[Federal Register Volume 87, Number 74 (Monday, April 18, 2022)]
[Proposed Rules]
[Pages 22823-22843]
[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.

    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:
     Federal eRulemaking Portal: https://www.regulations.gov
(our preferred method). Follow the online instructions for submitting
comments.
     Email: [email protected]. Include Docket ID No. EPA-
HQ-OAR-2021-0845 in the subject line of the message.
     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.
     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:<PHONE_NUMBER>; email address:
[email protected].

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

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

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

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

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.
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    \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.
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    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

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

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

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

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

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

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 (gCO2e) per pound of canola oil used as
feedstock relative to scenario absent this hydrotreated canola oil
biofuel production (``gCO2e 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 CO2e
emissions.
---------------------------------------------------------------------------

    The 40 gCO2e 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 gCO2e 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 gCO2e 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
gCO2e 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 gCO2e 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 <IP_ADDRESS> (``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-CO2 emissions some cases
(e.g., when land is cleared with fire). Overall, we estimate a small
reduction of 5 gCO2e 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
gCO2e per pound of canola oil. The canola shock is
associated with changes in livestock production that reduce GHG
emissions by 72 gCO2e per pound of canola oil. Changes in
rice production results in a small decreased of 3 gCO2e 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 gCO2e 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

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 gCO2e 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 gCO2e 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 <IP_ADDRESS> (``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 <IP_ADDRESS>.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

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 CO2e 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 CO2e 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 gCO2e 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
gCO2e 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
gCO2e 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
gCO2e per pound of canola oil for seed transport and 13
gCO2e 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.

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

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 gCO2e 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\
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    \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.
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    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
gCO2e per MJ, and up to 5.9 gCO2e 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 gCO2e per MJ, and up to 4.7
gCO2e 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.
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    \70\ See for example the March 2013 Pathways I rule (78 FR
14190) and the August 2018 sorghum oil rule (83 FR 37735).
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    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.
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    \71\ U.S. Department of Energy. ``The Hydrogen Analysis (H2A)
Project.'' https://www.hydrogen.energy.gov/h2a_analysis.html. Last
accessed March 16, 2022.
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    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 gCO2e/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 gCO2e/MJ for renewable diesel and 12.7 to 20.7
gCO2e/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 gCO2e/MJ for renewable diesel and jet fuel and 0.6
gCO2e/MJ for renewable naphtha and LPG.
11. Fuel Use
    For this analysis, we applied non-CO2 fuel use GHG
emissions factors from

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 gCO2e/MJ for
renewable diesel, 0.1 gCO2e/MJ for renewable jet fuel, and
0.5 gCO2e/MJ for renewable naphtha and LPG.
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    \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 CO2
emissions from combusting it cancel each other out. Therefore,
instead of presenting both the carbon uptake and tailpipe