Greenhouse Gas Mitigation Potential in the U.S. Forestry and Agriculture Sector

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The analysis builds on work presented in
the 2005 report Greenhouse Gas Mitigation
Potential in U.S. Forestry and Agriculture
to provide a contemporary perspective on
greenhouse gas abatement options for the
U.S. land use sectors using updated and
expanded modeling frameworks.

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Greenhouse Gas Mitigation Report

ACKNOWLEDGMENTS

This report was developed under a contract between the U.S. EPA's Office of
Atmospheric Protection, Climate Change Division and RTI International, Inc.
The main authors of the report are Sara Bushey Ohrel, Jared Creason, Shaun
Ragnauth, and Allen Fawcett of EPA; Chris Wade, Alice Favero, Yongxia Cai,
and Kemen Austin of RTI International; Justin Baker of North Carolina State
University; Brent Sohngen of the Ohio State University; Greg Latta of the
University of Idaho; Stefan Frank and Nicklas Forsell of the International
Institute for Applied Systems Analysis (NASA); Bruce McCarl of Texas A&M;
and Jason Jones of ICF. Support for the report's production was provided by
RTI International, Inc.

This report is dedicated to the memory of Darius M. Adams.

PEER REVIEW

This report was peer reviewed by four external and independent experts in a
process independently coordinated by ERG. EPA gratefully acknowledges the
following peer reviewers for their useful comments and suggestions: Ruben
Lubowski of Columbia University and Lombard Odier Investment Managers;
Alison Eagle of the Environmental Defense Fund (EDF); Gert-Jan Nabuurs
of Wageningen University; and Hongli Feng of Iowa State University. Robert
Beach, Senior Economist of RTI International, provided an internal technical
review. Finally, Elizabeth Marshall at the United States Department of
Agriculture reviewed and provided comments to the report. The information
and views expressed in this report do not necessarily represent those of the
peer reviewers, who also bear no responsibility for any remaining errors or
omissions.

RECOMMENDED CITATION

EPA. 2024. Greenhouse Gas Mitigation Potential in the U.S. Forestry
and Agriculture Sector. U.S. Environmental Protection Agency, Office of
Atmospheric Protection. Washington, DC. EPA.

CONTACT US

For more information, contact Sara Bushey Ohrel, ohrel.sara@epa.gov,
or Jared Creason, creason.jared@epa.gov, U.S. Environmental Protection
Agency.

DATA AVAILABILITY

Data from the analyses in this report can be accessed on the EPA website.

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Greenhouse Gas Mitigation Report

Contents

Executive Summary	1

Key Takeaways	3

Looking Forward	7

1. Introduction	8

1.1	Report Objectives	12

1.2	Trends in Forest and Agriculture Land Use and GHG Emissions	14

1.3	Overview of Mitigation Opportunities	17

1.4	Assessment Approach	19

1.4.1	Different. Approaches for Estimating Mitigation Potential	19

1.4.2	Modeling Approach Used in this Report	21

1.4.3	Multi-Model Comparison	23

1.4.4	Harmonization	26

1.4.5	Interpretation of Results	26

1.5	Report Organization	27

2 Methods and Scenario Design	28

2.1	Background Information on Models Applied and Modeling Approach	28

2.2	Forest and Agricultural Sector Optimization Model with Greenhouse

Gases (FASOMGHG)	30

2.2.1	History and Model Applications	30

2.2.2	Economic and Biophysical Features	31

2.2.3	Land Sector in the Model	32

2.2.4	Greenhouse Gas Emissions	32

2.2.5	Land-based Mitigation Strategies	32

2.3	Global Biosphere Management Model (GLOBIOM)	33

2.3.1	History and Model Applications	33

2.3.2	Economic and Biophysical Features	33

2.3.3	Land Sector in the Model	34

2.3.4	Greenhouse Gas Emissions	35

2.3.5	Land-based Mitigation Strategies	35

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Greenhouse Gas Mitigation Report

2.4	Global Timber Model (GTM)	36

2.4.1	History and Model Applications	36

2.4.2	Economic and Biophysical Features	36

2.4.3	Land Sector in the Model	37

2.4.4	Greenhouse Gas Emissions	38

2.4.5	Land-based Mitigation Strategies	38

2.5	Similarities and Differences in Models' Attributes	38

2.5.1 Mitigation Opportunities Across Models	46

2.6	Model Input Harmonization and Baseline Scenario	49

2.7	Mitigation Scenarios	50

2.8	Stand-Alone Analyses	54

3 Baseline and Mitigation Scenario Results	55

3.1	Future Baseline Projections	57

3.1.1	Baseline Emissions from the Land Use Sector Across Models	57

3.1.2	Baseline Land Projections and Market Dynamics Across Models	62

3.2	MACCs	68

3.2.1	AFOLU MACCs	68

3.2.2	Gas-Based MACCs	76

3.3	Activity-Based MACCs	79

3.3.1	Mitigation in Forests	79

3.3.2	Mitigation in Agriculture	90

3.4	Mitigation Across Land-Based Activities	102

3.4.1 Regional mitigation portfolio	103

3.5	Investments in Land-Based Mitigation Activities	107

4 Discussion and Future Research	110

4.1	Context for the Report Results	112

4.1.1	GHGI Historical Emissions and Projected Trends	113

4.1.2	Mitigation Projections in the Report and Comparison to Recent
Literature	116

4.1.2	Mitigation Projections in the Report and Comparisons to Recent
Literature	118

4.1.3	Mitigation Across Land-Based Activities	120

4.2	Potential Applications of the Results	120

4.2.1	Application 1: Abatement Potential and Cost	122

4.2.2	Application 2: Sensitivities to Model Frameworks and Primary
Scenario Parameters	122

4.2.3	Application 3: Unintended Consequences	122

4.3	Limitations and Future Research	124

5 References	126

¦¦

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Greenhouse Gas Mitigation Report

LIST OF FIGURES

FIGURE ES-1 By the numbers	6

FIGURE 1-1 U.S. greenhouse gas emissions and sinks by sector

(in MtC02e, 1990-2021)	14

FIGURE 1-2 U.S. greenhouse gas emissions and sinks from agriculture and

LULUCF sectors (in Mt C02e, 1990-2021)	15

FIGURE 1-3 Average mitigation potential per price range ($l-$35/Mt C02e
and $36-$200/Mt C02e) across land-based activities in the
United States	22

FIGURE 2-2 U.S. land area categories included by model (2020)	40

FIGURE 2-3 Greenhouse gas categories included in each model	42

FIGURE 2-4 Mitigation technologies and management strategies available in

each model	48

FIGURE 2-5 GHG prices in $/t C02e applied to each model in the mitigation

scenarios (2020-2050)	52

FIGURE 3-1 GHG emissions by model under baseline scenario

(in MtC02e yr\ 2025-2050)	58

FIGURE 3-2 Baseline U.S. average annual carbon-equivalent flux within

each decade by GHG category by model (in Mt CO e, 2020-2059) 60
FIGURE 3-3 Annual emissions, by GHG (CO , CH4 and N20), under the

baseline scenario (in Mt C02e, 2020-2059)	61

FIGURE 3-4 U.S. forest area under baseline scenario (in million acres,

2025-2050)	63

FIGURE 3-5 U.S. cropland area under baseline scenario (in million acres,

2025-2050)	65

FIGURE 3-6 U.S. pastureland area under baseline scenario (in million

acres, 2025-2050)	67

FIGURE 3-7 A) AFOLU marginal abatement cost curves; B) AFOLU absolute
emissions under baseline and mitigation scenarios in 2030
and 2050	70

FIGURE 3-8 Average annual change in GHG flux in the land sector from the

baseline across mitigation scenarios and models, by GHG category
(in MtC02eyr\ 2025 to 2050)	72

FIGURE 3-9 Average annual change in GHG flux in the land sector by GHG
from the baseline across scenarios and models (in Mt C02e yr1,

2025 to 2050)	76

FIGURE 3-10 GHG-based marginal abatement cost curves in (A) 2030 and

(B) 2050	78

FIGURE 3-11 A) Forest marginal abatement cost curves; B) Forest

absolute emissions under baseline and mitigation scenarios in 2030
and 2050	80

FIGURE 3-12 Average annual change in total forest area from the baseline

across scenarios and models (in million acres, 2025-2050)	81

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Greenhouse Gas Mitigation Report

FIGURE 3-13 A) Forest marginal abatement cost curves; B) Forest absolute

emissions under baseline and mitigation scenarios in 2030 and 2050 91
FIGURE 3-14 MACCs for cropland in 2030 and 2050	92

FIGURE 3-15 Average annual change in total cropland area from

baseline (in million acres, 2025-2050)	94

FIGURE 3-16 Marginal abatement cost curves for livestock in 2030 and 2050	98

FIGURE 3-18 Share of mitigation by main activity by model, 2025-2050	102

FIGURE 3-19 U.S. regional GHG emissions from agriculture and forestry under

baseline scenario, FASOMGHG (in Mt C02e yr"1, 2025-2050)	103

FIGURE 3-20 U.S. regional distribution of cumulative mitigation by activity under the

$50 at 3% scenario, FASOMGHG (2025 to 2050)	105

FIGURE 4-1 Historic and projected adjusted GHG emissions for U.S. agriculture,

forestry, and net AFOLU (in Mt C02e yr1, 1990-2050)	114

LIST OF TABLES

TABLE 1-1 Land-based mitigation options, as defined in the literature	17

TABLE 3-1 Average annual mitigation (Mt C02e yr"1) per range of annual

investments in land-based mitigation activities (in billion US dollars) 108

TABLE 4-1 Mitigation potential in the land sector per price range from literature

review and this report	119

TABLE 4-2 Average mitigation potential per land-based mitigation activities in

the literature	121

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Greenhouse Gas Mitigation Report

Executive Summary

The forestry and agriculture sectors are central pillars
of federal and state regulations and strategies aimed at
greenhouse gas (GHG) emissions and removals.

Recent federal policies have recognized the value of land-
based abatement strategies by allocating funds to preserve
forest as natural sinks, enhance land storage capacity,
and increase forest resilience. For instance, the Inflation
Reduction Act (IRA) directs large investment in land-based
mitigation programs; the Infrastructure Investment and Jobs
Act (IDA) allocates a share of the funds into forest-based
projects (e.g., reforestation); and the U.S. Department of
Agriculture Climate Smart Commodities Program is designed
to incentivize activities that reduce agricultural emissions
and improve soil health (IDA, 2021).

Insights into future potential mitigation trends of the
land sector, and the environmental, economic, and other
conditions that drive those trends, are necessary for the
design of effective mitigation policies, as these trends can
influence the magnitude and costs of the mitigation portfolio
in the short, medium, and long terms across sectors.

This report provides updated estimates of the GHG
mitigation potential associated with various abatement
activities in the agriculture and forestry sector in the United
States between now and 2050. The analysis builds on work
presented in the 2005 U.S. Environmental Protection Agency
(EPA) report Greenhouse Gas Mitigation Potential in U.S.
Forestry and Agriculture (EPA, 2005) and integrates new

modeling tools and frameworks to provide a contemporary
perspective on GHG abatement options for the U.S. land use
sector.

The report uses three economic models of the land sector
with detailed biophysical sectoral coverage and spatial
data: the Forest and Agricultural Sector Optimization Model
with Greenhouse Gases (FASOMGHG), the Global Timber
Model (GTM), and the Global Biosphere Management Model
(GLOBIOM). Each model has been extensively applied in
the literature for a variety of objectives, including projecting
land management, market, and environmental changes
across different policy, environmental, and macroeconomic
scenarios, and has been used in various official government
modeling applications. Each model provides different
perspectives into the report by focusing on only the land
sector in the United States (FASOMGHG), the global land
sector (GLOBIOM), and the global forestry sector (GTM).

A total of 24 land-based mitigation activities across eight
GHG emission categories have been identified across the
three models. All three models explicitly capture important
feedbacks that occur when market changes influence the
opportunity costs of investing in land-based mitigation
options, and hence affect the resulting potential magnitude
and cost of different GHG mitigation activities over time.

1

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Mi

Recent federal policies have recognized
the value of land-based abatement
strategies by allocating funds to preserve
forest as natural sinks, enhance land
storage capacity, and increase forest
resilience.

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Greenhouse Gas Mitigation Report

To evaluate net mitigation potential for different GHG
categories (or specific activities) in the land sector, model-
specific baseline scenarios with no mitigation pricing
policies in place are run using harmonized parameters
(e.g., key socioeconomic drivers). Under this scenario,
only market and biophysical conditions drive future land
use and land management decisions. To model mitigation
activities, each mode! includes 10 alternative GHG price
path scenarios and selects the optimal emission path and
combination of mitigation activities in response to the
prices based on tradeoffs between land use, markets, and
GHG reductions.

Two modern hog barns in Northwest Iowa.

Key Takeaways

Though the U.S. land sector is projected to remain a net sink through
midcentury, land use GHG emissions are projected to increase over time
under the baseline scenarios.

In the baseline, the U.S. Agriculture, Forestry, and Other Land Use (AFOLU) sector is expected to remain a net
GHG emissions sink, with projected net C02 sequestration of about 90 million metric tons of C02 equivalents
per year (Mt C02e yr"1) in FASOMGHG and 120 Mt C02e yr"1 in GLOBIOM in 2050. Baseline results generally
align with national GHG inventory historic values.

In the forestry sector, the three models project that the carbon sink will either remain relatively constant
or decline over time as forests age and harvesting activities grow, driven by an increase in population and
corresponding demand for forest-based products.

In 2050, the expected average annual carbon sequestration rate is 405 Mt C02 yr"1 in FASOMGHG,

431 Mt C02 yr1 in GLOBIOM, and 641 Mt C02 yr"1 in GTM (compared to an estimated rate of 688 Mt C02e yr"1

in 2020 in the EPA Greenhouse Gas Inventory 2023) (EPA, 2023).

In the agricultural sector, which includes both crops and livestock, both FASOMGHG and GLOBIOM project an
increase in GHG emissions over time as rising populations and gross domestic product (GDP) lead to increases
in demand for agricultural commodities, despite projected increases in crop yields.

continued

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Greenhouse Gas Mitigation Report

Across 10 mitigation scenarios, emissions reduction in the AFOLU
sector are projected to be 32-364 Mt C02e yr1 in FASOMGHG and 163-
309 Mt C02e yr1 in GLOBIOM in 2050 for GHG prices ranging from $7/t C02e
to $243/t C02e.

in 2050, at a GHG price of $100/t C02e, the AFOLU sector (including both agriculture and forestry) is projected
to abate about 250-350 Mt C02e yr"1.

Across all models, forest-based activities offer the highest level of mitigation potential. In GLOBIOM, forest
management provides, on average, more than half of the mitigation from the land sector, while afforestation has
the largest share of total mitigation in FASOMGHG and GTM. Under prices higher than $50/tC02e, in GLOBIOM
and FASOMGHG, the forestry sector is still the primary contributor to mitigation, but its share declines as more
land-based activities become cost-effective in livestock and cropping systems.

In all mitigation scenarios, the agricultural sector remains a net emitter of C02e; however, emissions reductions
of up to 16% from croplands and 18% from livestock activities are feasible by 2050, while still maintaining
production.

The forest sector has the capacity to reach net sequestration of 1 Gt C02e
yr1 in 2050 under half of the mitigation scenarios in GTM.

To achieve the U.S. Long Term Strategy (LTS) goal of net-zero emissions by 2050 requires important contributions
from land-based activities and other carbon removal activities. The findings presented in this report show
that the forest sector has the capacity to significantly increase net sequestration over the next three decades;
however, reaching a level of around 1 Gt C02e yr"1 in 2050 could require investments of more than $15 billion
per year between the present and 2050.

The land sector alone has the capacity to reduce its methane emissions by
30% below current levels in 2030.

The Global Methane Pledge launched in 2021 by the United States and the European Union aims at reducing
global methane emissions by 30% below 2020 levels by 2030. The results in this report show a potential
reduction in U.S. methane emissions of 30-33% relative to 2020 by 2030 from the land sector only (the global
pledge includes all methane-intensive sectors). This level of methane abatement could be achieved under GHG
prices higher than $116/t C02e in 2030.

continued

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Greenhouse Gas Mitigation Report

Under annual investments of $2 billion in the next decade, the land sector
can deliver around 50-78 Mt C0,e annual mitigation at an average cost of
about $25 per ton.

Under cumulative investments of $20 billion in the next decade directed to all land-based activities, the
expected cumulative abatement could reach a maximum of 800 Mt C02e. This works out to a per-ton average
mitigation cost of about $25.

Mitigation potential of the land sector in the report is within the lower bound
range of 5-1,188 Mt C02e presented in the literature because the models
account for land use competition, tradeoffs between mitigation activities,
and market dynamics that may not be reflected in other studies.

In the literature, estimated abatement varies significantly due to different approaches and assumptions with a
range of 5-624 Mt C02e of potential mitigation from the land sector under GHG prices below $35/t C02e. For
prices up to $200/t C02e, the potential range is even greater with projections of 550-1,168 Mt C02e.

The mitigation potential from recent techno-economic analyses, which usually sum across a range of mitigation
activities and sources, is higher than the results in this report where there is an explicit representation of
economic tradeoffs, land use competition, and market responses. This effect is significant under higher GHG
prices. Moreover, some of the recent bottom-up studies include new mitigation options (e.g., biochar) that are
not included in the models used for this report because of the lack of data in the Inventory of U.S. Greenhouse
Gas Emissions and Sinks (GHGI).

Field of mustard seed cover crop,
used as weed suppression and
pest control in Santa Cruz County,
California.

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Greenhouse Gas Mitigation Report

FIGURE ES-1

By the numbers

AFOLU

2030

2040

2050

• • • — •

• ¦

• •

-500 -400 -300 -200 -100

Mt C02e/yr

The U.S. AFOLU sector could
remain a net sink in 2050
under business-as-usual
conditions (without additional
mitigation policies targeting GHG
emissions). Under GHG mitigation
scenarios, it could increase its
net sequestration up to 309-364
Mt CO„,e relative to the baseline in
2050 depending on the model.

Agriculture & Livestock

2030

2040

2050

• ¦

100

200 300

Mt C02e/yr

400

500

In the U.S. agricultural sector,
which includes both crops and
livestock, GHG emissions are
projected to increase over time,
converging to a similar value
in 2050 under the baseline
scenario. Under GHG mitigation
scenarios, GHG emissions
are projected to decline by a
maximum of 85-110 Mt CO„,e
relative to the baseline in 2050
depending on the model.

Forestry

• M • M

2030

2040

2050

• •

• •• • •• «|

• •

• • • • •

>• • •

-1,500

-1,000 -500

Mt C02e/yr

The U.S. forestry sector will either
maintain a constant flux of net
sequestration or slowly decline
its sequestration over time under
the baseline scenario. Under GHG
mitigation scenarios, forest net
sequestration could increase by
a maximum of 200-832 Mt CO„,e
relative to the baseline in 2050
depending on the model.

¦ Baseline • Mitigation • FASOMGHG • GLOBIOM • GTM

All figures show absolute emissions under baseline and mitigation scenarios for net emissions from agriculture, forestry, and other
land use (AFOLU) in FASOMGHG and GLOBIOM; net emissions from forestry in FASOMGFIG, GLOBIOM, and GTM; and net emissions
from agriculture and livestock for FASOMGFIG and GLOBIOM. Each figure has a different x-axis scale. Results are presented in terms of
atmospheric accounting. Therefore, positive flux equates emissions; negative flux represents sequestration. Initial values in each model
differ due to varying GFIG pools included in each model, as discussed in Chapter 2, such as FASOMGFIG including emissions from on-farm
fuel consumption, which GLOBIOM does not. Additionally, GTM and GLOBIOM include representation of Alaska, while FASOMGFIG does not.

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Greenhouse Gas Mitigation Report

Future research should expand the
sensitivity test presented in the
report and include additional climate
change impacts on land to assess
the sensitivity of these findings to
changing climate conditions.

Heavy rains and storms in the Midwest have caused field
flooding and corn crop damage.

Looking Forward

The mitigation scenarios simulated in the report represent
an optimal framework in which all the agents are subjected
to GHG prices, they respond to the price mechanism
in a rational way with perfect information, there are no
transaction costs associated with their mitigation actions,
and free riding is not possible. Future efforts could expand
the sensitivity tests presented in the report, including
comparing the potential from incentivizing single land-based
mitigation activities to the potential found from the approach
presented in the report.

The models respond to the price mechanism by selecting the
most cost-effective composition of mitigation actions across
a range of 24 options, which represent the activities used on
a largescale at the present. Further research should expand
the portfolio of GHG reduction activities available for the
land and other sectors (e.g., bioenergy production, wetland
conservation, agroforestry, biochar) and strategies available
to maintain and enhance land sink and resilience (e.g., land
conservation) and those that seek to address food security
issues.

The models used for the analysis include economic and
biophysical characteristics of land and function as if climate
conditions will affect land productivity and availability
following historical trends. Future research should expand
the sensitivity test presented in the report and include
additional climate change impacts on land to assess the
sensitivity of these findings to changing climate conditions.
These efforts could include the role of changing temperature
and precipitation patterns, fluctuations in crop growing
regions, and changes in occurrences of natural disasters
such as drought, floods, and fires.

For each GHG price pathway, each model provides the cost-
effective composition of land-based mitigation activities
without considering macroeconomic costs and benefits. The
report does not estimate the social and economic benefits
of reducing GHG emissions from the atmosphere in terms
of avoided climate damages and the role of land adaptation
strategies. Future research should include these additional
layers of analysis by estimating, for example, the benefits in
terms of avoided carbon emissions and potential co-benefits
on biodiversity together with equity and environmental
justice considerations on where land-based activities will be
implemented.

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Greenhouse Gas Mitigation Report

1 Introduction

The forestry and agriculture sectors are central pillars
of global and U.S. greenhouse gas (GHG) emissions and
removals portfolios.

GHGs associated with the land sector include carbon
dioxide (C02), methane (CH4), and nitrous oxide (N20). For
comparison, amounts of these gases are often presented
as carbon dioxide equivalents (C02e). Land management
and land use change (LUC) activities can increase an
area's ability to hold carbon and act as a carbon sink or
can increase GHG emissions to the atmosphere. Carbon
sinks represent net removals of C02 from the atmosphere
via sequestration, which is defined as increasing carbon
content in a carbon pool other than the atmosphere
(Intergovernmental Panel on Climate Change [IPCC], 2000).
According to the IPCC, lands globally constituted a net
carbon sink of -6.6±5.2 gigatons (Gt) C02e annually from
2010 to 2019 (IPCC Working Group III, 2022). Agriculture,
forestry, and other land use (AFOLU) also represents a
source of emissions. About 22% (13 Gt C02e) of total net
global anthropogenic GHG emissions in 2019 came from
AFOLU, half of which were a result of land use change
activities (largely deforestation, though estimated global
deforestation rates are declining). Compared with other
sectors, GHG emissions estimates from the land sector are
generally more uncertain, largely due to the uncertainty of
the data underlying estimated emissions and sequestration
in land use, land use change, and forestry (LULUCF), which
depend on biological variation, differences in biophysical
conditions, and heterogeneity in management systems
across regions (IPCC Working Group III, 2022).

Land-based activities are globally recognized as having
substantial GHG mitigation potential, and these activities
have received renewed focus in many countries' GHG
reduction commitments made as part of the Paris
Agreement (United Nations Framework Convention on
Climate Change, 2015) and the last two IPCC special
reports (IPCC, 2018, 2019b). The recent IPCC 6th
Assessment Report stated that there is high confidence on
the substantial mitigation (and adaptation) potential from
opportunities in AFOLU that could be upscaled in the near
term across most regions (IPCC Working Group III, 2022).
In that report, the mitigation potential of AFOLU activities is
projected as 8-14 Gt C02e yr"1 between 2020 and 2050, at
costs below $100/t C02e, and 30%-50% of the potential
is available at less than $20/t C02e. The largest share
(4.2-7.4 Gt C02e yr"1) is projected to come from reduced
deforestation, improved management, and restoration of
forests and other ecosystems. Improved crop and livestock
management and carbon sequestration in agriculture
represent other key mitigation strategies with a potential of
1.8-4.1 Gt C02e yr"1.

In addition to increased international focus on this topic,
the United States has recognized that dedicated programs
and policies focusing on its land sector, including efforts to
reduce emissions from natural disturbances and to bolster
the health of vital ecosystems, have the potential to confer

8

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Forest hillside landst
selective logging of r
trees with seed tree:
Pennsylvania.

The Long-Term Strategy proposes avoided
forest land conversion, shifts to longer harvest
rotations, reforestation on degraded forested
lands, and reduced natural disturbance through
management, all of which can result in both
near- and long-term net carbon benefits.

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Greenhouse Gas Mitigation Report

substantial climate benefits and play an important role in
meeting the nation's decarbonization goals. To advance
those goals, U.S. federal actions—like the release of the
U.S. Long Term Strategy (LTS) (U.S. Department of State &
the U.S. Executive Office of the President, 2021) and the
passage of legislation like the Infrastructure Investment and
Jobs Act (IIJA, 2021) and the Inflation Reduction Act (IRA,
2022), which include funding for actions addressing climate
change—seek to substantially augment efforts to capitalize
on lands-based mitigation opportunities. Specifically,
the LTS proposes avoided forest land conversion, shifts
to longer harvest rotations, reforestation on degraded
forested lands, and reduced natural disturbance through
management, all of which can result in both near- and long-
term net carbon benefits. While managers of agricultural
lands can implement practices such as reduced tillage,
rotational grazing, and residue management to reduce
emissions from crop and livestock production, most of
these activities result in near-term emissions reductions.
The IRA is a major step in supporting these activities by
providing nearly $20 biilion in investment to natural and
working lands to preserve, restore, and conserve vital
landscapes, as well as investments in innovative on-farm
activities to reduce emissions. The United States has
also endorsed the Global Methane Pledge, a multilateral
agreement to take voluntary actions consistent with a
collective effort to reduce global CH4 emissions by at least
30% from 2020 levels by 2030. In the AFOLU context,
this agreement applies primarily to CH4 emissions from
rice cultivation and livestock production. There are also
dedicated actions taking place at the local and state levels
(The U.S. Conference of Mayors and the Center for Climate
and Energy Solutions, 2017; U.S. Climate Alliance, 2022).
Increased levels of public and private policy enactment and
investments in natural climate solutions may increase the
acreage, productivity, and overall health of, and realized
mitigation from, U.S. forested and agricultural lands.

The goal of this report is to provide estimates of the GHG
emissions mitigation potential associated with various
abatement activities in the agriculture and forestry sectors

in the United States over the next several decades, with
particular focus on results in 2050.

This report identifies and estimates mitigation options
and related costs that can be used to support informed
prioritization of abatement activities, target investments,
and improve the likelihood of achieving an overall GHG
reduction goal. This information is therefore valuable to a
broad range of stakeholders, including the designers of
national and regional GHG mitigation and land management
programs, private land managers, private-sector investors,
the broader research community, and the public.

The analysis builds on work presented in the 2005 U.S.
Environmental Protection Agency (EPA) report Greenhouse
Gas Mitigation Potential in U.S. Forestry and Agriculture
(EPA, 2005) to provide a contemporary perspective on
GHG abatement options for the U.S. land use sectors using
updated and expanded modeling frameworks.

Like the original report, this report evaluates GHG emissions
mitigation potential via simulated future conditions and
outcomes using an economic modeling framework that
uses biophysical data and modeling, cost parameters,
elasticities, and other inputs to explore the relationships
among drivers of decisions related to agricultural production
activities, forestry management, and other related land use

This report expands on the
2005 EPA report because
that report employed only
one model, a domestic
partial equilibrium (RE)
model of the U.S. forest
and agriculture sectors
with land use competition
between them and limited
linkages to international
trade (Forest and Agriculture
Sector Optimization Model
with Greenhouse Gases,
FASOMGHG).

and land use change activities.

u ^ ji Greenhouse Gas Mitigation
Potential in U.S. Forestry
and Agriculture

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Specifically, this report expands on the 2005 EPA report
because that report employed only one model, a domestic
PE model of the U.S. forest and agriculture sectors with
iand use competition between them and limited linkages
to international trade (Forest and Agriculture Sector
Optimization Model with Greenhouse Gases, FASOMGHG).
This analysis applies an updated version of FASOMGHG
and two additional economic models, the Global Biosphere
Management Model (GLOBIOM) and the Global Timber
Model (GTM). All three models explicitly capture important
feedbacks that occur when market changes influence the
opportunity costs of investing in land use sector mitigation,
and hence affect the resulting potential magnitude and
cost of different GHG mitigation activities over time. Each
model is well established in the economics, agricultural,
forestry, and land use literature (see Chapter 2 for detailed
information on these models). Since the use of FASOMGHG
in the 2005 EPA report, that model and the other two
models applied in this study have been expanded and
updated to incorporate additional mitigation options as well
as improved underlying scientific data and methods from key
data sources like the U.S. Forest Service's Forest Inventory
and Analysis (FIA) database (U.S. Forest Service, 2017). The
improved methods and data inputs, along with an expanded
suite of tools used, allow for a more robust assessment of
mitigation potential than the previous report.

Another key aspect of these three models is that they can
directly incorporate a monetary incentive to reduce GHG
emissions and increase carbon sequestration, and track
the estimated land use, market, and GHG consequences
of incentivizing the reduction of GHG emissions in forestry
and agricultural activities relative to a baseline scenario
(without mitigation policy). A GHG price mechanism is an
appropriate and broadly applied mechanism for deriving the
cost-effective portfolio of mitigation actions available in the
targeted sector (for this report, the sector is land). Moreover,
price-based mechanisms could be used to emulate different
programs—such as direct investments on all or specific
land-based activities—and estimate their possible outcomes.
Moreover, results in the report are presented as marginal
abatement cost curves (MACCs), which represent the annual
GHG mitigation (in C02e) associated with each GHG price
incentive across different price levels in each model.

The use of MACCs is a well-known and often-applied
approach in the literature to illustrate the estimated amount
of emissions reduction potential at varying GHG price levels
(e.g., EPA, 2005, 2019b). Model-derived MACCs and total
abatement levels are good candidates for informing high-
level policy or investment analysis that seeks to understand
full opportunity costs of mitigation investment within the
context of complex market or macroeconomic systems.
MACCs represent an extremely valuable policy tool to
measure the mitigation potential under a selected GHG
price or the required GHG price to meet a defined level of
emissions reduction in different time periods and across
activities (see Lubowski et al„ 2006).

Though the number of studies published on this topic
has been increasing recent years, many of those studies
are techno-economic and/or provide a synthesis of other
studies (Fargione et al., 2018). This report complements
such studies and offers fresh insights as to cost-effective
competitive potential of land-based GHG mitigation options
domestically by applying well-known models with interactive
biophysical and economic components.

Young pine stand growing in Governor Knowles State Forest in
Northern Wisconsin.

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1.1 Report Objectives

We estimated these reductions and additional sequestration
by comparing the GHG outcomes and economic activities
associated with a set of alternative future scenarios
across three economic land use models with the projected
business-as-usual or baseline trajectory of each model.
The estimated mitigation results presented in this report
are additional to the projected baseline activities and
related GHG emissions or sequestration rates. The results
produced are shown as MACCs for the land sector as well as
disaggregated by gas, activity, and region to provide detailed
descriptions of the mitigation potential of different activities
across models, time, and space.

Specifically, this report seeks to examine the following

questions:

•	What is the magnitude of total U.S. GHG mitigation
potential from a range of forestry and agriculture
activities over time and at different levels of GHG
reduction incentives?

•	How does the portfolio of forestry and agriculture
mitigation activities change over time given the differing
growing cycles of different crops and tree species and
related carbon dynamics?

•	What are the most efficient mitigation options, taking
economic opportunity costs, implementation costs, and
market impacts into consideration?

•	What is the estimated mitigation potential for different
GHGs, specifically CH4 as a potent short-lived climate
forcer vs. the long-lived gases C02 and N20?

•	What is the estimated regional distribution of GHG
mitigation opportunities?

•	How do baseline trends impact mitigation potential?

•	How do leakage and other potential effects from
mitigation activities affect overall mitigation outcomes?

•	How do mitigation results from global systems models
(e.g., GLOBIOM and GTM) compare to those from a
domestic model (e.g., FASOMGHG)? How does the
inclusion of global market feedback and resource
utilization impact net mitigation results for the United
States?

•	How does the different treatment of time among models
(dynamic recursive vs. forward-looking) affect the
results?

The goal of this report is to contribute
a new assessment of the estimated
cost-effective potential GHG emissions
reductions and additional carbon
sequestration associated with specific
activities in the agriculture and
forestry sectors in the United States
over the next several decades.

All three models explicitly capture important feedbacks that
occur when market changes influence the opportunity costs
of investing in land-based mitigation options, and hence
affect the resulting potential magnitude and cost of different
GHG mitigation activities over time. The alternative future
scenarios applied for this analysis are GHG price incentives
(presented in $/t C02e) that target reduced GHG emissions
and increased carbon sequestration, while capturing
resource competition and market feedback between the
forestry and agriculture sectors. These scenarios emulate a
hypothetical federal-level policy that aims to address climate
change by mitigating net GHG emissions and promotes
adoption of climate-smart activities on managed U.S. lands
at the minimum cost.

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Mitigation Report

It is important to note that simulated future scenarios like
those presented in this report are not meant to serve as
predictions—rather, they estimate potential future outcomes
under specified future conditions, offering policymakers
insights about what GHG emissions and land use outcomes
may result from varied future conditions and policy designs
and their potential distribution across activities, space, and
time. The scenarios represent a stylized ideal future where
all agents respond in a rational way to the price incentives
which are applied globally (in case of global models) to all
land-based GHG emitting activities. The scenarios assume
perfect information, no transaction costs associated with
mitigation activities, and no free-riding. In addition, there
are other policy instruments to advance climate change
mitigation outside GHG price incentives used for this
analysis. GHG prices have been selected for the study to
simulate future projections under an economically efficient
(optimal) framework in which all emitting activities pay
an equal fee and all activities that increase sequestration
receive an equal reward. By covering all the agents under
the same price, this instrument drives abatement to
be implemented in a cost minimization way where the
maximum mitigation is achieved per dollar invested. This
optimal framework could be compared to other policy
designs to understand their effects and inform and support
the policy-making decision process. Finally, the report
provides some examples of different policy designs and their
effects on future iand mitigation potential in the focus boxes
presented in Chapter 3.

The study harmonizes, to the extent possible, model inputs
and parameters. Employing three different models with
harmonized data and parameters, such as macroeconomic
variables and application of a single set of scenarios,
reduces the implicit bias inherent in single-model projections
and improves understanding of systems-level sensitivity to
key parameters, which increases confidence in the main
findings. Furthermore, it allows for isolation of the effects
of climate change mitigation actions and their costs. Use
of models with different functional forms in a harmonized
effort can also provide important insights on why model
results differ and enable researchers to identify and better
understand the drivers of those differences to improve
future model development and applications, including
supporting policy design and implementation.

This next section of this chapter lays out the recent status
of U.S. lands and related GHG emissions and provides
an overview of potential GHG mitigation options. The
last section further discusses the models used in the
report, including a discussion about how assessments
of lands-based mitigation can be done in different ways.
More details on methods and data are in Chapter 2, the
results are presented in Chapter 3, and discussion of the
results is in Chapter 4.

Results of a controlled bum
in a pine forest in Alabama.

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1.2 Trends in Forest and
Agriculture Land Use and
GHG Emissions

This report focuses on activities
and land management that occur in
AFOLU, which encompass emissions
and sequestration categories included
in the agriculture sector as well
as those in the LULUCF sector, as
determined by IPCC guidelines (IPCC,
2006).

FIGURE 1-1

U.S. greenhouse gas emissions and sinks by sector (in Mt C02e, 1990-2021)

1,000

6,000

>



'E

LU

-2,000

1990

2000

2010

2021

Net Emissions

Agriculture	Energy	Industrial

LULUCF ¦ Waste

Gray line shows net emissions from all sectors. Data source: 2023 U.S. GHGI (EPA, 2023).

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Greenhouse Gas Mitigation Report

The U.S. landscape represented 8% of the world's forests
(766 million acres) (FAO, 2020a) and 8% of global
agricultural lands (including cropland and grasslands, about
988 million acres) in 2016 (FAO, 2020b). The Inventory
of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI)
reported a net increase in carbon stocks (i.e., net C02e
removals from the atmosphere) of 832 million metric tons of
carbon dioxide equivalent (Mt C02e) in the LULUCF sector

in 2021.1 CH4 and N20 emissions from LULUCF activities in
2021 were 66 and 12 Mt C02e, respectively, and thus the
overall estimated net flux from LULUCF resulted in a removal
of 754 Mt C02e (EPA, 2023). On the other hand, emissions
from the U.S. agriculture sector (crop and livestock systems)
were 598 Mt C02e the same year. Therefore, the U.S.

AFOLU sector combined constituted a net C02e sink of
approximately 156 Mt C02e in 2021.

FIGURE 1-2

U.S. greenhouse gas emissions and sinks from agriculture and LULUCF
sectors (in Mt C02e, 1990-2021)

1000

o
o

o

CO
CO

E

LU

500

-500

-1000

1990

2000

2010

2021

LULUCF Emissions	Agriculture	LULUCF Removals	Net Emissions

1 Per IPCC guidelines (IPCC, 2006), the LULUCF sector includes reporting of fluxes related to changes within and conversions between all land-use types
including: forest land, cropland (including the soil carbon pool), grassland, wetlands, and settlements as well as other land.

Data source: 2023 U.S. GHGI (EPA, 2023).

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Greenhouse Gas Mitigation Report

The 2023 GHGi (EPA, 2023) indicates that while the AFOLU
sector currently is a net sink, the estimated net sink over
time has been getting smaller.

Carbon fluxes associated with forested lands make up
most of the LULUCF net removals; in 2021, 793 Mt C02e
was stored in forests of which 695 Mt C02e came from
existing forests and 98 Mt C02e from lands converted to
forests. However, since 1990 the net sink from forests
has decreased by 126 Mt C02e. As forested land area has
stayed relatively constant in the last 30 years (in 2021, the
United States had 692 million acres of managed forest land,
which is less than a 1% decrease compared to 1990), the
decline in the sink was primarily driven by a reduction in the
rate of net carbon accumulation in forests (EPA, 2023). This
slowing rate of carbon accumulation has multiple drivers,
including the age of U.S. forests (Wear & Coulston, 2015)
and the increase in natural disturbances (EPA, 2023). In the
future, possible increased levels of natural disturbances-
such as fires, insects, diseases, droughts, and storms
largely due to climate change—could potentially reduce the
net forest carbon sink even further (Seidl et al., 2017).

These trends may be counterbalanced by increasing timber
demand and/or emerging demand for new forest-based
products and bioenergy that will encourage continued and
new investments in forested lands and restoration activities
(Tian et al., 2018; Wade et al., 2022). Also, mounting
evidence of C02 fertilization effects (the phenomenon
that higher atmospheric levels of C02can enhance tree
growth) can in some regions boost tree growth and increase
corresponding carbon sequestration per hectare of forests
(Baker et al., 2023; Davis et al., 2022; Mendelsohn et al.,
2016; Norby & Zak, 2011). These countervailing drivers
make the possible future of U.S. forest carbon levels
uncertain. Some studies and modeling tools estimate that
U.S. forests may become a net source of emissions in the
next 10-40 years (Coulston et al., 2015; U.S. Department of
Agriculture Forest Service, 2012; Wear & Coulston, 2015),
whereas other studies estimate that the net forest

carbon sink may be maintained or even increase in the
coming decades (Favero et al., 2021; Tian et al., 2018;
U.S. Department of State, 2014, 2022), and still others
point out that forests could either increase or decrease
their future carbon storage (Ryan et al., 2012). Differences
driven by things such as different research goals, modeling
approaches, study design, and data inputs are discussed in
general below and for specific studies in Chapter 4.

The agriculture sector emits GHGs through various activities
including livestock management (generally to include enteric
fermentation and manure management), rice cultivation,
liming, urea application, field burning of agricultural
residues, and agricultural soil management.2 Total
agricultural GHG emissions (from both crop production and
livestock practices) were 598 Mt C02e (9% of total U.S. GHG
emissions) in 2021 (EPA, 2023), a 51 Mt C02e increase
since 1990. Compared to economy-wide emissions that
decreased by 2% from 1990 to 2021, emissions from the
agriculture sector increased by 7% over the same timeframe.
In 2021, enteric fermentation was the largest anthropogenic
source of CH4 emissions, accounting for 195 Mt C02e
(about 27% of total CH4 emissions). This level represents
a 6% increase since 1990, largely due to increasing cattle
populations (EPA, 2023). Manure management emissions
increased 62% between 1990 and 2021 (from almost 50
Mt C02e to about 80 Mt C02e) due to rising populations
of key livestock species and intensification of livestock
production. Agricultural soil management activities were
the largest contributors to U.S. N20 emissions in 2021
(accounting for 74%), and these levels have been relatively
constant since 1990. In the future, as the population and
economy continue to grow, the national and international
demand for agricultural commodities is expected to increase
which could drive more land to be converted to cropland
and/or subjected to intensified production. These changes
in land use and land management are expected to increase
GHG emissions over the next several decades under
business-as-usual practices (Wade et al., 2022).

2 Per IPCC reporting guidelines, the U.S. GHGI includes soil carbon stock changes on agricultural lands as part of the LULUCF sector.

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Greenhouse Gas Mitigation Report

1.3 Overview of Mitigation
Opportunities

A broad range of different potential
mitigation actions currently exist-
some have been employed for
decades, whereas others are still in
research and development stages.

These activities generally focus on preserving and enhancing
existing carbon pools and GHG-rich landscapes, and on
increasing active sequestration via removals from the
atmosphere and directly reducing GHG emissions.

Table 1-1 includes key examples of land-based mitigation
options sourced from different studies (starting with
practices that are widely deployed followed by lower-
adoption and/or emerging options). Not all the options listed
in the table are included in this report but they are included
here for comprehensiveness (for many emerging mitigation
options, there are not yet sufficiently comprehensive
datasets for such practices applied in the United States).

Estimated magnitudes of different mitigation options from
different studies are highlighted in Figure 1-3 and are
discussed in more detail in comparison with the results of
this study in Chapter 4.

TABLE 1-1

Land-based mitigation options, as defined in the literature

Land-based



Level of

mitigation options Description

References

Included in this report adoption

Afforestation and

Increase in above and below ground carbon

Busch et al. (2019)

Yes

High

reforestation

sequestration from conversion of land to forest
that either historically has not contained forests
(afforestation) or has recently contained forests
(reforestation).







Reduce

Increase in above and below ground carbon

Austin et al. (2020);

Yes (but in the United

High

deforestation/

sequestration from actions that avoid the

Busch et al. (2019)

States, it includes only



reduce forest

conversion of forest to non-forest. While



reduced conversion



conversion8

deforestation still occurs globally, it mainly occurs
in the tropics.15



because widespread
deforestation is not
expected to occur in the
country in the future)



Improved forest

Increase in above and below ground carbon

Austin et al. (2020);

Yes

High

management

sequestration from improved forest management
strategies which include extending timber harvest
rotations and increasing the productivity of forests
through thinning diseased and suppressed
trees, decreasing competition by removing brush
and short-lived trees, increasing stock levels in
understocked areas, and maintaining stocks at
high levels."

Sohngen & Brown
(2008); Van Winkle et
al. (2017)





Forest C02 products

Carbon storage via production of long-lived wood
products; substitution of wood products for carbon-
intensive materials like cement in buildings.

Griscom et al. (2017);
Sohngen & Brown
(2008)

Yes (substitution
of carbon-intensive
materials is not
included)

High

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Land-based



Level of

mitigation options Description

References

Included in this report adoption

Forest C02 soil	Enhance soil organic carbon sequestration in	Jiang & Koo (2013) Yes	High

forests.

Cropland non-C02 Management activities to reduce/avoid N20 and Beach, Creason, et al. Yes	High

CH4 emissions associated with nitrogen application (2015); EPA (2019a,
and through nutrient management, residue	2019b)

management, water management, dry seeding and
combinations of these activities; avoided N20 from
reducing total fertilizer application.

Cropland C02

Reduce C02 emissions from fossil fuel use for
agriculture production.

Wade et al. (2022)

Yes

High

Agricultural C02
soils

Enhance soil organic carbon sequestration in
croplands by shifting from, for example, current
management to no-till management, changes in
residues management and crop mixes

Pape et al. (2016);
Roe et al. (2021)

Yes

High

Livestock non-C02

Management activities to reduce/avoid CH4
emissions from ruminant livestock enteric
fermentation (e.g., changing diets, feed additives),
and CH4 and N20 emissions from improved manure
management practices (e.g., adoption of improved
anaerobic digesters).

Archibeque et al.
(2012); Beach,
Zhang, et al. (2015);
EPA (2019a); Hristov
et al. (2013)

Yes

High

Pasture and

rangelands

management

Retain carbon stocks (e.g., in soils, root systems)
by avoiding LUC and improving grazing practices.

Baker et al. (2020);
Bogaerts et al.
(2017); Claassen et
al. (2018); Jones &
O'Hara (2023)

Yes

High

Improved resilience
to natural
disturbances

Avoided emissions from natural disturbances
(e.g., fires) via practices such as hazardous fuel
removals.

Griscom et al. (2017)

No (emerging option)

Low

Bioenergy with
carbon capture and
storage (BECCS)

Carbon sequestration from electricity generation
derived by combusting crop-based or forest-based
biomass and combined with carbon capture and
storage (CCS).

Hanssen et al. (2020)

No (emerging option
with high uncertainty of
future demand)

Low

Reduce land
degradation and
restore natural
lands

Avoided emissions from degradation and/or loss of
carbon stocks in mangrove ecosystems, wetlands,
and degradation of peatlands (emerging options).

Griscom et al. (2017);
Humpenoder et al.
(2020); The White
House (2016)

No (emerging option)

Low

Other practices

Alternative solutions such as increased adoption
of riparian buffers, solid separators, agroforestry
practices, application of biochar, and enhanced
weathering.

Pape et al. (2016)

No (emerging options)

Low

a This category does not include forestiand managed through periodic harvesting for timber production.

b The IPCC Special Report: on Climate Change and Land defined deforestation as the conversion of forest land to non-forest land (IPCC, 2019a).

Improved forest management could also increase forests' adaptability to climate change, making them less susceptible to future wildfire, drought, and pests
(as shown in Anderegg et al., 2020).

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1.4 Assessment Approach

Insight into future potential trends in
forest and agriculture GHG emissions
fluxes, and the environmental and
economic conditions that drive those
trends, is necessary for the design
of effective mitigation programs and
policies, as these trends can influence
the magnitude and costs of various
GHG abatement activities (Baker et
al., 2017; IPCC, 2019b; Van Winkle et
al., 2017).

This report is intended to establish a foundation for
evaluating the broad potential of AFOLU mitigation across
the United States and as such must incorporate market
feedback effects. To achieve this end, this study focuses
on application of economic simulation models, which
integrate detailed land use and biophysical processes with
land management responses to market drivers and costs
under various scenarios of future conditions to explore
the relationships between policies and other drivers on
decisions related to agricultural production activities,
forestry management, and other related land use and land
use changes.

The three models simulate baseline and alternative scenario
projections of U.S land use activities and characteristics
including land use management, land use change, demand
and supply of commodities produced, associated costs
and GHG fluxes. These projections were constructed
using historical ecological data (e.g., detailed land GHG
information from process models such as DAYCENT),
economic parameters (e.g., cost data pertaining to specific
forest management activities) and specified future economic
and technological conditions. These future conditions
include socioeconomic elements that can significantly

influence how land resources are used and managed, such
as future GDP and population growth and assumptions on
technological innovations impacting agricultural productivity.
Future socioeconomic assumptions are harmonized across
models and unchanged under policy scenarios. Finally,
mitigation scenarios are explored in the models in forms of
payments for GHG abatement and carbon sequestration
activities. The inclusion of fiscal incentives like these
payments modifies business-as-usual trends and allows for
evaluating how those incentives change the U.S. land use
activities and characteristics and associated GHG fluxes
relative to the baseline.

Moreover, all three models explicitly capture important
feedbacks that occur when market changes influence the
opportunity costs of investing in land use sector mitigation,
and hence affect the resulting potential magnitude and cost
of different GHG mitigation activities over time.

1.4.1 Different Approaches for Estimating
Mitigation Potential

The development of projections of future GHG fluxes in the
forest and agriculture sectors is particularly challenging due
to spatial and temporal variability in land carbon stocks
and GHG flux processes, dynamic and interconnected
global markets for forestry and agricultural commodities
(Forest2Market, 2018; Latta et al., 2016; Ohrel, 2019;
Schmitz et al., 2014), diversity among land owners and their
management responses to market signals (Habesland et
al., 2016; Sohngen & Mendelsohn, 2003), and uncertainty
regarding the effects of policies that influence land use and
commodity markets directly and indirectly (e.g., bioenergy
policies) (Favero et al., 2020; Guo et al., 2019; Latta et al.,
2013; Wise et al., 2014).

Different models are designed to address these challenges
in various ways, and with different levels of complexity,
spatial and temporal detail, input data, model structure
and specification, sectoral coverage, macroeconomic
assumptions, and analytical objectives (e.g., Latta et al.,
2018; Sj0lie et al., 2015; van Meijl et al., 2018; Wade et
al., 2019). Because modeling tools are often developed in
different ways and for different purposes, they can produce

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divergent estimates of mitigation potential (Ohrel, 2019;
U.S. Department of State, 2014; U.S. Department of State &
the U.S. Executive Office of the President, 2021).

Various approaches and modeling tools have been used
in the literature to simulate future trends in forest and
agriculture GHG fluxes and assess mitigation potential of
land, including biophysical or ecological process models
(Law et al. 2021; Law et al. 2018), techno-economic
approaches (Cook-Patton et al., 2021; Eagle et al., 2022;
Fargione et al., 2018), econometric models (Lubowski et
al., 2006), PE models (Baker et al., 2013; EPA, 2005),
computable general equilibrium (CGE) models and
integrated assessment models (lAMs) (Calvin et al. 2019;
Golub et al. 2009).

Ecological models are used to consider future biophysical
characteristics and potential GHG profiles of, for example,
forests and they provide useful information to assess
biophysical parameters such as maximum yields, forest
ecosystem dynamics, and climate change impacts on

A reseeded forest managed plot in Shasta County, California, in
the northeastern section of the state.

forests. This methodology usually does not include human-
induced changes in forest productivity via changes in
management activities driven by policies and/or market
signals (e.g., demand shifts); therefore, their findings might
provide only a partial perspective of the mitigation potential
of land.

In AFOLU applications, techno-economic (or bottom-
up) approaches generally aggregate individual marginal
abatement costs from different sources to provide the
cumulative abatement available from the land sector.

This approach usually lacks a representation of resource
competition across sectors or mitigation options, which
may yield outcomes that overestimate the potential GHG
mitigation from the resource or underestimate the cost
of abatement activities. Other studies provide a static
representation of mitigation opportunities by measuring the
maximum technical and economic mitigation potential under
specific engineering assumptions (e.g., EPA, 2019a).

Econometric models are used to estimate carbon
sequestration potential by simulating the effects of carbon
subsidies on land rent and land abatement activities
and corresponding changes in carbon sequestration.
Sampling-based and simulation approaches such as the
one devised by Jiang and Koo (2013) use current census
data and producer preferences to simulate future mitigation
potential of land. Usually, both of these methodologies can
assess mitigation costs at the regional level, but they make
implicit assumptions about land availability with simplified
representations of biophysical constraints.

PE models, such as the three used for this report
(FASOMGHG, GLOBIOM, and GTM) equate supply and
demand in one or more markets such that prices stabilize at
their equilibrium level. PE models tend to have a high level of
detail in the land sector, but limited interactions with other
sectors relative to lAMs, as discussed below.

CGE and lAM models are more comprehensive in their
representation of the economy, reflecting feedback effects
among all economic sectors and factors of production,
such as capital and labor. CGE models are the broadest in
economic scope but tend to lack detail in their physical and
technologicai representations. lAMs are the broadest in their

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representation of the interactions between human (e.g.,
economic) and Earth (e.g., biophysical) systems but often
lack detail in their representation of particular sectors (e.g.,
finance, labor) and technologies. For example, CGE models
are designed to track resources in terms of their monetary
value and require subsequent accounting methods to
estimate physical quantities. On the other hand, lAMs
incorporate complex market interactions, as these models
typically are global and economy-wide in scope and have
extensive economic representation, but often do not have
the level of detailed biophysical or mitigation activity cost
information needed for national and subnational analyses
of land-based mitigation, especially for forestry (e.g., Calvin
et al., 2019). Furthermore, these complex models provide
results at the global or regional level without detailed
descriptions of country-specific mitigation potential. Further
discussion of general attributes related to these different
approaches can be found in Ohrel (2019).

When considering tradeoffs between these methodological
options, one must consider the goals of the analysis and
whether cross-sectoral impacts are potentially influential
on the overall results. For this specific report, the analysis
primarily focused on detailed behavior within the land
sector. PE models are well suited for this type of analysis as
they provide a detailed representation of economic factors
driving the markets of land commodities together with a
sophisticated representation of biophysical characteristics
of land. Moreover, the three PE models selected for this
report have some important differences that allow them
to portray a range of possible outcomes on the future of
land mitigation in the United States and consider resource
competition in their mitigation assessment across activities.

A review of peer-reviewed articles and reports from 2000
to 2022 conducted for this report identified 39 studies
assessing land-based mitigation potential using six main
methodologies. Because multiple methodologies have
been developed to estimate abatement opportunities in
the land sector, the literature presents a large range in
recent studies' estimated mitigation potential for activities
in the forestry and agriculture sectors, driven largely by
model type and different underlying scenario parameters
(Figure 1-3). For example, estimates of mitigation potential

from improved forest management in the United States
differ by a factor often due to variations in macroeconomic
assumptions, abatement policy formulations, and economic
modeling approaches (Van Winkle et al., 2017). Despite the
differences, the studies reviewed and reflected in Figure
1-3 indicate that the average mitigation potential of forest-
based activities is likely to be higher than agriculture-based
activities, but the associated degree of uncertainty also is
higher. Moreover, the mitigation potential of forest-based
activities is likely to be more sensitive to the assumed price
incentive; for example, the average mitigation of forest
management increases by 90% under the $36-$200/
t C02e price range relative to the lower price range.

1.4.2 Modeling Approach Used in this Report

Historic U.S. land use and land use management changes
and related levels of GHG fluxes were shaped over time
by a variety of environmental, social, and economic
conditions, and thus simulation of future trends in this
arena should be informed by these key elements. U.S. lands
are heterogeneous, and the commodities and markets
related to lands are as well, making it crucial that tools
applied to assess GHGs associated with U.S. lands reflect
these varied biophysical and economic aspects. Exploring
the dynamic roles that forest and agricultural lands can
play in U.S. mitigation efforts requires tools that 1) include
both biophysical and economic capabilities (meaning
competition between resources is reflected), 2) are based
on historical data, and 3) can simulate baseline trends as
well as project potential impacts of different future market,
social-economic, environmental, and technical conditions,
including mitigation policies and incentives.

This analysis applies three detailed economic models that
simulate future potential GHG fluxes, land cover change, and
commodity production in the forestry and agriculture sectors
using detailed regional biophysical and economic land
input data. The three models incorporate the capabilities
and detailed attributes necessary to generate projected
outcomes that consider the important interactions among
managed natural resources, markets, and other key
socioeconomic and biophysical components. Of particular
importance in the selection of these tools is the ability to

21

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FIGURE 1-3

Average mitigation potential per price range ($l-$35/Mt C02e and $36-$200/
Mt C02e) across land-based activities in the United States

1,400 -

g 1,200

^ cP1-

J* &
«S°V ^ J*

J* ^ ^

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incorporate competitive market interactions, which means
that projections of mitigation potential and associated costs
from these tools can provide additional insight beyond
what is provided via purely biophysical or techno-economic
assessments. The models used in this report include data
from a variety of sources (discussed further in Chapter 2),
including biophysical or process models.3

The models simulate both a future without new or additional
land-based mitigation policies in place (baseline scenario)
and multiple scenarios that can emulate different levels
of mitigation incentives or other policy incentives. GHG
mitigation strategies in this study are represented in each
model as GHG price incentives under different initial values
and growth rates. By including alternative future mitigation
scenarios, the report presents a future range of land
abatement potential driven by the price level associated with
GHG emissions.

By including the same GHG price pathways in three different
well-recognized models, the report presents the effects of
the underlying models' parameters, assumptions, scale,
and scope on the mitigation potential estimates. In this way,
multiple levels of uncertainty are explored and considered
to present a most likely (if possible) outcome of the future
mitigation potential of the land sector. Moreover, each
model is uniquely suited to provide different perspectives
and insights related to key drivers of land-based mitigation
activities in the United States—global market competition
(GLOBIOM), cross-sector interactions (FASOMGHG), and
forestry investments (GTM), all of which have particular
importance for assessment of this sector.

Finally, because the baseline does not incorporate recent
and proposed policies, the results could be used to estimate
the effects of different policies on land use and land
management. Moreover, price incentive scenarios could be
used to emulate alternative programs and their results could
be used to assess the potential effects of the program on
land use and land mitigation potential.

1.4.3 Multi-Model Comparison

It is critical that the forest and agriculture modeling
communities continue to evaluate the performance of their
models both independently and as part of larger model
comparison efforts (Daigneault et al., 2022; Fujimori
et al., 2019; Rosenzweig et al., 2014). The analysis in
this report compares three independently developed
models built on observed (and modeled) economic and
biophysical data. Comparing multiple models allows for
more robust evaluation of different potential outcomes,
reduces potential bias inherent in single-model projections,
and provides a deeper understanding of model results'
sensitivity to input data, structural features, and underlying
assumptions. This multi-model approach allows for more
transparent representation of uncertainties and more
robust understanding of the directionality and magnitude of
mitigation potential and costs than a one-model approach.
Identifying results that are consistent and robust across
different models and assumptions can build confidence
in projections (e.g., Schmitz et al., 2014; Waldhoff et al.,
2015).

The multi-model approach applied in this report provides
insight into the variability in projected baseline pathways
together with the projected portfolio of abatement
opportunities and associated costs across models under
different levels of incentive. The variability is driven by the
individual attributes of each model framework despite
some key data and parameters being harmonized across
models. Moreover, the multi-model approach can elucidate
the potential role of globally integrated markets and global
availability of mitigation opportunities on U.S. domestic
mitigation quantities and costs (see van Meijl et al., 2018,
for a similar example). Finally, this approach allows for a
direct comparison between domestic and global frameworks
to understand the relative impacts of policies implemented
domestically versus those implemented globally and
supports evaluation of the role of international trade
dynamics on domestic mitigation cost estimates.

3 For example, the FASOMGHG model uses data from the DayCent model to inform its crop analysis. DayCent is a biogeochemical model that tracks soil
processes in daily time steps to allow scheduling of management practices (IPCC Tier 3 method). To initialize levels of soil organic matter pools, DayCent
estimates pre-settlement vegetation and historical cropping practices from 1900-to the present (Del Grosso et al., 2012). Whereas process models often
require data from previous decades to establish initial or equilibrium conditions, economic-based future simulation models do not rely on and therefore do
not include decades of historic data. Relatedly, validation exercises for these types of models are often conducted via sensitivity tests as opposed to valida-
tion via comparison of measured historic data with projected results for historic time periods (Canova, 1995; Ohrel, 2019).

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Box 1

FOCUS: Representation of
resource competition in partial
equilibrium models

PE models, like those used in this report, endogenously account for market opportunity costs
as commodity market adjustments occur in response to mitigation investments. Conversely,
techno-economic estimates of marginal abatement costs taken in isolation do not account
for market opportunity costs and could thus represent an overly optimistic perspective on
mitigation potential at a given price incentive. Some studies (such as Fargione et al., 2018),
may directly compare or add together results from a variety of studies (e.g., a mix of biophysical
and technical potential analyses and competitive market potential estimates) to estimate
maximum mitigation potential. Applied in this manner, this approach may overestimate
mitigation potential at a given price because it does not incorporate important resource
competition, opportunity costs, and market interactions that would arise as different mitigation
practices across sectors are implemented simultaneously, thus reducing mitigation potential.

Figure B1 provides a simple illustration of market opportunity costs. If commodity supply and
demand (left-hand side) are explicitly linked to the total abatement from some mitigation
strategy (depicted by the MACCs on the right-hand side), then the level of abatement could
impact the total supply of the commodity. As a hypothetical example, N20 emissions reduction
from alternative nitrogen (N) fertilizer management strategies could induce a small yield loss in
certain contexts. A traditional MACC framework will reflect the farm-level opportunity costs of
this yield loss valued at the original crop price, through the corresponding increase in mitigation
price (Pc) required to "break even" or keep farm revenue constant. This effect can be shown
as movement along MACC1 with higher levels of abatement (A1 to A2 in this hypothetical
example) from an increase in the mitigation price (P% to P2C).

In a linked model, the yield loss would also affect the market equilibrium for crops. Lower yields
would result in a shift of the commodity supply function (e.g., from S1 to S2). This supply shift
results in higher market prices overall (Pq to Pq), and lower equilibrium production levels
(iQ1 to Q2).

As these two mechanisms work together, the higher market prices for crops are passed back
to the MACC model on the right. That is, higher market prices raise the marginal costs of
abatement by increasing the opportunity costs of forgoing production. In this example, this
price change results in a shift in the MACC (MACC1 to MACC1) to reflect higher opportunity
costs of foregone commodity production, thus lowering total abatement from
A2 to A3, in a combined model, these processes iterate until convergence is achieved.

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While this simple conceptual diagram illustrates the market potential of a particular strategy
by reflecting market opportunity costs, PE models can reflect market opportunity costs for
multiple abatement sources and associated commodity markets simultaneously. That is, PE
models can also quantify the competitive market potential of a particular abatement strategy,
which acknowledges resource competition across sectors (e.g., finite land resources) and
competition across different abatement strategies under a given set of market conditions and
mitigation incentives.

Returning to the nitrogen fertilizer management example, consider an alternative situation
where incentives are available for a second abatement strategy: afforestation. In this
case, afforestation incentives could increase the competition for land resources, because
as mitigation prices may induce tree planting on agricultural lands. This shift in land use
pressures commodity supply (again shown as a leftward shift of the crop supply function in
the left panel) and further raises the opportunity costs of reducing yields from alternative
nitrogen management strategies (right panel). Thus, the competitive market potential
of nitrogen management change as a mitigation strategy in conjunction with changes in
afforestation could be lower than the market potential of nitrogen management considered
in isolation (see Ohrel, 2019, for a more comprehensive discussion of market vs. competitive
market mitigation potential).

By reflecting commodity market dynamics and resource competition, PE models provide
a more comprehensive estimate of mitigation potential. Further, unlike techno-economic
analyses, these models offer flexibility in simulating different policy or market frameworks
and associated performance metrics. For example, PE frameworks can simulate outcomes
of different policy or pricing designs, such as those that identify specific mitigation activities
or those that look at the effects of regional programs targeting AFOLU strategies, as well as
associated indirect consequences (e.g., leakage, as discussed in Box 4) (Fingerman et al.,
2019; Latta et al., 2011).

Figure Bl: Conceptual illustration of market opportunity costs for a hypothetical commodity
market and MACC for an abatement strategy that generates a loss in yield or total production

The panel on the left shows a hypothetical
commodity market with demand D and initial
supply S. The panel on the right shows the MACC
for an abatement strategy where A is abatement
and P the marginal cost of A abatement (e.g., P£
represents the marginal costs of abating up to
A1). In this simple conceptual example, the market
opportunity cost is the direct feedback between the
market price change associated with a change in
the mitigation quantity and the marginal costs of
abatement.

MACC2

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1.4.4 Harmonization

Key underlying factors—such as future macroeconomic
variables and underlying biophysical data like U.S. forest
representation—are harmonized across the models
to mitigate the degree of variability in projected GHG
outcomes stemming from differences in these influential
variables (see Wade et al., 2022). The goal of this analysis
was not to exactly align the baseline projections across
models but instead to harmonize specific data inputs and
future conditions to a reasonable extent and then explore
changes in emissions between the baselines and various
counterfactual price scenarios. This limited harmonization
approach is recognized and regularly applied in the literature
as well as in numerous U.S. government official reports and
submissions to, for example, the UN Framework Convention
on Climate Change (U.S. Department of State, 2014, 2016,
2021, 2022).

The models are run in parallel, not in a linked or interactive
manner (where outputs from one or more models are fed
into another model). The conceptual framework of the
applied approach here progressively zooms in on different
topics across the models that highlight the strengths
of each model—starting with a global model capturing
global biophysical and economic interactions, followed
by a domestic model offering detailed results on the
U.S. agriculture and forest sector land use and market
interactions, and then a global forestry model focusing on
the more specific U.S. forestry and forest market dynamics
and how they relate with global markets. Linking these tools
in an iterative or linked fashion could offer useful insights
and thus presents an opportunity for future research.

Part of this analysis's objective was to study the
independent projected outcomes of the individual models
selected. All three tools include a detailed representation of
U.S. forest and agricultural lands and associated GHG fluxes.
Although the GHG gases and GHG mitigation activities are
not the same across the models, the intent is to evaluate
the extent to which the outcomes across the models as
they are generally applied align or differ and to understand
why. In addition to offering insights into cost-effective GHG
mitigation opportunities available in the United States, this

aspect of the evaluation—how tools can approach mitigation
assessment differently and how that affects outcomes—can
also be useful to policymakers as they look to different types
of modeling tools to evaluate different policy designs to
address climate change.

1.4.5 Interpretation of Results

The three economic models projected the future of the
land sector in terms of land cover, management, carbon
stocks, and GHG emissions under specific ecological,
socioeconomic and policy assumptions that represent
the main drivers of land demand (e.g., GDP) and land
availability and productivity (e.g., regional supplies of land-
based commodities). The results are indeed estimates
of potential outcomes under specific assumptions about
future socioeconomic, environmental, technology, and policy
conditions. They are intended not to serve as predictions of
the future but rather to offer insights into what might occur
given a certain set of conditions. Although these types of
models are simplifications or abstractions of reality, they
provide valuable insights to policymakers designing and
implementing policies that affect forestry and land use
about the potential directionality and magnitude of policy
outcomes given certain conditions, assumptions, and
constraints while acknowledging related uncertainties.

In the report, results are presented in different forms:

• Absolute values such as GHG emissions (in Mt C02e),
land area (in million acres), timber harvest (in million
metric tons), and crop and livestock production (in
million tons).

• Average annual change from the baseline scenario. In
the report, land mitigation potential and abatement
potential are interchangeable terms used to describe
the change or "delta" in emissions between the baseline
scenario without GHG price scenarios and the GHG
price scenarios. Other key results such as changes in
forest area, pastureland, and cropland from the baseline
scenario are presented in the report.

• Relative change from a base year. Projected changes in
GHG emissions aligned to a specific year.

• Distribution of mitigation activities by regions and by
sectors under specific GHG price scenarios.

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• In addition, MACCs are calculated by combining the
projected abatement with the GHG price driving that level
of abatement in each reference year across GHG price
scenarios. Annual observations are interpolated using
a polynomial function that represents the curve. MACCs
for subsectors and for each GHG are also presented in
the report and they represent the potential abatement
achieved by specific activities under each GHG price
scenario.

The results from each model should be viewed as
complementary to one another as they can provide
different perspectives on outcomes generated using
common scenarios. Each set of results provides a valuable
source of information on GHG abatement trends that
can inform detailed policy or research applications. This
complementarity is important, for example, in cases
where end users are developing regionally focused climate
strategies or investment decisions, which may require
spatially disaggregated results from the domestic model;
or in the case of practitioners in the global climate finance
community, who may need results that capture trade
impacts from the global models.

Finally, the results presented in the report can offer useful
insights to different stakeholders. The results can, for
example, help identify opportunities for landowners to
participate in offset markets or other conservation initiatives
to boost rural economic development and save money by
reducing fertilizer applications or improving soil health. The
report also provides further guidance on the interpretation
of results from each model and suggests key considerations
for determining the most appropriate set or range of
mitigation results for a given policy or research application.

1.5 Report Organization

The remainder of this report is structured as follows:

• Chapter 2: Methods and Scenario Design introduces
the models and details the scenario design used in this
analysis.

• Chapter 3: Baseline and Mitigation Scenario Results

presents information on key baseline trends, reviews
mitigation cost estimates and abatement portfolios at
the national and subnational levels in the United States
and overtime, and provides the multi-model comparison
of mitigation outputs.

• Chapter 4: Discussion and Future Research offers key
takeaways from this analysis, caveats and limitations,
results from sensitivity analyses, and general guidance
on the practical use of mitigation estimates from this
report. It also compares results from this analysis to
those from previous studies, including the 2005 EPA
report, which also presented estimates of mitigation
potential in U.S. forest and agriculture sectors. This
chapter also discusses limitations of the report and
future research needs.

• Supplemental appendices offer more detailed
information on the three specific modeling frameworks
applied and models' outputs.

Twelve boxes provide either stand-alone analyses (FOCUS)

or sensitivity tests of models' assumptions and the results

(SENSITIVITY).

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2 Methods and Scenario
Design

This chapter describes the models, methods, and scenarios
used in this technical report to estimate GHG mitigation
potential and costs in the U.S. forest and agriculture sector.

The first section describes the models used in this report
and provides key details on those models. The next section
discusses how certain elements have been harmonized
between the selected models. Lastly, the chapter discusses
the scenarios applied in the analysis.

2.1 Background Information
on Models Applied and
Modeling Approach

This report uses three well-recognized
land use models that include detailed
economic and biophysical sectoral
coverage: FASOMGHG, GLOBIOM, and
GTM.

The report uses three models—FASOMGHG, GTM, and
GLOBIOM—that include detailed economic and biophysical
sectoral coverage, detailed spatial data, and temporal range.
Each of these models has been extensively applied in the
literature for a variety of objectives, including projecting
land management, market, and environmental changes

across different policy, environmental, and macroeconomic
scenarios. They have also been used in various official
government modeling applications. For example, FASOMGHG
and GTM were used to evaluate land-based mitigation
potential in legislative policy proposals (EPA, 2009) and
LULUCF projections in several U.S. government reports (e.g.,
U.S. Department of State, 2022; U.S. Department of State &
the U.S. Executive Office of the President, 2021). GLOBIOM
has been used by the European Commission to build the EU
Reference Scenario 2020, the policy scenarios for delivering
the European Green Deal, the EU Climate Target Plan impact
assessment, and the in-depth analysis of the EU Long-Term
Strategy (European Commission, 2018, 2020; European
Commission Directorate-General for Energy, n.d.).

This section discusses the models considered in the report:
FASOMGHG, GLOBIOM, and GTM. A summary of each of
these models is provided, including their history, sectoral
representation, spatial coverage and resolution, temporal
representation, and GHG emissions representation. Links
to detailed documentation and discussion of previous
applications for each of the models are provided. As
previous versions of each model have been thoroughly
documented through past technical reports and academic
manuscripts, model characteristics discussed here are
limited to recent model updates and attributes most
pertinent to this analysis. Section 2.5, provides an overview

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The Pacific coast of Califc
with farmland close to th<
cities of Salinas and Mon

Each of the three land use models used in the
report has been extensively applied in the literature
for a variety of objectives, including projecting
land management, market, and environmental
changes across different policy, environmental, and
macroeconomic scenarios. They have also been used
in various official government modeling applications.

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of similarities and differences across models, including
more details on the models' attributes, and highlights
model elements that were harmonized for this analysis.
Section 2.6 provides information on how the models were
aligned under baseline scenarios. Section 2.7 presents
background on the mitigation scenarios implemented in
each model. Finally, Section 2.8 introduces stand-alone
analyses that were implemented in each model to further
take advantage of each model's unique capabilities.

2.2 Forest and Agricultural
Sector Optimization Model
with Greenhouse Gases
(FASOMGHG)

FASOMGHG is a dynamic non-linear optimization model of
the forestry and agriculture sectors in the United States,
developed initially by Dr. Bruce McCarl at Texas A&M
University, Dr. Darius Adams at Oregon State University,
and Dr. Ralph Aligatthe U.S. Forest Service (USFS) (Adams
et al., 2005; Adams et al., 1996; Beach et al., 2010) in
collaboration with researchers at RTI International, EPA,
North Carolina State University, University of Idaho, and
Texas A&M for the version used in this study.

Since the use of FASOMGHG in the 2005 EPA report,
the model has been substantially updated to reflect new
data and technologies as well as improve applicability
to emerging environmental and policy issues. In-depth
documentation reports of model updates include Adams

et al. (2005) and Beach et al. (2010), with additional
supporting documentation on intermediate updates
presented in Jones et al. (2019) and Wade et al. (2022).

Since 2010, FASOMGHG has undergone extensive
development to update its forest sector representation.
The FASOMGHG forestry side is based on spatially and
temporally aggregated inputs from the spatially detailed
Land Use and Resource Allocation (LURA) modeling system,
described in Latta etal. (2018). The LURA framework
includes a spatially explicit supply-side representation of
the U.S. forest resource system based on 2015 USFS Forest
Inventory and Analysis (FIA) National Program inventory
data and new empirically estimated yield growth curves
that vary by region, site class, forest type, ownership, and
management intensity. Plot-level information from the LURA
model is aggregated to FASOMGHG regions to maintain a
consistent inventory and age-class distribution of different
forest types by site class (Latta et al., 2018). Additional
information on the LURA-to-FASOMGHG development
process, plus other relevant updates to agricultural sector
data and inclusion of new mitigation technologies, can be
found in Jones et al. (2019).

Moreover, Wade et al. (2022) updated FASOMGHG to
include alternative baseline assumptions for each of
the five Shared Socioeconomic Pathways (SSP) (Riahi
et al., 2017). This update included revised parameters
for urbanization expansion, using projected urbanization
rates from each SSP coupled with historical rates of land
conversion to development based on the 2015 National
Resources Inventory (U.S. Department of Agriculture
Natural Resources Conservation Service, 2017). Exogenous
demands for agricultural products in FASOMGHG were
adjusted according to the different levels of GDP per capita
and dietary assumptions in each SSP. Finally, differences
across SSPs in the forest sector were reflected as changes
in domestic demand for harvested wood products (HWP),
shifts in biomass for energy demand based on Annual
Energy Outlook (AEO) 2022 projections (EIA, 2022), and
changes to forest product exports based on Daigneault and
Favero (2021).

FASOMGHG is a U.S.-only,
intertemporal optimization economic
model of the agricultural and forestry
sectors.

2.2.1 History and Model Applications

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In terms of policy applications, FASOMGHG has been used
to assess adaptation to environmental change by Beach et
al. (2015) and Zhang etal. (2014), GHG mitigation potential
of and associated economic impacts of mitigation and
renewable energy policies (Alig et al., 2010; Baker et al.,
2010; Latta et al., 2013; Ogle et al.. 2016), and biofuel
policy analysis with global systems models (Mosriier et al.,
2013). Different versions of the model have been used to
support federal policy and research efforts, including for
the Renewable Fuels Standard (RFS2) Regulatory Impact
Analysis (Beach and McCarl, 2010) and illustrative case
studies published in EPA's draft biogenic C02 assessment
framework report (EPA, 2014). The latter relied on improved
model representation of biopower generation and costs from
alternative agriculture and forestry feedstocks, including
regional boiler capacity constraints and co-firing options
from Latta et al. (2013). Moreover, Galik et al. (2019)
uses the version of the model presented in Latta et al.
(2013) to evaluate a range of federal incentives designed
to reduce emissions from agriculture and forestry. Cai et al.
(2018) incorporate new supply curves for afforestation and
compare mitigation outcomes across cost specifications.
Jones et al. (2019) present emissions projections and policy

analysis using the updated version of the model with a
redesigned forest sector, and Wade et al. (2022) evaluate
mitigation potential across a range of socioeconomic
scenarios.

2.2.2 Economic and Biophysical Features

This model is a detailed dynamic non-linear intertemporal
optimization model of the U.S. forestry and agriculture
sectors with representations of regional production
processes, land management potential, and commodity
market feedbacks, along with spatial heterogeneity in
forestry and agriculture activity productivity and production
costs.

FASOMGHG uses 63 subregions for agriculture, 11 market
regions for forestry and bioenergy (Appendix A, Figure A-l),
and a limited representation of bilateral trade with specific
regions outside of the United States. The dynamic nature
of the FASOMGHG model yields multi-period equilibrium on
a 5-year time-step basis over a period of 85 years in this
study,4 resulting in dynamic simulation of prices, production,
consumption, management, and GHG implications in

A large industrial crane at a logging mill lumberyard along the Yaquina River near the Oregon coast.

4 FASOMGHG was run for a period between 2015 and 2100, while results included in this report are for the period 2020-2070 to limit the potential impacts
of terminal conditions.

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the forest and agriculture sectors. Historical production,
consumption, and prices for both agricultural and forestry
commodities are used to calibrate the initial year (2015) to
observed levels. Additionally, land areas are aligned with the
National Resources Inventory and USFS FIA (Jones et al.,
2019).

The model maximizes the total present value of consumer
and producer surplus (net welfare) in the land sector
(forestry and agriculture) over dynamic intervals. The model
solves all time periods simultaneously via intertemporal
optimization. This model function allows actors within the
model (farmers and timberland managers) to have what is
called "perfect foresight" on expected future environmental,
economic, and policy conditions. Intertemporal optimization
is an important model attribute, particularly for the forestry
sector, because forestry investments are made today
with expected returns in the future, often decades out.
Investments in the forest resource base are an attempt
to neither overinvest nor underinvest based on the
current period's expectations of the future. Furthermore,
intertemporal dynamics play a role in agricultural
management since the two sectors are linked via
competition for land resources and soil carbon management
in agriculture follows a dynamic process.

2.2.3 Land Sector in the Model

In FASOMGHG, there are six major land cover types:
cropland, cropland pasture, pasture, forest, lands enrolled
in the Conservation Reserve Program (CRP), and developed
land.

FASOMGHG represents both privately managed and public
timberlands, though public harvest levels are held fixed
and exogenous, as management decisions regarding public
lands are less driven by and responsive to markets than
those regarding private lands. Forestry in FASOMGHG is
represented using FIA plot-level data, aggregated to each
of the 11 regions included in the model. Characteristics
of forests included in the FIA data, such as age class, cite
class, forest type, and management level, are all retained to
accurately represent the domestic forest base.

Agricultural land uses represented in the model include
cropland (supports the production of traditional crops and
dedicated biofuel crops), pasture (medium-productivity
grassland systems that are passively managed), cropland
pasture (managed land suitable for crop production but
currently being used as pasture land; for this reason,
cropland pasture and pasture are combined when
presenting pasture results), and rangeland (typically lower-
productivity grassland and rangeland in the Western United
States).

The model allocates land between alternative uses
(cropland, forestry, pasture, and cropland pasture) to
produce primary and secondary agricultural commodities
and forest products, and to meet biomass demand, when
applicable. The model also includes a bioenergy sector
with first- and second-generation biofuels and biomass
power plants. Bioenergy products include ethanol, cellulosic
ethanol, biodiesel, and bioelectricity from agricultural
and forestry feedstocks. More details can be found in
Section 2.5.

2.2.4 Greenhouse Gas Emissions

Comprehensive GHG accounting for AFOLU is implemented
in the model, including carbon stored in above- and
belowground biomass and other pools for forests, C02
emissions from energy-intensive input use in agriculture,
carbon fluxes related to soil management, and non-C02
emissions from crop and livestock production systems, as
detailed below.

2.2.5 Land-based Mitigation Strategies

Mitigation incentives in FASOMGHG are implemented via
a symmetric price on GHG emissions. The model responds
to the price by abating emissions and increasing carbon
sequestration through different activities across the country,
up to the point in which the cost of reducing the additional
ton of emission is equal to the GHG price. This approach is
further described in Baker et al. (2010), Alig et al. (2010),
and Ogle et al. (2016).

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2.3 Global Biosphere
Management Model
(GIOBIOM)

GLOBIOM is a global, recursive
dynamic economic model of the
agricultural and forestry sectors.

2.3.1 History and Model Applications

The GLOBIOM mode! is a spatially disaggregated,
recursive dynamic, PE model developed and applied by
the International Institute for Applied Systems Analysis
(NASA). The model was developed in the late 2000s based
on FASGMGHG to assess the impact of climate change
mitigation policies of biofuels and other land-based efforts
at the global level. There are several modei versions of
GLOBIOM available for different applications and contexts.
More detailed descriptions of the GLOBIOM model structure
and key parameters, including additional references to
recent publications, are provided in Havlik et al. (2011),
Valin et al. (2014), Havlik et al. (2014), Baker et al. (2018),

A farm in the hillsides of the Green Mountains, Vermont.

and Janssens et al. (2020). A sample of GLOBIOM code is
available to the public, and an open-source version is under
development.5

The GLOBIOM modeling framework has been applied
extensively to evaluate GHG mitigation potential from the
land use sectors. Recent mitigation-focused analyses
with the model include Frank et al. (2018; 2021), which
looked at agricultural non-00., and AFOLU wide GHG
mitigation potentials; Hasegawa etal. (2018) and Fujimori
etal. (2022), which each looked at food security under
climate change mitigation scenarios; Lauri et al. (2019)
and Daigneault et al. (2022), which analyzed the role of
the forest sector under mitigation policies; and Frank et al.
(2019) and Wu et al. (2023), which looked at the impact
of diet changes on GHG emissions. The model has also
been incorporated into multi-model assessments of climate
stabilization futures, including by Riahi et al. (2017), to
assess the impacts to the global land use sector under
alternative socioeconomic futures.

2.3.2 Economic and Biophysical Features

GLOBIOM is a detailed PE model that integrates the
agricultural, bioenergy, and forestry sectors with the aim of
simulating commodity trade flow patterns between spatially
separated supply and demand markets (Havlik et al., 2011).
GLOBIOM represents the world partitioned into 37 economic
regions (Appendix A, Figure A-2), in which a representative
regional consumer optimizes their consumption, depending
on income, preferences, and product prices in a 10-year
time step framework. The model manages land in a
recursive dynamic fashion across simulation decades, with
each modeled time step being influenced by the previous
time-step's solution, but without considering future price
projections (in contrast to FASOMGHG and GTM). The model
solution reveals an optimal combination of measures and
land allocation across regions. In every period, GLOBIOM
finds market equilibrium that maximizes the sum of producer
and consumer surplus subject to resource, technological,
demand, and policy constraints. Producer surplus is defined
as the difference between market prices at a regional

6 See GLOBIOM, "Model Code," h»ns://iiasa.github.io/GI OBIOM/model
code.html.

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level and the product's supply curve. The supply curve
accounts for labor, land, capital, and other purchased input.
Consumer surplus is based on the level of consumption of
each market and is arrived at by integrating the difference
between the demand function of a good and its market
price. The model uses linear programming to solve, although
it also contains some non-linear functions that have been
linearized usingstepwise approximation (NASA, 2023). The
first three periods (2000, 2010, and 2020) are used as
a calibration step where parameters such as production,
land use, and emissions are aligned at the regional level
based on global datasetssuch as the Food and Agriculture
Organization (FAO) Global Forest Resources Assessment,
and country-level reporting to the United Nations Framework
Convention on Climate Change (UNFCCC).

2.3.3 Land Sector in the Model

There are nine land cover types in GLOBIOM, and six of
these are modeled dynamically: cropland, grassland, short
rotation plantations, managed forests, unmanaged forests,
and other natural vegetation land. The other three land cover
categories are represented in the model but kept constant;
they include other agricultural land, wetlands, and not
relevant (ice, waterbodies, etc.).

The detailed grid cell-level spatial coverage for GLOBIOM
includes more than 10,000 spatial units worldwide. The
model represents 18 crops globally using FAOSTAT, FAO's
statistical database of food and agriculture data, as the
primary database for crop statistics. Crop modeling includes
differentiation in management systems and multi-cropping.

GLOBIOM also features highly detailed livestock
representation, based on FAOSTAT data. The model
includes seven animal products, which can be produced
in differentiated production systems. For ruminants, there
are eight production system possibilities, including grazing
systems in different climatic locations such as arid and
humid, mixed crop-livestock systems, and others. Pigs and
poultry are classified under either small-holder or industrial
systems. Based on the production system, animal species,
and region, GLOBIOM differentiates diets, yields, and GHG

emissions. For instance, dairy and meat herds are modeled
separately, and their diets are differentiated. Poultry in
industrial systems is split into laying hens and broilers,
again with different dietary needs. For ruminants, livestock
production is modeled spatially in GLOBIOM's gridded
cell structure. At the cell level, animal yields for bovine
and small ruminants are estimated using the GLOBIOM
module, RUMINANT. RUMINANT calculates a production
yield that matches plausible feed ratios and checks this
against regional-level data of livestock production. Feed for
animals is also differentiated in the RUMINANT model and
can be composed of feed crops, grass, stover, and other
feed. Monogastric productivities are calculated based on
FAOSTAT and assumptions of potential productivities of both
small holders and industrial livestock systems. Livestock
production is allowed to intensify or extensify, thereby
altering the amount of feed or grass consumed.6 Because
for ruminants this is modeled spatially, any changes in
grassland consumed due to changes in production systems,
animal type, yield, and GHGs are captured in the spatially
relevant areas. Each final livestock product is considered a
homogeneous good with its own specific market (apart from
bovine and small ruminant milk).

Forestry in GLOBIOM is captured through the Global
Forest Model (G4M) module (Gusti, 2010; Kindermann
etal., 2013) and includes detailed representation of the
sector and its supply chain and a differentiation between
managed and unmanaged forest areas (Shchepashchenko
and Kindermann, 2023). GLOBIOM includes bilateral trade
for agricultural and wood products. These products are
assumed to be homogeneous and traded based on the least
expensive production costs, though transportation costs and
tariffs are also included.

The model also includes a bioenergy sector with first- and
second-generation biofuels and biomass power plants.
Perennial crops and short-rotation coppice are included as
inputs to the bioenergy sector. GLOBIOM represents biofuel
coproducts including distillers grains and oilseed meals.

6 Intensifying involves increasing livestock output without expanding the
area of pastureland by grazing more livestock per area of land, increasing
feed relative to grazing, or using feedlots. Extensifying involves expanding
pasture area in order to increase livestock production.

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These coproducts can be traded either in their processed or
whole forms. Coproducts that can be used for livestock feed
are incorporated into the livestock RUMINANT module and
can substitute other forms of feed depending on protein and
metabolizable energy content (Valin, Sands, et al., 2014).

2.3.4 Greenhouse Gas Emissions

GHG emission coverage includes 12 sources of emissions
that cover crop cultivation, livestock, above- and
belowground biomass, soil organic carbon, and peatland.
Although GLOBIOM does not track terrestrial carbon stocks
dynamically, carbon fluxes from LUC are calculated with
equations, following IPCC guidelines, that estimate changes
over time and allocate the average annual emissions to the
period in which the LUC occurs.

2.3.5 Land-based Mitigation Strategies

Comprehensive GHG accounting for AFOLU is implemented
in the model, and mitigation incentives are implemented via
price mechanisms. For modeling GHG mitigation potentials
from the full AFOLU sector, GLOBIOM is coupled with the
G4M model to explicitly simulate forest management,
afforestation (including reforestation) and deforestation
activities, and GHG implications. Specifically, mitigation
incentives in GLOBIOM are introduced as a direct payment
on land-related emissions and carbon sequestration
activities.

For forestry, G4M models the reduction of deforestation
area, increase of afforestation area, change of rotation
length of existing managed forests in different locations,
change of the ratio of thinning versus final fellings, change of
harvest intensity (amount of biomass extracted in thinning
and final felling activity), and change of harvest locations
(Gusti, 2010). The introduction of a GHG price gives an
additional value to the forest through the carbon stored
and accumulated in it. In general, an introduction of a price
incentive tends to decrease deforestation and increase
afforestation and reforestation. However, this might not
happen at the same intensity across all locations and all
activities, and market interactions can result in negative
feedbacks. For example, a reduction in deforestation
increases land scarcity and might therefore decrease
afforestation relative to the baseline scenario. The existing
forest under a GHG price is managed with longer rotations
of productive forests and shifting harvest to less productive
forest. Where possible, the model increases the area of
forests used for wood production, meaning a relatively larger
area is managed relatively less intensively. This modeling
approach also implies changes of the thinning versus
final felling ratio toward more thinning (which affects the
carbon balance less than final fellings). Forest management
activities can influence emissions from deforestation by
increasing or decreasing the average biomass in forests
prior to their being deforested. They also influence rates of
biomass accumulation in newly planted forests, depending
on whether these forests are used for production or not.

For agriculture, GLOBIOM represents a set of structural
and technological non-C02 mitigation options as well as
changes in consumption levels in response to a mitigation
policy. Structural options are represented through
different livestock and crop production systems that vary
in GHG intensity. The model can choose to move to more
GHG-efficient management practices on site, reallocate
production to more productive areas within a region, or
reallocate through international trade across regions. In
addition, technological mitigation options such as anaerobic
digesters, animal feed supplements, and others are
represented based on the EPA mitigation option database
(Beach, Creason, et al., 2015).

Land use in GLOBIOM allows for both intensification and
extensification. When land is converted, this is endogenously
determined in the model based on conversion costs, and
the profitability of primary products, coproducts, and final
products. Costs increase as the area converted expands.
Additionally, there are biophysical land suitability and
production potential restrictions. LUC is determined at the
grid cell level. There is a land transition matrix that sets
the options for land conversion for each cell and is based
on land conversion patterns specific to that region and
conversion costs depending on the type of land converted.

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2.4 Global Timber Model
(GTM)

GTM is a global, intertemporal
optimization economic model of the
forest sector.

2.4.1 History and Model Applications

GTM is an intertemporal economic optimization model of
the global forest sector, based on the dynamic approach
described in Sedjo and Lyon (1990), Binkley et al. (1987),
and Sohngen and Sedjo (1998).

GTM is a well-known global forest sector model that has
been applied to a variety of different applications in
numerous peer-reviewed publications and many scenarios
reviewed by the IPCC. The GTM framework has been applied
extensively to evaluate GHG mitigation potential from forests.
GTM has been used to assess climate change impacts in
the forest sector (see Sohngen et al., 1999, 2001; Tian
et al., 2016); forest sector carbon sequestration potential
under climate change mitigation incentives (Baker et al.,
2019; Kindermann et al., 2008; Sohngen and Mendelsohn,
2003, 2007); GHG emissions under alternative market and
environmental change scenarios (Tian et al., 2018); forest
bioenergy policy analysis (Daigneault et al.. 2012; Kim et al.,
2017); forest carbon sequestration and woody bioenergy in

Tug boat moving logs in Juno, Alaska.

comprehensive economy-wide analysis of climate change
mitigation and stabilization scenarios via links with an
1AM model (Favero and Mendelsohn, 2014; Favero et al.,
2017; The White House, 2016); and the effects of forest-
based mitigation activities on surface albedo (Favero,
Sohngen, et al., 2018). Baker et al. (2017) conducted a
U.S.-focused assessment of GHG mitigation potential, while
Baker et al. (2018) and Favero et al. (2020) addressed
policy complementarity between carbon sequestration
and bioenergy policies and Austin et al. (2020) quantified
economic costs of carbon sequestration.

2.4.2 Economic and Biophysical Features

GTM generates projections of future timber resource
and market conditions, and related carbon implications,
using detailed biophysical and economic forestry data for
different countries and regions, including the United States.
Specifically, GTM is a dynamic PE model that maximizes total
welfare in timber markets over time across approximately
350 world timber supply regions by managing forest stand
ages, compositions, management intensity, and acreage
given production and land rental costs over 200 years.

Land classes in the model were linked to vegetation types
represented in biophysical models such as LPX-Bern (Favero,
Mendelsohn, et al., 2018; Favero et al., 2021) and MC2
(Kim et al., 2017; Tian et al., 2016). Though the version of
GTM used for this report does not include ciimate change
impacts that could vary under different GHG emissions
pathways, the model does incorporate historical climate
change, as the yield functions for the land classes in the
mode! are consistent with current climatic conditions.
Moreover, the model incorporates overall land limits on
areas derived from the ecological models, such that only
land that is capable of naturally supporting forests can be
used for timber production. Finally, the model is calibrated
to regional forest inventory to the extent possible, and
recent analysis indicates that future market and land use
projections are robust to parametric uncertainty related to
forest growth and land supply parameters (Sohngen et al.,
2019). Another GTM paper provides a historical calibration
exercise with the model performing a simulation of a
historical time to illustrate the important contributions of
management to the evolution of terrestrial carbon stocks
historically (Mendelsohn & Sohngen, 2019).

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GTM provides a long-term view of forest resource use and
product supply under assumed future market, policy, and
environmental conditions. The model optimizes the net
market surplus of the timber sector by selecting the optimal
levels of timber harvests, timber investments, and land use
over time. When forests are harvested, forest owners have
the option to allow land to regenerate naturally or convert to
a more intensively managed/planted system depending on
the future market expectation (e.g., higher timber prices).
Like FASOMGHG, the model relies on forward-looking
behavior and solves all time periods at the same time
via intertemporal optimization. This dynamic optimization
approach means that landowners incorporate future
market expectations into land use and forest management
decisions today to reflect future expectations (i.e., decisions
anticipate future potential net returns). The model is
global in scale, with 16 individual regions represented
(including the United States) (Appendix A, Figure A-3). GTM
has more than 150 disaggregated U.S. forest types and
over 200 forests and management types globally. Recent
developments have added heterogeneous forest product
demand to explicitly represent pulpwood and sawtimber
demand. The model has a 150-to 200-year time horizon
to account for the long time intervals between harvest and
regeneration in many of the world's forests.

2.4.3 Land Sector in the Model

Like FASOMGHG, GTM maximizes the net present value
of consumers' and producers' surplus (net welfare) in the
forestry sector. Consumers' surplus for timber markets is
derived from inverse timber demand functions calculated
from timber prices and consumption quantities that are
endogenous to the model solution. Producers' surplus is
composed of the gross returns to timber harvests minus
the costs of managing and holding timberland. The costs of
managing timberland include the costs of replanting timber,
the costs of harvesting, accessing, and transporting timber,
and the opportunity cost of maintaining land in forests
rather than switching to agriculture for crop cultivation and
livestock grazing.

The model solution determines how much to harvest in each
age class and period, how many hectares to regenerate

Aerial photograph of logging clear cuts in the forest
near Yachats in Lincoln County, Oregon.

in each forest type in each period, how intensively to
regenerate the hectares when they are planted, and how
many new hectares of high-value plantations to establish.
As a dynamic intertemporal economic optimization model,
GTM relies on forward-looking behavior and solves all time
periods at the same time.

GTM is a detailed model of the global forest sector that does
not explicitly model agricultural production and commodity
demand systems, but it considers the competition of
forestland with farmland using a rental supply function
for land. In the model, the rental functions are shifted
exogenously over time to simulate rising demand for land
to be used in agriculture, resulting in land use change.
The parameters used to model land rents have been
calibrated to past land use change and reflect assumptions
about future demand for agricultural products by world
regions (Austin etal. 2020). The rental supply functions
are restricted to agricultural land that is naturally suitable
for forests, and an assumption is made that the least
productive crop- and pastureland will be converted first
and that rental rates increase as more land is converted
and thus becomes scarcer. That is, as more farmland is
devoted to forestland, the opportunity costs of converting
an additional acre of land into forests rise to reflect the

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underlying inelastic price of food. The total amount of
forestland in GTM is therefore endogenous and driven by
the demand for timber products, GHG price incentives,
opportunity cost of land, and management costs.

2.4.4 Greenhouse Gas Emissions

In GTM, forest carbon stock is measured as the sum of
carbon stock in four different carbon pools: above, soil,
market, and slash carbon.

Aboveground carbon accounts for the carbon in all tree
components, including stem, stump, branches, bark, seeds,
and foliage, as well as carbon in the forest understory and
the forest floor. Aboveground carbon in the GTM framework
does not include dead organic matter such as from slash,
which is contained in a separate pool. Market carbon pool
is the GTM classification for carbon stored in HWP under
assumed rates of product turnover in markets and resulting
oxidization and decay. GTM classification of market carbon
is consistent with the U.S. GHGI definition of HWP pools that
affect "Changes in forest carbon stocks."

Soil carbon includes carbon stored in mineral and organic
soils (including peat). GTM models changes in soil carbon
storage from forest LUC but does not capture nuanced soil
carbon dynamics associated with forest operations. Finally,
slash carbon measures carbon stored in slash that remains
on site, resulting from timber harvesting operations.

2.4.5 Land-based Mitigation Strategies

Mitigation policies are included in GTM as carbon rental
payments and direct subsidies to carbon sequestration.
Specifically, forest owners receive carbon payments
(subsidies equal to the GHG price) for the carbon
permanently stored in wood products and are compensated
by annual rent7 for providing annual carbon sequestration
in forests. The change in soil carbon when land switches
between forests and agriculture is also valued at the
GHG price. Mitigation options available in the model
include lengthening timber harvest rotation, increasing
forest management intensity, avoiding forest conversion,
converting agricultural lands to forest, and increasing carbon
stored in wood products.

7 In GTM, carbon rent R,: at time t is related to carbon price P,: as follows: R,:
(t)= Pr (t)-Pr (t+l)/(l+r), where r is the discount rate equal to 5%.

2.5 Similarities and
Differences in Models'
Attributes

Each model has specific
characteristics that make it a
comprehensive and valuable tool
for this study, and their individual
frameworks complement each other.

The three models selected for this assessment include
specific economic and environmental attributes that make
them uniquely positioned to provide robust projections of
potential future baseline and GHG mitigation quantities
and associated costs under alternative policy scenarios.

Each model has specific characteristics that make it a
suitable tool for this study, and their individual frameworks
complement each other. Specifically, GLOBIOM captures
global interactions within the land sector, FASOMGHG
provides a detailed description of the U.S. agriculture and
forest sector market interactions, and GTM produces a
very specific representation of the U.S. and global forestry
dynamics. As this approach employs different frameworks
and scopes, it allows for a broader spectrum of analysis and
investigation of results from different perspectives. Some
specific attributes are similar across models, while others
diverge significantly, as described below and shown in
Figure 2-1.

First, all three models are economic models. The models
are price endogenous, meaning output prices are part of
the model solution and are impacted by the initial stock of
resources, scenario specifications, and the competition for
resources both within and across sectors. Each model has a
primary objective to maximize economic surplus by choosing
efficient levels of supply and consumption given other
scenario-specific inputs.

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FIGURE 2-1

Primary model attributes (similarities and differences)

GLOBIOAf

Sector Cover-
age: Forestry
(175 product types),
bioenergy (forest

• Time Horizon:

2015-2100, 5-yr
time steps

Geographic Coverage:

U.S. (11 regions)

International Trade:

Region-specific import and
export demand functions for
major crops; Exogenous forest
product import/export growth

Where the circles overlap, the attributes are similar, and they are different where the circles do not overlap.

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Second, the models include a detailed biophysical
representation of the land sector. Specifically, they include
a representation of spatial heterogeneity in biophysical
and economic conditions to capture important variations
in crops, species, production practices, natural resource
availability, infrastructure and related costs, and markets.
Moreover, forestry and agricultural production processes
include spatially disaggregated information on crop yields
and forest productivity by type and management regime.
Each model relies on recent biophysical data and trends to
the extent possible to inform future projections by including
and/or being calibrated to recent publicly available forestry,
agricultural, and land use statistics. All three models rely
on officially published forest inventory data: FASOMGHG

and GTM utilize the USFS FIA for U.S. forest characteristics
such as initial forest biomass and land area, while GLOBIOM
uses data published by the United Nations (UN) FAO,
which is informed by USFS data. Despite this alignment,
discrepancies do exist due to multiple differences across
models, such as the inclusion of Alaska8 in GLOBIOM and
GTM, but not FASOMGHG, and different land categories
considered in each model (Gidden et a I., 2023)9
(Figure 2-2).

! In the U.S. GHGI (EPA, 2023), forest remaining forest in Alaska has been
estimated to range from a net sink of CO2 of 19 Mt C02e yr1 to a net
source of 111 Mt C02e yr1 from 1990 to 2021 compared to a national net
sink of 823-611 Mt C02e yr1 for the same pool.

' Note that discrepancy also exists between land models and National GHG
Inventories because national inventories incorporate a wider definition of
managed land (see Gidden et al. (2023) for more details).

FIGURE 2-2

U.S. land area categories included by model (2020)

GLOBIOM

FASOMGHG

4 24

GTM

Forest

Cropland

Pasture

CRP

Developed Land

Land categories in million acres for each model. The size of each donut represents total land area included per model. Land
categories do not include grassland and wetlands. FASOMGHG forest area does not include Alaska. Source: FASOMGHG and GTM
utilize the USFS FIA for U.S. forest characteristics such as initial forest biomass and land area, while GLOBIOM uses data published by
the UN FAO, which is informed by USFS data*

'Note: GLOBIOM uses country-level data published by FAO to be consistent across regions. The U.S. data submitted to FAO is from the USFS FIA.

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Third, GHG gases and land-based mitigation practices are
well detailed in each model as summarized in Figure 2-3.
The report identifies and discusses eight GHG categories
to both assess future baseline projections and future
mitigation potential in the land sector. FASOMGHG reports
emissions projections from all GHG categories listed in
Figure 2-3. In the non-C02 emission categories, FASOMGHG
only accounts for direct and indirect sources of N20
emissions from crop production directly related to fertilizer
use and does not include other soil N20 fluxes (including
from residue management, organic soil amendments, and
mineralization and asymbiotic fixation). In the C02 emission
categories, FASOMGHG includes on-farm C02 emissions
(from activities such as energy-related emissions from
groundwater pumping, commodity storage, and on-farm fuel
use) and off-farm C02 emissions from fertilizer production.
The model explicitly tracks U.S. soil carbon changes over
time and across sectors. GLOBIOM does not include
agricultural C02 emissions from fossil fuels, carbon stored
in HWP, or soil carbon emissions or removals on agricultural
land. Instead, it tracks changes in above- and belowground
biomass due to conversion of natural lands to agriculture,
which this report includes in the agricultural soils category
(see Box 2).

All the GHG projections in Chapter 3 will be reflected in C02e
using the global warming potential (GWP) rate of 25 for CH4
and 298 for N20 (IPCC, 2007).10The detailed representation
of gases and activities allows the direct estimation of
the abatement potential across technologies or land
management strategies, with associated costs. These
models have the ability to produce MACCs for the whole
sector or for only specific practices or gases.

Fourth, all models consider resource competition in terms
of land use competition between sectors—either directly
through LUC possibilities or indirectly through economic land
supply functions and parameters and land management

10 For consistency, all three models used the same GWPs established by
the IPCC Fourth Assessment Report (AR4) during the analysis phase of
this report's development, which took place from 2020 to 2023. Use of
AR4 GWPs was appropriate as it followed with reporting guidelines as
defined by UNFCCC at that time. Updated guidelines will require countries
including the United States to adopt the IPCC Fifth Assessment Report
(AR5) (2013) 100-year GWP values for national GHG inventory reporting
in 2024. More information is available at httnsV/www.ena.gov/svstem/
files/documents/?0??-04/us-ghg-inventorv-?0??-annex-6-additional-in-
formation.ndf.

change opportunities at the intensive margin—for example,
conversion to planted or otherwise intensively managed
forestry systems, and input intensity, irrigation, and crop/
livestock mix changes for agriculture.

Fifth, all three models well represent market dynamics
for specific sectors (agriculture and forestry) where the
demand for land-based commodities must be met by the
supply at any time. That is, each model includes exogenous
drivers (e.g., population and GDP) for timber demand, crops
demand, and livestock demand, and solves by selecting the
optimal allocation of land resources to supply each demand.
In this way, at any period, the land market (only forestry for
GTM and both agriculture and forestry for FASOMGHG and
GLOBIOM) is in equilibrium.

Finally, their economic structure allows the introduction of
policy scenarios using a similar methodology introducing a
common price signal on all GHG emissions. Furthermore,
each model's framework allows for resource management
to respond to market or policy signals at the intensive
and extensive margins. Extensive margin investment in
forestry and agriculture requires land use expansion (e.g.,
afforestation or expansion of crop production on other
land uses). In forestry, an intensive margin investment
could include thinning to enhance productivity, or planting
forests post-harvest instead of allowing forests to naturally
regenerate. In agriculture, intensive margin expansion could
include more input use intensity per unit area or changes
in regional crop mixes to more input-intensive systems.
FASOMGHG and GLOBIOM each have the flexibility to allow
for variable rates of fertilizer use, irrigation intensity, and
implementation of alternative cropping patterns. While
each model has some representation of the bioenergy
and/or biofuels market, the analysis in this report does
not incentivize additional use of bioenergy and/or biofuel
as part of the mitigation scenarios. Specifically, under the
mitigation scenarios developed for this study, bioenergy/
biofuel demand is not changed relative to the baseline
scenario to isolate only the effect of direct incentives on
land-based abatement activities.

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FIGURE 2-3

Greenhouse gas categories included in each model

Cropland C02

C02 emissions from
farm fuel usage for
irrigation, fertilizer, and
pesticide applications,
and drying of grains

Agricultural C02 Soils

C02 sequestration in
agricultural soils
(including both
cropland and
pastureland)

Cropland Non-COz

NzO and CH4 from the

burning of agricultural
residues, NzO from fertilizer
applications, and CH4 from
rice cultivation

Forest COz (existing forests)

C02 sequestration in existing forested

lands, including above-and below-
ground biomass, litter, and down- and
standing-dead trees through forest
growth and forest management activities
such as altered harvesting schedules,
tree planting, and thinning activities

Forest C02
Products

Carbon stored in
long-lived wood
products

Livestock Non-CO

CH4 emissions from

enteric fermentation,

and N20 and CH4

emissions from
livestock manure

Forest CO.

CO.

~2 (new forests)

^2 sequestration in newly
re/afforested lands, including above-
and below-ground biomass, litter, and
down- and standing-dead trees on
re/afforested lands

Forest COz Soils

C02 sequestration in forest soils in both
existing forest and afforested lands

FASOMGHG includes all the eight categories, while only two GHG categories are included in the three models: Forest C02 (existing
forests) and Forest C02 (new forests). GLOBIOM accounts for agricultural soil carbon fluxes, but not forest soils.

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Box 2

FOCUS: Representation of carbon
in agriculture and forest soils

Forest CO, Soils

More C02 sequestered in forest soil
because of changes in land use from t0
to t, (net forest land in t1 is lower than
net agricultural land in t0)

FORESTLAND

CROPLAND

Pasture converted to Cropland

PASTURE

Agricultural C02 Soils

Less C02 sequestered in agricultural soil
because of changes in land use from t0
to t., (net agricultural land in t., is lower
than net agricultural land in t0)

Cropland converted to Pasture

It is important to highlight how carbon sequestered and stored in soils is allocated in
FASOMGHG. As the soil carbon basically "moves" with any land that transitions in and out of
different land uses, this movement can cause the soil carbon pools between the various sectors
in the model to appear to have large fluctuations in the estimated volumes. This outcome is
due to land use changes, not large changes in the actual volumes of sequestration and storage
of carbon by the soil pools. The figure above shows how FASOMGHG accounts for carbon
sequestered in land soil by considering the current stock of soil carbon in each land use and the
change in land uses from two different time periods (t0 and it).

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Agricultural C02 soils include both the amount of carbon sequestered in existing agriculture
land soils (including both cropland and pasture) and the difference between increasing soil
carbon stored due to more land converted to agriculture and decreasing soil carbon due to
agriculture land converted into other uses from one period to the next period. In the case shown
in the figure, net agriculture land declines, driving a contraction in agricultural C02 soils from
t0 to ti.

Similarly, forest C02 soils include both the amount of carbon sequestered in existing forests
soils and the difference between increasing soil carbon due to more land converted to forests
and decreasing soil carbon due to forestland converted into other uses from one period to the
next period. In the case shown in the figure, net forestland increases, driving an increase in
forest C02 soil from t0 to ti.

Note that all the land categories included in the figure could be converted to other land uses
(e.g., development). These changes are included in the estimated changes in soil carbon.

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As there are similarities across the modeling frameworks,
there are also differences. These include the economic
modeling approach (including treatment of time dynamics),
geographic coverage, sectoral coverage, land use categories,
land use competition, international trade dynamics, and the
time horizon employed.

First, all three models are optimization frameworks that
can reflect long-term scenario timeframes, but there are
differences in their treatment of time dynamics. FASOMGHG
and GTM are intertemporal optimization (sometimes referred
to as perfect foresight) models, which simultaneously
optimize the entire solution period—85 years for FASOMGHG
and 200 years for GTM in this analysis. Both models include
a discount rate of 5%. GLOBIOM offers an alternative,
recursive dynamic optimization structure (often referred to
as myopic) in that it optimizes each successive period on the
basis of past and current conditions as it steps through time.
Both approaches are widely used in modeling applications
to project future conditions and quantify the impacts of
potential policy changes to inform decisions today. However,
investors, land managers, and program designers have
neither perfect foresight nor myopia, so it is important
to understand the implications of the treatment of time
dynamics on the model results. Employing these different
modeling outlooks in this exercise is purposeful, as it allows
for better understanding of similarities and differences
between projected outcomes with tools using different
temporal outlooks. Moreover, FASOMGHG and GLOBIOM
run up to 2100, while GTM runs from 2020 to 2200. In
forward-looking models like FASOMGHG and GTM, different
terminal years of the modeling timeframe (i.e., FASOMGHG's
timeframe ends in 2100 whereas GTM's ends in 2200) are
likely to affect the results even in the short-term because
future long-term GHG prices drive investment decisions
at any period. To explore different future scenarios, this
report does not harmonize the terminal year across models
but rather tests the sensitivity of the results to this factor
(discussed in Box 6 of Chapter 3).

Second, variations in model attributes can influence
resulting mitigation projections in the United States'
context in potentially different and significant ways. For
example, differences in spatial scale can affect the regional

distribution of mitigation action in response to an exogenous
policy driver. GTM and GLOBIOM are global models, which
capture general market feedbacks in a global setting
as changes in global market conditions and trade flows
can change the opportunity costs of pursuing mitigation
opportunities. For example, if all countries face similar
policy incentives to reduce emissions or increase carbon
sequestration, countries with a comparative advantage
(meaning lower opportunity costs) in GHG mitigation
activities relative to traditional commodity production could
see increased relative investments to reduce emissions,
and supply could shift dramatically. Countries such as the
United States, with large amounts of productive lands and
highly developed technologies, could maintain a strong
comparative advantage in both mitigation and traditional
agricultural and forest product supplies, so mitigation
potential in the United States could be heavily influenced by
global market conditions and shifting trade patterns. Global
models are needed to assess these potential international
market interactions (Baker et al., 2018).

On the other hand, FASOMGHG is a domestic (U.S.-only)
model that does not explicitly capture market and policy
feedback with the rest of the world. The model does include
global trade flows between the United States and other
regions with the use of endogenous supply functions in the
agricultural sector and constant supply functions from other
regions in the forestry sector; hence, trade adjustments
can be introduced exogenously under different market
and policy conditions (Jones et al., 2019). The advantage
of a domestic model like FASOMGHG is that it provides
additional detail on the production processes and offers a
more activity-scale disaggregation, including a wider range
of product markets, than global models. Furthermore,
domestic models typically capture greater levels of spatial
heterogeneity in cost structures and production activities.
Reflecting more detailed domestic markets and land use
production processes, as well as capturing intra-regional
interactions and spillovers, domestic models offer the
capability of evaluating policy-induced changes in mitigation
costs and portfolios overtime and across domestic regions.
Ultimately, the selection of models with different geographic
scales allows for evaluation of how projected outcomes are
affected by that variable.

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Third, sectoral coverage also varies across models and can
impact net mitigation outcomes. GTM, for example, offers
highly disaggregated detail of the global forestry system and
markets for pulpwood and sawtimber, which allows for more
detailed assessment of global forest market interactions
and related land use and forest carbon outcomes than
with the other two models used in this study. However,
competition for agriculture in this model is only represented
indirectly through land rental functions and land supply
elasticity parameters (Kim et al.. 2017; Sohngen et at.,
2019). While GTM's focus on forestry reflects the rising
opportunity cost of bringing additional land into forestry
at the expense of agriculture, the model's lack of direct
resource competition with crop and livestock production
systems results in mitigation portfolios that ignore
endogenous market responses in agriculture to climate
policies (e.g., impacts on changes in agriculture practices
and demand for land). Conversely, FASOMGHG and
GLOBIOM explicitly represent agricultural components and
therefore provide a more comprehensive description of land
use and related market competition between sectors but
offer less detail within the global forestry sector than GTM.

Finally, each model uses a slightly different approach
for incentivizing GHG emissions reduction relative to the
baseline. GTM includes a carbon rental payment (Favero
et al., 2020; Sohngen & Mendelsohn, 2003). The rental
payment approach pays forest owners an annual rental rate

for carbon sequestered on standing stocks and the GHG
price for storage in HWP. If forested land is converted to
non-forest use, that benefit (positive rental payment in the
objective function) is lost in perpetuity. If forests are simpiy
harvested, then it takes time to rebuild the carbon stock and
accompanied carbon rents, though the rental payment can
influence management changes, such as longer rotations
and management intensification (including forest planting or
interventions that boost productivity). GLOBIOM treats this
price incentive as a direct payment from landowners for GHG
emissions released. Similarly, but within an intertemporal
optimization framework, FASOMGHG uses a symmetric
GHG mitigation price incentive that rewards emissions
reductions relative to the baseline with a welfare payment
and penalizes emissions above baseline levels.

2.5.1 Mitigation Opportunities Across Models

The models used for this analysis have been selected to
evaluate aggregate mitigation potential at large scales,
which requires addressing market feedbacks, as well as
considering an idealized and comprehensive implementation
approach (a GHG price applied to all land emissions).
Moreover, this analysis does not restrict the models to only
consider options that are present across all the models
but leverages models' specific attributes to present a
comprehensive assessment of direct land mitigation in the
United States. Each model has specific mitigation activities

;3M

Planting soybeans into corn residue and wild mustard using a no-till planter on a farm in Vincennes, Indiana, on May 13, 2021. (Indiana Natural Resources
Conservation Service photo by Brandon O'Connor)

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available across the eight GHG categories and responds to
the price incentive by finding its specific mix of abatement
options for any period. A total of 24 mitigation activities have
been identified across the three models: from 5 in GTM
focusing only on the forestry sector to 21 in FASOMGHG
that account for the largest number of abatement options
available. GLOBIOM has 20 abatement activities available to
respond to the price incentive (Figure 2-4).

In the forestry sector, five activities can be implemented
in response to the GHG price, namely avoiding forest
conversion, lengthening timber harvest rotation, converting
agricultural lands to new forest, increasing forest
management intensity, and increasing production of wood
products to increase carbon sequestered in long-lived
wood timber products. Management intensification in U.S.
forestry is an important factor for developing long-term
carbon projections (Jones et al., 2019; Tian et al„ 2018;
Wade et al., 2022), which is endogenously captured in
all three models. However, GLOBIOM does not represent
all forest management intensification options consistent
with current management patterns in the United States,
such as plantation forestry, but it does allow for changing
of rotation lengths, change of the ratio of thinning versus
final fellings, change of harvest intensity, and change of
harvest location. Finally, all models account for emissions
at the time of harvesting as foregone carbon sequestered
in forests. FASOMGHG and GTM assume that some carbon
will be tied up in wood products for a number of years.
Specifically, carbon in timber products may be released
to the atmosphere many years in the future as products
decompose; the decomposition rate varies depending on
the products (e.g., short-lived or long-lived) and models
(see Winjum et al. 1998 for estimates used in GTM and
Skog 2008 for estimates used in FASOMGHG). GLOBIOM
does not include the HWP carbon pool over time but reports
these as emissions from forest management at the time of
harvesting.

In the agriculture sector, mitigation activities are divided
into cropland-based activities and livestock-based activities
for C02, N20, and CH4. In both FASOMGHG and GLOBIOM
the following mitigation activities are included: adoption
of automatic fertilization systems for rice production,

Brush management practice has opened the rangeland for cattle to better
graze and improved the land near Sauerbier Ranches LLC, in southwest
Montana (August 27, 2019). (USDA Photo by Lance Cheung)

the application of nitrification inhibitors, the usage of
dryland rice production and direct seeding, implementing
conservation and no-till practices, increased crop residue
incorporation into soils, reduced fertilizer application,
and mid-season draining of rice. Additionally, FASOMGHG
allows for reduced on-farm fossil fuel emission and
changes to irrigation intensity, while GLOBIOM includes split
fertilization applications, automated fertilizer techniques
on other crops11 in addition to rice, and no-till on rice.
For livestock, both models include the usage of plug flow,
covered lagoon anaerobic, and complete-mix digesters;
the administration of bovine somatotropin (bST) to dairy
cattle; the administration of propionate precursors to dairy
cattle; the administration of antimethanogen treatment for
cattle; improved feed conversion; changes to antibiotics
administered to cattle; and changes to grazing activities.
Both FASOMGHG and GLOBIOM include spatially explicit,
crop-, and livestock-specific emissions reductions for these
activities to account

SG Auto-fertilization refers to advanced methods of soil analysis to deter-
mine the optimal quantity of fertilizer to maximize crop yields. Soil plH or
plant characteristics can be analyzed to determine the nutrient quality of
soils and determine the timing and quantity of fertilizer application. Pre-
cision fertilization can reduce input fertilizer costs for farmers in the case
of overfertilization in addition to increasing yields (Oberoi et al., 2017).

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FIGURE 2-4

Mitigation technologies and management strategies available in each model

globio m

Split fertilization applications
Automate fertilizer techniques
Change from conventional tillage to
no-tillage (rice)

Administer bST to dairy cattle
Use covered lagoon anaerobic digesters
Use complete mix anaerobic digesters
Use plug flow digesters
Administer propionate precursors to dairy cattle
Administer antimethanogen treatments for cattle
Improve feed conversion for cattle
Administer antibiotics to cattle
Use intensive grazing §

Automating fertilization techniques
(rice)

Apply Nitrification inhibitors
Use dryland rice/direct seeding
Change from conventional tillage to
conservation- or no-till
Increase residue incorporation
Reduce fertilizer application
Drain mid-season (rice)

Change crop mix

Reduce fossil fuel-related emissions
associated with crop tillage change
Change irrigated/dry land mix

FASOMGHG

Each circle includes the list of activities for each model; circles overlap where models have the same activities included.

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for heterogeneity across space and agricultural systems.
Across both models, alternative input mixes, which may be
utilized to reduce emissions, will influence crop yields. For
example, in FASOMGHG, reducing nitrogen input by 15%
on corn fields has a median yield decrease of 14%. At the
same time, changing tillage practices can result in either an
increase or decrease in corn yields depending on location,
nitrogen input, and irrigation levels.

In addition to model-specific abatement activities, each
model has the flexibility to change where activities such as
crop planting, livestock grazing, afforestation, and forest
harvesting occur to maximize the net benefit from both
agricultural and forestry products entering markets, and
from incentives placed on GHG mitigation outcomes. In
this analysis, all mitigation categories are treated in each
model without consideration of their level of risks related
to permanence, additionality, and leakage (see Box 4 in
Chapter 3 for a specific example of the potential effects of
leakage on the results presented in this report).

Finally, all models include bioenergy, but the GHG price does
not apply to bioenergy supply; therefore, it does not receive
any incentive/disincentive in the GHG price scenarios
relative to the baseline case. This scenario design was
selected to focus the report on direct mitigation from the
land sector without considering future demand for land and
changes in land management driven by decarbonization
activities from other sectors (e.g., energy sector). Moreover,
there is uncertainty in modeling future demand for bioenergy
and biofuels as a GHG reduction strategy, uncertainty of
adoption of dedicated energy crops such as switchgrass,
and ongoing policy changes in renewable fuel programs
that would increase the degree of uncertainty in the direct
estimates of land-based mitigation potential. Other direct
land mitigation options (such as agroforestry) are not
represented in the models, largely due to a lack of adoption
to date domestically, which means no comprehensive
historic data on environmental outcomes and costs, plus
a lack of GHG reporting guidelines (i.e., no established
guidelines for reporting this activity in IPCC GHG reporting
guidelines).

2.6 Model Input
Harmonization and Baseline
Scenario

In each model, pertinent exogenous
parameters and input data have been
harmonized.

To evaluate net mitigation potential for different GHG
categories (or specific activities) in the land sector, model-
specific baseline simulations are run using harmonized
parameters and input data, as described below, to facilitate
comparison among model outputs. In each model, baseline
scenarios reflect no mitigation policies (e.g., GHG incentives,
state-level renewable standards) in place, and market
and biophysical conditions drive future land use and land
management decisions. Moreover, climate change impacts
on land are not included in the assessment.

This analysis does not attempt to align the baseline
projections across the models exactly but instead to
harmonize data inputs and model parameters for key drivers
to a reasonable extent and to then explore changes in
emissions between the baselines and various counterfactual
GHG price scenarios. This limited harmonization approach
is regularly applied in the literature, including numerous U.S.
government reports and official submissions to the UNFCCC
(e.g., Biennial Reports, U.S. Department of State, 2014,
2016, 2021, 2022). Aligning all underlying parameters to
achieve similar baseline projections would not support the
goal of this analysis, which is to evaluate the estimated
magnitude and directionality of projected GHG mitigation
versus a baseline—the "delta" between the simulated policy
case and business-as-usual baseline. As applied, this
harmonization effort narrows the primary focus to comparing
differences in modeled emissions projections between
baseline and GHG mitigation scenario pathways, and
discussion of variability in results driven by key structural or
data-oriented differences between the models.

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Specifically, this effort harmonizes overarching
macroeconomic drivers, such as U.S. population and GDP,
that can materially affect projected outcomes (Riahi et al.,
2017), as well as key biophysical inputs such as U.S. forest
data. Specifically for this report, initial conditions in each
model were aligned to age-class distribution from the 2015
U.S. Forest Inventory and Analysis datasetto reflect recent
U.S. forest resource conditions (USFS, 2017).

For the macroeconomic drivers, each model aligns with U.S.
population and GDP growth rates from the AEO Reference
Case scenario, as these factors drive total demand growth
for agricultural and forestry commodities (EIA, 2022).
All three models use AEO 2022 projections of U.S. GDP
and population until 2050. For other regions of the world,
GLOBIOM and GTM apply population and GDP growth rates
from the SSP2 scenario (Riahi et al., 2017). In FASOMGHG,
growth rates for the United States after 2050 follow the
SSP2 macroeconomic growth rates, and trade projections
are calibrated to SSP2 growth projections for the rest of the
world. This SSP2 scenario generally considered a "middle
of the road" case, in which macroeconomic trends follow
their historical patterns and population and economic
growth is moderate through 2100, which has implications
for agriculture and forest product demand and the land use
sector broadly (Daigneault et al., 2019; Popp et al., 2017;
Riahi et al., 2017).

By harmonizing socioeconomic and technological
specifications across models using SSP2, this report
does not test the effects of those specifications on future
mitigation potential of land. However, each model has been
tested under different SSP scenarios and Box 3 provides a
summary of the main findings for each of them.

Aside from the harmonized elements specifically discussed
here, the models are applied using parameters and other
specifications as generally applied by the modeling teams
(meaning that there was no further harmonization for
elements such as estimated future CRP enrollment or future
RFS volume mandate updates).

Finally, the harmonization process does not align 2020
values across models because initial values in each model
differ due to varying GHG pools included in each model, as
discussed above, such as FASOMGHG including emissions
from on-farm fuel consumption, which GLOBIOM does not.
Additionally, GTM and GLOBIOM include representation of
Alaska, while FASOMGHG does not. Moreover, some models
start in 2015 (FASOMGHG), while GTM begins in 2020, and
GLOBIOM begins in 2000, creating possible discrepancies in
2020 results.

2.7 Mitigation Scenarios

To assess land mitigation potential,
10 alternative GHG price scenarios,
ranging from $5 to $100/t C02e in
2020 and reaching $7 to $281/t C02e
in 2050, have been applied to each
model.

To evaluate net mitigation potential of land, a set of
consistently defined GHG price scenarios is run within each
model, and the results of the baseline runs are compared to
the mitigation scenario runs to estimate the change in GHG
emissions and sequestration driven by the price path.

To incentivize mitigation, each model applies 10 alternative
GHG price path scenarios (as all GHGs evaluated in the
report are reflected as C02e, the GHG prices are shown
in $/t C02e). These scenarios include five initial prices
beginning in the year 2020—$5, $20, $35, $50, and
$100/t C02e—in combination with two real price growth
rate scenarios of 1% and 3% annually. The growth rates
of 1% and 3% were selected to be consistent with the
average economic growth rate presented in the AEO 2022
(EIA, 2022) and SSP2 (Riahi et al., 2017) and were used
to simulate future socioeconomic pathways in this report.
Moreover, these rates are in line with the average 2020-
2100 growth rate of the prices in the Integrated Assessment
Modeling Consortium (IAMC) 1.5 °C Scenario Explorer and
data hosted by NASA (Huppmann et al., 2019).

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Box 3

FOCUS: Models' results under
alternative socioeconomic
scenarios

Socioeconomic and technology specifications about future conditions substantially influence
projected levels of land use sector mitigation potential. The three models used for this report
have been tested under multiple socioeconomic pathways, and the results are presented in
Wade et al. (2022) for FASOMGHG and Daigneault et al. (2022) for GTM and GLOBIOM.

Each SSP scenario reflects different assumptions of GDP, population growth, urban
development, demand growth for agricultural and forest commodities due to changes in
population, dietary preferences, trade, and shifts in agricultural productivity growth.

Below is a summary of the main takeaways of the effects of the SSPs in each model.

• Income and demand growth are positively related to GHG emissions from agriculture,
(FASOMGHG and GLOBIOM) and to carbon sequestration from forestry (FASOMGHG
GLOBIOM, and GTM).

• The models select a different portfolio of mitigation activities across sectors depending on
socioeconomic conditions.

• Under scenarios with a high population and economic growth scenario (as in SSP5 or
SSP3), it is likely that agriculture sector mitigation will be a relatively more important
component of a domestic climate strategy (FASOMGHG and GLOBIOM); whereas under
scenarios with lower growth and reduced agricultural and forest product trade (as in SSP1
or SSP4), forest sector mitigation is likely to be more cost-effective (FASOMGHG and
GLOBIOM).

• Across all SSPs, forest area increases under climate change mitigation scenarios from the
baseline (FASOMGHG, GLOBIOM, and GTM).

• Across all SSPs, forest management provides the greatest source of mitigation across all
SSPs within the forest sector (FASOMGHG, GLOBIOM, and GTM).

• Variation in agricultural sector mitigation potential is driven primarily by the differences in
baseline demand for agriculture products across the scenarios. Low baseline demand for
agricultural products projects higher mitigation from agriculture because of lower marginal
abatement costs (FASOMGHG and GLOBIOM).

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Figure 2-5 and Appendix A, Table A-l present the GHG
price paths over time. The lower bound corresponds to the
recent global average carbon credit price in the voluntary
carbon market of around $7/tC02e, and nature-based
credits have a value of around $5/t C02e (World Bank,
2023). On the other hand, the higher bound is in line with
the new central estimated social cost of carbon between
$61 and $168/t C02e (Rennert et al., 2022) and mimics
current carbon excises of about $130/t C02e in Switzerland,
Uruguay, and Sweden (World Bank, 2023).

Each model treats GHG prices as direct fees for landowners
for the GHG emissions resulting from land use and land
management activities and direct payments to landowners
for carbon stored through sequestration activities. This

function translates to monetary incentives to reduce
emissions and to increase sequestration relative to the
baseline without GHG prices. Note that while GLOBIOM and
FASOMGHG include one-time direct payments for all forest-
based sequestration activities, GTM uses a carbon rent
approach to "reward" aboveground carbon stock in forest
annually.

By using different mitigation price paths, the study can
represent likely bounds on the magnitude of overall GHG
mitigation and gauge how different levels of incentives
and growth rates influence the projected mix of mitigation
activities. Dynamic economic models are particularly
impacted by applied growth rates for mitigation incentives.
Therefore, by including different growth rates, this study can

FIGURE 2-5

GHG prices in $/t C02e applied to each model in the mitigation scenarios
(2020-2050)

300 -

200 ¦

O
O

100-

0 -

2020

2025

2030

2035

2040

2045

2050

$5 at 1%
$5 at 3%

$20 at 1%
$20 at 3%

$35 at 1%
$35 at 3%

$50 at 1%
$50 at 3%

$100 at 1%
$100 at 3%

GHG prices in $/t C02e applied to each model in the mitigation scenarios, 2025-2050.

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analyze the influence of mitigation price growth on potential
delay or anticipatory mitigation action in the land use system
(Baker et al„, 2018; Baker et al., 2017).

Estimated mitigation potential and related total costs are
calculated by comparing projected model-specific baseline
emissions, sequestration with emissions, or sequestration
under each price scenario at any time. Both projected
changes in cumulative emissions and annual flux changes
are compared against the baseline. Costs are calculated
for each simulation step (5-year or decadal increment,
depending on the model), and over time. These results
are then presented as MACCs. which represent the annual
GHG mitigation (in C02e) associated with each GHG price
incentive across different scenarios in each model, and
are then compared across models. MACCs show the
corresponding abatement under a selected GHG price or
the required GHG price to meet a defined level of emissions
reduction. The results, including MACCs, are presented in
Chapter 3.

Baseline trends are an important factor in determining
the estimated magnitude of net mitigation potential. For
instance, a projected baseline with relatively high expansion
in one sector (e.g., forestry) or specific management
activity (e.g., afforestation) would face a different set of
opportunity costs when pursuing mitigation strategies
(e.g., larger foregone forestry rents) or possibly lower levels
of future potential mitigation from that activity (e.g., less
projected afforestation in response to policy incentive).
The variability in estimated future baseline conditions can
impact mitigation costs and abatement portfolios even

for a single model (e.g., different SSP pathways applied in
one model, as seen in Wade et al. [2022] and Daigneault
et al. [2019]). Aside from the harmonized macroeconomic
drivers, mitigation activities wili be driven primarily by the
suite of available abatement options and technologies and
associated costs in each model. That is, the estimated
mitigation potential from each model is the cost-effective
quantity of net GHG emission reductions achievable by a
specific set of mitigation options and related costs relative
to a specified baseline. If mitigation options are restricted
(i.e., a smaller set of mitigating options are incentivized),
the relative costs and mitigation levels of the remaining
mitigation options are likely to change (e.g., Latta et al.,
2013; Tian et al.. 2018).

Note that the baseline and future scenario projections
presented in this report are illustrative and are not intended
to replicate any specific policy or program. They do not
explicitly seek to address or evaluate other topics, such as
the interplay of GHG mitigation policies with biodiversity
preservation or how to achieve socio-economically equitabie
policy outcomes or future environmental conditions, such
as climate change impacts. It is also important to note that
historic climate change impacts are captured implicitly
within the data inputs used in the models (e.g., climate
change impacts such as changes in natural disturbance, like
increased incidences of pests and wildfires). Expanding this
analysis to include other issues, including specific radiative
forcing scenarios, collectively across this suite of models
(as they have been applied individually in previous studies
| Favero. Sohngen, et al., 2018]) is an area of future potential
research. Other limitations and areas of future research are
highlighted in Chapter 4.

Prescribed grazing and forage and biomass planting of pastures in Sheridan, Arizona, on June 27, 2019. (USDA Photo by Lance Cheung)

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2.8 Stand-Alone Analyses

Model results have been validated
through five sensitivity tests.

in addition to the mitigation scenarios applied across all
three models, a set of sensitivity scenarios was conducted
with individual models to examine how estimated future
abatement portfolios and mitigation costs might change
when specific analytic parameters or variables are modified.
Specifically, this approach allows for gauging how sensitive
the model and model results are to changes in key variables
and scenario design parameters. Sensitivity analysis is
regularly practiced in modeling exercises to observe how
uncertainty in model outputs relates to input uncertainty to
better evaluate the robustness of results.

The report presents and briefly discusses estimated results
under five sensitivity tests presented in separate boxes in
Chapter 3, many of which will be pursued in more depth
in future research endeavors based on this report. These
sensitivity tests were carried out with individual models
based on each's ability to reflect the target scenario.
Box 4 uses GLOBIOM to observe what happens when
global versus national emissions reduction policies are

implemented. Box 5 adds additional runs from GTM to
assess how inclusion of future climate scenarios and C02
concentrations in the atmosphere can affect estimated GHG
mitigation outcomes. Box 6 shows how altering parameters
around land use conversion and the effects of holding GHG
prices steady after the end of the century are investigated
in GTM, with the latter assessing the role that a longer,
running model may have on overall mitigation results. Box 7
illustrates how outcomes are sensitive to implementation
of a mitigation program that involves full participation
versus one with lower participation rates, including potential
leakage effects in FASOMGHG. Box 8 offers a snapshot
of how specific activity eligibility constraints can affect
results by restricting the geographic location of eligible
mitigation activities in FASOMGHG. Specifically, it looks at
what happens when there is no financial incentive for re/
afforestation in the Corn Belt region (emulating a policy
intent to ensure food security or landowner decisions to
not adopt that mitigation practice in that region). Finally,
three additional boxes in Chapter 3 provide some additional
in-depth analyses by comparing the technical mitigation
potential of livestock and the economic potential measured
in the report (Box 9), providing an overview of long-term
mitigation potential of the land sector beyond 2050
(Box 10), and discussing a hypothetical application of the
models' results at specific investment levels (Box 11).

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3 Baseline and Mitigation
Scenario Results

This chapter provides an evaluation of U.S. land sector GHG
mitigation potential by presenting the projected GHG emissions under
the baseline and alternative mitigation scenarios across the three
models included in this report: FASOMGHG, GTM and GLOBIOM.

For simplicity, all GHG emissions estimates (e.g., livestock
non-C02) are presented in C02e emissions. The results
presented largely focus on the year 2050, though Box 10
looks at some key results through 2070. As discussed in
Chapter 2, key parameters have been harmonized across
the three models. However, the goal of this analysis was
not to align every initial parameter across the models,
but rather to assess the directionality and magnitude of
the estimated mitigation potential by looking at the delta
between the alternative scenarios' results and the baselines
of each respective model. The chapter presents results from
each model to allow for the extraction of specific insights
the different models provide given their relative strengths
to inform the evaluation of the final results. Therefore,
the results from each model are presented separately but
discussed jointly to provide a comprehensive framework of
the future mitigation potential of land in the United States.
In the next chapter, the results will be discussed in relation
to the U.S. GHGI, compared to other literature projecting
emissions and mitigation from the land sector, and further
discussed in terms of how these findings can be applied in
real-world contexts.

The chapter is divided into five sections.

• Section 3.1 presents estimated baseline emissions
and sequestration for each model with discussion of the

factors driving the differences in the future projections
across models.

• Section 3.2 discusses the MACCs for the entire land
sector first and then decomposes them by GHG (C02,
CH4 and N20) across models and time.

• Section 3.3 provides a detailed description of the
MACCs by sector (forestry and agriculture) and activity
across models and time.

• Section 3.4 presents the mitigation potential of land
across each activity and compares the results across
models and mitigation scenarios.

• Section 3.5 assesses the required investments needed
to drive specific levels of abatement across models and
time.

In this chapter, eight stand-alone analyses are presented
as separate boxes. Five boxes test either the sensitivity
of the MACCs and mitigation potential to some specific
variables (e.g., physical parameters, model specifications)
or the sensitivity of the results to the policy design and
provide some practical applications. The boxes are not
intended to be exhaustive studies but instead serve to
assess how sensitive the results are to certain parameters
and/or assumptions in the main study, highlight areas of
uncertainty, determine the directionality of impacts under
varied parameters, and spur future research endeavors.

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An aerial view of a forest being cleared
using the slash and burn method,
Hendersonville, North Carolina.

Though the U.S. land sector is projected to
remain a net sink through midcentury, land use
GHG emissions are estimated to increase over
time under the baseline scenarios.

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3.1 Future Baseline
Projections

Though the U.S. land sector is
projected to remain a net sink through
midcentury, land use GHG emissions
are estimated to increase overtime
under the baseline scenarios.

3.1.1 Baseline Emissions from the Land Use
Sector Across Models

The AFOLU sector is expected to sustain a net GHG
emissions reduction of between 90 and 120 Mt C02e yr"1 in
both FASOMGHG and GLOBIOM in 2050 under the baseline
scenario (Figure 3-1).

FASOMGHG projects that activities such as existing forest
management and afforestation/reforestation efforts will
lead to a continued stable net sink from the U.S. forestry
sector through 2050, though emissions from cropland and
livestock production increase slightly over time and thus the
overall magnitude of the net sink decreases overtime.
Under the GLOBIOM baseline scenario, the U.S. land sector

remains a net sink, but the net sink declines by 2050 and
trends toward becoming a net source of GHG as agricultural
emissions rise to meet growing demand and the forestry
sink declines, due to limited investment in replanting and
afforestation/reforestation activities over the projection
timeframe (driven by the recursive dynamic nature of
GLOBIOM) and forest aging (as older tree inventories absorb
less carbon) (He et al., 2012).

In the forestry sector, the three models project that the
net carbon sink will remain relatively constant or decline
slightly over time as unman aged or natural forests age
and harvesting activities in managed forests grow, driven
by an increase in population and corresponding demand
for forest-based products (Figure 3-1). The expected
average annual carbon sequestration rate in 2050 is
405 Mt 002 yr1 in FASOMGHG, 431 Mt C02 yr-1 in GLOBIOM,
and 641 Mt C02 yr'1 in GTM. Both FASOMGHG and GTM
project increases in plantation forest establishment at
the baseline as a response to growing demands for forest
products. Responsiveness to this growing demand helps
the forest sector remain at a relatively constant annual sink
in both models, countering slowing sequestration rates on
older, mature stands. Additionally, global trade impacts each
model's decision on investment levels in forestry activities
differently, which will be discussed further in the next
section.

A small herd of cows grazing in the fog as the sun sets in the hills of Virginia.

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FIGURE 3-1

GHG emissions by model under baseline scenario (in MtC02e yr\ 2025-2050)

o
o

750

500

250

-250

-500

-750

2025

2030

2035

2040

2045

2050

Year

— Agriculture and Livestock, GLOBIOM

— NetAFOLU, GLOBIOM

— Forestry, GTM

Agriculture and Livestock, FASOMGHG
Forestry, FASOMGHG

NetAFOLU, FASOMGHG
Forestry, GLOBIOM

Emissions from agriculture and livestock, forestry, and net AFOLU. Net AFOLU emissions are calculated as the sum of emissions from
agriculture and livestock and forestry. Results are presented in terms of atmospheric accounting. Therefore, positive flux equates with
emissions; negative flux represents sequestration. Initial values in each model differ due to varying GHG pools included in each model as
discussed in Chapter 2, such as FASOMGHG including emissions from on-farm fuel consumption, which GLOBIOM does not. Additionally,
GTM and GLOBIOM include representation of Alaska, while FASOMGHG does not.

FASOMGHG projects a relatively stable sink in existing
forests, with fluctuations over time driven by spatial and
temporal patterns in forest growth, aging, and harvests.
Further, shifting land use and production patterns in and
between segments of the forestry and agriculture sector
result in changes to the forest carbon sink over time. For
instance, in future decades afforestation/reforestation
increases the net carbon sink asforestland expands, but
this increase in the sink diminishes in later periods as
afforested stands, predominately plantations, experience
higher harvest levels (Figure 3-2). This increase in
afforestation/reforestation is driven by a growing demand for
forestry products overtime as population and GDP increase.

At the same time, agricultural productivity is increasing,
which results in the relative rental rate of forestland to
be higher than that of cropland over time. Forestland
transitioning into agricultural or developed uses results in
declines in biomass carbon as well as soil carbon stocks
as lands leave forests, thus resulting in a net emission
from forest soils before midcentury (after that the rate
of conversion of forestland to developed land declines).
Specifically, an exogenous shift of 10 million acres of
forestland is converted to developed land in FASOMGHG by
2050, based on historical LUC measures from the National
Resources Inventory (U.S. Department of Agriculture Natural
Resources Conservation Service, 2017) and projected

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expansion of built-up areas (Riahi et al., 2017). Ultimately,
carbon sequestration rates of U.S. forests (existing and
newly forested lands) remain relatively constant across the
time horizon, resulting in the forest sector remaining a net
sink past midcentury.

GLOBIOM projects that the U.S. forestry sector will diminish
its carbon sink over the next several decades, with average
annual flux from forestry declining from 551 Mt C02e yr"1
in 2025 to 431 Mt C02e yr"1 in 2050 (Figure 3-2). This
outcome results from a slowing of annual carbon storage
as current forest stands age, coupled with afforestation/
reforestation and re-establishment of harvested forests
activities that occur at a relatively low rate. GLOBIOM limits
investment in forest management and afforestation/
reforestation activities while increasing harvesting slightly
over time, resulting in a declining annual flux.

GTM projects that the carbon sequestration from the
U.S. forestry sector will remain stable at around 621 to
641 Mt C02 yr"1 from 2025 to 2050, driven by improved
management activities on managed forests counteracting
slowing sequestration rates from aging unmanaged forests
as well as a growing forest land base (Figure 3-2). Moreover,
carbon stored in HWP is expected to remain constant at
around 69 Mt C02 yr"1 as new long-lived timber products
enter the market driven by increasing consumption per
capita despite the decay of carbon stored in existing wood
products (Figure 3-2).

In the agricultural sector, which includes crops and livestock
production, both FASOMGHG and GLOBIOM project an
increase in GHG emissions overtime despite projected
increases in crop yields as rising populations and GDP
lead to increases in demand for agricultural commodities
both domestically and outside the United States (Figure
3-1). Specifically, FASOMGHG projects emissions from the
agricultural sector to rise from about 217 Mt C02e yr"1 in
2025 to 314 Mt C02e yr"1 in 2050 while GLOBIOM shows an
increase from 298 Mt C02e yr"1 in 2025 to 311 Mt C02e yr"1
in 2050. Despite the overall similarity in estimated baseline
emissions outcomes, FASOMGHG and GLOBIOM rely on
slightly different mechanisms to reach these levels, as
explained below.

In FASOMGHG, the change overtime in baseline crop and
livestock emissions is driven by several factors. First, the
initial stock of carbon stored in cropland soils is affected
by several variables such as conversion to other land
uses (including from cropland and pasture to exogenously
determined development and endogenously determined
conversion of cropland to pastureland and vice versa),
some continued use of conventional tillage practices
resulting in diminished soil organic carbon, and changes
in crop mix (with residues from some crops and cover
crops contributing to the soil sequestration totals), which
on net diminish soil carbon stock over time. The baseline
scenario in FASOMGHG reflects continued adoption of
conservation and no-till techniques, which accumulates
carbon for a time until additional sequestration capacity
saturates (Stewart et al., 2008). Additionally, consistent
with recently observed trends in the United States (Baker
et al., 2020; Kuck & Schnitkey, 2021), pastureland in
FASOMGHG increases slightly over time to meet growing
demand for meat products. Moreover, similar to the forestry
sector, FASOMGHG reflects cropland and pastureland
conversion to developed land as populations continue to
rise (driven in part by macroeconomic parameters in AEO
2022 and SSP2; further details are presented in Wade et
al., 2022). This approach differs from GLOBIOM and GTM,
which do not reflect changes in development area, and
thus total natural and working land area remains constant
in those models. Finally, livestock sector emissions in
FASOMGHG are projected to increase slightly over time
as demand growth for livestock products is driven by
increasing population and income (Figure 3-2). This demand
shift requires more livestock on the landscape, yielding
higher emissions from enteric fermentation and manure
management practices. In FASOMGHG, total agricultural
input usage and production levels remain relatively constant
as technological improvements increase yields over time,
thus relaxing extensification pressure on agricultural land
use. This increase in productivity allows the model to utilize
crop inputs such as fertilizer, pesticides, and irrigation at
relatively constant rates over time, which limits the increase
in non-C02 cropland emissions.

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FIGURE 3-2

Baseline U.S. average annual carbon-equivalent flux within each decade by GHG
category by model (in Mt C02e, 2020-2059)

FASOMGHG

GLOBIOM

GTM

500 -

250 -

o
o

0 -

-250 -

-500 -

-750 -

2020-
2029

2030-
2039

2040-
2049

2050-
2059

2020-
2029

2030-
2039

2040-
2049

2050-
2059

2020-
2029

2030-
2039

2040-
2049

2050-
2059

Cropland C02
Forest Soils

Cropland Non-C02
Forest CO,

Livestock Non-C02
Forest Products

Agricultural Soils

AFOLU

Agriculture and Livestock

Forestry

Results are presented in terms of atmospheric accounting. Therefore, positive flux equates with emissions; negative flux represents
sequestration. Lines show total emissions and sequestration from the combined AFOLU (middle lines), agriculture and livestock (top lines)
and forestry (bottom lines) sectors as reported in Figure 3-1 for each model. Initial values in each model differ due to varying GHG pools
included, as discussed in Chapter 2, such as FASOMGHG including emissions from on-farm fuel consumption, which GLOBIOM does not.
Additionally, GTM and GLOBIOM include representation of Alaska, while FASOMGHG does not. Between 2030 and 2050, FASOMGHG
projects that emissions from LUC between forestry and agricultural lands will lead to net emissions from existing forest soils. This outcome
is due to how emissions from LUC activities are modeled in FASOMGHG as described in Box 2. Emissions from land use conversions (i.e.,
forestland converted to agricultural land) occur in the period that the land conversion occurs. However, for land conversions that result
in higher levels of stored carbon in soils (i.e., cropland converted to forestland), the accumulation of additional soil carbon occurs over
a 100-year time horizon. In the mitigation scenarios presented later in this chapter, mitigation from afforestation and improved forest
management are presented separately for GTM; however, in its current format, this differentiation is not possible in the baseline GTM runs.

In contrast, the factors driving the rise of baseline
agricultural emissions in GLOBIOM reflect the global
prominence of U.S. agriculture. First, the United States
maintains a strong role in both crop and livestock commodity
production relative to other regions, so production
expands to meet global demand12 (U.S. Department of
Agriculture, n.d.) relative to the model's assumed increase
in technologically driven yield growth13 (Figure 3-2).

Similarly, total livestock populations and emissions grow
slightly over time, as underlying model parameters from the
SSP2 scenario (Riahi et al., 2017) project populations and
wealth across the world will increase, driving up demand
for meat products (Figure 3-2). Further, GLOBIOM livestock
sector emissions are approximately 33% higher than those
in FASOMGHG across the full time horizon due to higher
livestock sector production levels in the baseline.

12 Note that by the United States increasing its role as a global agricultural producer, there is the potential to lower global emissions because other regions
that may use less efficient production systems can reduce output.

13 In GLOBIOM, exogenous yield growth requires that a level of input use intensification accompanies yield gains..

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Figure 3-3 shows the projected baseline trends in land
emissions divided by gas. In both FASOMGHG and GLOBIOM,
CH4 and N20 emissions are projected to increase slightly
overtime, as agricultural production increases to meet
growing demands. FASOMGHG projects that by 2050,
CH4 emissions will have increased by 3% relative to 2025
levels (increasing from 170 Mt C02e yr"1 in 2025 to 175
Mt C02e yr"1 in 2050). GLOBIOM projects similar rates of
growth in CH4 emissions with 2050 emissions 5% higher
(181 Mt C02e yr"1) than 2025 (173 Mt C02e yr1). Both
models include CH4 emissions from rice cultivation, manure,

and enteric fermentation from livestock, and burning of
agricultural residuals. FASOMGHG and GLOBIOM also
project similar growth over time for N20 emissions from
the agricultural sector. FASOMGHG projects that 2050
emissions will be less than 1% higher than 2025; GLOBIOM
projects an increase of 5% in the same period.

FASOMGHG tracks N20 emissions from volatilization
and leaching from nitrogen applied to soils, fertilizer
applications, pasture and manure for livestock production,
draining of agricultural soils, and burning of agricultural

FIGURE 3-3

Annual emissions, by GHG (C02, CH4 and N20), under the baseline scenario (in
Mt C02e, 2020-2059)

o
o

c
o
in
w

750-

500-

250-

-250-

-500-

-750-

2025

2030

2035

2040

2045

2050

CH4, FASOMGHG
CH4, GLOBIOM

C02, FASOMGHG
CO,, GLOBIOM

C02, GTM
N,0, FASOMGHG

N20, GLOBIOM

Results are presented in terms of atmospheric accounting. Therefore, positive flux equates with emissions; negative flux represents
sequestration. Initial values in each model differ due to varying GHG pools included in each model as discussed in Chapter 2, such as
FASOMGHG including emissions from on-farm fuel consumption, which GLOBIOM does not. Additionally, GTM and GLOBIOM include
representation of Alaska, while FASOMGHG does not. C02 values represented here are net estimates.

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residues. GLOBIOM includes N20 emissions from fertilizer
applications, pasture and manure for livestock production,
and burning of grassland and agricultural biomass. Under
the baseline, FASOMGHG projections for the magnitude of
N20 emissions are lower than GLOBIOM due to GLOBIOM
including more sources of emissions from soil management
activities including synthetic fertilizer application, manure
applied to soils, organic soils, and crop residues, while
FASOMGHG accounts for N20 emissions from residue
burning, fertilizer applications, histosols, leaching, manure,
pasture, and volatilization. For C02, both FASOMGHG and
GLOBIOM project that the AFOLU sector will remain a net
sink through 2050, while similarly, GTM projects that the
forest sector will continue to be a net sink from 2025 to
2050. FASOMGHG and GLOBIOM estimate the net C02 sink
will have declined by 29% (134 Mt 002) and 18% (114 Mt
C02) by 2050 respectively. On the other hand, GTM projects
that the forest sector will slightly increase as a net sink, with
sequestration increasing by 3% in 2050 relative to 2025.

Loblolly pines grown as a comrnerical crop in northern Florida.

3.1.2 Baseline Land Projections and Market
Dynamics Across Models

GHG emissions dynamics in the three models incorporate
estimated changes in land use, land management, and
expected demand for land-based products. The following
sections summarize the results for land use and commodity
production for forestry, cropland, and livestock across
models under the baseline scenario.

3.1,2.1 Forestry

All three models project that forest area will either remain
unchanged or expand in the U.S. baseline from 2025 to
2050 and beyond to meet growing national and global
demands for HWP driven by population growth and
increasing consumption per capita in the future. FASOMGHG
projects 10 million acres and GLOBIOM projects 5 million
acres of net forest land expansion in the baseline by
2050, while GTM projects that nearly 19 million acres of
net afforestation/reforestation occurs during the same
period (Figure 3-4). Note that the initial forest area varies
across models as they rely on different underlying datasets
(FASOMGHG and GTM utilize FIA, GLOBIOM uses values
presented by UN FAO) and the inclusion of Alaska in
GLOBIOM and GTM, but not in FASOMGHG.

In FASOMGHG, plantation forest area increases by about 1
million acres per year from 2025 to 2050 (increasing from
about 76 million acres to 106 million acres). GTM projects
less expansion of plantation forests, with plantation forest
area growing from 56 to 67 million acres from 2025 to 2050
(Appendix Table A-2).

Despite the varying amounts of intensification and
extensification in the forestry sector across each model,
overall harvest (both sawtimber and pulpwood) is consistent
across the models, with gradual increases in harvest levels
in FASOMGHG and GLOBiOM, and a relatively constant rate
of harvesting in GTM. In 2025 the models project harvest
levels of 361 million metric tons (mmt) in FASOMGHG, 369
mint in GLOBIOM, and 392 mmt in GTM. In 2050, harvest
levels equal 399 mmt in FASOMGHG, 414 mmt in GLOBIOM,
and 363 mmt in GTM (Appendix Table A-3). The slight
increase in harvest levels in FASOMGHG and GLOBIOM is
mainly driven by high income elasticity combined with the

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FIGURE 3-4

U.S. forest area under baseline scenario (in million acres, 2025-2050)

800-

600-

£
o
<
c
o

400-

200-

2025

2030

2035

2040

2045

2050

FASOMGHG

GLOBIOM

GTM

Initial values in each model differ due to varying types of forest areas included in the initial data sources: GLOBIOM is based on FAO data
while FASOMGHG and GTM are based on FIA data. Additionally, GTM and GLOBIOM include representation of Alaska, while FASOMGFIG
does not.

income growth parameters that translate into increasing
demand for things such as housing leading to expansion
of (domestic or global) timber production. In GTM, global
demand for timber increases in the future while the U.S.
market share remains relatively constant.

Trade parameters for timber products vary across the
models, affecting respective GHG emissions results.
FASOMGHG has constant exogenous trade levels for forest
products based on work by Daigneault and Favero (2021).
GLOBIOM includes endogenous global trade, with bilateral
trade represented as well. GTM does not explicitly model
trade, but instead includes a global demand for timber
products that each region meets according to its domestic
supply. All three models project that global demand will
increase for forest commodities over time largely driven

by the harmonized global consumption per capita, and in
response, the domestic production in the United States
changes, with FASOMGHG and GLOBIOM projecting an
increase while GTM shows a decline by 2050 relative
to current levels. As domestic wood products demand
increases, GLOBIOM projects also that the United States
will increase imports overtime. Imports for wood products
including plywood and sawnwood increase by about 29%
(1.1 million m3) and 53% (16.7 million m3) from 2025 to
2050 respectively, while the slight increase in forest harvest
levels is mainly attributed to energy uses. These increases
in domestic demand and imported harvest wood products
contribute to a declining carbon sink from the U.S. forestry
sector in GLOBIOM, as investments in reforestation do not
outweigh the increase in harvesting activities and aging of
forests.

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Aerial view of a tractor spraying fertilizer on plants in an
agricultural field, California.

To meet the rising demand (global or domestic), GTM
and FASOMGHG project new investments in expanding
forest area and in increasing highly productive plantation
forests.14 Some of these plantation investments are made
post-harvest to convert formerly naturally regenerating (and
slower growing) forests into plantations, consistent with
forest planting trends observed in the United States since
the 1950s (McEwan et al., 2020; Wade et a!., 2019). On
the other hand, GLOBIOM does not significantly increase
management investments or change forest type in response
to economic market signals of timber products, largely
because in a single time step the model only incurs the
upfront costs from these activities, while the future benefits
are not considered given its recursive modeling framework
(see Daigneault et a!., 2022 for a comprehensive discussion
on differences between GTM and GLOBIOM).

3.1.2.2 Cropland

Future cropland area projections in FASOMGHG and
GLOBIOM are driven by different yield growth parameters
and endogenously determined crop mixes, as well as by
how each model responds to global markets. Specifically,
FASOMGHG uses higher yield growth rates, based on higher
expectations of technological improvements overtime, than
GLOBIOM. This difference will lead to a different demand
for cropland under the same socioeconomic scenario. For
instance, for the same projected food demand growth, a
model with low agriculture productivity will project a higher
increase in land requirements for food production than
a model with a high productivity rate. As the demand for
cropland increases over time, the cost of converting land
into forests increases.

In FASOMGHG, technologically driven yield growth is based
on USDA National Agricultural Statistical Service national
data of crop and livestock yields from 1960 to 2009 and
projected into the future (for additional information see
Baker et al., 2013). From 2025 to 2050, FASOMGHG
projects that cropland area will decline by about 30 million
acres, or about 1 million acres annually (Figure 3-5).
In GLOBIOM, yield growth rates are econometrically
estimated using historical data from 1980 to 2010 and then

14 GTM increases plantation forest area to meet future global demand
post 2050. In the medium term, domestic harvest rates decline relative
to 2020 levels, with domestic demand being met through increased
imports.

projected using GDP per capita from SSP2 (as seen in Fricko
et al., 2017). The yield growth rates in GLOBIOM are lower
than FASOMGHG which drives a lower decline in the U.S.
cropland than FASOMGHG. GLOBIOM projects that between
2025 and 2050, about 5 million acres (or about 200,000
acres annually) of cropland will convert to other uses, which
results in a slight increase in crop-related non-C02 emissions
due to increased usage of agricultural inputs such as
fertilizer (Figure 3-5).

Finally, GTM does not explicitly model cropland, but it does
include rental supply functions. For example, if global timber
prices rise relative to farmland values, GTM projects that
timber owners will rent suitable farmland for at least one
rotation (increasing total forestland). Similarly, if global
timber prices fall relative to farmland values, forestland will
be converted back to farmland upon harvest. This approach
reflects that the least productive crop- and pastureland will
be converted first and that rental rates increase as more
land is converted and thus becomes scarcer in the future.

Projected crop mixes in 2050 between the two models
vary, with shifts in global demand patterns represented in
GLOBIOM leading to a shifting crop mix in the United States
overtime, while FASOMGHG has a relatively stable crop
mix from 2025 to 2050 (Appendix Table A-4). For instance,
in 2050, FASOMGHG projects that about 25% of cropland

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area is devoted to both corn and wheat, 20% to soybeans,
and about 15% to hay. GLOBIOM projects a higher level of
specialization in corn and soybean production in the United
States with about 35% of total cropland area dedicated to
each crop, while wheat production covers only about 10% of
cropland area, and cotton covers about 7% (Appendix Figure
A-4). In GLOBIOM, wheat production declines in the United
States overtime. Shifts in crop production within each
model also impact livestock production due to changes in
feed market dynamics, as discussed further in the following
section.

Finally, global demand for crop commodities impacts the
baseline in both FASOMGHG and GLOBIOM. In FASOMGHG,
major U.S. export products, such as corn and soybeans,
are projected to remain at relatively consistent levels of
exports relative to 2025, with changes being within ±5%
of today's values from 2025 to 2050. GLOBIOM projects
that the United States will expand its role as a net exporter
of soybeans with annual exports increasing by about 1%
annually from 2025 to 2050. At the same time, corn exports
are expected to drop, with levels 20% lower than today in
2050.

FIGURE 3-5

U.S. cropland area under baseline scenario (in million acres, 2025-2050)

400-

300-

£
o
<

200-

100-

2025

2030

2035

2040

2045

2050

FASOMGHG

GLOBIOM

Cropland is not explicitly modeled in GTM, so that model is not included. Initial values in FASOMGHG and GLOBIOM differ due to varying
types of areas included in the initial inventory.

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3.1.2.3 Livestock

FASOMGHG projects increased livestock production in the
baseline scenario, including growth for chicken, beef, and
pork driven by increasing demand through growing per
capita GDP. For instance, beef production in FASOMGHG
grows from about 13 million tons in 2025 to 16 million
tons a year by 2050, while both chicken and pork output
increase from about 17 million tons to 21 million tons
(Appendix Table A-5). Additionally, FASOMGHG projects that
about half of the increase in production of meat is exported
from the United States, while the other half is consumed
domestically. As discussed further in the mitigation section
below, the increase in meat production in FASOMGHG in
the baseline allows for more mitigation opportunities in the
livestock sector once price incentives are implemented.

GLOBIOM differs in its demand growth rates, which results
in steady levels of production for both pork and beef
over the projection period (remaining at about 12 and
10 million tons from 2025 to 2050 respectively), while
poultry production increases from about 20 million tons to
24 million tons annually (Appendix Table A-5). The share
of total caloric intake met by meat products in the United
States remains fairly constant from 2025 to 2050, with
increasing overall consumption of chicken meeting the
growing demand. Similar to the change in production, net

trade of meat products in the United States is projected to
remain relatively constant from 2025 to 2050 as increasing
domestic production is used to meet increasing demand.

Both GLOBIOM and FASOMGHG project that pastureland
area will increase in the baseline scenario to meet growing
demands for animal products (Figure 3-6). Between 2025
and 2050, FASOMGHG projects 10 million new acres of
pasture (about 400,000 acres per year) while GLOBIOM
projects lower rates of conversion over the same period,
with pastureland area increasing by 2 million acres by 2050
(about 60,000 acres per year) as GLOBIOM relies more on
livestock production through intensive feeding operations.

FASOMGHG and GLOBIOM vary in the baseline projections
of livestock commodity exports from 2025 to 2050.
FASOMGHG projects that the United States will expand
its export quantities of beef, chicken, and pork to satisfy
a growing global demand for meat products. Conversely,
GLOBIOM projects that exports of U.S. meat products will
remain relatively consistent with ievels of exports in 2050
being ±2% relative to 2025 levels, as other regions increase
production of meat products to meet domestic demand and
rely less on U.S. imports.

Aerial view of poultry houses and farm in Tennessee.

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FIGURE 3-6

U.S. pastureland area under baseline scenario (in million acres, 2025-2050)

300-

200-

b
<
c
o

100-

2025

2030

2035

2040

2045

2050

FASOMGHG

GLOBIOM

Initial values in FASOMGHG and GLOBIOM differ due to different land use categories included in the initial model inventory. Specifically,
GLOBIOM does not differentiate between pasture and grasslands. However, the productivity of these lands varies greatly to reflect
heterogeneity in ability to provide grazing opportunities. Pastureland is not explicitly modeled in GTM so it is not included.

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3.2 IVIACCs

In 2050 at a GHG price of $100/t
C02e, the land use sector can abate
around 250-350 million t C02e.

This section uses the results from the multi-model
assessment to estimate the cost to reduce GHG emissions
across different mitigation options in the land sector and the
magnitude of those projected reductions. Specifically, the
analysis estimates the cost of removing an additional ton of
C02e across different land use-based activities by creating
the MACCs for the land sector from each economic model.

MACCs have been used in a variety of contexts by
policymakers, investors, land managers, and economic
modeling teams to inform mitigation assessments (e.g.,
EPA, 2009, 2019b). Moreover, model-derived projections
of abatement potential that align with widely utilized
macroeconomic scenarios such as the SSPs (Riahi et
al., 2017) are useful in informing policy or integrated
assessment modeling efforts because they directly capture

Small wheeled loader moving logs around the log yard at a local
sawmill in Oregon.

economic opportunity costs of investing in alternative
mitigation technologies in a competitive market (Calvin,
2016; Daigneault& Favero, 2021; Doelman et a I., 2018;
Wade et al., 2022; Wei et al., 2018).

Furthermore, MACCs are time-dependent, and therefore
can be used to assess how mitigation opportunities change
for different simulation time-steps and across models. This
temporal disaggregation is particularly useful in a policy
context when mitigation commitments are made over
different timeframes. Moreover, mitigation assessments
that treat marginal abatement costs as constant over long
time horizons could over- or understate mitigation potential
(Fargione et al., 2018), relative to a dynamic modei that
cumulatively tracks opportunity costs and market feedback
(Austin et al., 2020; Cai et al., 2018; Wade et al., 2023).

To build the MACCs, the analysis aggregates the projected
GHG mitigation results across the 10 price incentive
scenarios and two growth rate pathways presented in
Section 2.7 for each modei (see Appendix Table A-l). It is
important to note that the price values only GHG abatement
actions and does not consider possible co-benefits or side-
effects such as gains or losses of biodiversity from specific
land-based mitigation activities.

In the following sections, the MACCs are presented for
the whoie land sector and followed by a more detailed
discussion of the MACCs by GHGs (CC2, N20, and CH4) and
by sector (forestry, cropland, and livestock).

3.2.1 AFOLU MACCs

The AFOLU MACCs reflect the economically efficient mix of
projected GHG mitigation actions across the land sector
as modeled by FASCMGHG and GLOBICM. These results
therefore present a comprehensive, cost-effective mix of
mitigation activities within the forestry, crop, and livestock
sectors. Each model responds with its cost-effective
composition of mitigation activities, which varies across
GHG prices and periods. That is, while the results indicate
reasonable alignment in total projected cost-effective
mitigation potential across a broad range of U.S. forest and
agriculture-based mitigation strategies, there are important
differences between GHG mitigation opportunities utilized

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in each model under each price scenario, as discussed
below. Furthermore, MACCs show how sensitive sector-
specific abatement opportunities are to the GHG price
and to if and when each abatement opportunity reaches a
maximum level of mitigation capacity. Specifically, by looking
at the steepness of each MACC, it is possible to measure
how much an incremental increase in the value of C02e
is likely to increase the level of abatement. For instance,
a very steep MACC shows that it is very costly to abate an
additional ton of C02e in that specific activity compared to
another activity with a flatter MACC. The steep slope of the
MACC in one sector relative to the other sector reflects high
opportunity costs of abatement per unit area in that sector.

Figure 3-7 shows MACCs that aggregate all land-based
(both agriculture and forestry) activities in FASOMGHG and
GLOBIOM in the short- and medium-term time horizons
(2030, 2050) for each price growth scenario (1% and 3%).
Moreover, the figure shows the projected emissions in the
sector under the baseline scenario and the GHG mitigation
scenarios. Abatement presented in the MACCs is measured
as the difference between emissions in the baseline and the
emissions under each GHG price scenario for each model.

In the short term (2030), the mitigation scenario results
show that under a GHG price of $100/t C02e, the land use
sector can abate about 205-300 Mt C02e yr"1 in FASOMGHG
and around 195 Mt C02e yr"1 in GLOBIOM. This outcome
corresponds to about doubling the net sink in GLOBIOM
relative to the baseline. GLOBIOM shows potential for
significant abatement even under low GHG prices with
abatement above 100 Mt C02e yr1 under a GHG price of
$5/t C02e. The mitigation is derived mainly from increased
carbon sequestration from changes in forest management
on existing stands, including changes to harvest schedules.
In 2050, at a price of $100/t C02e, FASOMGHG projects
annual rates of mitigation of 250-350 Mt C02e yr"1 while
GLOBiOM estimates 280-300 Mt C02e yr"1 of mitigation.
Compared to 2030, the same price of $100/t C02e in 2050
could deliver more abatement because there is more time
to achieve the long-term mitigation benefits of strategies in
forest management and afforestation/reforestation, given
that the stock has more time to increase than in the short
term.

Figure 3-7 highlights how price growth rates have different
impacts in each model depending on its structure. For
instance, an intertemporal optimization model like
FASOMGHG selects a cost-effective mitigation portfolio

ft#* t.

Herd of cattle grazing in a fenced-in field at sunset, Warrenton, Virginia..

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FIGURE 3-7

A) AFOLU marginal abatement cost curves; B) AFOLU absolute emissions under
baseline and mitigation scenarios in 2030 and 2050

FASOMGHG

GLOBIOM

200

100

o
o

200

100

200

300

100

200

300

Mt C02e
1 % Growth — 3% Growth

2030

• ••• •

• ••• • • H •

• • ••••••

2050

• • • • • •

• •

-500

-400

-300 -200

Mt C02e/yr

• • ¦

-100

FASOMGHG • GLOBIOM

Baseline # Mitigation

A) MACCs are built using the abatement under each GHG price scenario starting at $5/t C02e. Five observations per year are used to
build each MACC. MACCs show the level of abatement in Mt C02e (x-axis) associated with a specific monetary value of GHG emissions
in $/t C02e (y-axis) for a specific reference year (2030 and 2050). GTM is not included in the figure because it does not explicitly model
agriculture. B) Absolute emissions under baseline and mitigation scenarios for net emissions from agriculture, forestry, and other land use
(AFOLU) in FASOMGHG and GLOBIOM. Results are presented in terms of atmospheric accounting. Therefore, positive flux equates with
emissions; negative flux represents sequestration.

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by considering current and future projected GHG prices.
Under this specific framework, contemporaneous and future
land use and land management decisions depend on the
expected price growth overtime. Changes in growth rates
have implications even in the short term. Specifically, high
growth rates (e.g., 3% in this report) have the potential
effect of driving a lower adoption of mitigation actions in
the short term relative to low growth rate scenarios. The
model anticipates higher prices in the future and thus
higher returns on mitigation actions in the future when
postponed. In comparison, at the same initial price the
lower growth rate (1%) yields relatively higher adoption of
short-term mitigation activities because future anticipated
returns are lower. For instance, under the same GHG price
path in 2030, FASOMGHG projects more mitigation under
the 1% growth rate when future prices are not expected to
grow as fast as under the 3% case (as shown in Figure 3-7
by the gap between the 1% and 3% growth MACCs). Given
the same forward-looking nature of GTM. similar effects will
occur in the forest-only MACCs presented in Section 3.3.1.
Conversely, mitigation potentials projected by GLOBIOM,
a recursive dynamic model, are not sensitive to future
expectations on key variables such as the GHG price (the
gap between the two MACCs is not significant).

Across the mitigation scenarios, emissions reductions as
calculated versus the baseline for the AFOLU sector are
projected to be 32-364 Mt C02e yr"1 in FASOMGHG and
163-309 Mt C02e yr"1 in GLOBIOM by midcentury, meaning
the two models expected a similar maximum magnitude of
mitigation potential from the land sector (Appendix Figure
A-5). Under the lowest price scenarios (scenarios $5 at 1%,
$5 at 3%, $20 at 1%, $20 at 3%), FASOMGHG projects an
average annual mitigation potential of 32-132 Mt C02e
yr"1 by 2050. These scenarios correspond to a GHG price
level below $50/t C02e in 2050. Under the highest price
scenarios (starting at $35 at 1%), the range of abatement
increases to 239-364 Mt C02e yr"1. On the other hand,
under the same high price scenarios, GLOBIOM estimates
an average annual mitigation potential of 245-309 Mt C02e
yr"1 in 2050 (between $85 and $243/t C02e).

Looking at individual mitigation activities, results show that
at low and moderate price scenarios, each model achieves
similar mitigation levels but through different activities
(Figure 3-8).

Each model chooses to implement different cost-effective
land-based activities to reduce emissions or increase
sequestration as a specific price response. For example, the
average annual mitigation from 2025 to 2050 is projected
to be 32-364 Mt C02e yr"1 in FASOMGHG and GLOBIOM,
but GLOBIOM projects higher volumes of mitigation from
the livestock sector and forest management compared to
FASOMGHG. In FASOMGHG, the livestock sector contributes
14% of total reduction on average, while in GLOBIOM the
proportion is 18%. Forest management contributes 27%
in FASOMGHG and 59% in GLOBIOM. On the other hand,
anticipating future returns from forestry, FASOMGHG invests
in higher rates of afforestation/reforestation compared to
GLOBiOM, which only looks at the current year incentives.
Thus, FASOMGHG projects higher increases in both
carbon sequestered in new forests and increased carbon
sequestration in forest soils (afforestation and reforestation
contributes 42% of total reduction on average in FASOMGHG
while in GLOBIOM, the contribution is 8%).

Domestic MACCs are likely to be affected not only by
interna! market dynamics and land competition but also by
policies, land use practices, and demand for land-based
products from the rest of the world. The results presented
in this chapter assume that the same GHG price incentives
are applied to the land sector at the global level; thus,
landowners around the world have the incentive to reduce
high-emitting activities and start/increase sequestration
activities. These dynamics are assessed and discussed
using the specific attributes of GLOBIOM as a global model
with a detailed description of the land sector in Box 4.

Log barge, Kachemak Bay; Alaska.

aaa-. ¦¦ m.rskiw.

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FIGURE 3-8

Average annual change in GHG flux in the land sector from the baseline across
mitigation scenarios and models, by GHG category (in Mt C02e yr\ 2025 to 2050)

o
o

50
0
-50
-100
-150

100
0

-100
-200

100-
0-

CO -100
-200
-300
100
0

Eo -100

-200
-300

o
o

-200-

-400-

FASOMGHG
1 % Growth

FASOMGHG
3% Growth

GLOBIOM
1 % Growth

GLOBIOM
3% Growth

2020- 2030- 2040- 2050-
2029 2039 2049 2059

Net Emissions

Cropland C02
Cropland Non-C02

Livestock Non-C02

Afforestation/

Reforestation

Forest Management
Forest Products

Agricultural Soils
Forest Soils

Baseline GHG emissions from the land sector by GHG category are presented in Figure 3-1. GHG categories are described in Figure 2-3.
FASOMGHG includes all GHG categories, while GLOBIOM does not include forest products, forest soils, or cropland C02. As the results are
presented in atmospheric accounting terms, negative values equal more mitigation than the baseline. GTM is not included because it does
not explicitly model emissions from agriculture. Initial values differ among the models because they measure the change from baseline
values and because the models respond differently to different GHG prices. Note the differences in scale across the graphics. See Box 2 for
further discussion on movement of soil carbon across land use categories when land use change occurs in FASOMGHG.

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Box 4

SENSITIVITY: Estimated
differences in outcomes between
global and domestic-only GHG
emissions approaches

The MACCs presented above show the mitigation potential of the land sector in the United
States under a GHG price incentive applied worldwide (for the global models used in this
report). While a worldwide GHG price is the most efficient and the cheapest route to cut GHG
emissions, public support for carbon pricing varies across the world and technical and political
complexity limits the practicality (Haites, 2020; Pollitt, 2019; Steinebach et al., 2021). It is
more likely that before a global carbon pricing scheme is agreed upon, individual countries will
unilaterally continue to set policies aimed at GHG reductions. For instance, as countries aim
to meet their Paris Agreement targets, many have already set carbon pricing schemes (e.g.,
the European Union, Japan, Australia) (World Bank, 2023). To test the effects of a more likely
near-term policy scenario, a domestic set of GHG prices on land emissions in the United States
is implemented in GLOBIOM and compared to the global GHG price scenarios presented in the
report.

Results show that a domestic U.S.-only GHG price scenario always projects more abatement in
the United States than the global GHG price scenarios in GLOBIOM. This dynamic is driven by
the changes in the opportunity costs of land-based mitigation activities in the United States
relative to the rest of the world when the price incentive is applied globally. Specifically, when
the price is applied globally, abatement in the United States becomes relatively more expensive,
therefore less domestic mitigation is reported under the same price. Under a global program,
more land is projected to be converted into forests, with correspondingly less agriculture and
thus higher land prices. This effect is more significant under higher GHG price scenarios and in
the long term. For instance, in 2050 under the price scenario of $50/t C02e, the United States
sequesters 7% more than it would with the same price applied globally while under a high price
level of $135/t C02e, it sequesters 10% more than the global price scenario (Figure B4).

The United States reaches more abatement under the domestic GHG price scenario by
converting more cropland and pasture to forest than under the global price scenarios, as well as
by retiring land from agricultural production. For instance, in 2050 under a GHG price of $50/t
C02e, the United States converts 5 million acres of cropland, 1 million acres of pasture, and
2 million acres of other natural land to forests, while gaining 8.1 million acres of forested area
relative to the global GHG price scenario. In the highest price scenario of $100 at 3%, cropland

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loss would be 31 million acres, pasture loss would be 12 million acres, and gains in forested
area would be 7 million acres. Other natural land (undeveloped land which is not agriculture,
pasture, or forestry) increases by 35 million acres, as cropland with limited tree-growing
capacity is retired from agricultural production.

A unilateral GHG price is likely to indirectly affect agricultural production in the United States
relative to the rest of the world, lowering the United States' competitiveness because of the
additional domestic costs included in GHG-intensive production processes and goods. For
instance, under the highest price scenario, the U.S. share of global agricultural production falls
from 9% in the global price scenario to 7% in the U.S.-only price scenario. Further, demand for
land-intensive agriculture products, such as meat and dairy, are expected to increase in the
coming decades (see for example Komarek et al., 2021), and much of the supply for those
products might be displaced to potentially less efficient production systems in other countries,
resulting in higher meat prices and lower consumption.

Moreover, results show that a unilateral policy (like a domestic GHG price) is likely to create
GHG leakage. As more domestic mitigation actions are implemented in response to the GHG
price, more emissions will be produced in the rest of the world to make up for commodity supply
losses caused by the management changes driven by the GHG price, possibly offsetting total
cumulative abatement from the land sector globally. Table B4 shows that as much as 11% of the
estimated mitigation gains achieved in the United States are erased by changes in production
patterns and mitigation strategies elsewhere in the world under the U.S.-only GHG price. These
results are similar to findings in a GTM analysis that quantifies U.S. forest sector mitigation
potential for unilateral and global policy scenarios (Baker et al., 2017). It is important to note
that the leakage effect could occur when other countries implement GHG mitigation strategies,
but the United States does not.

The AFOLU sector is globally interconnected, and the model runs described here illustrate
how unilateral climate policy by one country (in this case the United States) might affect LUC,
likely shifting some of the country's commodity production as well as displaced emissions to
other parts of the world. They also highlight the importance of accounting for global market
interactions and related GHG outcomes as countries are developing national policies aimed at
climate and other sustainability targets.

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Figure B4: Marginal abatement costs of the U.S. land sector in 2030 and 2050 under U.S.-only GHG price scenarios
and global GHG price scenarios, GLOBIOM

o
o

O
lO

300-
200-
100-
0

300
200-
100-
0-

1 % Growth

3% Growth

100

200

300

100
Mt C02e/yr

200

300

Global

U.S. Only

Table B4: Average annual mitigation for the land sector in the United States and in the rest of world (ROW) under a
global GHG price and under the U.S.-only GHG price scenario from 2025 to 2050 (Mt CO.e yr1)

Global GHG Price Scenario

U.S.-Only GHG Price Scenario

Average GHG Price in
$/t C02e (2025-2050)

U.S. Mitigation
(Mt C02e yr1)

ROW Mitigation
(Mt C02e yr1)

U.S. Mitigation
(Mt C02e yr1)

ROW Mitigation
(Mt C02e yr1)

$5-20

175-185

2,822-3,321

179-191

(4)-(3)

$21-40

228-249

4,653-5,085

246-268

(16)—(12)

$41-90

265-302

5,553-6,277

288-333

(35)-(16)

$91-165

327-339

6,544-6,724

358-373

(41)-(31)

The Average GHG Price column shows the 2025-2050 average prices across the 10 mitigation scenarios. Positive mitigation values reflect
net mitigation and values in parentheses represent net emissions.

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3.2.2 Gas-Based MACCs

This section presents estimated mitigation opportunities in
the land sector by focusing on each GHG across FASOMGHG,
GLOBIOM, and GTM (C02 only).

Figure 3-9 shows the projected changes in emissions from
each GHG (C02, N20, and CH4) under GHG price scenarios. In
2050 under the highest GHG price scenario of $100 at 3%,

the maximum C02-only emission reductions are expected to
be 302 Mt C02 in FASOMGHG, 204 Mt C02 in GLOBIOM, and
800 Mt C02 in GTM (it is important to note that C02 is the
only gas represented in GTM). Under the same scenario, CH4
emissions are expected to decline relative to their baseline
projections by 60 Mt C02e in FASOMGHG and 65 Mt C02e
in GLOBIOM. Finally, for N20 the abatement is 7 Mt C02e in
FASOMGHG and 40 Mt C02e in GLOBIOM.

FIGURE 3-9

Average annual change in GHG flux in the land sector by GHG from the baseline
across scenarios and models (in Mt C02e yr\ 2025 to 2050)

FASOMGHG GLOBIOM GTM

-200-

O -400-

-600-
-800-

2025 2030 2035 2040 2045 2050

$5 at 1 % $20 at 1 % $35at1% $50at1% $100 at 1%

$5 at 3% $20 at 3% $35 at 3% $50 at 3% $100 at 3%

Baseline GHG emissions from the land sector by GHG are presented in Figure 3-1. Because the results are presented in atmospheric
accounting terms, negative values equal more mitigation than at the baseline. GTM models only C02 in the forestry sector. Initial values
are different because they measure the change from baseline values, because the models respond differently to different GHG prices, and
because the models include different mitigation strategies across GHGs. Note the differences in scale across the graphics.

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Figure 3-10 presents the MACCs for each gas in the short
and medium term.

in 2030, at a GHG price of $100/t C02e, mitigation of C02
varies across models. FASOMGHG projects about 130-
254 Mt C02 yr1. GLOBIOM projects 110 Mt C02 yr"1, and
GTM projects 350-500 Mt C02 yr"1. Across CH4-MACCs and
N20-MACCs, GLOBIOM consistently projects higher rates of
mitigation than FASOMGHG for each price tested, in 2030,
at a GHG price of $100/t C02e, CH4 mitigation is projected
to be 50 Mt C02e yr"1 and 34 Mt C02e yr"1 in GLOBIOM and
FASOMGHG, respectively. N20 is lower than both C02 and
CH4—equal to 3 Mt C02e yr"1 and 18 Mt C02e yr"1 at a price
of $100/t C02—in FASOMGHG and GLOBIOM. in 2030, CH4
emissions are projected to be below 30% relative to 2020
levels at a GHG price of 116$/t C02e in GLOBIOM.

In 2050, the C02-only mitigation potential is higher than
2030 across models per GHG price tested. For instance,

at a GHG price of $100/t C02e, FASOMGHG projects
200-280 Mt C02 yr"1, GLOBIOM projects 200-210 Mt C02
yr"1, and GTM projects 350-500 Mt C02 yr"1. For CH4-
MACCs, both FASOMGHG and GLOBiOM show increasing
maximum mitigation potential to 42 and 57 Mt C02e yr"1
at a GHG price of $100/t C02e, respectively. Finally, N20
abatement potential also increases at the same GHG price
of $100/t C02e in 2050 relative to 2030 in FASOMGHG
and GLOBIOM, with projections of 4 and 22 Mt C02e yr"1,
respectively.

While mitigation potential may be smaller for non-C02
gases, the MACCs show that there are ample cost-effective
opportunities available for both CH4 and N20 and that they
could play a role in achieving GHG mitigation goals. Finally,
the forward-looking structure of FASOMGHG is likely to affect
only C02 abatement opportunities while non-C02 MACCS are
not sensitive to the growth rate of GHG prices because the
benefits do not accumulate over time.

Fields of crops in a valley in the desert climate of Arizona just outside of Phoenix.

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FIGURE 3-10

GHG-based marginal abatement cost curves in (A) 2030 and (B) 2050

FASOMGHG

GLOBIOM

GTM

(A)

200-

150-

100-

50-

200-

150-

100-

O
LO

200-

150-

100-

O
LO

}

0 100 200 300 400 500

0 100 200 300 400 500 0 100 200 300 400 500

(B)

300-

200-

100-

300-

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3.3 Activity-Based MACCs

This section presents and describes
the MACCs of the land sector
divided by three subsectors: forestry,
agriculture, and livestock.

Each subsection discusses the role of all the mitigation
activities included in each model as shown in Figure 2-6,
within the three main land categories (forests, cropland, and
pastureland) to help interpret the MACCs results.

3.3.1 Mitigation in Forests

This section presents the MACCs for the forestry sector
only across the three models in the short and medium term
(2030 and 2050). FASOMGHG and GTM each represent
all five abatement options (Appendix Figure A-6), while
GLOBIOM includes three of the mitigation activities.

Individual pools include existing forest (above- and
belowground), new forest (above-and belowground), carbon
stored in HWP, and soil organic carbon in forested lands. The
models include multiple activities that can increase carbon
storage in existing forests, and all three models can extend
(or curtail) timber harvest rotation lengths. FASOMGHG and
GTM allow for increased management intensity through
activities such as intensive planting and thinnings, and
all three models allow for avoided forest conversion. Each
model also represents afforestation/reforestation and land
use competition and tracks additional carbon stored in
new or reforested lands. FASOMGHG and GTM also track
carbon stored in HWP and each model can change the
production levels of these products to optimize the amount
of carbon stored in standing biomass and long-lived timber
products. Each model also tracks carbon stored in soils
on forestlands; however, there are not explicit mitigation
activities that increase soil carbon. Instead, carbon stored in
soils is a byproduct of LUC activities such as afforestation/
reforestation.

Figure 3-11 shows the MACCs and the projected emissions
from the forestry sectors in 2030 and 2050 across

the three models. In 2030, at a price of $100/t C02e,
FASOMGHG projects abatement levels from forestry options
of 260 MtCC02 yr1, GLOBIOM of 110 Mt C02 yr"1, and
GTM of 350-400 Mt C02 yr"1. In 2050, at a GHG price of
$100/t C02, FASOMGHG projects abatement levels of 250-
286 Mt C02 yr1, GLOBIOM projects 180-210 Mt C02 yr1,
and GTM projects 350-500 Mt C02 yr"1. Moreover, in
2050, GLOBIOM projects the largest abatement at low
prices ($5 at 1% scenarios), with net sequestration from
forests increasing by 31% relative to baseline, while GTM
and FASOMGHG increase net sequestration by 6% and 8%
respectively.

Despite different representations of timber markets and
different availability of forest-based mitigation activities
(including management techniques), the three models
project a similar net mitigation potential of approximately
32-244 Mt C02 yr"1 under lower GHG prices at $50/t C02
in 2050. This outcome is in line with previous mitigation
analyses of the U.S. forest sector (Baker et al., 2018; Cai
et al., 2018; Daigneault et al., 2022; Van Winkle et al.,
2017). Chapter 4 compares the outcomes of this report with
other literature in more detail. On the other hand, mitigation
estimates diverge significantly under GHG prices higher than
$100/t C02e, as GTM projects high rates of afforestation/
reforestation. In general, GTM projects a much higher rate of
mitigation available from the forestry sector compared to the
other two models. Each of these two models increases forest
area at increasing rates as GHG prices rise, but because
of their explicit representation of agricultural markets, as
agriculture production declines initially, commodity prices
rise, which in turn increases the value of agricultural
land. This progression leads to endogenous limits on
afforestation/reforestation due to market responses, which
result in lower projected amounts of overall mitigation in
FASOMGHG and GLOBIOM compared to GTM.

Appendix Figure A-7 shows the projected reductions in
emissions from the forestry sector until 2050. By 2050
under the highest GHG price scenario of $100 at 3%, the
maximum emissions reduction is equal to 375 Mt C02e yr"1
in FASOMGHG, 200 Mt C02e yr"1 in GLOBIOM, and 832
Mt C02e yr"1 in GTM. The wide range is explained by different
GHG price pathways, alternative mitigation options available
in each model, market dynamics, and other uses of land

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FIGURE 3-11

A) Forest marginal abatement cost curves; B) Forest absolute emissions under
baseline and mitigation scenarios in 2030 and 2050

FASOMGHG

GLOBIOM

GTM

300-

250-

200-

C«)

150-

100-

50-

(1)

300-

250-

200-

lO
o

150-

100-

tn
o

100 200 300

120 160 200 0 250 500 750
Mt CO,e

1 % Growth

3% Growth

( M • «

2030

• • •

2050

»• • •

-1500

-1000

-500

Mt C02e/yr

Baseline

Mitigation

FASOMGHG

GLOBIOM

GTM

A) MACCs are built using the abatement from the forestry sector under each GHG price scenario starting at $5/t C02e. GHG price applies to
all GHG emissions from the land sector but only the abatement from the forestry sector is used to build the curves presented in this figure.
Five observations per year are used to build each MACC. MACCs show the level of abatement in Mt C02 (x-axis) associated with a specific
monetary value of GHG emissions in $/t C02e (y-axis) for a specific reference year (2030 and 2050). Note that the x-axis is different for
each model. B) Absolute emissions under baseline and mitigation scenarios from the forestry sector in FASOMGHG, GLOBIOM and GTM in
2030 and 2050. Results are presented in terms of atmospheric accounting. Therefore, positive flux equates with emissions; negative flux
represents sequestration.

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discussed below. Across the landscape, the introduction of
a price incentive on carbon sequestration drives changes in
land management and land use as well as changes in forest
product markets.

Figure 3-12 compares changes in forest area relative to the
baseline across the different models. All models project an
increase in managed forestland and total forest area under
mitigation scenarios. Projected investment in new forest
stands is quite similar for GTM and FASOMGHG in early
periods, with an increase in afforestation/reforestation of
about 5-75 million acres between 2025 and 2035 across
all mitigation scenarios. Overtime, GTM continues to invest
in afforestation/reforestation and forest management

(especially under GHG price starting above $50/t C02e)
while FASOMGHG slows investment driven by high relative
afforestation/reforestation cost parameters (see Cai et
al., 2018) and begins to stabilize. In 2050, GLOBIOM and
FASOMGHG project 2-80 million acres of new forest relative
to the baseline (about l%-9% increase from present), while
GTM projects 8-112 million acres (about 1%-19% increase
from present). Historically, absent major non-market
incentives to increase forest area, the U.S. forest land
base has remained relatively stable at about 690 million
acres (excluding Alaska and Hawaii) (U.S. Forest Service,
2012). These different responses help explain the range of
mitigation from the forest sector across each model after
midcentury.

FIGURE 3-12

Average annual change in total forest area from the baseline across scenarios
and models (in million acres, 2025-2050)

FASOMGHG

GLOBIOM

GTM

125 ¦

100 ¦

75 -
£
o
<
c
o

50 ¦

25 ¦

0 ¦

2025 2030 2035 2040 2045 2050 2025 2030 2035 2040 2045 2050 2025 2030 2035 2040 2045 2050

$5 at 1 %
$5 at 3%

$20 at 1 %
$20 at 3%

$35 at 1 %
$35 at 3%

$50 at 1 %
$50 at 3%

$100 at 1 %
$100 at 3%

Baseline forest area by model is presented in Figure 3-4. Positive values equal an increase in forest area from the baseline. Initial values
are different because FASOMGHG starts in 2015, GLOBIOM begins in 2000, and GTM starts in 2020.

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Not only is more land projected to be converted to forests
under mitigation scenarios, but the relative magnitude of
intensively managed forest is projected to increase. When
GHG prices are implemented, both FASOMGHG and GTM
increase plantation forest area, with FASOMGHG increasing
plantation forest by as much as 12 million acres, and GTM
increasing by as much as 32 million acres in 2050 relative
to the baseline (Appendix Figure A-8). The expansion of
plantation forests results in both faster growing and higher
carbon density stands relative to passively managed
forests. This forest management decision allows each of
these models to increase the mitigation potential while
still supplying high amounts of HWP in the mitigation
scenarios. GLOBIOM does not include explicit representation
of intensively managed or plantation forest, which results
in GLOBIOM increasing the imports of HWP relative to the
baseline as the model capitalizes on U.S. forests' ability to
efficiently sequester carbon.

The introduction of a GHG price affects the production of
wood products since carbon stored in standing forests
together with carbon in HWP is rewarded, but the magnitude
and directionality (increased or decreased production) of
HWP projected volumes varies across the models.

Interestingly, only the lower price scenarios within GTM
result in an increase in forest timber products relative to the
baseline while all GHG price scenarios in FASOMGHG and
GLOBiOM, and moderate and high GHG prices in GTM, result
in a decrease in harvesting and ensuing HWP. Specifically,
from 2025 to 2050 under GHG price scenarios at or below
$35 at 1%, GTM projects that total harvest will increase
by Q.4%-1.5% (5-20 mnit cumulatively) relative to the
baseline. On the other hand, under all GHG price scenarios,
FASOMGHG and GLOBIOM project a decrease in harvesting
up to 10% from 2025 to 2050 (Appendix Table A-6).

Moreover, harvesting decisions under GHG price incentives
vary in GTM and FASOMGHG due to different modeling
approaches, including a global versus domestic modeling
approach. For instance, in GTM, when GHG prices are
included, the United States expands its forest sector to both
increase carbon sequestration and increase HWP production
long-term after 2050, while other regions (including the
tropics) reduce production to set aside larger portions
of their forest biomass and instead receive carbon rents
(Austin et a!., 2020). This outcome leads to an increase
in carbon sequestered in the U.S.-derived forest products
in GTM under low-priced GHG scenarios (1-5 Mt C02 yr"1

A completed active timber harvest
in the McBride Plantation area just
southwest of Mount Shasta in the
Shasta-Trinity National Forest in
February 2023. The spacing, healthy
crowns, and same-sized, straight,
trees are the goal for what the
plantation should look like. (USDA
Forest Service photo by Paul Wade)

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A fuel treatment area in the Accelerating
Longleaf Pine Ecosystem Restoration
Project in the Osceola National Forest,

Florida. (Forest Service photo by Scott Ray)

in 2050 under the $5 at 1%, $20 at 1%, and $35 at
1% scenarios). On the other hand, FASOMGHG reduces
domestic production of wood products by as much as 27
mint by 2050 relative to the baseline scenario under high
mitigation price assumptions (compared to total output
of 396 mint in 2050 in the baseline). This reduction in
production is due to it being more cost-effective to sequester
carbon through preservation in older forests and receive
carbon payments than to produce HWP for consumption.
Ultimately, GTM and FASOMGHG find the marginal benefit
of leaving some forest stands on the landscape to continue
storing carbon to be more valuable than harvesting them
for forest products under high price scenarios, which leads
to a smaller carbon sink stored in HWP relative to the
baseline. That is, rising GHG prices will drive forest owners
to hold trees for a longer time, effectively increasing the
stock of carbon in forests and increasing the sink capacity
while decreasing harvesting and carbon in HWPs. Tradeoffs
between timber revenue and revenue from carbon stored in
HWP relative to revenue from forest carbon rents are fully
included in the models when they find the optimal solution.

Finally, as mentioned above, terminal years might affect the
results in the models. Since GTM simulations are run for
a much longer timeframe (200 years), there is a temporal
shift in abatement across regions as mitigation price
incentives increase in the long term. Near-term mitigation
action is centered primarily in the tropics, supporting both
reduced deforestation and net forest expansion through
plantation systems because they are more cost-effective
than other forest-based actions (as discussed in Austin et

al., 2020). After 2050, a greater proportion of mitigation
is expected in the temperate and boreal regions globally.
Moreover, terminal years play an important role in explaining
the difference in projected area and timing of plantation
investments between GTM and FASOMGHG. With a shorter
simulation timeframe, FASOMGHG increases plantation
investments early in the simulation horizon and shifts forest
harvests to newly established and converted plantation
systems over time, while GTM has the incentive and ability
to spread investment in plantation forests into the future
because the model is run over a longer time horizon and
planted forests can still be harvested and create a financial
return prior to the terminal year.

Furthermore, projected mitigation results from the forestry
sector are sensitive to the applied biophysical characteristics
of forests, implementation of carbon sequestration
payments, and intrinsic characteristics of the model used
for the analysis. The following focus boxes use the detailed
description of the forestry sector in GTM and FASOMGHG
to test the results presented in this section under
alternative scenarios to better inform the interpretation
of the results. Specifically, Box 5 uses GTM to explore
the effect of the potential mitigation and related costs of
carbon sequestration in forests taking into consideration
C02 fertilization effects. Box 6 explores how sensitive GTM
results are to the terminal year and land availability for new
forests. Box 7 provides a theoretical framework to explore
the effects of endogenously determined participation rates
in a forest sequestration program on the overall mitigation
potential of forests with FASOMGHG.

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Box 5

SENSITIVITY: Representing
effects of C02 fertilization
on forest mitigation

Forest carbon mitigation potential is likely to be affected by future changes in climate
conditions that vary across regions and scenarios. Changes in climate conditions are going to
affect forestland availability and productivity through changes in dieback rates, tree migration,
and C02 fertilization (Favero, Mendelsohn, et al., 2018; Favero et al., 2021; Gonzalez et al.,
2010; Kirilenko & Sedjo, 2007; Schimel et al., 2015; Sohngen & Tian, 2016). This box focuses
on the effects of C02 fertilization on forest mitigation that emerge by running the same baseline
and GHG price scenarios in GTM as found in the main report under an alternative assumption
on C02 fertilization that includes climate change impacts. The version of GTM used in this box
includes carbon fertilization effects in the function that represents regional forest natural
productivity. Regional values of carbon fertilization are based on the estimates presented
in Schimel et al. (2015) and Davis et al. (2022) and are in line with the Representation
Concentration Pathway 4.5 scenario (RCP 4.5).

Under the baseline scenario, results show that when C02 fertilization is included, forests in the
United States are likely to increase their sequestration potential by 28% by 2050. Since forests
are more productive per hectare under the baseline scenario, less land is required to produce
the same amount timber. That is, under the baseline scenario, for the same demand for timber
products, 9 million less acres of land will be converted into forests in the United States when
fertilization is included. Furthermore, fertilization affects regional timber supplies differently
depending on the location and forest types. For instance, under the fertilization scenario, the
United States is expected to increase its average supply by 15% relative to the same global
demand scenario without fertilization between 2025 and 2050.

Under the mitigation scenarios, results show more abatement from the forestry sector when
fertilization is considered. Specifically, when fertilization effects are applied along with the
GHG price scenarios, more sequestration is projected to occur under each GHG price scenario,
with the highest increase in absolute and relative terms under the $100 at 1% scenario.

These changes will affect the MACCs under the same GHG price: that is, carbon fertilization is
projected to abate 29%-82% more by 2030 depending on the price level. By 2050, the forestry
sector—including above- and belowground biomass, forest soil carbon, and HWP—is projected
to sequester between 31% and 58% more than the same price scenario without fertilization
(Figure B5).

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These findings show the importance of testing climate change impacts when estimating the
mitigation potential of forests. Future research should explore other important impacts outside
C02 fertilization.

Figure B5: Marginal abatement costs for forests in 2030 and 2050 with and without C02 fertilization, GTM

1 % Growth 3% Growth

Mt C02e

— With C02 Fertilization — Without C02 Fertilization

MACCs for forests in 2030 and 2050 with and without C02 fertilization in GTM by growth rate scenarios (1% and 3%). MACCs are built using
the abatement under each GHG price scenario starting at $5/t C02e. Five observations per year are used to build each MACC. MACCs show
the level of abatement in Mt C02e (x-axis) associated with a specific monetary value of GHG emissions in $/t C02e (y-axis) for a specific
reference year (2030 and 2050). C02 fertilization is included in the GTM as the change in natural primary productivity in all forests around
the world and it is specific to regions and forest types (Davis et al., 2022; Schimel et al., 2015). The Without C02 Fertilization Scenario
reports the results presented in the main text of Chapter 3.

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Box 6

SENSITIVITY: Accounting for
price and land constraints

Results show that the U.S. forestry sector plays a considerable role in land use mitigation
activities across scenarios and models applied in this report. When both sectors (agriculture
and forest) are available, GLOBIOM and FASOMGHG show that more than 67% of future
mitigation in the United States could come from forest by midcentury. Moreover, under
moderate GHG price scenarios ($35 and $50/t C02e at 1% and $35/t C02e at 3%) forest
mitigation potential across the three models is within a similar range (32-244 Mt C02e yr1).
However, as more ambitious GHG prices are introduced in the system, GTM estimates higher
potential than the other two models (e.g., 832 Mt C02e yr1 vs. 375 Mt C02e yr1 in FASOMGHG
and 200 Mt C02e yr1 in GLOBIOM in 2050). The discussion in this box tests two possible drivers
of this gap in the projections.

First, the assessment looks at whether the forward-looking framework of GTM combined
with long-term terminal conditions (200-year simulation horizon) could drive more short-term
mitigation actions than the other two models that have a shorter time horizon (they both end in
2100). To test this, the GHG price scenarios with 3% growth are fixed to their 2100 values for
2110-2200 (Fixed Price Scenario after 2100) in GTM. Under these scenarios, future GHG prices
do not exceed $1,066 t/C02 after 2100. As a second test, the study assesses whether the
representation of land available to be converted into forests using the GTM land rental functions
is a cause of its higher estimated mitigation volume relative to FASOMGHG and GLOBIOM. For
example, in 2050 under the $100/t C02 at 3% scenario, GTM projected 128 million acres of new
forestland while FASOMGHG projected 82 million acres and GLOBIOM 61 million acres. In this
sensitivity case, the same GHG price scenarios with 3% growth are simulated under a constraint
on additional land available for conversion to forest of 82 million acres (Land Constraint
Scenario). This value was chosen as it aligns with the volume estimated by FASOMGHG under
the $100 at 3% scenario.

Results show that GTM mitigation potential estimates are more sensitive to the land constraint
than to the cap on the GHG price (Figure B6 presents MACCs between the unconstrained
model, the fixed price model, and the land constrained model). Interestingly, the price cap
incentivizes a little more short-term mitigation largely because of the forward-looking structure
of the modeM/vith higher long-term GHG prices in the uncapped GHG price runs, the model
waits until later periods to make large investments in forestry to capitalize on the higher
expected returns. This outcome is more likely to happen under high GHG prices in the short

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term, with mitigation potential projected to change relative to the unconstrained model within a
range from an increase of 0.7% to a decrease of 0.5% in 2050.

On the other hand, constraining the amount of land available for afforestation/reforestation in
the future means that some mitigation opportunities are lost, reducing mitigation to a maximum
of 5% in 2050 under the highest GHG price scenario. For GHG price scenarios starting at a value
higher than $20/t C02e, the constraint has a large effect on the level of abatement and the
effect becomes even larger under higher GHG price scenarios. For instance, under the $100 at
3% Land Scenario, 68 million acres that were converted in the unconstrained scenario are not
converted into forests, with a corresponding loss of 46 Mt C02e yr1 in mitigation by 2050.
Overall, these results suggest that the continued growth of GHG prices in GTM relative to
FASOMGHG and GLOBIOM has little effect on differences in the projected mitigation potential
across the models. However, when the afforestation/reforestation potential of the model is
limited in GTM, it projects mitigation potential closer to that of GLOBIOM and FASOMGHG.

Figure B6: Marginal abatement costs for forests in 2030 and 2050 with and without land and price constraints using
GHG price scenarios with 3% annual growth rates, GTM

2030

300-

200-

100-

O
O

250

500

2050

750

1000

300-

200-

100-

250

500
Mt CO,e

750

1000

MACCs are built using the abatement under
each GHG price scenario starting at $5/t C02e.
Five observations per year are used to
build each MACC. MACCs show the level of
abatement in Mt C02e (x-axis) associated with
a specific monetary value of GHG emissions
in $/t C02e (y-axis) for a specific reference
year (2030 and 2050). The Fixed Price After
2100 Scenario holds GHG prices fixed to
their 2100 values for the 2110-2200 period;
therefore, future GHG prices do not exceed
$1,066 t/C02 after 2100. The Land Constraint
Scenario assumes a maximum of 82 million
acres available to be converted to forests. This
value was chosen as it aligns with the land size
estimated by FASOMGHG under the $100 at
3% scenario. The Unconstrained Scenario has
no constraints on GTM and reports the results
presented in the main text of Chapter 3.

Fixed Price After 2100
Unconstrained

Land Constraint

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Box 7

SENSITIVITY: Accounting
for opt-in program design

The market-based approaches employed in the models used in this report represent a
theoretical method to evaluate mitigation potential of the land sector. It applies a GHG pricing
system in which all carbon pools are tracked, and all agents and activities are affected by the
price incentive. In effect, this approach simulates outcomes as if all carbon pools are monitored
and market players make decisions knowing that a carbon payment will be delivered each year
(i.e., there are no market failures or transaction costs).

In this box, a more applied approach explores the potential effects of implementing an opt-in
system, more like the offset programs that have emerged in regional mitigation programs or
the voluntary offset market. Offset providers choose to participate in these programs and are
subject to carbon pool accounting and verification for a set amount of time. This modeling
framework is adapted to simulate a 100-year improved forest management carbon offset
program by expanding the land base representation to allow two classes of forest landowners:
one that voluntarily participates and has the price incentive applied to their carbon accounts,
and another that does not participate and does not have the price incentive applied to their
carbon accounts. Moreover, payments are limited to easily observable/quantifiable/verifiable
pools of live trees and HWP without rewarding participating landowners for increased carbon
sequestration in soil and other forest pools (e.g., understory and shrubs).

Prior studies have expanded FASOMGHG to include voluntary participation for either forest
(Latta et al., 2011) or agricultural (Wang et al., 2021) carbon offset programs. In each case,
an opt-in program resulted in a shift of the MACC up and in (toward the y-axis) indicating less
mitigation potential at a given GHG price than when the same scenario is modeled as an all-in
program design.

To demonstrate the degree to which increases in nonparticipating land emissions can
potentially reduce net programmatic emissions reductions, the voluntary approach of Latta
et al. (2011) is applied to the forestry sector in the updated FASOMGHG version of Wade et al.
(2022) under the GHG price scenarios used in this report. To facilitate comparison, annualized
C02e emissions reductions results are presented in Figure B7. Panel (A) shows net emissions
MACCs for two scenarios: 1) All-in participation in a forest sector only simulation (no interaction
or competition with the agriculture sector), and 2) Opt-in participation in a forest sector only

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simulation. Panel (B) deconstructs the opt-in participation MACC of scenario 2 to show a MACC
for land participating in something like the voluntary forest offset program (participants), for
nonparticipating land (nonparticipants), and for all land (total).

Looking at Panel (A), the scenario that considers the forest sector under the all-in program
results in 50 Mt C02e yr1 at $50/t C02. There is a reduction in expected mitigation of
35 Mt C02e yr1 at $50/t C02 when the model considers only voluntary participation for carbon
pools that are easily verified in the program. Panel (B) breaks the voluntary MACC out by
the GHG emissions effects of offset program participants and nonparticipants. At $50/t C02
participants in the voluntary program sequestered 23 Mt C02 but the market participation
of some forest lands in the program led to increased offsite, or outside the project scope,
emissions of 8 Mt C02 from nonparticipating lands that is, a leakage effect. This leakage
phenomenon occurs as the management activities of the market participants change to reduce
emissions and/or increase sequestration (such as extended rotations and reduced harvest
levels), and as other land managers not participating in the program react to the reduced supply
of timber and related higher prices on the market accordingly by increasing harvest levels, thus
reducing the overall mitigation achieved by the program.

Future work using FASOMGHG with this opt-in construct will leverage the model's ability to
differentiate between participants and nonparticipants to address leakage within voluntary
forest carbon offset programs. It will also take an approach similar to that of Wang et al. (2021)
and evaluate how quantification rules in a voluntary offset methodology contribute to that
leakage.

Figure B6: Marginal abatement costs for forests in 2030 and 2050 with and without land and price constraints using
GHG price scenarios with 3% annual growth rates, GTM

(A) (B)

Panel (A) shows net emissions MACCs for two scenarios: 1) All-in participation scenario run and 2) Forest sector only opt-in participation.
Panel (B) deconstructs the partial participation MACC of scenario 2 (forest sector only partial participation) to show a MACC for lands
participating, lands not participating, and all land (Total). Please note the difference in the x-axis scale.

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3.3.2 Mitigation in Agriculture

This section presents the MACCs for agriculture across
FASOMGHG and GLOBIOM where each model chooses
among a combination of cost-effective abatement options in
this sector.

Results presented in Figure 3-13 show the MACCs for
agriculture and the projected emissions under the baseline
and GHG mitigation scenarios for the two models in 2030
and 2050. Despite the agriculture sector remaining a net
emitter of GHG emissions under GHG mitigation scenarios,
both models show significant mitigation potential in the
sector with emissions projected to decline up to 15%
(FASOMGHG) and 30% (GLOBIOM) in 2030 and 22%
(FASOMGHG) and 36% (GLOBIOM)in 2050. Mitigation
opportunities in each model are described below and are
divided by sector (cropland and livestock).

3.3.2.1 Cropland

This section presents the MACCs for cropland in 2030 and
2050. In the cropland sector, mitigation options include the
activities presented in Appendix Figure A-9 divided by GHG
categories.

In 2030, under a price of $100/t C02e, FASOMGHG projects
abatement levels of 12-16 Mt C02e yr"1 and GLOBIOM
projects 24-30 Mt C02e yr"1 from cropland. In 2050, under
a GHG price as high as $100/t C02e, FASOMGHG projects
abatement levels of 16-26 Mt C02e yr"1 and GLOBIOM
projects 30-47 Mt C02e yr"1.

There is variation in activities used for agricultural mitigation
across each model. For instance, FASOMGHG includes
agricultural soil carbon changes from inputs and activities
as a component of overall GHG flux while GLOBIOM only
includes changes to above- and belowground biomass due
to land conversion into and out of cropland. In FASOMGHG,
the cost-effective mitigation mix favors soil carbon
sequestration through tillage change, change in cover
cropping applications, and LUC on agricultural lands (e.g.,
afforestation).15

Like the EPA (2005) report, projected mitigation from
agricultural soil carbon sequestration in FASOMGHG
decreases over time and is smaller in magnitude under
higher mitigation prices relative to low price scenarios.
Historic and projected future soil carbon dynamics help
explain this result: early investments in practices that
enhance soil carbon (e.g., conservation tillage), as well as
land use changes, eventually result in a new soil carbon
balance equilibrium, and the model cannot effectively
invest in many additional soil carbon-enhancing practices
in the absence of significant cropland expansion in the
GHG scenarios (see Box 2 for a description of soil carbon
dynamics in FASOMGHG). Furthermore, soil carbon practices
typically generate small net carbon gains per unit area
(< 11 C02e per acre) (Dell & Novak, 2012; Johnson et al.,
2005), so at higher price incentives the model opts for
mitigation strategies that provide greater returns per unit
area and per dollar investment (such as afforestation or
improved forest management). FASOMGHG projects that
other on-farm activities such as changes in input intensity,
burning of crop residues, and diesel usage could result in
mitigation rates of between 1 and 18 Mt C02e yr"1 by 2050.
In 2050, FASOMGHG projects nitrogen fertilizer consumption
decreases by 0%-10% relative to the baseline projected
application amount of about 13 million tons, though fertilizer
usage intensity increases with total cropland area declining
at higher rates (0%-18%).

Conversely, GLOBIOM does not model soil carbon fluxes
in croplands due to management changes in this analysis
and thus projected mitigation activities concentrate on crop
non-C02 and livestock non-C02 sources (discussed in Section
2.3). Crop non-C02 mitigation projections in GLOBIOM range
between approximately 16 and 51 Mt C02e yr"1 by 2050.
Projected mitigation potential for GLOBIOM clusters at the
moderate and high mitigation price scenarios (e.g., those
higher than $20/t C02e as a starting point). This result
occurs primarily through varying levels of representation of
mitigation options. As a global model, GLOBIOM has a less
detailed representation of the U.S. agricultural mitigation
options such as practices that retain SOC or on-farm fossil
fuel usage reductions. Additionally, GLOBIOM forecasts that
there are more efficient agricultural mitigation opportunities

15 FASOMGHG results do not fully disaggregate emissions/soil carbon changes from tillage practices, cover cropping, or LUC on agricultural lands. Recent
meta-analyses of the effect of tillage changes on soil carbon found that soil organic carbon (SOC) storage can be higher under no-till management in some
soil types and climatic conditions even with redistribution of SOC; and it can contribute to reducing net GHG emissions. However, uncertainties tend to be
large (Ogle et al., 2019).

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FIGURE 3-13

A) Forest marginal abatement cost curves; B) Forest absolute emissions under
baseline and mitigation scenarios in 2030 and 2050

FASOMGHG

GLOBIOM

300-

250-

200-

C«)

150-

100-

50-

(1)

300-

250-

200-

lO
o

150-

100-

50-

100

125 0
Mt CO,e

2030

2050

1 % Growth

• ••• •

3% Growth

100

125

«•

• •

H •

100

200

• • mm ••

300

400

500

Mt C02e/yr

FASOMGHG • GLOBIOM ¦ Baseline # Mitigation

Panel (A) MACCs are built using the abatement under each GHG price scenario starting at $5/t C02e. Five observations per year are used
to build each MACC. MACCs show the level of abatement in Mt C02e (x-axis) associated with a specific monetary value of GHG emissions
in $/t C02e (y-axis) for a specific reference year (2030 and 2050). GTM is not included in the figure because it does not explicitly model
agriculture. Panel (B) Absolute emissions under baseline and mitigation scenarios from agriculture in FASOMGHG and GLOBIOM in 2030
and 2050. Results are presented in terms of atmospheric accounting. Therefore, positive flux equates with emissions; negative flux
represents sequestration.

Changes in GHG fluxes in agricultural soils due to land use change are not included in the MACCs because this factor, on net, is a transfer
of stored carbon from agriculture soils to forest soils. On the other hand, changes in GHG fluxes in agricultural soils due to land use change
are included in the bar figure showing agriculture emissions under the baseline and GHG mitigation scenarios.

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FIGURE 3-14

MACCs for cropland in 2030 and 2050

FASOMGHG

GLOBIOM

300-

250-

200-

C«)

150-

100-

50-

300-

250-

200-

lO
o

150-

100-

50-

60 0

Mt C02e

1 % Growth 3% Growth

MACCs for cropland only in 2030 and 2050 by models and growth rate scenarios (1% and 3%). MACCs are built using the abatement from
cropland under each GHG price scenario starting at $5/t C02e. GHG price applies to all GHG emissions from the land sector but only the
abatement from cropland is used to build the curves presented in this figure (changes in soil carbon are not included). Five observations
per year are used to build each MACC. MACCs show the level of abatement in Mt C02e (x-axis) associated with a specific monetary value of
GHG emissions in $/t C02e (y-axis) for a specific reference year (2030 and 2050).

outside of the United States, as the domestic agricultural
sector is currently producing at relatively low GHG intensities.
GLOBIOM reduces nitrogen fertilizer usage considerably in
the higher mitigation price scenarios, with usage declining
by as much as 23% from 2025 to 2050 (with baseline
nitrogen fertilizer application projected at 16 million tons per
year over the same time horizon). This reduction in fertilizer
application results in declining yields relative to the baseline
scenario. At the same time, GLOBIOM expands irrigation to
increase crop yields while overall crop area declines. This is
the opposite of what happens in FASOMGHG partly because
GLOBIOM does not explicitly account for emissions from
groundwater pumping.

The introduction of GHG prices disincentivizessome
common GHG-intensive agricultural activities in the two
models such as the use of fertilizer, which—under high
prices—can decrease the profitability of some cropland,
resulting in a decline in total cropland area under these
mitigation scenarios relative to the baseline in both models
(Figure 3-15).

In FASOMGHG, total cropland area is more responsive
to GHG prices than in GLOBIOM, with cropland area in
2050 either remaining constant relative to the baseline,
or decreasing by up to 58 million acres. GLOBIOM projects
a decline of between 0 and 6 million acres over the

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same timeframe. As presented in the earlier section, this
difference in the magnitude of cropland area decline is
partially driven by the difference between how the two
models handle temporal dynamics, with FASOMGHG relying
heavily on afforestation/reforestation to maximize the net
benefit of sequestration under the GHG price scenarios
(hence more land use conversion from agricultural lands).
Agricultural input usage and intensity also vary across
each model; under the $35 at 3% scenario, FASOMGHG
and GLOBIOM project a relative reduction in the use of
nitrogen fertilizer by 7% and 16% relative to the baseline in
2050 (13 million tons in FASOMGHG and 17 million tons in
GLOBIOM). Moreover, the contraction is higher under high
GHG price scenarios (e.g., $100 at 3%) because emissions
from fertilizer applications are costlier, with reductions of
N fertilizer increasing to 10% iri FASOMGHG and 23% in
GLOBIOM. FASOMGHG projects that the change in irrigation
water consumption will range from a slight increase of 2% to
a decrease of 8% across all GHG price scenarios, resulting in
a relatively stable irrigation intensity level. GLOBIOM projects
a slight increase in irrigation water consumption across ail
GHG price scenarios of 0% to 2%, but at the same time,
irrigated area is increasing at a higher rate, resulting in a
decline in irrigation intensity as GHG prices rise. Additionally,
due to its global coverage, GLOBIOM also reflects tradeoffs
between mitigation potential and agricultural production
domestically versus internationally. Under the GHG price
scenarios, the United States continues to have a competitive
advantage over many regions in agricultural production,

which results in the United States' relative reduction in
agricultural production being less than in many other regions
in GLOBIOM.

Each model also relies on crop switching as a response
to GHG prices. The proportions of cropland dedicated to
corn, soybean, wheat, and cotton all increase under the
$50 starting price, growing at 3% annually relative to the
baseline in GLOBIOM. At the same time, smaller crops, such
as rice, beans, canola, and sugar, decline in their proportion
of total crop area. Like GLOBIOM, FASOMGHG increases
the proportion of cropland dedicated to corn, while the
proportion of area dedicated to wheat remains constant,
and the proportion of area dedicated to soybean declines.
Other smaller crops, such as sorghum and barley, remain
relatively constant. FASOMGHG also achieves mitigation in
the agriculture sector by reducing domestic rice cultivation
in most GHG price scenarios (>$5/t 002e) and relying on
reduced exports and increased imports to meet domestic
demand, with overall rice exports in 2050 decreasing by as
much as 90% in the GHG price scenarios.

Since cropland is projected to decline to some degree under
all the GHG price scenarios, Box 8 assesses how mitigation
opportunities may change under policy scenarios reserving
specific lands for agricultural production by running
the same mitigation scenarios as the main report with
restrictions on afforestation/reforestation activities inside
the Corn Belt region.

Ripe barley in field
below Wellsville
Mountains, Utah.

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FIGURE 3-15

Average annual change in total cropland area from baseline (in million acres,
2025-2050)

FASOMGHG

GLOBIOM

-10-

v> -20
£
o
<

-30-

-40-

-50 "¦—r

2025 2030 2035 2040 2045 2050 2025 2030 2035 2040 2045 2050

$5 at 1 %

$5 at 3%

$20 at 1 %
$20 at 3%

$35 at 1 %
$35 at 3%

$50 at 1 %
$50 at 3%

$100 at 1%
$100 at 3%

Baseline cropland area by model is presented in Figure 3-5. Positive values equal an increase in cropland area from the baseline. Initial
values are different because FASOMGHG starts in 2015 while GLOBIOM starts in 2000. GTM is not included because it does not explicitly
model cropland.

Corn growing in rural Augusta, Kansas.

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Box 8

SENSITIVITY: Implications
of limiting mitigation
activities geographically

Land-based mitigation actions are likely to affect LUC and land use management decisions
with possible implications for related commodities. As a general matter, applying a GHG price
incentive to GHG reduction activities can drive land use management decisions to maximize net
benefits for the land sector. Because the GHG price incentives are expected to increase over
time, rational actors will seek to implement the activities that maximize GHG sequestration
and GHG reductions over time, not solely in the immediate term. The model function is akin
to how timber-related management decisions are often made—planting a tree today with
expected returns decades into the future. Because trees sequester C02, store more and more
carbon over time, and also provide some GHG benefits post-harvest in the form of HWPs,
activities in forestry—especially those that generate the highest levels of sequestration/GHG
reductions—are the most lucrative and selected by rational actors in the models. These highest
levels of GHG benefits from forestry are often realized in places where lands have higher
productivity rates—the reason being that trees, like crops, will grow faster on more productive
lands, sequestering more carbon and thus earning higher returns under a GHG pricing system.
Therefore, when modeling GHG mitigation across a suite of activities in FASOMGHG, many
rational actors in the model will respond to the higher future returns offered in the long term
via the activities that yield the most GHG benefits (e.g., forestry activities like afforestation and
reforestation) on the most productive lands in the model, including in the U.S. Corn Belt, which
has been shown to be one of the most productive areas in the world at growing crops (Guanter
et al., 2014). This outcome is reflected in the regional results presented in this report.

While models including FASOMGHG are built to reveal both the most cost-efficient and/or
welfare-maximizing outcomes favored by rational actors and the specific parameters of the
modeling components and study design, these tools do not yet have the capabilities to reflect
all the possible policy and other important monetary and non-monetary considerations that
landowners incorporate into decision-making in practice. Examples include the choice to
retain certain lands and/or land management practices as part of family legacies or common
local social practices, or conforming with local, state, or federal land management or other
policies. For this reason, researchers may elect to let the models generate the most cost-
effective solutions without constraints, to conduct sensitivity analyses, or to evaluate simulated
outcomes under specific conditions and constraints.

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To that end, this box estimates the effects of preserving land allocation in the Corn Belt by
limiting the amount of current agricultural land converting to forestry in FASOMGHG in response
to the GHG mitigation price paths. Specifically, as a case study, 10 additional GHG price
scenarios were run in FASOMGHG where afforestation/reforestation activities were not allowed
in the U.S. Corn Belt region.

This land constraint emulates scenarios presented in other studies (e.g., Fujimori et al., 2019)
where land conversion is constrained in the most productive land areas to preserve agriculture
lands as a means to guarantee food security or to reflect landowner preferences to retain the
land in agriculture despite the GHG price incentives.

As discussed in Section 3.3.6, under the highest GHG price scenario in the main analysis,
agricultural production for major feedstocks is projected to decline by 9% (corn), 20% (wheat),
and 27% (soybeans) in 2050 relative to the baseline in FASOMGHG. Livestock production is
also expected to decrease with a contraction of 21% (for beef), 10% (poultry), and 23% (pork)
in 2050 under the same scenario. These results are driven by the conversion of agricultural
land area into forests as the response to GHG price, despite FASOMGHG including increasing
marginal costs associated with converting agricultural lands to forestry based on historical
payments under the Conservation Reserve Program (Cai et al., 2018).

Under the land constraint scenario, national agricultural production under the mitigation
scenarios is still projected to decline relative to the baseline scenario but at a slightly lower
rate for all products than scenarios without the Corn Belt land constraint. That is, the impact
the mitigation prices have on agriculture and livestock production is smaller in this sensitivity
analysis, with a projected decline of 6% (corn), 16% (wheat), 16% (soybeans), 19% (beef), 8%
(poultry), and 20% (pork) in 2050 under the highest GHG price scenario. Overall, restricting
land conversion in the region does not reflect a significant change in agricultural commodity
production, with the largest benefit in relative terms occurring to soybeans.

On the other hand, results show that under the land constrained scenarios, national mitigation
drops by 2%-30% depending on the GHG price (Figure B8). This reduction in mitigation potential
is largely isolated to the Corn Belt—it reflects that mitigation opportunities implemented
outside of the Corn Belt are not cost-effective because of the differences in land productivity.

These results emphasize how scenario designs (e.g., limiting land conversion in specific
regions) and more broadly, policy designs, could affect the total mitigation potential under
the same GHG price scenario. This example illustrates how there may be tradeoffs associated
with limiting what lands, landowners, and land management activities are eligible for GHG
mitigation incentives, which should be considered during policy development.

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Figure B8: Projected national MACCs in 2050 from the agriculture sector (top) and forestry sector (bottom) from
FASOMGHG under the GHG price scenarios with no restrictions and scenarios with no afforestation in the Corn Belt
region

1 % Growth

3% Growth

O
O

300-

2 200

< 100

300-

200-

100-

100

200

300 0
Mt CO,e

100

200

300

No Afforestation in the Corn Belt

Unrestricted

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3.3.2.2 Livestock

Mitigation options in the livestock sector are summarized in
Appendix Figure A-10 and the MACCs for livestock in 2030
and 2050 are presented in Figure 3-16.

In 2030, at GHG prices of $100/t C02e, FASOMGHG projects
abatement levels of 32-38 Mt C02e yr"1 and GLOBIOM
of 42-48 Mt C02e yr"1 from livestock. In 2050, at prices
of $100/t C02e, FASOMGHG projects abatement levels
of about 34-41 Mt C02e yr"1 and GLOBIOM of about 50-
53 Mt C02e yr"1.

Appendix Figure A-ll provides a comparison of projected
livestock abatement activities (through either manure

management or reduction in enteric fermentation) for
FASOMGHG and GLOBIOM in 2050. In general, FASOMGHG
livestock sector mitigation is projected to be more costly
than in GLOBIOM, resulting in lower adoption rates of
manure management and enteric fermentation abatement
practices. At low GHG prices, FASOMGHG relies mostly on
reducing enteric fermentation through changes in animal
feeds. As GHG prices rise, manure management activities
become price competitive and become the most prominent
activity utilized to reduce emissions from livestock
production. The response is similar in GLOBIOM even though
manure management systems require higher GHG prices to
become cost competitive, but eventually it projects higher
abatement via enteric fermentation than in FASOMGHG.

FIGURE 3-16

Marginal abatement cost curves for livestock in 2030 and 2050

FASOMGHG GLOBIOM

300-

250-

200-

300-

250-

200-

Mt C02e

1 % Growth 3% Growth

MACCs are built using the abatement from livestock under each GHG price scenario starting at $5/t C02e. GHG price applies to all GHG
emissions from the land sector but only the abatement from livestock is used to build the curves presented in this figure. Five observations
per year are used to build each MACC. MACCs show the level of abatement in Mt C02e (x-axis) associated with a specific monetary value of
GHG emissions in $/t C02e (y-axis) for a specific reference year (2030 and 2050).

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This slight divergence occurs for several reasons, including
differences in baseline emissions projections (which are
slightly higher for GLOBIOM, 190 Mt C02e yr"1 compared
to 150 Mt C02e yr1 in FASOMGHG) and cost structures
(more regional variation in FASOMGHG technology-specific
abatement cost assumptions, as documented in the
supplemental appendix of Jones et al., 2019).

In addition to mitigation activities through livestock
management, both models utilize LUC and global markets
to achieve mitigation from the livestock sector. For example,
under higher mitigation prices, GLOBIOM maintains U.S.
livestock production at relatively consistent levels and
invests more in abatement technologies that reduce
enteric fermentation emissions, while FASOMGHG reduces
domestic production and exports of meat products. Changes
in land use dedicated to livestock production across both
FASOMGHG and GLOBIOM are relatively consistent, with
reductions of pastureland in 2050 between 2% and 27% (4
to 70 million acres) in GLOBIOM, and between 3% and 21%
(4 to 31 million acres) in FASOMGHG relative to the baseline
(Figure 3-17).

Despite similar trends in pastureland, U.S. meat production
in GLOBIOM is expected to decline by no more than 3%
across all mitigation scenarios, while FASOMGHG projects
a maximum decline of more than 21% from their respective
baselines in 2050. This difference in production highlights
how national and global models may differ in their
responses to mitigation incentives.

In GLOBIOM, all regions respond to the GHG price, finding
their optimal cost-effective combination of abatement
activities and livestock consumption and production
quantities. Overall, the U.S. production declines under GHG
price scenarios but its share of global supply is expected
to increase because it has relatively cheaper abatement
opportunities than other meat-producer regions. Under
GHG price scenarios, the United States has a comparative
advantage over many other countries (especially developing
regions such as Brazil) in producing animal commodities
at less carbon-intensive levels. Because of this, the U.S.
increases exports and market share under GHG mitigation
price scenarios relative to the baseline (see Appendix A,
Table A-7).

In FASOMGHG, trade dynamics are limited and the supply
of livestock from the rest of the world (outside the United
States) is not affected by the GHG price, which makes
domestic production relatively more expensive than the
rest of the world. This drives a reduction in U.S. exports to
meet domestic consumption under GHG price scenarios.
Under the highest price scenarios, domestic consumption of
chicken, beef, and pork all decline over time, in with chicken
declining by about 8%, and beef and pork both declining by
around 20%. While consumption is declining relative to the
baseline, compared to current levels, future consumption
increases for each commodity under all scenarios.

Production and exports show similar trends: declines
reach as much as 20% relative to the baseline but increase
relative to current levels.

These results show how underlying model parameters and
inputs (e.g., available abatement options) and structure
(e.g., national versus global, forward-looking versus recursive
dynamic) affect the mitigation strategies and potentials.
Moreover, Box 9 discusses how modeling results diverge
from techno-economic assessments and how their results
can inform different policy-related questions using livestock
as a case study.

An Indiana poultry farm's storage shed for manure that will be applied to
the fields later. (Photo by Brandon O'Connor/lndiana-NRCS)

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Box 9

FOCUS: Technical potential
vs. cost-effective potential—
the case of livestock

Figure B9 compares MACCs from FASOMGHG and GLOBIOM for the livestock sector, including
enteric fermentation and manure management opportunities in 2050 with the technical
potential for the livestock sector from the U.S. EPA Non-C02 Mitigation Report (EPA, 2019b).
The livestock sector can achieve a maximum technical potential of 75 Mt C02e yr1 at a price
of $250/t C02e according to the 2019 U.S. EPA Non-C02 Mitigation Report, while in this
report FASOMGHG projects a maximum mitigation potential of about 55 Mt C02e yr1 and
GLOBIOM reaches nearly 58 Mt C02e yr1 for the same GHG price. The technical potential is not
achieved by the livestock sector under the GHG price scenarios simulated via the competitive
market approach used in this report. While the technical potential presents the maximum
abatement available by a single sector/technology without considering market tradeoffs and
the opportunity cost of a scarce resource like land, the results from FASOMGHG and GLOBIOM
consider both the cost information of individual abatement activities and the interactions
among activities, including competition for resources. The figure shows that with market
opportunity costs included, the mitigation potential declines by about 29%-36% under a GHG
price as high as $240/t C02e in 2050. That is, the difference in mitigation potential from the
modeled outputs from FASOMGHG and GLOBIOM and the technical potential from EPA is largely
due to the market opportunity cost and resource competition discussed in Box 1, Chapter 2.
This comparison shows that economic tradeoffs, such as those involving land use competition
and synergies, are important to consider for estimating the potential impact of future strategies
aimed at reducing emissions or increasing sequestration potential from the AFOLU sector.

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Figure B9: Marginal abatement cost curves for livestock sector from FASOMGHG and GLOBIOM, and technical
mitigation potential from EPA (2019b) in 2050

300-

200-

o
o

100-

Mt CO,e

FASOMGHG

GLOBIOM

MACC

MACCs from FASOMGHG (red) and GLOBIOM (blue) are built using the abatement from livestock under each GHG price scenario starting
at $5/t C02e under both growth rate scenarios. GHG price applies to all GHG emissions from the land sector but only the abatement from
livestock is used to build the curves presented in this figure. Ten observations per year are used to build each MACC. MACCs show the level
of abatement in Mt C02e (x-axis) associated with a specific monetary value of GHG emissions in $/t C02e (y-axis) for a specific reference
year (2030 and 2050). The Technical Mitigation Potential from EPA (2019b) shows the abatement achieved under each $/t C02e level as
the black area underneath the curve.

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3.4 Mitigation Across Land-
Based Activities

For each GHG price scenario, each
model not only projects the mitigation
potential of the land sector but
also determines the cost-effective
composition of land-based activities
in response to each price at any time,
considering tradeoffs and synergies
among activities.

Results across all models suggest that there is mitigation
potential across multiple activities, with forest-based
activities offering the highest level of abatement (Figure
3-18). There is not a dominant forest-based strategy across
models. In GLOBIOM forest management provides, on
average, more than half of the mitigation from the land
sector while afforestation/reforestation has a larger share of
total mitigation in FASOMGHG and GTM. The cost-effective
combination of mitigation strategies is sensitive to the
GHG price scenario and the time horizon. For instance,
under GHG price scenarios higher than $50/t C02e, in
GLOBIOM and FASOMGHG, the forestry sector is still the
primary source of mitigation, but its share declines as more
land-based activities become cost-effective in livestock
and cropping systems. Moreover, GLOBIOM shows a larger
variation in the share of domestic mitigation delivered

FIGURE 3-18

Share of mitigation by main activity by model, 2025-2050

Agricultural Soils C02 Soils Crop Non-C02

Forest Management

Afforestation/Reforestation

Livestock Non-C02

0.25-

2025 2030 2035 2040 2045 2050 2025 2030 2035 2040 2045 2050 2025 2030 2035 2040 2045 2050

0.00-

B FASOMGHG B GLOBIOM B GTM

Solid lines indicate means and shaded areas show upper and lower bounds across models. Mitigation activities are described in Figure 2-4.
All GHG price scenarios and growth rates are included in this figure.

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by croplands and livestock relative to FASOMGHG, with
a minimum of 9% of total mitigation from crop non-C02
and 5% from livestock and a maximum of 38% from crop
non-C02 and 52% for livestock. This is driven by the limited
response of the forest sector in GLOBIOM compared to
a forward-looking model like FASOMGHG. Under high
prices, FASOMGHG invests heavily in afforestation and
forest management activities to take advantage of high
future GHG prices. Results from GLOBIOM show also that
mitigation activities from the livestock and agriculture
sector could provide more short-term reductions compared
to forestry. Despite the variation, the models indicate
mitigation activities in forestry may be dominant between
now and 2050. Additionally, in all mitigation scenarios in
both FASOMGHG and GLOBIOM, the agricultural sector is
projected to remain a net GHG emitter.

3.4.1 Regional mitigation portfolio

This section presents U.S. regional results from FASOMGHG
by starting with future emissions trends under the baseline
scenario followed by the regional distribution of mitigation
potential under the GHG price scenario of $50 at 3% used in
the main report.

The version of FASOMGHG used in this report includes
subnational representation of the land use sector, through
the delineation of 11 different regions. Figure 3-19 shows
regional emissions for agriculture and forestry from
FASOMGHG at the baseline. At the regional level, some
regions such as the Corn Belt (CB), Lake State (LS), and
South Central (SC) project a significant increase in baseline
emissions from agriculture driven by expanded production

FIGURE 3-19

U.S. regional GHG emissions from agriculture and forestry under baseline
scenario, FASOMGHG (in Mt C02e yr1, 2025-2050)

Agriculture

Forestry

400-

200-

IN r\ _l

o o-
o

-200-

-400-

2020-
2029

2030-
2039

2040-
2049

2050-
2059

2020-
2029

2030-
2039

2040-
2049

2050-
2059

CB | LS ^ PNWE ^ PSW ^ SC
GP NE ¦ PNVWV RM SE

Appendix Figure A-l shows the 11 regions included in FASOMGHG.

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of agriculture commodities like corn, soybeans, and wheat.
On the other hand, regions such as Great Plains (GP) and
Rocky Mountains (RM), despite representing a large share of
current emissions, are not expected to increase their share.
Similarly, the remaining regions—Northeast (NE), Southeast
(SE), Pacific Southwest (PSW), Pacific Northwest-east
(PNWE), Pacific Northwest-west (PNWW), and Southwest
(SW)—are projected to result in constant emissions levels
from agriculture. In the forestry sector, SC and SE are
expected to increase net GHG emissions reductions from
49 Mt C02e yr"1 and 17 Mt C02e yr"1 in 2025 respectively to
58 Mt C02e yr"1 and 56 Mt C02e yr"1 in 2050, as investments
are made to continue to increase biomass growth in these
productive regions to meet higher demand in the future. On
the other hand, LS and OB are projected to experience a
decline in forest carbon stock under the baseline scenario,
driven by forest conversion to cropland. Finally, none of the
other regions show significant changes from the current
levels of carbon sequestration in forests in the baseline.

The mix of mitigation activities and mitigation levels varies
across the nation as a function of spatially heterogeneous
land productivity, production costs, and projected baseline
land use and management. Moreover, regions respond
differently to the price signal; some of them may find it more
economically beneficial to increase production of agricultural
and/or forestry products while other regions opt to reduce
GHG-intensive activities and/or products. Figure 3-20 shows
the distribution of cumulative abatement across each of
the 11 regions, by mitigation activity under a GHG price of
$50 at 3% from 2025 to 2050. Under this scenario, the SE
invests in reducing livestock non-C02 emissions around hog
production, and the SC reduces agricultural C02 and non-
C02 through reductions in rice production. The NE, LS, CB,
RM, PSW, and PNWE all utilize afforestation/reforestation
along with forest management as primary roles in mitigation.
The CB and SC also reduce emissions from cropland
production through reduction of fertilizer application and
reduced on-farm fuel usage. CH4 reduction from dairy farms
is a key mitigation strategy in the NE and PSW, while the
RM region has limited feasible emissions reductions from
agricultural activities. The GP and SW achieve mitigation
through the implementation of methane digesters and

changes in grazing and feed mixes to reduce livestock non-
C02. Finally, the PNWW (which only includes the forest sector
in FASOMGHG), increases rotation lengths of existing forests
to increase carbon sequestration.

Regions that rely heavily on afforestation/reforestation
(CB and LS) and forest management (SC and SE) activities
have the highest level of mitigation potential at low and
moderate investment levels, while regions that rely more
on livestock (GP, SW) or crop-based mitigation activities are
more costly and smaller in total potential. Box 8 explores
the dynamics of land use change under mitigation scenarios
and how limiting land conversation will affect the abatement
potential of the region. The U.S. regions with smaller total
land area (PNWE. PNWW, NE, and PSW) are projected to
have smaller mitigation potential due to high levels of land
use competition and limited potential for low-cost mitigation
through afforestation. Overall, a range of activities must
be utilized to achieve the maximum mitigation from the
domestic land sector; however, afforestation/reforestation
and forest management activities are potentially the most
cost-effective strategies to reach midcentury emissions
reduction targets across much of the United States.

Pigs in a hoop barn on a central Iowa farm.

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FIGURE 3-20

U.S. regional distribution of cumulative mitigation by activity under the $50 at
3% scenario, FASOMGHG (2025 to 2050)

800,000
420,000
49,000

Afforestation/Reforestation
Cropland Non-C02
Agricultural Soils
Forest Management

Agricultural-C02
Livestock Non-C02
Forest Products
Forest Soils

The size of the pies represents the absolute level of abatement available in each region. Appendix Figure A-l shows the map of the 11
regions included in FASOMGHG and Figure 2-4 describes the mitigation activities included in FASOMGFIG.

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Box 10

FOCUS: Beyond 2050

This section focuses on the modeling results after 2050 to provide some insights on long-term
dynamics in the land sector with and without mitigation policies targeting the sector.

Under the baseline scenario, models project that the AFOLU sector will sustain a net GHG
emissions reduction but at a declining rate. While FASOMGHG projects increasing emissions
from agriculture and livestock counterbalanced by a stable net sink of U.S. forests that will
preserve the net AFOLU sector from being a net emitter, GLOBIOM expects stable emissions
from agriculture and a decrease in the forest carbon sink. By 2070, the U.S. land sector in
GLOBIOM is near to becoming a net source of GHG emissions. By comparison, the Forest
Service 2020 Resources Planning Act Assessment projects that the U.S. forest sector will
decline as a sink, with the potential to become a net source of emissions by 2070 (U.S. Forest
Service, 2023).

Under the mitigation scenarios, the long-term trends do not diverge significantly from short- or
medium-term results until 2050 with more abatement achieved in 2070 across GHG prices.
When projected across longer time horizons (out to 2070), mitigation rates increase for all
models compared to medium- and short-term projections. This outcome is driven largely
by forest growth dynamics and further shows how investments made in earlier periods in
afforestation/reforestation and forest management can maximize the growth rate of timber,
and thus sequestration, in forestlands in the long term.

By 2070 under the lowest price scenarios FASOMGHG projects an average annual net
mitigation potential of the land sector between 54 and 327 Mt C02e yr1 while under the highest
price scenarios the range increases to 306-422 Mt C02e yr1. On the other hand, GLOBIOM
estimates an average annual mitigation potential that ranges from 213 to 447 Mt C02e yr1 in
2070 across all scenarios.

The largest uncertainty in terms of abatement potential is projected in the forestry sector
where in 2070, under a price as high as $440/t C02e, FASOMGHG projects abatement levels
of 336 Mt C02e, GLOBIOM projects 306 Mt C02e, and GTM projects 1258 Mt C02e. On the
other hand, agriculture shows a more defined trend, with annual mitigation ranging from 12 to
108 Mt C02e yr1 in FASOMGHG and 39 to 134 Mt C02e yr1 in GLOBIOM.

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3.5 Investments in Land-
Based Mitigation Activities

In the next decade, under investments
of $2 billion per year in the land
sector, this study estimates a potential
to mitigate 50 to 78 Mt C02e yr"1.

This section uses the output from the three models to
assess the estimated levei of investments needed to
achieve specific GHG mitigation volumes overtime. For
each GHG price scenario and each model, the average
future investments are calculated ex-post by discounting
annual expenditures (GHG price times GHG abatement)
to compensate landowners for their land-based mitigation
activities. This approach is similar to the one presented in
Austin et al. (2020).

Since each model responds differently to mitigation
incentives, the level of abatement under different
investments diverges significantly. Table 3-1 presents
the average annual mitigation achieved under a range of
possible investments in land-based abatement activities for
the short term (2025-2035) and the medium term (2025-
2050) for each model.

In the first decade, under annual investments between
$50 million and $2 billion in the land sector, there is a
potential to mitigate 78 Mt C02e yr"1 under FASOMGHG
and 50 Mt C02e yr"1 under GLOBIOM. Moreover, if the
same amount of investment is devoted only to forests, the
results from GTM show a potential average mitigation of
50 Mt C02e yr"1. Increasing the level of investments up to $5
billion per year, increases mitigation by 14% in FASOMGHG,
and it is 32% higher in GLOBIOM and 74% higher in GTM.

By midcentury, more abatement opportunities are estimated
to be available than in 2030 since investments made in
forestry activities take longer to realize gains from forest
growth dynamics. Ail models project that for the same level
of annual investments in land-based mitigation activities
(e.g., $5 billion-$15 billion yr"1) more abatement can be
achieved in 2050 than in 2030 per dollar invested. For
instance, under annual investments of $500,000 to $2
billion, FASOMGHG and GLOBIOM project that the land
sector can efficiently mitigate an average of 81 Mt C02e yr"1
and 131 Mt C02e yr"1 respectively between 2025 and 2050.
Under the same range of investments and time period, GTM
projects a mitigation potential of 94 Mt C02e yr"1.

Paint Creek mountain
view in East Tennessee.

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Average annual mitigation (Mt C02e yr1) per range of annual investments in land-
based mitigation activities (in billion US dollars)

(A) 2025-2035 range of
annual investments (Inv) in
billion US dollars

2025-2035 Expected annual average mitigation (Mt C02e yr1)
under each range of investments

FASOMGHG

GLOBIOM

GTM (forest only)

Inv < 0.5

0.5 < Inv < 2

2 < Inv < 5

5 < Inv <15

133

125

Inv >15

232

N/A

195

(B)2025-2050 range of
annual investments (Inv) in
billion US dollars

2025-2050 Expected annual average mitigation (Mt C02e yr1)
under each range of investments

FASOMGHG

GLOBIOM

GTM (forest only)

Inv < 0.5

0.5 < Inv < 2

131

2 < Inv < 5

155

158

193

5 < Inv <15

228

189

275

Inv >15

304

N/A

458

For each GHG price scenario and each model, the average future investments are calculated ex-post by discounting annual expenditures
to compensate landowners for their land-based mitigation activities. This approach is similar to the one presented in Austin et al. (2020).
To be consistent with the discount rate included in the models, future public finance to support land-based mitigation actions are
discounted using a 5% discount rate. GLOBIOM does not provide abatement levels above investments of $15 billion.

Restoration thinning with understory
removal in the Mark Twain National
Forest, Missouri, 2012. (Forest Service
photo by Michael Stevens)

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Box 11

FOCUS: Investments in land
mitigation

This box shows how the results presented in the report may be used to assess the mitigation
potential of specific levels of investments across certain mitigation activities using
FASOMGHG's results as an example.

While elsewhere in this report estimated mitigation potential is presented for a given GHG
price, it is possible to use these same results to evaluate potential cost-effectiveness of
investments across the AFOLU sector. Cost-effectiveness is a method for combining cost
estimates with projected outcomes. It compares one scenario to another scenario by
estimating how much it costs to gain a unit of the outcome, GHG mitigation in this case. This
study lays out 10-year cumulative estimated abatement associated with specific levels of
investments from FASOMGHG (Figure Bll). For example, under a $20 billion investment, the
potential abatement is projected to be around 780 Mt C02e (around 78 Mt C02e yr1). Using
these results, the average cost per ton of abatement is estimated to be about $25 per ton of
C02e.

This analysis indicates how results from the policy-agnostic analysis of the main report
presented here can be used to assess a hypothetical investment being made in specific GHG
reduction efforts across the land sector.

Figure Bll: Projected 10-year mitigation potential vs. 10-year cumulative investments in land-based mitigation
activities, FASOMGHG

For each GHG price scenario, the
average future investments are
calculated ex-post by discounting annual
expenditures (GHG price times GHG
abatement) to compensate landowners
for their land-based mitigation activities.
This approach is similar to the one
presented in Austin et al. (2020). To
be consistent with the discount rate
included in the models, future public
finance to support land-based mitigation
actions are discounted using a 5%
discount rate.

200 400 600 800 1000
10-year Cumulative Abatement (Mt C02e)

1200

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4 Discussion and Future

Research

The United States has set ambitious climate mitigation targets in the
short- to- medium term, including the national goal of reducing net
GHG emissions by 50-52% by 2030 from 2005 levels and becoming
net-zero by 2050 under the U.S. Long Term Strategy (LTS) (National
Climate Advisor, 2021; U.S. Department of State and the U.S.
Executive Office of the President, 2021).

Meeting these goals requires a portfolio of mitigation actions
that spans multiple sectors and implementation times,
and in this context, the land sector is expected to be a key
player. For instance, the LTS projects a net sequestration
level of about 1 Gt C02e yr"1 level of net sequestration from
carbon removal activities including land-based activities,
and other technologies16 (U.S. Department of State and the
U.S. Executive Office of the President, 2021), in 2050. The
LTS also includes non-C02 abatement activities in the land
sector (e.g., methane emission reductions from livestock
management) as key strategies to achieve the 2050 target
with projected reductions of methane and nitrous oxide
from agriculture of about 72 Mt C02e yr1 and 8.8 Mt C02e
yr"1, respectively (U.S. Department of State and the U.S.
Executive Office of the President, 2021).

Recent federal policies also recognize the value of
land-based abatement strategies by allocating funds
to preserve forests' natural capacity to sequester and
store carbon, to implement agricultural GHG mitigation

actions, and to increase forest resilience. For instance, the
Inflation Reduction Act has directed investments in land-
based mitigation programs, including the Environmental
Quality Incentives Program, the Regional Conservation
Partnership Program, the Conservation Stewardship
Program, the Agricultural Conservation Easement Program,
the Conservation Technical Assistance Program, and
the Agricultural Conservation Easement Program.17 The
Bipartisan Infrastructure Investment and Jobs Act of
2021 provides the USFS over $5 billion to tackle pressing
issues, including wildfire fuel removals, to develop national
reforestation plans, and to encourage innovation in the wood
product industry and bio-based product development.18
Finally, the USDA has directed approximately $3.1 billion to
selected projects under the Partnerships for Climate-Smart
Commodities program.19

In this context, it is important to create a technical
foundation of projected mitigation potential of the land
sector that can be used, among other applications, to

1 The LTS aggregates the mitigation potential of carbon removal technologies, including the LULUCF sector and direct air capture, so it is not possible to

determine the exact contribution of the LULUCF sector.

9 Inflation Reduction Act | USDA (https://www.usda.gov/ira)

18 https://www.fs.usda.gov/managing land/infrastructure

19 https://www.usda.gov/ciimate-solutions/climate smart-commodities

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A large reforestation project
replacing native trees in previously
cleared lands. This project, one of
many nationwide, is located in north
Florida near Jacksonville.

Greenhouse Gas

dura

Meeting GHG emissions goals requires a
portfolio of mitigation actions that spans
multiple sectors and implementation times,
and in this context, the land sector is expected
to he a key player.

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inform future policy development and implementation
and investment strategies. Moreover, given the potential
contribution of the land sector to national GHG emissions
reductions and sequestration targets, it is essential to
update and refine estimates of the magnitude and cost
of GHG mitigation activities from this sector in the short
and medium terms. This report provides information on
estimated future cost-effective levels of GHG mitigation
potential of U.S. land sector activities across multiple
models that consider a detailed representation of forestry
and agricultural resource management land-based
commodities markets, and GHG accounting under certain
conditions. The results presented in this report could
help stakeholders understand the general implications
of different future conditions and GHG reduction strategy
designs and the potential outcome of investments in the
land sector across time and activities.

This chapter's outline is as follows. First, the results from
this report are compared to historic annual emissions fluxes
and emissions trends from the U.S. GHGI (EPA, 2023) and
to the recent literature. Second, some practical applications
of the results are discussed. The chapter concludes with
a summary of the limitations and directions for future
research in land-based mitigation actions.

Planting corn into a stand of cover crop in Porter County,
Indiana, May 2023. (Photo donated to USDA by Jacob Tosch,
Porter County Soil and Water Conservation District)

4.1 Context for the Report
Results

Mitigation potential of the land sector
in this report is within the ranges
presented in previous versions,
the U.S. GHGI, and the broader
literature. The results tend to be more
conservative than some recently
published estimates as this analysis
accounts for land use competition,
tradeoffs between mitigation
activities, and market dynamics.

This report updates and expands the estimates of the
potential magnitude and cost of GHG mitigation from the
land sector presented in the 2005 EPA Report to provide a
range of future mitigation pathways for the land sector. Both
EPA reports use economic tools with biophysical information,
but this new report advances the research in the field by
using two additional partial equilibrium models of land
(only FASOMGHG was included in 2005), identifying eight
GHGs emissions categories (seven categories in 2005) and
24 mitigation activities in the land sector (23 activities in
2005). Since the 2005 report, FASOMGHG has also been
updated with revised representation of the U.S. forestry-
sector based on FIA data, which reflects the evolution of
the U.S. forest inventory over the last two decades and
new macroeconomic inputs (e.g., U.S. population and
GDP growth rates) from the AEO (EIA, 2022). Moreover,
the current report's use of increasing GHG price scenarios
compared with either fixed or constant prices drives to
different outcomes versus the 2005 report. Specifically,
by using a constant price in forward-looking models like
FASOMGHG, the incentive to invest in long-term mitigation
strategies would be minimized, reducing the potential for
higher abatement levels in the future. Finally, by applying

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global land models (GTM and GLOBIOM) in conjunction with
a domestic model, this report considers possible effects of
trade dynamics on the U.S. abatement potential that were
not included in the previous report.

In this report, all models run a baseline scenario that does
not include hypothetical GHG price scenarios, and future
land emissions are driven by market dynamics (e.g., demand
for timber products), other socioeconomic conditions,
and changes in biophysical characteristics (e.g., aging
trees) in the baseline and via the 10 GHG price scenarios.
Baseline projections are key elements to assess the GHG
mitigation potential of specific activities or sectors across
time because the estimated mitigation is determined by the
difference between future emissions projected under the
baseline and future emissions projected under the applied
scenarios. For this reason, key socioeconomic drivers are
harmonized across models, and the same price incentives in
the form of carbon-equivalent payments are used uniformly
across the three models in this study. Each model responds
to the price incentive by finding the most cost-effective mix
of land use activities and production, and the resulting GHG
reductions. Consequently, model results are comparable,
in a relative sense, as the difference between the results
under the baseline and the GHG price scenarios.

4.1.1 GHGI Historical Emissions and Projected
Trends

In this section, model results from the report are compared
with the historical emissions from 1990 to 2021 from the
Inventory of U.S. Greenhouse Gas Emissions and Sinks
(EPA, 2023). It is important to put the estimated results
in the context of the GHGI to get a sense of where and
to what extent historic and projected trends (in terms of
directionality and magnitude) align or not. For example, in
the AFOLU sector, according to the GHGI, net sequestration
decreased by 5.9 Mt C02e yr"1 from 2000 to 2020 and,
using results from this study, is projected to decrease by
5.3 Mt C02e yr"1 from 2030 to 2050 under the baseline
scenario.

To compare the direction of future emissions trends for the
baseline and mitigation scenarios to historical changes, the
models' projections are pegged to the emissions reported
in 2020 in the 2023 GHGI (see Figure 4-1 for a detailed
description of how projected future emissions have been
aligned to 2020). This adjustment, in effect, puts the
estimated projection results of this study in the context of
the GHGI estimates in a simplified manner.20 In essence, the
baseline trends and mitigation volumes are appended to the
terminal reporting year of the GHGI for illustrative purposes.
This comparison between adjusted projections and historical
data shows that they generally follow the trend of increasing
emissions.

In the baseline scenarios, the adjusted projected emissions
from the agricultural sector indicate a slight increase relative
to historical emissions reported in the 2023 GHGI. From
1990-2020, annual net emission rates for these emissions
ranged from 587 to 663 Mt C02e yr"1, while projected
net adjusted emissions are 652 to 728 Mt C02e yr"1 in
2050. Under the GHG mitigation scenarios, the adjusted
agricultural emissions would be in line to the late 1990s in
the 2023 GHGI, with levels of just over 640 Mt C02e yr"1 in
2050.

In the forestry sector, in the adjusted projected emissions,
the maximum net carbon sink under the baseline scenario
across all three models is about 708 Mt C02e yr"1 in 2050,
which falls between the historical bounds as reported over
the last 30 years—the annual net sequestration rate ranged
from 657 to 846 Mt C02e yr"1 from 1990-2020. Moreover,
the minimum projected net carbon sink in the baseline of
about 646 Mt C02e yr"1 is near the lower bound of what
has been experienced in the same period. Box 12 provides
a detailed discussion of the historical evolution of GHGI
emissions and a comparison with projected emissions from
the forests.

20 Adjusted baseline emissions from the AFOLU sector are projected to be higher than the results presented in Chapter 3 because they are adjusted to be in
line with 2020 values.

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FIGURE 4-1

Historic and projected adjusted GHG emissions for U.S. agriculture, forestry, and net
AFOLU (in Mt C02e yr4,1990-2050)

500-

>
"53

o
o

-500-

-1000-

Agriculture and Livestock GHGI

Net AFOLU GHGI

Forest GHGI

1990

2000

2010

2020

2030

2040

2050

Baseline
Mitigation

Historical emissions for agriculture, forestry, and net AFOLU are from the 2023 GHGI. Included GHG emissions pools from the GHGI are
crop cultivation, livestock, and fuel combustion for agriculture and livestock; and land converted to forest, forestland remaining forestland,
and LULUCF emissions for forestry. Historical forestry sector emissions were calculated as the sum of three categories included in the U.S.
GHGI: (1) Forestland remaining forestland, (2) land converted to forest, (3) LULUCF emissions.

Projected emissions (E) from each model i for each sector (Agriculture and Livestock, AFOLU, and Forest) at anytime t are aligned to 2020
values from the 2023 GHGI following this formula:

E{i,t + n) = E{GHGI,2020)+ £ E(i,t + n)- E{it)

Where t is the initial year of the results from the models (2025) and n is equal to 5 years. In the figure, for visualization purposes, 2025
values are estimated as the average between 2020 and the 2030 adjusted value for each model. For agriculture from 2020 to 2050,
adjusted results are from GLOBIOM and FASOMGHG and include C02 and non-C02 emissions while adjusted forest emissions show results
from GLOBIOM, FASOMGHG, and GTM from 2020 to 2050. Net AFOLU emissions, aggregate adjusted agriculture and forest emissions
from GLOBIOM and FASOMGHG. Shaded areas show upper and lower bounds of the baseline scenario and the GHG price scenarios
across models.

Each model's results were adjusted to 2020 reference levels from the 2023 GHGI; therefore, the results presented in this figure offer a
different perspective compared to Chapter 3, where the GHG scenario results do not estimate the net AFOLU sink to become a net source.

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Across recent studies, there is a low agreement on future
U.S. forest sequestration trends under baseline scenarios.
Some studies project U.S. forests will constitute a net sink
but with a declining sequestration rate in the future, mainly
due to forest dynamics, including disturbances and aging
(Jones et al., 2019; Latta et al., 2018; U.S. Department
of Agriculture Forest Service, 2012; Wear and Coulston,
2015), while other studies show an increased carbon sink
in the future driven by investments in more forestland and
more managed forests than current levels (Austin et al..
2020; Daigneault et al., 2022; Nepal et al., 2012; Tian et
al., 2018). Finally, other studies show that under a business-
as-usual scenario, U.S. forests will become a net source of
emissions before or by 2050 (Nepai et al., 2012; Oswalt et
al., 2014; Ryan et al., 2012). The 2020 Resource Planning
Assessment (RPA) (U.S. Forest Service, 2023) is another
example of a study that uses different tools to address
different analytical questions—in this case, to assess future
potential land-based resource outcomes. That study applies
a range of future socioeconomic and climate scenarios to
project the potential availability and condition of forest and
rangeland resources over the next 50 years to offer insights
about how underlying socioeconomic and climate drivers
can affect the natural resources in the United States. Since
the 2010 RPA (U.S. Forest Service, 2012), this analysis
uses selected socioeconomic and climate scenarios to
assess future potential land resource outcomes but does

not specify a specific baseline or apply GHG mitigation
scenarios, and therefore does not offer estimated mitigation
potential. While it does not assess mitigation potential,
the RPA does offer projections on future forest carbon
stocks. The RPA projects across 4 future climate and 20
socioeconomic scenarios that the forest sector net sink
could decline, similar to the general baseline trends from
FASOMGHG and GLOBIOM in this report; and in some cases,
the RPA projects that the forest sector will become a net
source after midcentury, with projections ranging from -165
to 95 Mt C02 in 2070.

Under GHG mitigation scenarios examined in this study,
forestry-related emissions show larger variation (and
greater mitigation potential) in projected changes relative
to historical levels from 1990 to 2020. Note that the levels
of GHG incentives in this report under the hypothetical GHG
pricing scenarios, especially in the longer term, constitute
levels of financial incentive that have not yet existed for
GHG mitigation in the land sector. The upper bounds of
these results illustrate the potential magnitude of GHG
mitigation practices adopted in response to GHG prices over
the simulated timeframe of this study, including high GHG
price incentives by the medium term, which could spur high
rates of afforestation and increasing investments in forest
management in the United States.

Tree and shrub canopy in the Chicago region of Illinois. (USFS photo by Preston Keres)

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Box 12

FOCUS: Historical and projected
carbon fluxes from forests

Every year, the historic data in the GHGI (from 1990 to the present) is re-estimated according
to the updated data and methodologies as required by IPCC guidelines.21 This annual re-
estimation can at times significantly change the estimated flux in a specific year or years
across inventories. Focusing on total forest ecosystem carbon fluxes, Figure B12 shows the
historic data from 1990 to 2021, using data from 16 GHGIs published between 2007 and
2023, and compares them with the projected results from this report (baseline and GHG price
mitigation scenarios). The comparison shows that future baseline projections from the three
models are projected to be within the range of historical estimates. Furthermore, emissions
under the GHG mitigation scenarios are projected to be within the range, except for a few high
GHG price runs from GTM. While it is insightful to compare projected GHG estimates to historic
values, these historic values are indeed estimated and revised annually to incorporate scientific
and technological advances. Moreover, the large uncertainty of historical emissions can also be
driven by changes in carbon pools included and changes in measurements.

21 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006).

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Figure B12: Conceptual illustration of market opportunity costs for a hypothetical commodity market and MACC
for an abatement strategy that generates a loss in yield or total production

-1500-

1990 2000 2010 2020 2030 2040 2050

GHGI

2007

GHGI,

2013

GHGI

2019

— FASOMGHG Baseline

GHGI

2008

GHGI,

2014

GHGI

2020

— FASOMGHG Mitigation

GHGI

2009

GHGI,

2015

GHGI

2021

— GLOBIOM Baseline

GHGI

2010

GHGI,

2016

GHGI

2022

— GLOBIOM Mitigation

GHGI

2011

GHGI,

2017

GHGI

2023

GTM Baseline

GHGI

2012

GHGI,

2018

GTM Mitigation

Historic emissions from 1990 to 2021 are sourced from the GHGIs published between 2007 and 2023, and the legend shows the
year in which the GHGI was published. Projected fluxes from 2025 to 2050 report the results presented in this report from baseline
and mitigation scenarios by models. Note that GHGI emissions included here reflect only forest remaining in forests, while the result
from the report also includes flux from land converted to forests.

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4.1.2 Mitigation Projections in the Report and
Comparisons to Recent Literature

As discussed in Chapter 1, there are different methodologies
to assess land mitigation potential, and recent literature
presents a large range of estimated future abatement
opportunities in the forestry and agriculture sectors, driven
largely by model type and different underlying scenario
parameters. Chapter 1 identified 39 studies, including
both peer-reviewed articles and reports published between
2000 and 2022, where land-based mitigation potential was
assessed using one or multiple GHG price scenarios. Among
the 39 studies, 8 provide the total aggregate potential of
the land sector across different GHG price scenarios (see
Appendix B). Across these studies, the abatement estimated
varies significantly due to different methodologies, input
data, and assumptions, with a range of 5-624 Mt C02e for
GHG prices below $35/t C02e and 550-1,168 Mt C02e for
GHG prices up to $200/t C02e between the present and
2050 (Table 4-1). These estimates are higher compared to
this report's findings of 63-181 Mt C02e under GHG prices
below $35/t C02e and 268-269 Mt C02e under GHG prices
up to $250/t C02e, and the difference could be explained by
several factors.

Recent bottom-up studies, such as Cook-Patton et al. (2021)
and Eagle et al. (2022), found that the U.S. land sector
could mitigate around 1 Gt C02e yr"1 and 700 Mt C02e yr"1
by 2030, respectively, at less than $100/t C02e, while this
study finds much lower mitigation rates, with maximum
values of around 200-300 Mt C02e yr"1 in 2030 under
the same price. The difference lies mainly in differing
methodologies and different data, as well as modeling
a shorter overall timeframe, which limits the mitigation
potential from afforestation and reforestation activities.
Bottom-up approaches used in Fargione et al. (2018),
Cook-Patton et al. (2021), and Eagle et al. (2022) usually
aggregate estimated abatement potential under specific
GHG price ranges from a variety of different sources and
models. By aggregating the results ex-post, this methodology

does not endogenously account for land use competition
and economic tradeoffs, and potentially overestimates the
rates of mitigation relative to PE models where there is an
explicit representation of economic tradeoffs, land use
competition, and market responses. By not representing
these interactions and tradeoffs between agriculture and
forestry activities, bottom-up assessments of land mitigation
usually provide higher abatement estimates for the same
level of GHG price than PE models. This effect is particularly
significant under high GHG prices (>$50/t C02e) and in the
long term. On the other hand, the effect is smaller under
low GHG prices and/or short- to medium-term time horizons
when PE models might find opportunities for mitigation
activities to complement each other (Baker et al., 2019;
Galik et al., 2019). Another driver of higher mitigation
potential estimates from bottom-up studies lays in the
inclusion of new/nascent mitigation options (e.g., biochar,
agroforestry) that are not included in the models used for
the report to date because there are not yet sufficiently
comprehensive datasets for such practices applied in
the United States and/or consistent reporting guidelines
established by IPCC or other coordinating bodies.

Comparing the results presented in this report with recent
studies using PE models like FASOMGHG, there are still
some important differences driven by different parameters
and assumptions rather than methodological frameworks.
Different projections in mitigation from the land sector may
be driven by, for example, different study objectives that
affect choices in scenario design, either in the baseline or
in the portfolio of mitigation activities available at the sector
level. For instance, Wade et al. (2022) used FASOMGHG to
apply different socioeconomic pathways (SSPs; Riahi et al.,
2017) that affected both baseline emission projections and
the cost-effective composition of the mitigation portfolio.
On the other hand, Baker et al. (2013) selected a different
set of abatement options available in the same model.
Specifically in their analysis, bioenergy is included in the
land-based mitigation portfolio, and authors found higher
mitigation potential for the same GHG price range.

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Other factors that help explain the difference in results
across approaches and models include recent changes
in data and other parameters, such as upfront costs for
different technologies, maximum technical potential of
technologies, new land use and market conditions (e.g.,
new timber products), changes in carbon dynamics of
terrestrial ecosystems (e.g., natural productivity of land),
and management responses to market incentives.

Mitigation potential in the land sector per price range from literature review and this
report

Estimated Annual Average

GHG Price

Mitigation Potential per

Range

Study

Price Range (Mt C02e)

($/t C02e)

Method

Each methodology used to calculate mitigation potential
provides information useful for different stakeholders,
such as decision, makers and the broader GHG modeling
community, despite differences in methods and outcomes.
For example, in the GHG reduction strategy design process,
technical potential is valuable to assess the estimated
upper bound of potential of specific technologies.

EPA (2005)*

$l-$35

627

$36-$200

Roe et al. (2019)

550

$36-$200

Techno-economic / Bottom-up

Roe et al. (2021)

957

$36-$200

Techno-economic / Bottom-up

Fargione et al. (2018)

300

$l-$35

1,100

$36-$200

Cook-Patton et al. (2021)

1,168

$36-$200

Techno-economic / Bottom-up

Eagle etal. (2022)

827

$36-$200

Techno-economic / Bottom-up

Baker et al. (2013)

624

$l-$35

Partial Equilibrium Model (PE)

Wade etal. (2022)

386

$l-$35

Partial Equilibrium Model (PE)

EPA (2024) - FASOMGHG**

$l-$35

269

$36-$200

EPA (2024) - GLOBIOM**

181

$l-$35

Partial Equilibrium Model (PE)

Annual average mitigation potential of the land sector in Mt C02e per price range in the United States has been calculated using data from
Van Winkle et al. (2017) and original sources listed in Appendix B. Price ranges are considered for 2020-2100. However, many studies
report mitigation potential until 2030, whereas other studies (e.g., Cook-Patton et al., 2020; Roe et al., 2021) do not explicitly mention
the time horizon used in their analyses. Some studies (e.g., Fargione et al., 2018) include mitigation activities not included in this report.
Some studies present more estimates per price range; these estimates have been averaged in the table.

*Tables 3.8, 4.A.1, and 4.A.4 (EPA, 2005). For comparison purposes, all values from EPA (2005) exclude biofuels.

**Results from this report are reported as average values for 2050 and include all GHG price scenarios that reach a maximum GHG price
of $240/tC02e in 2050.

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4.1.3 Mitigation Across Land-Based Activities

in this report, each model not only projects the GHG
abatement potential of various land sector mitigation
activities but also estimates the cost-effective composition
of land-based activities in response to the mitigation
incentive. Results across all models in this report suggest
that forest-based activities offer the highest level of
mitigation potential. Despite different methodologies,
parameters, and inputs, recent studies broadly agree with
the results of this study that improved forest management
and afforestation are the practices with the largest and/
or cheapest GHG mitigation potential in the U.S. land
sector (Table 4-2). For instance, Roe et al. (2021) shows
that afforestation/reforestation has the largest maximum
mitigation potential of 307 Mt C02e yr"1 under a GHG price
between $36 and $200/t C02e. On the other hand, Eagle et
al. (2022) estimated that improved forest management and
avoided forest and grassland conversion provide the largest
mitigation potential at a GHG price scenario of $10/t C02e,
while other mitigation actions will be available at higher GHG
prices.

Table 4-2 also shows the high variability of estimated
mitigation potential from soil carbon activities, with recent
studies projecting lower potential relative to previous
estimates (e.g., Schneider and McCarl [200]). Note that
there has been increased focus on the high uncertainty
related to the biophysical potential of soil carbon mitigation
activities due to the lack of physical observations and data
(Ogle et al., 2019).

Finally, a recent USDA report (Jones and O'Hara, 2023)
estimated the technical mitigation potential of agriculture
and livestock-based activities to be in the range of 38-
140 Mt C02e (cropland non-C02) and 26-40 Mt C02e
(livestock non-C02), depending on the price range. The
results presented in the report fall within this range with
a maximum of 50 Mt C02e (GLOBIOM) and 12 Mt C02e
(FASOMGHG) potential for cropland non-C02 and 58 Mt C02e
(GLOBIOM) and 55 Mt C02e (FASOMGHG) for livestock in
2050. The estimated potential from livestock is below the
maximum technical potential of 75 Mt C02e presented in the
EPA Non-C02 Mitigation Report (EPA, 2019b), as discussed
in Box 9.

4.2 Potential Applications of
the Results

This technical report could be used by different stakeholders
across different applications, such as supporting policy
design assessment and improving current modeling
frameworks and the state-of-knowledge on mitigation
potential assessments. Some theorical applications are
described below.

First, the integrated assessment and energy modeling
community could use results from this report to reflect the
potential magnitudes and costs of abatement from the
agriculture and forest sectors (not related to bioenergy
supply), as well as baseline emissions from the land sector.

As discussed in the IPCC AR6 WGIII, Chapter 7 (Nabuurs
et al., 2022), the number of land-based measures used
in lAMs is limited compared with sectoral models like
those used in this report. In addition, the resolution of
land-based measures in lAMs is less granular compared
to sectoral models and may lead to higher uncertainty on
the mitigation potential of a single land-based mitigation
strategy. Specifically, lAMs usually represent limited or less
detailed representations of land sector ecosystems and
markets, and therefore represent a limited set of mitigation
possibilities from forests and agricultural systems through
management changes and technologies. Furthermore, lAMs
often do not represent detailed landscape carbon dynamics
via afforestation and avoided deforestation, like the three
models used in this study.

Given these different characteristics, sectoral models could
be used to augment the results from lAMs, while lAMs could
be used to provide a more comprehensive representation of
dynamics across sectors. The results from this report could
help close these gaps by improving the representation of the
land sector baseline emissions and mitigation potential (C02
and non-C02) across a wide range of GHG price scenarios
and abatement activities.

Second, public- and private-sector entities could use results

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TABLE 4-2

Average mitigation potential per land-based mitigation activities in the literature

Mitigation Activities in the U.S.

Estimated Range of Average Mitigation Potential GHG Price Range
per Price Range (Mt C02e yr"1) ($/t C02e)

Afforestation/reforestation/avoided deforestation

3-918

$l-$35

10-1,290

$36-$200

Forest management

10-413

$l-$35

12-1,256

$36-$200

Soil carbon sequestration

1-546

$l-$35

6-195

$36-$200

Cropland non-C02

3-150

$l-$35

3- 140

$36-$200

Livestock non-C02

11-71

$l-$35

16-75

$36-$200

Average annual mitigation potential in Mt C02e per price range of $l-$35/t C02e and $36-$200/t C02e across five key land-based
activities in the United States. Estimated range of average mitigation potential shows the minimum and maximum amounts of abatement
reported across the 33 studies listed in Appendix B. Price ranges are considered for 2020-2100; many studies report mitigation
potential until 2030, while other studies (e.g., Cook-Patton et al., 2020; Roe et al., 2021) do not explicitly mention the time horizon
assumed in their analyses. The table considers studies using different methodologies. For instance, it includes recent estimates from a
USDA report on mitigation from agriculture (Jones and O'Hara, 2023) and the EPA Non-C02 Mitigation Report (EPA, 2019b). Both reports
provide a static representation of maximum technical potential of abatement opportunities as they represent annual potential mitigation
consistent with a given cost; therefore, they diverge from the methodological approach used in this report. As discussed in Chapter 3, the
two approaches complement each other by proving different sets of information.

from this study to prioritize land mitigation investment and
strategies designed to increase carbon sequestration and
other beneficial GHG outcomes. Results from this report
offer insights into how mitigation efforts could be prioritized
across activities and over time to maximize emissions
reductions given budget constraints or mitigation price
thresholds. Depending on a stakeholder's primary needs,
the use of model results from a single model or set of
scenarios could represent a different strategic choice.

Such users could include federal, state, or regional
government stakeholders, nongovernmental organizations
investing financial resources in GHG mitigation and related

land conservation initiatives, and private-sector entities
seeking to decarbonize their supply chain through various
investments in the land sector. Specifically, the wide range
of mitigation potential and cost estimates provided in this
report can help implementors evaluate which types of
projects to invest in, where to focus investment and outreach
efforts, and related considerations on investment timing.
Moreover, the U.S. regional results could lend insights
to those interested in the potential mitigation outcomes
and economic tradeoffs of different mitigation options in
different regions. However, these subnational results might
not provide sufficient insight for stakeholders interested in
understanding abatement costs for a specific location or
under specific localized circumstances.

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Appendix C presents all the key results discussed in the
report, and the next subsections present three examples
of practical applications to show possible ways to use the
results.

4.2.1 Application 1: Abatement Potential and
Cost

Policymakers, investors, and members of different research
communities might be interested in using the results
presented in this report in their own analyses. One potential
application is in the estimation of abatement potential from
various sectors and associated costs. Alternatively, users
can use the results to calculate per-ton mitigation costs of
various targets, dependent on time and scope. Finally, the
results can be utilized to assess the potential contribution
from the AFOLU sector in meeting long-term climate
stabilization targets or the feasibility of specific land-based
mitigation goals, as the two examples below show.

The U.S. LTS (U.S. Department of State and the U.S.
Executive Office of the President, 2021) projected about a
1 Gt C02e yr"1 level of net sequestration from land-based
activities and other carbon removal activities to achieve
the net-zero emissions goal by 2050. Overall, the findings
presented in this report estimate that the forestry sector has
the capacity to increase its net sequestration to 1 Gt 002e
yr"1 over the next three decades under a GHG price higher
than $67/t C02e.22

The Global Methane Pledge launched in 2021 by the United
States and the European Union aims to reduce global
methane emissions by 30% below 2020 levels by 2030
(The White House, 2021). Though this global goal includes
all the sectors emitting methane (e.g., energy, lands), the
results in this report could be used to estimate the potential
reduction of U.S. methane emissions in the land sector.
Results show that the land sector could reduce methane
emissions by 28% in FASOMGHG and by 31% in GLOBIOM
in 2030, relative to 2020, under a GHG price higher than
$110/t C02e.

4.2.2 Application 2: Sensitivities to Model
Frameworks and Primary Scenario Parameters

This report could provide insights into the selection of
policy design elements to help achieve different analytical
goals. While the main results presented in Chapter 3 rely
on a modeling framework in which a universal GHG price
is applied to the land sector and all agents respond to the
price in a rational way with perfect information, the boxes
explore alternative land-based mitigation policy designs by
combining the GHG price scenario with other policies (Box 8)
and by introducing an opt-in program (Box 7).

4.2.3 Application 3: Unintended Consequences

A third element of this report is the consideration of
unintended consequences and potential indirect effects of
land-based activities domestically. Through the application
of a suite of models with differing scope and detail, this
report allows for assessment of potential unintended
consequences stemming from hypothetical GHG pricing
scenarios. Unintended consequences can result from
market or non-market changes, and this report employs a
variety of models, scenarios, and sensitivity cases to bring a
broader range of results into consideration.

4.2.3.1 Leakage Effects

Results in this report demonstrate the advantages of
coordination of strategies across jurisdictions (landowner
type, sector, geography, and economic market are all
attributes of scope that can be considered) and estimate the
carbon leakage resulting from unilateral action (measured
as the difference between the abatement achieved under
the coordinated action and the abatement achieved under
unilateral action).

Box 4 compares the results of a uniform global GHG price
(main results of Chapter 3) with a unilateral GHG price
applied to the United States only. Results show that a
unilateral (domestic only) pricing scenario is likely to

22 Note that 1 Gt C02e is the projected net flux of CO2 from forests in GTM in 2050 under the $50 at 1% scenario, which is expected to deliver a net mitiga-
tion of about 362 Mt C02e relative to the baseline in 2050.

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create the conditions for GHG leakage with a possible 11%
increase in emissions from the rest of the world under a
U.S.-only GHG price. Also, note that there might be opposite
effects in terms of leakage if other jurisdictions/countries
implement policies independently.

4.2.3.2 Tradeoffs Between Land Conservation and
Mitigation Potential

The main results of this report generally reinforce the
understanding that the land sector can be responsive to
incentives for GHG mitigation by converting land from one
use to another use due to financial incentives. However,
some land use changes are anticipated to occur in certain
places where other forces (like cultural reasons or local
preservation goals) may limit conversion. Also, land use
changes may be restricted to achieve other goals, like the
cultivation of food crops. For these reasons, this report looks
at sensitivity analyses that investigate limits on land use
changes and the implications on estimated GHG outcomes.
In these specific cases, there are tradeoffs associated with
limiting what lands, landowners, and land management
activities are eligible for GHG mitigation incentives, which
should be considered during policy development and the
assessment of a specific policy's potential outcomes (which
is beyond the scope of this report).

Box 8 estimates the effects of preserving land allocation in
the Corn Belt by limiting land use transitions from cropland
into forestry. This land use conversion restriction could
mimic a situation in which landowners do not respond to the
GHG price signal by shifting to the most remunerative use of
land, but maintain the current use of land because of high
transaction costs or imperfect information on the mitigation
incentive. Conversely, the restriction could represent a
strategy to preserve agriculture land to avoid disruption
to crop and livestock production levels as a means to
guarantee food security. Results from the sensitivity show
that under the constrained scenarios, national estimated
GHG mitigation is reduced by about 20% with a $100/t C02e
GHG payment relative to the unconstrained scenario.
The case study represents a simplified application of the

model's results and does not provide an assessment of the
tradeoffs between land-based climate change mitigation
efforts and food security, which requires a stand-alone
analysis that goes beyond this report. Specifically, multiple
socioeconomic and technological scenarios, which include
alternative assumptions on economic growth, technological
innovation, and diet preferences, should be considered
when projecting future demand for food commodities and
the demand for land. Alternative demand scenarios for food
should be used to test the effects on the costs of specific
land-based mitigation activities, the MACCs, and the total
mitigation potential of land.

Cover crops are aerially seeded over corn at Scully Family Farms in
Spencer, Indiana, in September 2022. The cover crops mix includes cereal
rye, crimson clover, and rapeseed and was spread over 160 acres of
no-till farmland that will be planted with soybeans in the spring. (Natural
Resources Conservation Service photo by Brandon O'Connor)

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4.3 Limitations and Future
Research

Future research should advance
the knowledge of land mitigation
potential by including new mitigation
strategies, climate change impacts on
land availability and productivity, and
social and environmental co-benefits
associated with their implementation.

As with any simulation scenario analysis or multi-
model comparison effort, there are several data gaps
and limitations of this analysis that warrant additional
consideration and offer future research avenues, as
discussed below.

This study focuses only on direct land-based GHG mitigation
opportunities through land management and land use
without considering other mitigation activities outside
the land sector that are likely to affect land use and land
management and indirectly change its GHG balance. For
instance, while future demand for biofuels and bioenergy is
likely to compete for land with direct land-based abatement
strategies and with consequent implications on the
GHG emissions from land, these are not GHG mitigation
measures that could directly apply to the land sector to
address its future mitigation potential. That is, bioenergy
and biofuels are GHG mitigation strategies employed in the
energy sector as a response to climate mitigation actions
to reduce GHG emissions from energy; therefore, the
assessment of their effects on land is outside the scope
of this report. Similarly, this report does not include future
demand for land for solar or wind energy production, which
is likely to increase under decarbonization scenarios (van
de Ven et al., 2021). Finally, the models do not include
demands for non-traditional land-based commodities
(e.g., cross-laminated timber), which might be driven by
decarbonization activities outside the land sector and will
have effects on emissions fluxes from the land sector.

Moreover, there is high uncertainty on future demand
projections for bioenergy and biofuels, which would have
increased the complexity of the number of scenarios
simulated for the report and the inability to assess the
actual mitigation potential of land. For instance, a recent
analysis using the two lAMs projects an annual demand for
biomass in the United States of 0 to 3 exajoules per year
(EJ yr"1) when no GHG price is assumed, and increases to
approximately 9-22 EJ yr"1 in 2060 with a carbon price
trajectory of $26/t C02e in 2020 to $413/t C02e in 2060,
depending on the model (Vimmerstedt et al., 2023).

Finally, lAMs expect high demand for bioenergy associated
with carbon capture and storage (BECCS) under stringent
mitigation scenarios, which are associated to high GHG
prices (above $100/t C02) and in the long term (Favero
et al., 2023). Both of these conditions do not apply to the
report (only three scenarios have a GHG price higher than
$100/t C02e in 2050, and the results are reported up to
2050).

Given all these aspects, the implications of bioenergy and
biofuels production on the land sector emissions balance
would be better explored in a stand-alone analysis where
energy models and land models (like the models used for
this report) integrate the information in a dynamic fashion to
provide important insights on the mitigation potential of the
two sectors. As suggested in other studies, bioenergy and
mitigation from the land use sector could either complement
each other or create additional resource competition when
considered conjunctively (e.g., Baker et al., 2019; Favero
et al., 2020; Favero and Mendelsohn, 2014; Favero et al.,
2017). Further research on these topics should include the
effect of climate mitigation policies on biomass demand and
corresponding implications on land competition and MACCs.
In terms of direct land-based mitigation strategies, the report
does not include emerging options such as agroforestry and
new developments in livestock feed additives due to a lack
of comprehensive historic data on environmental outcomes
and costs. As more options are developed on a larger scale
and related data on costs and GHG outcomes become
available, they can be included in future assessments of the
abatement potential of the land sector using models from
this suite of tools. Furthermore, in the current mitigation
portfolio applied in this report, the study and the models
do not differentiate between available activities for their

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inherent riskiness related to permanence, additionally, and
leakage considerations, but those could be further explored
in future research.

In terms of future supply of mitigation options available
from the land sector, this analysis does not include potential
impacts associated with climate change on land availability
and productivity above what is inherently included via the
input data used in the models. Some key impacts that
are likely to affect the results presented in this report are
changes in crop productivity, natural disturbances, increased
C02 fertilization, and tree species migration, among others.
For example, Box 5 (Chapter 3) shows that forest carbon
fertilization driven by higher concentration of GHGs in the
atmosphere is likely to increase the projected mitigation
outcomes of GTM. Climate change feedbacks beyond
climate fertilization are particularly important to assess
investment incentives over longtime horizons (Baker et
al., 2023; Davis et al., 2022; Favero et al., 2021). Future
research should expand on this application to test the
current results under different climate change scenarios to
assess the sensitivity of the findings to changing climate
conditions by including the role of changing temperature and
precipitation patterns, fluctuations in crop growing regions,
and changes in occurrences of natural disasters such as
drought, floods, and fires.

The model results presented in this report show potential
future trends across different scenarios. Though this
report does have some sensitivity analysis to evaluate
uncertainties related to specific modeling variables and
other parameters, it does not provide a comprehensive
analysis of uncertainty of the results. However, the three
models have been used in several peer-reviewed studies to
explore: (1) key policy questions related to complementarity
of mitigation investments across sectors (Baker et al., 2018;
Favero et al., 2020); (2) indirect mitigation co-benefits of
asymmetric mitigation pricing schemes (Baker et al., 2019);
(3) the spatial and temporal distribution of forest sector
mitigation potential under different incentive structures
(Austin et al., 2019); (4) U.S. and global forest and
agricultural sector mitigation potential across alternative
SSP baselines (Daigneault et al., 2022; Wade et al., 2022);
(5) impacts of shifting diets on land sector emissions
(Kozicka et al., 2023; Latka et al., 2021; Wu et al., 2023);

and (6) impacts of climate change-driven water scarcity
on agricultural production (Awais et al., 2023; Fitton et al.,
2019). This comprehensive collection of complementary
analyses provides a fuller set of sensitivity tests for
individual models.

As shown by the sensitivity tests, the design of GHG
mitigation policies can affect projected mitigation potential
across sectors and time. Future research should explore
alternative policy designs and implications of multiple
policies targeting the land sector simultaneously. For
instance, this report is constructed with only a hypothetical
universal GHG price without considering other policies that
could either complement it or increase the costs of land
mitigation activities.

Finally, the results presented in this report provide an
estimated cost-effective composition of specific land-
based mitigation activities under a different range of GHG
price pathways under specific future conditions, without
considering macroeconomic costs and socioeconomic
benefits outside GHG mitigation. Future research should
include these additional layers of analysis by estimating, for
example, the social benefits of reducing GHG emissions and
the potential co-benefits on biodiversity together with equity
and environmental justice considerations.

A Midwest ethanol plant.

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