to read as follows:

    Authority: 42 U.S.C. 7414, 7521, 7542, 7545, and 7601(a).

Subpart M--Renewable Fuel Standard

0
2. Amend Sec.  80.1401 by adding in alphabetical order the definition 
of ``Canola/rapeseed oil'' to read as follows:


Sec.  80.1401  Definitions.

* * * * *
    Canola/Rapeseed oil means either of the following:
    (1) Canola oil is oil from the plants Brassica napus, Brassica 
rapa, Brassica juncea, Sinapis alba, or Sinapis arvensis which 
typically contains less than 2 percent erucic acid in the component 
fatty acids obtained.
    (2) Rapeseed oil is the oil obtained from the plants Brassica 
napus, Brassica rapa, or Brassica juncea.
* * * * *
0
3. Amend Sec.  80.1426 by:
0
a. Removing the text ``Table 1 to this section'' wherever it appears 
and adding, in its place, the text ``table 1 to paragraph (f)(1) of 
this section'';
0
b. Removing the text ``Table 1 to Sec.  80.1426'' wherever it appears 
and adding, in its place, the text ``table 1 to paragraph (f)(1) of 
this section'';
0
c. In paragraph (f)(1), removing the text ``Tables 1 and 2 to this 
section'' and adding in its place the text ``tables 1 and 2 to this 
paragraph (f)(1)'';
0
d. Redesignating table 1 to Sec.  80.1426 as table 1 to Sec.  
80.1426(f)(1);
0
e. In newly redesignated table 1 to Sec.  80.1426(f)(1), revising the 
entries ``G,'' ``H,'' and ``I'';
0
f. Redesignating table 2 to Sec.  80.1426 as table 2 to Sec.  
80.1426(f)(1).
    The revisions read as follows:


Sec.  80.1426   How are RINs generated and assigned to batches of 
renewable fuel?

* * * * *
    (f) * * *
    (1) * * *

    Table 1 to Sec.   80.1426(f)(1)--Applicable D Codes for Each Fuel
                   Pathway for Use in Generating RINs
------------------------------------------------------------------------
                                                  Production
               Fuel type         Feedstock          process       D-code
                                                 requirements
------------------------------------------------------------------------
 
                              * * * * * * *
G........  Biodiesel,        Canola/Rapeseed   One of the              4
            renewable         oil.              following:
            diesel, jet                         Transesterifica
            fuel, and                           tion with or
            heating oil.                        without
                                                esterification
                                                pre-treatment,
                                                or
                                                Hydrotreating;
                                                excludes
                                                processes that
                                                co-process
                                                renewable
                                                biomass and
                                                petroleum.

[[Page 22843]]

 
H........  Biodiesel,        Soy bean oil;     One of the              5
            renewable         Oil from annual   following:
            diesel, jet       covercrops; Oil   Transesterifica
            fuel, and         from algae        tion with or
            heating oil.      grown             without
                              photosynthetica   esterification
                              lly; Biogenic     pre-treatment,
                              waste oils/fats/  or
                              greases; Non-     Hydrotreating;
                              food grade corn   includes only
                              oil; Camelina     processes that
                              sativa oil;       co-process
                              Distillers        renewable
                              sorghum oil;      biomass and
                              Canola/Rapeseed   petroleum.
                              oil.
I........  Naphtha, LPG....  Camelina sativa   Hydrotreating...        5
                              oil; Distillers
                              sorghum oil;
                              Canola/Rapeseed
                              oil.
 
                              * * * * * * *
------------------------------------------------------------------------

* * * * *
[FR Doc. 2022-07598 Filed 4-15-22; 8:45 am]
BILLING CODE 6560-50-P


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