Greenhouse Gas Emissions Scenarios: Background, Issues, and Policy Relevance

June 3, 2021 R46807

Greenhouse Gas Emissions Scenarios:
June 3, 2021
Background, Issues, and Policy Relevance
Michael I. Westphal
Projecting future climate change, and what drives it, is difficult, with many uncertainties.
Analyst in Environmental
Computer models, however, can be useful tools for exploring the long-term implications of
Policy
climate change and evaluating policy options. For example, models can help construct plausible

scenarios of future greenhouse gas (GHG) emissions based on socioeconomic, environmental,
and technological trends and drivers.

Integrated assessment models (IAMs), coupled models of the economy, energy, land use, and climate systems, are used by
the Intergovernmental Panel on Climate Change (IPCC), the main international scientific body for assessing global climate
change. This report explores the results of a selected set of IAM scenarios consistent with keeping the increase in global
mean surface temperature to 1.5°C or 2°C above preindustrial levels in 2100, the temperature goal of the Paris Agreement.
The modeling indicates that the more stringent the temperature target, the earlier the dates would have to be for global peak
and net-zero carbon dioxide (CO2) emissions. In order to hold likely (with at least a 66% probability) warming to below 2°C
in 2100, the model results suggest that global annual CO2 emissions would need to decline to net-zero between 2080 and
2100. To keep likely warming below 1.5°C in 2100, the models project that global CO2 emissions would generally have
peaked around 2020 and would reach net-zero by 2060. In these scenarios, carbon removal would need to balance positive
GHG emissions. The IPCC scenarios indicate that the later the peak in CO2 emissions, the sharper the reductions would be
later in the century to hold the temperature increase below any given target.
With current technologies and projected future technology costs, the global IAM models in this report all generally rely on,
inter alia, a scaling up of energy efficiency, renewable energy, nuclear energy, electrification of end-use energy, and large-
scale deployment of negative emissions technologies to find lowest-cost solutions to keeping likely warming to 1.5°C or 2°C
in 2100. Under some scenarios considered in this report, the models indicate that renewable energy may scale up by 3-4
times, and carbon capture and storage capacity by 20 to more than 300 times in the next 30 years. In 2050, across the model
runs, assumed negative emissions represent half to more than double the level of positive CO2 emissions from energy,
transport, and industrial processes. The models project significant increases in the global demand for electricity by 2050—in
some scenarios, twice as much as current levels, due to a shift toward electrification, or the substitution of electricity for
fossil fuel use in engines, furnaces, and other devices. The models indicate that the energy intensity (energy per unit of GDP)
of the world economy would decline by roughly one-quarter to more than one-third in the 1.5°C- or 2°C-consistent scenarios
compared to the baseline in 2050. However, the IAMs have limitations in foreseeing what technologies may become
available and economically viable in the future. There are other possible energy futures if other factors besides costs and
technical potential are taken into consideration.
Role of Integrated Assessment Models (IAMs) in Policymaking
The projections and comparative results from the IAM scenarios may provide a foundation for Members of Congress who are
considering climate change mitigation proposals. While not without criticism and limitations, the scenarios have been
specifically designed to find technology deployments that meet specified climate or emissions constraints, typically in a
lowest-cost manner. One strength of IAMs is the ability to explore complex linkages and tradeoffs across energy, agriculture,
and land-use sectors that may occur with policy changes. IAMs are most useful not for precise estimates of the future
technology or fuel mix under different scenarios, but rather to compare relative results from different policy options.
If Members of Congress are interested in understanding GHG emissions choices, including net-zero emissions, model results
from IAMs can inform policy deliberations on possible GHG reduction targets, timing, and pathways. IAMs may help in the
consideration of legislative options, such as incentives to accelerate development and deployment of technologies to meet
emissions objectives. IAM results suggest that key technologies are in such areas as renewable energy, energy efficiency,
electrification, nuclear energy, carbon capture and storage, and carbon removal, among others.
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Greenhouse Gas Emissions Scenarios: Background, Issues, and Policy Relevance

Introduction
The use of scenario analysis began with military planning and gaming and moved into the business
world by the early 1960s as a way to analyze in a systematic way the long-term consequences of
strategic decisions.1 The goal of scenario analysis is neither to predict nor forecast, but rather
explore possible futures in order to understand uncertainties and key variables and aid in
decisionmaking.
Greenhouse gas (GHG) emissions scenarios are fundamental to understanding the long-term
implications of both future anthropogenic climate change2 and policy options to mitigate it. GHG
emissions scenarios are plausible emissions futures based on socioeconomic, environmental, and
technological trends and drivers.3 They are used as inputs in climate models to explore how changes
in GHG concentrations alter the earth’s radiative balance4 and thus affect the global climate.5
As Congress considers whether and how to address climate change, and particularly legislation
drafted with a policy objective to mitigate GHG emissions, Members may have emissions scenarios
as evaluations of their options. Moreover, President Biden has announced a number of climate
change targets in the Nationally Determined Contribution (NDC) submitted on April 21, 2021, to
the United Nations Framework Convention on Climate Change (UNFCCC) as part of the Paris
Agreement.6 The NDC includes a 50% reduction in GHG emissions by 2030 (compared to 2005)
and net-zero emissions by 2050.7 Congress may find it useful to better understand the models that
the Administration may use to evaluate and present its strategies. These models can inform
deliberations on the feasibility of achieving various emissions reduction trajectories and help to
identify policies and tradeoffs, such as competition for land, in meeting those emissions constraints.
This report provides background on emissions scenarios, some of the main economic-energy
models that have been used to construct emissions scenarios as part of the Intergovernmental Panel
on Climate Change (IPCC) and national policy processes (including those of the United States), and
some of the key findings of the scenarios consistent with keeping mean global warming to 1.5°C or
2°C. The report then concludes with observations for Congress.

1 Richard H. Moss et al., “The Next Generation of Scenarios for Climate Change Research and Assessment,” Nature, vol.
463, no. 7282 (February 11, 2010), pp. 747-756; Eric V. Larson, Force Planning Scenarios, 1945–2016: Their Origins
and Use in Defense Strategic Planning, Santa Monica, CA: RAND Corporation, 2019.
2 For a discussion of the scientific understanding and confidence regarding the drivers of recent global climate change, see
CRS Report R45086, Evolving Assessments of Human and Natural Contributions to Climate Change, by Jane A. Leggett.
3 Hereinafter referred to simply as emissions scenarios. Richard H. Moss et al., “The Next Generation of Scenarios for
Climate Change Research and Assessment,” Nature 463, no. 7282 (February 11, 2010), pp. 747-756, https://doi.org/
10.1038/nature08823; Aurore Colin, Charlotte Vailles, and Romain Hubert, “Understanding Transition Scenarios: Eight
Steps for Reading and Interpreting These Scenarios,” I4CE: Institute for Climate Economics, November 2019.
4 The radiative balance is the difference between solar irradiance (sun’s energy entering the atmosphere) and energy
radiated back to space.
5 For more discussion of the drivers of climate change, see U.S. Environmental Protection Agency, “Climate Change
Science,” May 12, 2017, at https://archive.epa.gov/epa/climate-change-science/causes-climate-change.html; R. K.
Pachauri et al., Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, eds. R. K. Pachauri and L. Meyer (Geneva,
Switzerland: IPCC, 2014).
6 U.S. Government, “Nationally Determined Contribution. Reducing Greenhouse Gases in the United States: A 2030
Emissions Target,” April 21, 2021, at https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/
United%20States%20of%20America%20First/United%20States%20NDC%20April%2021%202021%20Final.pdf.
7 For a discussion of net-zero emissions, see CRS In Focus IF11821, Net-Zero Emissions Pledges: Background and
Recent Developments, by Michael I. Westphal.
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Economic, Energy, and Climate Modeling: Use of
Integrated Assessment Models (IAMs)
Overview
The construction of GHG emissions scenarios is generally done with quantitative models, which are
abstractions, or simplified representations of reality. Models capture the essence of the relationships
in a system, but are reduced in their complexity to allow one to gain insights not possible simply
from available information.8 Models are often mathematical in nature, but not necessarily so. Best
practices for modeling include clearly stated assumptions and transparent relationships among
model variables.9
Integrated assessment models (IAMs) are a prominent type of economic-energy model that combine
elements of the human system (e.g., population, economy, and energy use) and the biophysical
earth system into one modeling framework.10 There are two basic types of IAMs: (1) relatively
simple IAMs11 that incorporate economic damages from climate change but have fairly limited
representations of the economy and are highly spatially aggregated,12 and (2) detailed, higher-
spatial-resolution, process-based IAMs that represent the drivers and processes of change in global
energy and sometimes land use systems linked to the broader economy, but typically lack a
comprehensive representation of climate impacts (e.g., changes in gross domestic product [GDP]
from physical climate impacts).13 The focus of this report is on the latter process-based type of
IAMs, which are discussed below in detail.
While one could use various models to generate emissions scenarios,14 analyses from these more
detailed, process-based IAMs have been a key component of the mitigation working group
(Working Group III) of the IPCC, the main international scientific body for assessing global climate
change.15 They have also been used in a number of countries’ scenarios for decarbonization—for

8 Katy Borner et al., “An Introduction to Modeling Science: Basic Model Types, Key Definitions, and a General
Framework for the Comparison of Process Models,” in Understanding Complex Systems, 2012.
9 Katy Borner et al., “An Introduction to Modeling Science: Basic Model Types, Key Definitions, and a General
Framework for the Comparison of Process Models,” in Understanding Complex Systems, 2012.
10 James A. Edmonds et al., “Integrated Assessment Modeling (IAM),” in Encyclopedia of Sustainability Science and
Technology, ed. Robert A. Meyers (New York, NY: Springer New York, 2012), pp. 5398-5428.
11 These include the DICE, PAGE, and FUND models. William Nordhaus, “Evolution of Modeling of the Economics of
Global Warming: Changes in the DICE Model, 1992-2017,” Climatic Change, vol. 148, no. 4 (June 2018), pp. 623-640,
at https://doi.org/10.1007/s10584-018-2218-y; David Anthoff and Richard S. J. Tol, The Climate Framework for
Uncertainty, Negotiation, and Distribution (FUND), Technical Description, Version 3.9, 2014; C. W. Hope, The PAGE09
Integrated Assessment Model: A Technical Description, Judge Business School, University of Cambridge, 2011.
12 They are spatially aggregated in that they typically operate at no smaller than the country-scale. They have been used to
calculate the social cost of carbon, a monetary estimate of the discounted climate change impacts to society over time
from an additional ton of carbon dioxide. See Delavane Diaz and Frances Moore, “Quantifying the Economic Risks of
Climate Change,” Nature Climate Change, vol. 7, no. 11 (November 2017), pp. 774-782 ; CRS In Focus IF10625, Social
Costs of Carbon/Greenhouse Gases: Issues for Congress, by Jane A. Leggett.
13 These are called process-based because they offer a detailed representation of the energy system, including energy
demand, future extraction, transformation, distribution, and use of energy and explore linkages with other sectors in the
economy, such as agriculture and land use. They have a higher spatial resolution in that they incorporate features at finer
spatial scales than the country-scale (for example, agro-ecological zones or hydrologic basins).
14 For an example of a web-based emissions scenario tool, see Energy Policy Simulator: Energy Innovation, “Energy
Policy Solutions,” at https://www.energypolicy.solutions/.
15 For a review of some of the main conclusions from the IPCC assessment reports over time, see CRS Report R45086,
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example, the U.S. midterm strategy for deep decarbonization developed during the Obama
Administration.16
These detailed, process-based IAMs17 are numerical, computer models. They vary considerably in
their sectoral (e.g., transportation, power generation, industry), technological, or macroeconomic
detail; geographic representation; availability of technologies and mitigation options; economic
structure; and solution approach (Appendix A).18 However, they are typically structured to include
several principal building blocks, or modules (Figure 1):19
 Macroeconomy System. This module uses outside (“exogenous” to the model)
macroeconomic inputs (e.g., population, labor productivity, sometimes GDP) to
estimate energy demands for each sector and world region. The most common
sectors include transport, buildings, industry, and agriculture.
 Energy System. This module typically includes a representation of the sources of
primary energy20 supply, modes of energy transformation (e.g., combustion of
fossil fuels into heat and electricity), and energy service demands (e.g., passenger
and freight transport, industry energy use, residential and commercial heating and
electricity). This building block allows the model to choose a wide range of fuels
and technologies to meet the energy demands and represents the costs and
performance (efficiency, lifetime) of the energy technologies.21 It would include
energy supply technologies (e.g., fossil fuels, nuclear, solar photovoltaics, wind), as
well as energy demand technologies (e.g., gas stoves and boilers, electric heat
pumps, internal combustion and electric vehicles, blast furnaces). This module
could also include energy demand from agriculture and water systems. The fuels
used to meet energy demand in each time period have associated emissions factors
that relate fuel combustion to greenhouse gas emissions. Many IAMs also represent
the nonenergy sectors, such as land use and agriculture, and include noncombustion
CO2, and non-CO2 GHGs, such as methane and nitrous dioxide. The ways in which
IAMs “choose” technologies and fuels vary with model structure and the criteria or
“objective functions” that the modelers specify, and these can explain many
differences across model results.
 Climate System. This module relates emissions over any time period to changes in
atmospheric concentrations of GHGs and the resulting changes in earth’s mean

Evolving Assessments of Human and Natural Contributions to Climate Change, by Jane A. Leggett
16 The White House, “United States Mid-Century Strategy for Deep Decarbonization,” November 2016, at
https://unfccc.int/files/focus/long-term_strategies/application/pdf/mid_century_strategy_report-final_red.pdf.
17 Henceforth, these detailed, process-based IAMs will simply be referred to as IAMs.
18 Integrated Assessment Modelling Consortium, “IAMC Wiki,” 2020, at https://www.iamcdocumentation.eu/index.php/
IAMC_wiki.
19 Ajay Gambhir et al., “A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address
These, Through the Lens of BECCS,” Energies, vol. 12, no. 9 (May 1, 2019), pp. 1-21; Joint Global Change Research
Institute, “GCAM v4.3 Documentation,” at https://jgcri.github.io/gcam-doc/.
20 Primary energy is energy found in nature and not subject to any human conversion process. Primary energy includes
fossil fuels (petroleum, natural gas, and coal), nuclear energy, and renewable sources of energy, such as wind and solar.
Secondary energy refers to resources that have been converted (for example, crude oil that is refined into fuels, coal that is
used in a coal-fired plant to generate electricity, or wind that is harnessed by a turbine to generate electricity).
21 Models very greatly in the amount of technological detail they contain. This can greatly affect the options available in
the model for responding to policy constraints, and ultimately the results from the model.
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Greenhouse Gas Emissions Scenarios: Background, Issues, and Policy Relevance

surface temperature. Some IAMs include reduced-form global climate carbon-cycle
models that include feedbacks among the atmosphere, soil, and oceans.22
One key distinction among IAMs is how they structure the economy. Equilibrium in economic
theory is reached when prices are found to match supply and demand in a market. General
equilibrium models represent the entire economy (though the sectoral detail could vary
significantly) and find a set of prices that have the effect of “clearing” all markets simultaneously.
Partial equilibrium models do so for just one or a couple of markets/sectors (e.g., energy,
agriculture), assuming prices in other markets remain constant.23
All IAMs generally are designed to meet some emissions limit or climate threshold in a cost-
effective manner.24 They vary in how they represent costs and whether they simulate future
emissions and technology paths, or whether they optimize them over time (i.e., least-cost pathway),
assuming perfect foresight.25 IAMs are often used to compare a baseline scenario26—an emissions
trajectory under current conditions/policies—with a policy scenario, where climate policies, targets,
constraints, or changes in the technology availability, cost, and mix are explored.
Figure 1. Illustrative Example of IAM Inputs, Building Blocks, and Outputs

Source: Adapted from CarbonBrief, “Q&A: How ‘Integrated Assessment Models’ Are Used to Study Climate
Change,” February 10, 2018, at https://www.carbonbrief.org/qa-how-integrated-assessment-models-are-used-to-
study-climate-change.
Note: IAMs vary in how they incorporate socioeconomics (for example, population and labor productivity may be
used to generate GDP estimates) and their sectoral representation.

22 GCAM, for example, has a global climate carbon-cycle model, Hector, that models carbon flux in the atmosphere, three
“pools” on land, and four “pools” in the ocean. Joint Global Change Research Institute, “GCAM v5.3 Documentation:
Earth System Module – Hector v2.0,” at http://jgcri.github.io/gcam-doc/gcam-usa.html.
23 Elizabeth A. Stanton, Frank Ackerman, and Sivan Kartha, “Inside the Integrated Assessment Models: Four Issues in
Climate Economics,” Climate and Development, vol. 1, no. 2 (2009), pp. 166-184.
24 Ajay Gambhir et al., “A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address
These, through the Lens of BECCS,” Energies, vol. 12, no. 9 (May 1, 2019), pp. 1-21.
25 Elizabeth A. Stanton, Frank Ackerman, and Sivan Kartha, “Inside the Integrated Assessment Models: Four Issues in
Climate Economics,” Climate and Development, vol. 1, no. 2 (2009), pp. 166-184.
26 Aurore Colin, Charlotte Vailles, and Romain Hubert, “Understanding Transition Scenarios: Eight Steps for Reading
and Interpreting These Scenarios,” I4CE: Institute for Climate Economics, November 2019.
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IAMs and the IPCC Assessment Process
The IPCC has used emissions scenarios since its First Assessment Report in 1990, which presented
a set of four scenarios—a baseline business as usual scenario and three policy scenarios.27 In 1992,
the IPCC reformulated the scenarios to include only no-climate-policy scenarios, spanning a range
of six plausible pathways, relying on internally coherent assumptions about how economies and
technologies may evolve.28 By 2000, the IPCC had developed the quantitative Special Report on
Emissions Scenarios (SRES) scenarios with four narrative storylines of population, economic
growth, and GHG emissions scenarios.29 When the IPCC revised the scenarios in the late 2000s, the
IPCC decided to separate the development of socioeconomic storylines from scenarios of global
warming that could occur by the end of the century, in order to speed up the climate modeling
process.30 This led to development of Representative Concentration Pathways (RCPs) and
associated Shared Socioeconomic Pathways (SSPs).
Scenarios of Global Warming and Socioeconomic Storylines
In response to a call from the IPCC for a research organization to lead the integrated assessment
modeling community in the development of new scenarios, the Integrated Assessment Modeling
Consortium (IMAC)31 was established in 2007. The IMAC developed the RCPs—scenarios that
represent different target levels in 2100 of radiative forcing,32 or how the earth’s energy imbalance
may change due to various climatic drivers, such as GHG concentrations in the atmosphere or
reflectivity of the earth’s surface. These RCP scenarios are used in analyses by global climate
models33 to understand the impact of changing radiative forcing on global and regional climate.34
For example, climate change projections made using the IPCC RCPs have been used in analyses as
part of the U.S. Fourth National Climate Assessment.35
The RCPs are in units of watts per meter squared (W/m2), a measure of the energy at the top of the
atmosphere.36 Higher values indicate greater forcing. Thus, RCPs can be considered a proxy for

27 Richard H. Moss et al., “The Next Generation of Scenarios for Climate Change Research and Assessment,” Nature, vol.
463, no. 7282 (February 11, 2010), pp. 747-756.
28 Jane Leggett et al., “Emissions Scenarios for IPCC: An Update,” in Climate Change 1992. The Supplementary Report
to the IPCC Scientific Assessment, Intergovernmental Panel on Climate Change, 1992, https://www.ipcc.ch/site/assets/
uploads/2018/05/ipcc_wg_I_1992_suppl_report_section_a3.pdf.
29 N. Nakicenovic et al., Special Report on Emissions Scenarios (SRES), A Special Report of Working Group III of the
Intergovernmental Panel on Climate Change (Cambridge: Cambridge University Press, 2000).
30 Richard H. Moss et al., “The Next Generation of Scenarios for Climate Change Research and Assessment,” Nature, vol.
463, no. 7282 (February 11, 2010), pp. 747-756.
31 The Integrated Assessment Modeling Consortium, at http://www.iamconsortium.org.
32 Radiative forcing is the difference between solar irradiance (sun’s energy entering the atmosphere) and energy radiated
back to space. For more discussion of the drivers of climate change, see U.S. Environmental Protection Agency, “Climate
Change Science,” May 12, 2017, at https://archive.epa.gov/epa/climate-change-science/causes-climate-change.html.
33 Intergovernmental Panel on Climate Change, “What Is a GCM?” Data Distribution Centre, accessed April 19, 2021, at
https://www.ipcc-data.org/guidelines/pages/gcm_guide.html.
34 Discussion of climate models is beyond the scope of this report. One major project to compare and continually improve
climate models is the World Climate Research Program, “Coupled Model Intercomparison Project (CMIP),” at
https://www.wcrp-climate.org/wgcm-cmip.
35 C. W. Avery et al., “Data Tools and Scenario Products,” in In Impacts, Risks, and Adaptation in the United States:
Fourth National Climate Assessment, vol. II (Washington, DC: U.S. Global Change Research Program, 2018), pp. 1413-
1430.
36 Watt is a unit of energy, so radiative forcing (W/m2) is a measure of energy per unit area.
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mean global temperatures. The initial four RCPs spanned the range of radiative forcing values for
the year 2100 found in the peer-reviewed literature at the time (i.e., from 2.6 to 8.5 W/m2); see
Table 1.37 The IPCC Fifth Assessment Report focused on the four RCPs listed in Table 1;
subsequently, RCP 1.9, RCP 3.4, and RCP 7.0 have been added for the Sixth Assessment Report,
due to be published beginning in 2021.
In IPCC parlance, likely refers to at least a 66% probability.38 RCP 2.6 indicates a likely 2100
temperature range of 0.3°C to 1.7°C (mean 1.0°C) above preindustrial levels.39 RCP 2.6 is
consistent with keeping likely mean global warming to 2°C (with at least a 66% probability) in
2100.40 RCP 4.5 indicates a likely 2100 temperature range of 1.1°C to 2.6°C (mean 1.8°C). In
contrast, the radiative forcing of RCP 8.5 could result in an increase in warming of nearly 5°C
(mean of 3.7oC and likely range 2.6oC to 4.8oC) above preindustrial levels by the end of the
century.41 Recently, there has been some criticism of RCP 8.5, with some groups saying it is not
very plausible;42 for example, reaching it would mean policy choices leading to a five-fold increase
in global coal use, which may be larger than estimates of recoverable reserves.43 The new RCP 1.9
is consistent with limiting the increase in global mean temperature in 2100 to 1.5°C with
approximately a 66% probability.44
Table 1. Overview of the RCPs
Temperature Increase (2081-
RCP
Description
2100) (°C)
RCP 2.6
Peak in radiative forcing at ~3 W/m2
0.3 to 1.7 (mean 1.0)
(~490 ppm CO2 eq) before 2100
and then decline (the selected
pathway declines to 2.6 W/m2 by
2100)
RCP 4.5
Stabilization without overshoot
1.1 to 2.6 (mean 1.8)
pathway to 4.5 W/m2 (~650 ppm
CO2 eq) at stabilization after 2100

37 Detlef P. van Vuuren et al., “The Representative Concentration Pathways: An Overview,” Climatic Change, vol. 109,
no. 1-2, SI (November 2011), pp. 5-31.
38 IPCC, “Summary for Policymakers,” in Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge, UK: Cambridge
University Press, 2013).
39 Table 2.1 in IPCC, “Summary for Policymakers,” in Climate Change 2013: The Physical Science Basis. Contribution
of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge, UK:
Cambridge University Press, 2013). Each RCP results in a range of temperatures in 2100. See also Table 1 in Detlef P.
van Vuuren et al., “The Representative Concentration Pathways: An Overview,” Climatic Change, vol. 109, no. 1-2, SI
(November 2011), pp. 5-31.
40 IPCC, “Summary for Policymakers,” in Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge, UK: Cambridge
University Press, 2013).
41 Matthew J. Gidden et al., “Global Emissions Pathways under Different Socioeconomic Scenarios for Use in CMIP6: A
Dataset of Harmonized Emissions Trajectories Through the End of the Century,” Geoscientific Model Development, vol.
12, no. 4 (April 12, 2019), pp. 1443-1475.
42 Zeke Hausfather and Glen P. Peters, “Emissions: The ‘Business as Usual’ Story Is Misleading,” Nature, vol. 577, no.
7792 (January 30, 2020), pp. 618-620.
43 Justin Ritchie and Hadi Dowlatabadi, “The 1000 GtC Coal Question: Are Cases of Vastly Expanded Future Coal
Combustion Still Plausible?” Energy Economics, vol. 65 (2017), pp. 16-31.
44 Joeri Rogelj et al., “Scenarios Towards Limiting Global Mean Temperature Increase below 1.5 °C,” Nature Climate
Change, vol. 8, no. 4 (April 1, 2018), pp. 325-332.
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Temperature Increase (2081-
RCP
Description
2100) (°C)
RCP 6
Stabilization without overshoot
1.4 to 3.1 (mean 2.2)
pathway to 6 W/m2 (~850 ppm CO2
eq) at stabilization after 2100
RCP 8.5
Rising radiative forcing pathway
2.6 to 4.8 (mean 3.7)
leading to 8.5 W/m2 (~1370 ppm
CO2 eq) by 2100
Source: Detlef P. van Vuuren et al., “The Representative Concentration Pathways: An Overview,” Climatic Change
109, no. 1-2, SI (November 2011), pp. 5-31, at https://doi.org/10.1007/s10584-011-0148-z; IPCC, “Summary for
Policymakers,” in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge, UK: Cambridge University Press,
2013).
Note: The temperature increases are based on 5% to 95% of model ranges.
The RCPs are complemented by SSPs, which are socioeconomic narratives of the future. While the
RCPs effectively set representative pathways for GHG concentrations and indicate likely end-of-
the-century warming, the SSPs indicate how society may transform and, consequently, how GHG
emissions may change over time. There are five SSPs, designed to span possible societal futures
and cover the societal trends that could make both climate change mitigation and adaptation more
or less challenging to undertake:
1. SSP1 (“Sustainability—Taking the Green Road”);
2. SSP2 (“Middle of the Road”);
3. SSP3 (“Regional Rivalry—A Rocky Road”);
4. SSP4 (“Inequality—A Road Divided”); and
5. SSP5 (“Fossil-Fueled Development—Taking the Highway”).45
The SSPs vary considerably in what they assume about economic growth, inequality, trade,
dependence on fossil fuels, and material consumption (see Appendix B for more details). For
example, SSP1 assumes medium economic growth, moderate international trade, low growth in
material consumption, low-meat diets, and an emphasis on renewable energy and energy efficiency,
while SSP5 assumes high economic growth, high international trade, high material consumption,
meat-rich diets, and a focus on the use of fossil fuels. They vary considerably in their trajectories
for two important socioeconomic variables: GDP and population (Figure 2). In 2100, global GDP
for SSP1 and SSP5 ranges from about $280 billion to $1,000 billion ($1 quadrillion), while
population ranges from 6.9 billion to 12.6 billion in 2100, respectively. Some of the authors have
characterized SSP2 as a “world that continues the historical experience.”46

45 Keywan Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas
Emissions Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168; Brian C.
O’Neill et al., “The Roads Ahead: Narratives for Shared Socioeconomic Pathways Describing World Futures in the 21st
Century,” Global Environmental Change, vol. 42 (January 2017), pp. 169-180.
46 Joeri Rogelj et al., “Scenarios Towards Limiting Global Mean Temperature Increase Below 1.5 °C,” Nature Climate
Change, vol. 8, no. 4 (April 1, 2018), pp. 325-332.
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Figure 2. SSPs and Population and GDP Assumptions

Source: CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10;
International Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) - Version
2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan Riahi et al., “The Shared
Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview,”
Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: There are different interpretations of the SSP socioeconomic variables. This projection for GDP was
provided by the Organization for Economic Co-operation and Development (OECD). GDP is in purchasing power
parity (PPP), $2005. This projection for population was provided by the International Institute for Applied Systems
Analysis-Wittgenstein Centre (IIASA-WIC).
For each SSP, the six IAM groups tried to find a solution that satisfied each RCP future warming
scenario, in order to understand how the energy and land-use systems could evolve in the future
(Appendix A). These SSP-RCP scenarios were then compared to a baseline, which is a reference-
case scenario without (1) climate change mitigation policies (no policies after 2010, including those
related to the Paris Agreement) and (2) feedbacks from climate change on socioeconomic or natural
systems. To be consistent and aid in comparison across IAM results, the IAM groups all used the
same climate model to convert from annual GHG emissions to concentrations and estimate
resulting global warming.47
Results from Emissions Scenarios Consistent with 1.5°C and 2°C
Warming
The United States is a party to the UN Framework Convention on Climate Change (UNFCCC),48
with its objective in Article 2 being

47 As Table 1 shows, the same GHG concentrations could result in different estimated warming. The IAMs used the same
climate carbon-cycle model (MAGICC Climate Modeling System, at http://www.magicc.org/) to aid in comparison of
IAM results.
48 U.N. Treaty Collection, Chapter XXIII. 7. President George H. W. Bush transmitted the signed treaty to the Senate for
its advice and consent in 138 Congressional Record 23902 (September 8, 1992). The U.S. Senate gave its advice and
consent to ratification in 138 Congressional Record 33527 (October 7, 1992). See also S. Treaty Doc. 102-38 (1992); S.
Exec. Rept. 102-55. President Bush signed the instrument of ratification and submitted it to the United Nations on
October 13, 1992. Depositary notification C.N.148.1993.
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stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent
dangerous anthropogenic interference with the climate system. Such a level should be
achieved within a time-frame sufficient to allow ecosystems to adapt naturally to climate
change, to ensure that food production is not threatened and to enable economic development
to proceed in a sustainable manner.49
The Biden Administration rejoined the Paris Agreement,50 a subsidiary agreement under the
UNFCCC.51 The agreement, with 191 parties as of the date of this publication, includes an aim of
strengthening the global response to climate change, including by
[h]olding the increase in the global average temperature to well below 2°C above pre-
industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-
industrial levels, recognizing that this would significantly reduce the risks and impacts of
climate change.52
These temperature targets are supported by the scientific consensus that the overall risks to
physical, ecological, and social systems (e.g., agricultural production, livelihoods) increase with
warming. One consideration is the level of climate change sufficient to trigger abrupt and
irreversible changes (tipping points). While precise levels remain uncertain, the risk associated with
crossing such thresholds increases with rising temperature.53 The IPCC’s assessments of the
temperature increase at which certain natural, managed, and human systems could experience at
least moderate risks have generally been revised downward over time, given more scientific
studies.54 There is now scientific evidence to suggest that some tipping points could be exceeded
between 1°C to 2°C of warming.55
While the Paris Agreement aims to achieve temperature goals, there is no specified global
emissions target.56 First, there is uncertainty around climate sensitivity,57 that is, the temperature
change projected to result from a change in the concentration of GHGs in the atmosphere. The
IPCC Fifth Assessment Report estimated that a doubling of atmospheric CO2 from preindustrial
levels would likely result in an increase in global mean surface temperature in the range of 1.5°C to
4.5°C.58 The ranges of temperature increase associated with each IPCC RCP (Table 1) reflect the

49 United Nations Framework Convention on Climate Change, May 9, 1992, S. Treaty Doc No. 102-38, 1771 U.N.T.S.
107.
50 The White House, “Paris Climate Agreement,” January 20, 2021, at https://www.whitehouse.gov/briefing-room/
statements-releases/2021/01/20/paris-climate-agreement/.
51 For more on the Paris Agreement and the UNFCCC, see CRS Report R46204, The United Nations Framework
Convention on Climate Change, the Kyoto Protocol, and the Paris Agreement: A Summary, by Jane A. Leggett.
52 United Nations, “Paris Agreement,” Article 2.1a, at https://www.un.org/en/climatechange/paris-agreement.
53 Pachauri et al., Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change.
54 Timothy M. Lenton et al., “Climate Tipping Points—Too Risky to Bet Against,” Nature, vol. 575, no. 7784 (November
28, 2019), pp. 592-595.
55 IPCC, Global Warming of 1.5°C (Intergovernmental Panel on Climate Change, 2018); IPCC, IPCC Special Report on
the Ocean and Cryosphere in a Changing Climate (Intergovernmental Panel on Climate Change, 2019).
56 For a discussion of the role of anthropogenic GHG emissions in climate change, see CRS Report R45086, Evolving
Assessments of Human and Natural Contributions to Climate Change, by Jane A. Leggett.
57 Tapio Schneider et al., “Climate Goals and Computing the Future of Clouds,” Nature Climate Change, vol. 7, no. 1
(January 1, 2017), pp. 3-5. The term equilibrium climate sensitivity is often used. This term refers specifically to the
global surface temperature increase that results after CO2 concentrations have doubled and the climate system has
equilibrated to this perturbation.
58 IPCC, “Summary for Policymakers,” in Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge, UK: Cambridge
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uncertainty about climate sensitivity. Second, there are many different global emissions pathways
that could result in warming that does not exceed 1.5°C/2°C in 2100, and hence there are also
different possible trajectories for individual countries. Many IAMs find a “least-cost” pathway
globally for achieving a specific temperature target, or impose other constraints, but policymakers
and stakeholders may consider other factors to be important, such as feasibility, capabilities of
different countries, and equity.59
Given countries’ commitments to the Paris Agreement’s temperature goals, the remainder of this
section analyzes IAM emissions scenarios consistent with warming of 1.5°C to 2°C by 2100.
Methodology for Scenario Selection
The IAMC, as part of its ongoing cooperation with the IPCC’s Working Group III on mitigation,
issued a call for submissions of scenarios that limit warming to 1.5°C or 2°C in the “long term”
(e.g., 2100) for inclusion in the IPCC Global Warming of 1.5°C report.60 In total, 19 modeling
groups submitted 529 scenarios, of which 90 were consistent with 1.5°C, and 132 were consistent
with 2°C. 61
However, the modeling scenarios differ in their socioeconomic assumptions, and not all have model
outputs that are publicly available. This section of the report examines the results from the six IAM
groups that have modeled the SSP-RCP scenarios. All of these IAMs used a consistent set of
socioeconomic assumptions and have model outputs available in the SSP public database, hosted by
the International Institute for Applied Systems Analysis (IIASA).62 These IAM modeling scenarios
will be a focus of the forthcoming IPCC Sixth Assessment Report. This report does not examine
other non-IAMC scenarios that may be compatible with 1.5°C or 2°C mean global warming. 63
RCP 1.9 and RCP 2.6 can be considered proxies for 1.5°C and 2°C pathways.64 As noted above,
RCP 2.6 is consistent with keeping likely65 mean global warming in 2100 to 2°C above preindustrial
levels,66 while RCP 1.9 is consistent with keeping likely mean global warming to 1.5°C above

University Press, 2013).
59 Yann Robiou du Pont et al., “Equitable Mitigation to Achieve the Paris Agreement Goals (Vol 7, Pg 38, 2017),” Nature
Climate Change, vol. 7, no. 2 (February 2017). p. 153, at https://doi.org/10.1038/NCLIMATE3210.
60 P. Forster et al., “2.SM Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development—
Supplementary Material,” in Global Warming of 1.5 °C (Intergovernmental Panel on Climate Change, 2018); J. Rogelj et
al., “Chapter 2: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development,” in Global
Warming of 1.5 °C (Intergovernmental Panel on Climate Change, 2018).
61 The term 1.5°C-/2°C-consistent refers to pathways with no overshoot, with limited (low) overshoot, and with high
overshoot of 1.5°C-/2°C in 2100. J. Rogelj et al., “Chapter 2: Mitigation Pathways Compatible with 1.5°C in the Context
of Sustainable Development,” in Global Warming of 1.5 °C (Intergovernmental Panel on Climate Change, 2018).
62 International Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) - Version 2.0,”
at https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan Riahi et al., “The Shared Socioeconomic
Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview,” Global
Environmental Change, vol. 42 (January 2017), pp. 153-168.
63 These include the International Energy Agency, research and consulting firms (e.g., Bloomberg New Energy Finance,
McKinsey), and a number of oil majors (e.g., BP, Shell, Equinor); Aurore Colin, Charlotte Vailles, and Romain Hubert,
“Understanding Transition Scenarios: Eight Steps for Reading and Interpreting These Scenarios,” I4CE: Institute for
Climate Economics, November 2019.
64 Joeri Rogelj et al., “Scenarios Towards Limiting Global Mean Temperature Increase Below 1.5 °C,” Nature Climate
Change, vol. 8, no. 4 (April 1, 2018), pp. 325-332.
65 Likely in IPCC parlance refers to at least a 66% probability.
66 IPCC, “Summary for Policymakers,” in Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge, UK: Cambridge
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preindustrial levels in 2100.67 The six IAMs all used the same climate model, Model for the
Assessment of Greenhouse Gas Induced Climate Change (MAGICC). For RCP 1.9, the range in the
increase in mean annual temperature in 2100 was estimated to be 1.3°C to 1.4°C, and for RCP 2.6,
1.7°C to 1.8°C in 2100.68 For RCP 1.9, all the IAMs do “overshoot” the 1.5°C temperature target in
the 2040s.69 For two of the six IAMs, SSP2-RCP1.9 was “infeasible,” meaning they could not find
a solution that avoided a 1.5oC increase.70 Throughout the rest of the report, the RCP 1.9 and RCP
2.6 are referred to as 1.5oC-consistent and 2oC-consistent scenarios, respectively.
The SSP database includes the SSP-RCP and baseline scenarios and provides outputs (2005-2100)
for a number of variables, such as primary, secondary, and end-use energy; land cover; agricultural
demand and production; GHG emissions; and climate (radiative forcing, temperature). This section
discusses global results, because the SSP database does not break out the data to the national scale.
Given the size of the U.S. economy and its contribution to GHG emissions, the same basic
conclusions from the global results may be instructive for technology deployments in the United
States consistent with meeting the global 1.5°C or 2°C targets. The focus of this section is an
analysis of some (but not all) of the key results for the middle-of-the-road SSP2 socioeconomic
scenario, referred to as a “world that continues the historical experience.”71 Given the large number
of possible combinations of SSP-RCP scenarios, IAMs, and output variables, a comprehensive
analysis of other scenarios and results is beyond the scope of this report. This section, in particular,
highlights a number of key results related to energy use and negative emissions technologies.
The focal year for the analysis is 2050, which is far enough in the future to discern differences
between the 1.5°C-/2°C-consistent scenarios and the baseline. Modeling results beyond this time
frame may be instructive, but uncertainties increase further into the future—for example, regarding
the cost and availability of various technologies.
Primary Energy Use
The IAMs provide results on future energy use under the different SSP-RCP scenarios. The results
from the six IAMs show significant differences in the modeled future energy mix, even with the
same socioeconomic assumptions (Figure 3). This is due to not only differences in model structure
and solution approach, but also the availability and costs of technologies and fuels. In the baseline
(no climate policy) scenario, all the models generally project a world dominated by fossil-fuel use.
Fossil fuels provide more than 80% of primary energy use across the IAMs in the baseline
scenarios.

University Press, 2013).
67 Joeri Rogelj et al., “Scenarios Towards Limiting Global Mean Temperature Increase Below 1.5 °C,” Nature Climate
Change, vol. 8, no. 4 (April 1, 2018), pp. 325-332.
68 International Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) - Version 2.0,”
at https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan Riahi et al., “The Shared Socioeconomic
Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview,” Global
Environmental Change, vol. 42 (January 2017), pp. 153-168.
69 Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) - Version 2.0,” at
https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan Riahi et al., “The Shared Socioeconomic
Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview,” Global
Environmental Change, vol. 42 (January 2017), pp. 153-168.
70 Joeri Rogelj et al., “Scenarios Towards Limiting Global Mean Temperature Increase Below 1.5 °C,” Nature Climate
Change, vol. 8, no. 4 (April 1, 2018), pp. 325-332.
71 Joeri Rogelj et al., “Scenarios Towards Limiting Global Mean Temperature Increase Below 1.5 °C,” Nature Climate
Change, vol. 8, no. 4 (April 1, 2018), pp. 325-332.
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The models indicate that keeping global mean temperature increase on a trajectory to below 2°C in
2100 requires a scaling up of nonbiomass renewable energy technologies across the globe in 2050,
increasing from between 4% to 10% of primary energy in the baseline scenario to between 10% and
29% in the 2°C-consistent scenarios and 11% to 40% in 1.5°C-consistent scenarios. The lower the
radiative forcing target, the greater the increase in renewables for providing primary energy. In
comparison, 10% of total primary energy worldwide came from nonbiomass renewables in 2019.72
The IAMs indicate a decrease in the share of fossil fuels in the energy mix in 2050 in the 1.5°C-
and 2°C-consistent scenarios. Coal in particular would see decreases, providing no more than 10%
and 14% of primary energy across IAMs in the 1.5°C- and 2°C-consistent scenarios, respectively,
compared to 25% to 35% of primary energy in the baseline. As a point of reference, in 2019, coal
provided 26% of global primary energy.73 The models project an increase in nuclear energy,
providing 4% to 12% of primary energy in 2050 in the 2°C-consistent scenario, compared to 1% to
3% in the baseline in 2050 (and 5% today).74 As will be discussed below, the IAMs vary
considerably in how much they rely on biomass to meet energy needs.
Figure 3 also shows that keeping likely warming to 1.5°C and 2°C in 2100 points to reduced
primary energy consumption in the modeling scenarios, largely as a result of energy efficiency
gains. Compared to the baseline, energy intensity (primary energy per unit of GDP) in 2050
declines 21% to 41% in the 2°C-consistent scenarios and 22% to 32% in 1.5°C-consistent
scenarios.75

72 International Energy Agency, “Global Primary Energy Demand by Fuel, 1925-2019,” at https://www.iea.org/data-and-
statistics/charts/global-primary-energy-demand-by-fuel-1925-2019. 1 MTOE is equivalent to 0.042 EJ.
73 International Energy Agency, “Global Primary Energy Demand by Fuel, 1925-2019,” at https://www.iea.org/data-and-
statistics/charts/global-primary-energy-demand-by-fuel-1925-2019.
74 International Energy Agency, “Global Primary Energy Demand by Fuel, 1925-2019,” at https://www.iea.org/data-and-
statistics/charts/global-primary-energy-demand-by-fuel-1925-2019.
75 Note that only four of six IAMs had runs for the 1.5°C-consistent scenarios (Figure 3). This is reflected in the range
differences. CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan
Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions
Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
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Figure 3. Global Primary Energy Mix in 2050, by IAM
SSP2 (“middle-of-the-road” socioeconomic scenario)

Source: CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan
Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions
Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: RCP 2.6 is consistent with keeping mean global warming to 2°C in 2100, while RCP 1.9 is consistent with
keeping mean global warming to 1.5°C in 2100. The database includes only four IAMs that could solve for the RCP
1.9 target for the SSP2 scenario. For IMAGE and WITCH models, no solution could be found. Renewables include
all nonbiomass renewables (hydro, solar, wind, geothermal, and other).
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Electrification
Electrification is the substitution of electricity for fossil fuel use in engines, furnaces, and other
devices.76 Climate change studies indicate that electrification is one of the main strategies for
decarbonization, along with decarbonization of the power supply (absent carbon capture) and
increased energy efficiency (i.e., reduced energy demand).77
The IAM results indicate that keeping likely warming to 1.5°C or 2°C in 2100 would entail an
increased reliance on electricity to meet energy needs (Figure 4). Electricity in final energy demand
nearly doubles in most of IAMs in the 1.5°C-consistent scenario compared to the baseline in 2050,
reaching 41% to 61% of final energy demand. By comparison, in 2019, electricity comprised 19%
of the world’s final energy demand.78
Figure 4. Global Electrification in 2050, by IAM
SSP2 (“middle-of-the-road” socioeconomic scenario)

Source: CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan
Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions
Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: RCP 2.6 is consistent with keeping mean global warming to 2°C in 2100, while RCP 1.9 is consistent with
keeping mean global warming to 1.5°C in 2100. The database includes only four IAMs that could solve for the RCP
1.9 target for the SSP2 scenario. For IMAGE and WITCH models, no solution could be found.

76 Chris Kennedy et al., “Keeping Global Climate Change Within 1.5°C Through Net Negative Electric Cities,” 1.5°C
Climate Change and Urban Areas 30 (February 1, 2018), pp. 18-25.
77 The GHG benefits of electrification depend on the carbon intensity of the electric grid. Except for the most fossil-fuel-
intensive grids, electrification will generally result in a net reduction of GHG emissions. (See Chris Kennedy et al.,
“Keeping Global Climate Change Within 1.5°C Through Net Negative Electric Cities,” 1.5°C Climate Change and Urban
Areas, vol. 30 (February 1, 2018), pp. 18-25.) This is due to the fact that electric devices are generally more efficient than
fossil fuel devices. For example, electric vehicles are currently two to five times more efficient than internal combustion
engines. See IEA, World Energy Outlook 2020 (Paris, France: International Energy Agency, 2020).
78 IEA, “Global EV Outlook 2020: Entering the Decade of Electric Drive?” (Paris: International Energy Agency, 2020), at
https://www.iea.org/reports/global-ev-outlook-2020.
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Peak and Net-Zero CO2 Emissions
An examination of the global CO2 emissions over time for all 1.5°C- and 2°C-consistent scenarios
reveals several key points policymakers may consider. First, as illustrated in Figure 5 and Figure 6,
the IAMs indicate that there are many different global CO2 emissions pathways that stay within
1.5°C and 2°C in 2100. Second, the models find that the more stringent the radiative forcing target
(i.e., RCP output), the earlier the dates for peak and net-zero emissions. In order to keep warming to
2°C in 2100, the models project that annual CO2 emissions will have to reach net-zero between
2080 and 2100 (Figure 5). To achieve a 1.5°C temperature target, the models estimate that CO2
emissions would have had to peak around 2020 and reach net-zero by 2060 (Figure 6).79 Emissions
of other GHGs remain positive in these 1.5°C- and 2°C-consistent scenarios through 2100.
According to the models, the later the peak in CO2 emissions, the sharper the reductions would have
to be later in the century to keep within the temperature targets.
Third, achieving the emissions reductions consistent with meeting the targets in 2100 for both
mitigation scenarios generally relies on “negative emissions”80 (or permanent CO2 removal,
discussed below), though the degree of availability of the technology and reliance on negative
emissions technologies vary across IAMs. Carbon removal (i.e., the removal of CO2 from the
atmosphere and storage in geological, terrestrial, or ocean reservoirs, or in products81) is needed to
balance positive emissions, including those of the other non-CO2 GHGs.

79 International Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) - Version 2.0,”
at https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan Riahi et al., “The Shared Socioeconomic
Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview,” Global
Environmental Change, vol. 42 (January 2017), pp. 153-168.
80 Negative emissions refers to the removal of greenhouse gases (GHGs) from the atmosphere by deliberate human
activities, in addition to the removals that occur via natural carbon cycle processes. See IPCC, Global Warming of 1.5°C
(Intergovernmental Panel on Climate Change, 2018).
81 For a discussion of carbon removal, see CRS In Focus IF11501, Carbon Capture Versus Direct Air Capture, by Ashley
J. Lawson; and CRS In Focus IF11821, Net-Zero Emissions Pledges: Background and Recent Developments, by Michael
I. Westphal.
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Figure 5. Global CO2 Emissions over Time Across 2°C-Consistent Scenarios

Source: International Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) -
Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan Riahi et al., “The Shared
Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview,”
Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: Each CO2 trajectory represents one IAM model run for different SSPs. The legend indicates the model
name, fol owed by the socioeconomic scenario (SSP) and radiative forcing (RCP). RCP 2.6 (denoted as “26” in the
legend) is consistent with keeping likely mean global warming to 2°C in 2100. RCP 2.6 refers to the radiative
forcing target of 2.6 Wm-2 radiative forcing in 2100.
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Figure 6. Global CO2 Emissions over Time Across 1.5°C-Consistent Scenarios

Source: International Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) -
Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan Riahi et al., “The Shared
Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview,”
Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: Each CO2 trajectory represents one IAM model run. The legend indicates the model name, fol owed by the
socioeconomic scenario (SSP) and radiative forcing (RCP). RCP 1.9 (denoted as “19” in the legend) is consistent
with keeping mean global warming to 1.5°C in 2100. RCP 1.9 refers to the radiative forcing target of 1.9 Wm-2
radiative forcing in 2100.

Negative Emissions Technologies
All of the 1.5°C- and 2°C-consistent scenarios illustrated above rely on negative emissions from
two main sources, although other technologies could emerge over time: (1) bioenergy with carbon
capture and storage (BECCS), where biomass is burned for energy and the resulting CO2 captured
and stored; and (2) terrestrial carbon removal through land use: conservation, restoration, and/or
improved land management actions that increase carbon storage and/or avoid GHG emissions in
forests, wetlands, grasslands, and agricultural lands (some refer to these as natural climate
solutions, or NCS).82

82 Bronson W. Griscom et al., “Natural Climate Solutions (Vol 114, Pg 11645, 2017),” Proceedings of the National
Academies of Sciences, vol. 116, no. 7 (February 12, 2019), p. 2776. See also CRS In Focus IF11693, Agricultural Soils
and Climate Change Mitigation, by Genevieve K. Croft; and CRS Report R46312, Forest Carbon Primer, by Katie
Hoover and Anne A. Riddle.
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By 2050, models indicate BECCS might remove between 0.8 and 5.9 gigatons (Gt) CO2 per year
across the 2°C-consistent scenarios, and between 1.3 and 12.8 Gt CO2 per year in the 1.5°C-
consistent scenarios (Figure 7). The reliance on BECCS to achieve the mitigation targets varies
considerably across IAMs, with some models (GCAM and REMIND-MAGPIE) depending much
more on the technology. BECCS has two impacts in the models, which is why it is often relied
upon: not only does it supply energy to meet energy demands, but it also removes CO2 from the
atmosphere. The 1.5°C- and 2°C-consistent scenarios presented here do not consider all potential
carbon removal options, such as direct air capture, enhanced weathering, biochar, soil organic
carbon, or ocean fertilization.83
Figure 7. Total Annual Global Carbon Capture from BECCS in 2050, by IAM
SSP2 (“middle-of-the-road” socioeconomic scenario)

Source: CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan
Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions
Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: BECCS is bioenergy with carbon capture and storage. RCP 2.6 is consistent with keeping mean global
warming to 2°C in 2100, while RCP 1.9 is consistent with keeping mean global warming to 1.5°C in 2100. The
database includes only four IAMs that could solve for the RCP 1.9 target for the SSP2 scenario. For IMAGE and
WITCH models, no solution could be found. The models include little or no BECCS in the baseline.
Industrial carbon capture and storage (CCS) includes not only BECCS, but also the capture of CO2
from fossil fuel combustion and other industrial processes (e.g., cement manufacturing).84 However,
BECCS is not possible without the use of CCS facilities. Currently, one industry association
estimates that there are 26 operational, commercial CCS facilities worldwide.85 In total, these

83 Joeri Rogelj et al., “Scenarios Towards Limiting Global Mean Temperature Increase Below 1.5 °C,” Nature Climate
Change, vol. 8, no. 4 (April 1, 2018), pp. 325-332; Jay Fuhrman et al., “Food–Energy–Water Implications of Negative
Emissions Technologies in a +1.5 °C Future,” Nature Climate Change, vol. 10, no. 10 (October 1, 2020), pp. 920-927.
84 While BECCS is a negative emissions technology, CCS by itself is not. CCS is a process in which a relatively pure
stream of CO2 from industrial and energy-related sources is separated (captured), conditioned, compressed, and
transported to a storage location for long-term isolation from the atmosphere. CCS can be used to capture CO2 from
fossil-fuel burning plants and other industrial facilities (e.g., cement plants), in which case it may be net neutral in CO2,
but not negative. See IPCC, Global Warming of 1.5°C (Intergovernmental Panel on Climate Change, 2018); and CRS
Report R44902, Carbon Capture and Sequestration (CCS) in the United States, by Peter Folger.
85 Global CCS Institute, “CCS. Vital to Achieve Net-Zero,” 2020, at https://www.globalccsinstitute.com/wp-content/
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operating facilities have been estimated to sequester 40 MtCO2 per year.86 BECCS as a technology
has not been widely scaled up; globally, there are three commercial BECCS plants in operation (all
associated with ethanol production), sequestering 1.39 MtCO2 per year.87 Given that the 1.5°C and
2°C scenarios described above project a range of carbon capture by BECCS of 800 MtCO2 to
13,000 MtCO2 per year in 2050, this would require global CCS capacity to increase by 20 to more
than 300 times in the next 30 years to match these projections.88
The models’ reliance on BECCS to meet the 1.5°C and 2°C temperature targets correspondingly
translates into greater bioenergy crop production compared to the baseline scenarios (Figure 8).
Without an increase in agricultural productivity or the conversion of other land uses to agriculture,
increased bioenergy production could put pressure on food production, prices, or availability.
Besides the issues pertaining to scalability, others have questioned the full carbon cycle impacts of
BECCS. A 2018 analysis using a different, more sophisticated vegetation model estimated that
carbon removed from the atmosphere through BECCS could be offset by losses due to land-use
change from the cultivation of bioenergy crops. According to that analysis, where BECCS involves
replacing high-carbon-storing ecosystems with energy crops, forest-based mitigation could be more
efficient for atmospheric CO2 removal than BECCS.89
As previously noted, the 1.5°C- and 2°C-consistent scenarios also include varying contributions
from terrestrial carbon removal, particularly reforestation and afforestation. Forest area generally
increases globally in 2050 in the IAM model results, although some models indicate there could be
forest loss in areas of high bio-crop potential.90 Combined with a general decreased demand for
livestock products (due to a shift in diets) in the 1.5°C- and 2°C-consistent scenarios, the models
find a decrease in pastureland in 2050.

uploads/2020/12/Global-Status-of-CCS-Report-2020_FINAL_December11.pdf.
86 Global CCS Institute, “CCS. Vital to Achieve Net-Zero,” 2020, at https://www.globalccsinstitute.com/wp-content/
uploads/2020/12/Global-Status-of-CCS-Report-2020_FINAL_December11.pdf.
87 Commercial facilities include those where (1) CO2 is captured for permanent storage as part of an ongoing commercial
operation, (2) storage is undertaken by a third party or by the owner of the capture facility, (3) the economic lifetime is
similar to the host facility whose CO2 they capture, and (4) there is a commercial return while operating and/or meeting a
regulatory requirement. Global CCS Institute, “CCS. Vital to Achieve Net-Zero,” 2020, at
https://www.globalccsinstitute.com/wp-content/uploads/2020/12/Global-Status-of-CCS-Report-
2020_FINAL_December11.pdf.
88 Author’s calculations, assuming CCS today sequesters 40 MtCO2 per year.
89 Anna B. Harper et al., “Land-Use Emissions Play a Critical Role in Land-Based Mitigation for Paris Climate Targets,”
Nature Communications, vol. 9, no. 1 (August 7, 2018), p. 2938.
90 Anna B. Harper et al., “Land-Use Emissions Play a Critical Role in Land-Based Mitigation for Paris Climate Targets,”
Nature Communications, vol. 9, no. 1 (August 7, 2018), p. 2938.
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Figure 8. Bioenergy Crop Production in 2050, by IAM
SSP2 (“middle-of-the-road” socioeconomic scenario)

Source: CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan
Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions
Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: The models do not assume all bioenergy crops would be used in BECCS (bioenergy with carbon capture
and storage. RCP 2.6 is consistent with keeping mean global warming to 2°C in 2100, while RCP 1.9 is consistent
with keeping mean global warming to 1.5°C in 2100. The database includes only four IAMs that could solve for the
RCP 1.9 target for the SSP2 scenario. For IMAGE and WITCH models, no solution could be found.
Similarly, to keep likely warming in 2100 to 1.5°C or 2°C, IAMs rely on reductions in land use
emissions compared to the baseline (e.g., increases in terrestrial carbon removal, reductions in
deforestation), though not all project absolute negative land use emissions in 2050. Some IAMs
(IMAGE, REMIND) project positive land use emissions in 2050 under the 1.5°C- and 2°C-
consistent scenarios. GCAM, in contrast, includes much higher levels of NCS, and projects large
overall negative land use emissions (i.e., net sequestration in the land use sector) in 2050. GCAM
estimates that land use removes more than 10,000 MtCO2 per year (net) from the atmosphere in the
1.5°C- and 2°C-consistent SSP2 scenarios by 2050 (Figure 9). To put this in perspective, one recent
review of natural climate solutions estimates that the maximum additional potential of NCS—when
constrained by food security, fiber security, and biodiversity conservation—is 23,800 MtCO2 per
year in 2030.91 This would be in addition to the 9,500 MtCO2 absorbed annually by terrestrial
ecosystems today.92

91 Bronson W. Griscom et al., “Natural Climate Solutions (Vol 114, Pg 11645, 2017),” Proceedings of the National
Academies of Sciences, vol. 116, no. 7 (February 12, 2019), p. 2776.
92 Based on 2014 data. Note that net emissions from the land use sector were 1,500 MtCO2 in 2014. Counteracting this
sequestering of carbon are positive emissions from forestry and agricultural activities. C. Le Quéré et al., “Global Carbon
Budget 2014,” Earth System Science Data, vol. 7, no. 1 (2015), pp. 47-85.
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Figure 9. Global Net Land Use Emissions in 2050, by IAM
SSP2 (“middle-of-the-road” socioeconomic scenario)

Source: CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan
Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions
Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: This figure shows net land use emissions. RCP 2.6 is consistent with keeping mean global warming to 2°C
in 2100, while RCP 1.9 is consistent with keeping mean global warming to 1.5°C in 2100. The database includes
only four IAMs that could solve for the RCP 1.9 target for the SSP2 scenario. For IMAGE and WITCH models, no
solution could be found.
To put the previous analysis in this report in perspective, Figure 10 shows how negative emissions
compare with positive CO2 emissions (i.e., fossil fuel combustion from energy and transport and
industrial processes) in 2050 under the 1.5°C-consistent temperature pathway. Across the IAM
model runs, negative emissions represent 49% to 207% of the positive CO2 emissions from energy,
transport, and industrial processes, underscoring how the IAMs rely on negative emissions in order
to keep likely warming to 1.5°C or 2°C in 2100.93 The IAMs vary as to whether BECCS or NCS is
the dominant source of negative emissions. One model (REMIND) projects positive land use
emissions (green bar in Figure 10) in 2050 under the 1.5°C-consistent scenario.

93 To keep likely warming within 1.5°C/2°C, the models need to select technologies that reduce GHG emissions—for
example, by reducing fossil fuel combustion (e.g., in electricity generation or transport). If positive emissions cannot be
reduced quickly enough in the models, then those emissions need to be offset with assumed negative emissions
technologies that remove CO2 from the atmosphere (i.e., BECCS) to the degree they are available in the model.
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Figure 10. CO2 Emissions in 2050, by IAM
SSP2 (“middle-of-the-road” socioeconomic scenario), RCP 1.9

Source: CRS analysis of data from International Institute for Applied Systems Analysis, “SSP Database (Shared
Socioeconomic Pathways) - Version 2.0,” at https://tntcat.i asa.ac.at/SspDb/dsd?Action=htmlpage&page=10; Keywan
Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions
Implications: An Overview,” Global Environmental Change, vol. 42 (January 2017), pp. 153-168.
Notes: Positive emissions include CO2 emissions from fossil fuel combustion (energy, transport), as well as
industrial CO2 emissions (e.g., cement). BECCS is bioenergy with carbon capture and storage. Some models have
net negative emissions (GCAM, REMIND), meaning that negative emissions exceed positive emissions. RCP 1.9 is
consistent with keeping mean global warming to 1.5°C in 2100. The database includes only four IAMs that could
solve for the RCP 1.9 target for the SSP2 scenario. For IMAGE and WITCH models, no solution could be found.
Strengths and Criticisms of IAMs
IAMs can assist researchers and decisionmakers in understanding how complex sets of assumptions
on the economic-energy system interact with the biophysical earth system and how various policy
actions (e.g., fiscal and regulatory policy) may help achieve objectives. IAMs have been
specifically designed to explore technology deployments that meet specified climate or emissions
or other constraints (as detailed above with 1.5°C- and 2°C-consistent scenarios) typically in a
lowest-cost manner—something that many other models or tools for emissions scenarios cannot
easily do. They can also be used to model emissions reductions from a complex suite of policies
across sectors and indicate resulting warming.94 IAMs have utility as structured frameworks to
explore various assumptions around costs, performance characteristics, and the availability of
different fuels and technologies.95 In contrast to single-sector models, one strength of IAMs is their
ability to explore linkages and tradeoffs among energy use, agriculture, and land use. Like all
models, they are most useful not for precise estimates of the future technology or fuel mix under
different scenarios, but rather to compare relative results from different policy options.

94 For the latter, see Nathan E. Hultman et al., “Fusing Subnational with National Climate Action Is Central to
Decarbonization: The Case of the United States,” Nature Communications, vol. 11, no. 1 (October 16, 2020), p. 5255.
95 Ajay Gambhir et al., “A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address
These, Through the Lens of BECCS,” Energies, vol. 12, no. 9 (May 1, 2019), pp. 1-21.
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The detailed, process-based IAMs discussed in this report have been criticized on a number of
fronts.96 For example, they have been critiqued for their lack of transparency and “black box”
nature; inappropriate input assumptions and outdated data; difficulty in updating the cost of rapidly
changing technologies; focus on supply-side technologies; lack of incorporation of innovation
processes; lack of incorporation of behavioral processes; limited integration with other policy goals;
limited consideration of social, political, economic, and technical barriers and drivers; and coarse
spatial and temporal resolution.97 IAMs vary in the degree to which they are publicly available and
the extent of transparency around model assumptions. IAMs tend to be “deterministic”—that is,
they often provide one set of results despite uncertainties in input, although one can make multiple
runs to explore sensitivities. Most IAMs are not “dynamic” in altering assumptions for a future
period based on modeling results from preceding periods. The underlying assumption in IAMs is
strong long-term economic growth.98 Although this is based on historical trends, there is no
certainty.
The detailed, process-based IAMs generally do not estimate the economic damages due to the
physical impacts of climate change.99 Thus, there are typically no feedback effects on economic
variables, such as GDP growth and labor productivity, from changes in climate. Different IAMs can
yield varying results for the same policy—for example, carbon prices—due to the model structure
and the available energy technology options. Model intercomparisons, such as the one described in
this report, can be instructive as to the outcomes of policy choices.100
One of the most prominent criticisms of IAMs has been their reliance on negative emissions
technologies, including BECCS. As discussed in the previous section, BECCS would need to scale
up by orders of magnitude in these IAM 1.5°C- and 2°C-consistent scenarios. Some have
questioned whether BECCS deployment at this scale is technically feasible or realistic,101
particularly considering the physical or technical limits of biomass production.102 Furthermore,
some contend increased use of BECCS could put additional pressure on biodiversity and ecosystem
services, freshwater systems, and biogeochemical cycles.103 In one 1.5°C scenario study, if BECCS

96 Ajay Gambhir et al., “A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address
These, Through the Lens of BECCS,” Energies, vol. 12, no. 9 (May 1, 2019), pp. 1-21.
97 Not every criticism is applicable to every IAM. Ajay Gambhir et al., “A Review of Criticisms of Integrated Assessment
Models and Proposed Approaches to Address These, Through the Lens of BECCS,” ENERGIES, vol. 12, no. 9 (May 1,
2019), pp. 1-21; Hiroto Shiraki and Masahiro Sugiyama, “Back to the Basic: Toward Improvement of Technoeconomic
Representation in Integrated Assessment Models,” Climatic Change, vol. 162, no. 1 (September 2020).
98 Graham Palmer, “A Biophysical Perspective of IPCC Integrated Energy Modelling,” Energies, vol. 11, no. 4 (April
2018), at https://doi.org/10.3390/en11040839.
99 The simpler IAMs (often called cost-benefit IAMs) do calculate losses to GDP in each period based on climate
damages. The divide between the two model types is not always clear-cut, though, with the full-scale IAM, WITCH, able
to undertake cost-benefit analysis through its incorporation of damages from increased temperature changes, and thus the
benefits of reducing temperature changes. See Delavane Diaz and Frances Moore, “Quantifying the Economic Risks of
Climate Change,” Nature Climate Change, vol. 7, no. 11 (November 2017), pp. 774-782.
100 Jordan T. Wilkerson et al., “Comparison of Integrated Assessment Models: Carbon Price Impacts on US Energy,”
Energy Policy, vol. 76 (January 2015), pp. 18-31.
101 Sean Low and Stefan Schäfer, “Is Bio-Energy Carbon Capture and Storage (BECCS) Feasible? The Contested
Authority of Integrated Assessment Modeling,” Energy Research & Social Science, vol. 60 (2020), p. 101326; Naomi E.
Vaughan and Clair Gough, “Expert Assessment Concludes Negative Emissions Scenarios May Not Deliver,”
Environmental Research Letters, vol. 11, no. 9 (August 2016), p. 095003.
102 Naomi E. Vaughan and Clair Gough, “Expert Assessment Concludes Negative Emissions Scenarios May Not
Deliver,” Environmental Research Letters, vol. 11, no. 9 (August 2016), p. 095003.
103 Vera Heck et al., “Biomass-Based Negative Emissions Difficult to Reconcile with Planetary Boundaries,” Nature
Climate Change, vol. 8, no. 2 (February 1, 2018), pp. 151-155.
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were constrained and not allowed to occur on land converted from natural ecosystems (and
assuming the absence of direct air capture technologies), then food prices could increase several
times by the end of the 21st century.104 Lastly, some have argued there is a moral hazard dimension
to relying on BECCS and negative emissions technologies more broadly; that is, expected reliance
on negative emissions later this century could potentially compromise current efforts to reduce
GHG emissions.105 However, these are not criticisms of IAMs themselves but of particular
assumptions used by modelers in structuring their research.
The modeling community has begun to address some of the criticisms of IAMs.106 For example,
there has been some effort to update the technology costs more frequently, to explore different
baseline scenarios of fuel use and energy efficiency, to examine tradeoffs with other policy goals
(e.g., Sustainable Development Goals), and to incorporate hourly electricity data. Moreover, some
recent efforts explore 1.5°C- and 2°C-consistent pathways that are not dependent on BECCS—for
example, by assuming lower future energy demand or lifestyle change (e.g., lower-meat diets, lower
home heating and cooling demands), additional reduction of non-CO2 GHGs, and more rapid
electrification of energy demand base.107 The availability of the public SSP-RCP database has made
model results more transparent, thus enabling, for example, the comparison in this report.108
Some have argued for further transformation or even eliminating the use of IAMs because of the
limitations identified above.109 Models do allow for the exploration of various low carbon emissions
scenarios and provide a method to examine the future extraction, transformation, distribution, and
use of energy and explore linkages with other sectors in the economy, such as agriculture and land
use.
IAMs are simplifications of reality, and all models have limitations. Trying to model the
implications of nascent technologies, such as direct air capture, and incorporate feedbacks among
policies and behavioral change decades into the future is difficult, and often speculative. The
uncertainty in future projections is an inherent limitation of any modeling exercise.
Concluding Observations
Congress may find the projections and comparative results from IAM scenarios useful when
considering climate change mitigation proposals. This section highlights selected issues raised by a
review of the particular IAM modeling results discussed in this report.

104 Jay Fuhrman et al., “Food–Energy–Water Implications of Negative Emissions Technologies in a +1.5 °C Future,”
Nature Climate Change, vol. 10, no. 10 (October 1, 2020), pp. 920-927.
105 Kevin Anderson and Glen Peters, “The Trouble with Negative Emissions,” Science, vol. 354, no. 6309 (2016), pp.
182-183.
106 For a fuller description of criticisms and the IAM community responses, see Ajay Gambhir et al., “A Review of
Criticisms of Integrated Assessment Models and Proposed Approaches to Address These, Through the Lens of BECCS,”
Energies, vol. 12, no. 9 (May 1, 2019), pp. 1-21.
107 Detlef P. van Vuuren et al., “Alternative Pathways to the 1.5 °C Target Reduce the Need for Negative Emission
Technologies,” Nature Climate Change, vol. 8, no. 5 (May 1, 2018), pp. 391-397; Arnulf Grubler et al., “A Low Energy
Demand Scenario for Meeting the 1.5 °C Target and Sustainable Development Goals Without Negative Emission
Technologies,” Nature Energy, vol. 3, no. 6 (June 1, 2018), pp. 515-527.
108 International Institute for Applied Systems Analysis, “SSP Database (Shared Socioeconomic Pathways) - Version
2.0,” at https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10.
109 Ajay Gambhir et al., “A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address
These, Through the Lens of BECCS,” Energies, vol. 12, no. 9 (May 1, 2019), pp. 1-21.
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The Role of IAMs in Climate Legislation
Policymakers may look to IAMs and their various GHG emissions policy scenarios to inform
legislative decisions regarding climate change objectives and potential mitigation options. IAM
results described in this report show model disagreement in some areas (e.g., energy supply mix,
the degree of reliance on BECCS). Moreover, two of six IAMs could not find a solution for the
1.5°C-consistent scenario with “middle of the road” socioeconomic assumptions. IAMs vary in
their absolute projections (e.g., those pertaining to future primary energy), but they can be
particularly instructive where they show agreement. With current technologies and projected future
technology costs, the models all generally rely on, inter alia, renewable energy, electrification of
end-use energy, and negative emissions technologies to find lowest-cost solutions. Understanding
where models disagree and why may also assist consideration of policy options. Other
considerations besides costs and technical potential would lead to different modeling results.
Technologies to Reduce GHG Emissions
The global IAM scenarios provide a lowest-cost solution to holding likely global warming to 1.5°C
or 2°C in 2100. According to the modeling results presented in this report, renewable energy may
need to scale up 3 to 4 times compared to today, and CCS capacity by 20 times to more than 300
times in the next 30 years, to be on track to not exceed those temperature goals in 2100. In 2050,
across the model runs, negative emissions may represent half to more than double the positive CO2
emissions from energy, transport, and industrial processes. The models in the study described here
project significant increases in the global demand for electricity by 2050—in some scenarios,
electricity demand could reach twice as much as current levels. The modeling results indicate that
the energy intensity (energy per unit of GDP) of the world economy in the 1.5°C- or 2°C-consistent
scenarios is projected to decline by roughly one-quarter to more than one-third compared to the
baseline in 2050. However, the IAMs are used to project the future energy system, but they have
limitations in foreseeing what technologies may become available and economically viable in the
future. They typically do not consider all potential carbon removal options, and nascent
technologies such as direct air capture have only recently been included in scenarios. Additionally,
their focus has been more on supply-side technologies than demand-side measures.
If net-zero GHG emissions is a goal, as some Members have stated110 and introduced legislation to
the effect,111 Congress may seek to consider legislative options, such as incentives to accelerate
development and deployment of technologies in such areas as renewable energy, energy efficiency,
electrification, nuclear energy, carbon capture and storage, and carbon removal, among others.

110 “The time for debate and discussion on why and how we must tackle this crisis is over. The science is clear: we must
achieve net zero emissions by 2050 in order to ensure a safe and prosperous future for ourselves and our posterity. Now is
the time for action and implementation of crucial efforts to save our planet.” (Sen. Robert Menendez et al., “Statements
on Introduced Bills and Joint Resolution,” remarks in the Senate, Congressional Record, daily edition, vol. 167 [April 19,
2021], pp. S2013-S2038); “The science is clear: We must achieve net-zero greenhouse gas emissions by 2050 if we’re to
avoid the most catastrophic consequences of climate change. And we must take decisive action this decade to ensure
we’re on a path to reaching that target.” (Opening statement of Chairman Frank Pallone, in U.S. Congress, House
Committee on Energy and Commerce, hearing on “Back in Action: Restoring Federal Climate Leadership,” 117th Cong.,
February 9, 2021).
111 For example, CLEAN Future Act, H.R. 1512. The bill includes a national, economy-wide goal of net-zero GHG
emissions no later 2050.
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Appendix A. Details of the IAMs
Table A-1. Comparison of IAMs Referenced in this Report

AIM/CGE
GCAM
IMAGE
MESSAGE-
REMIND
WITCH
GLOBIOM
Organization National
Pacific
PBL
International
Potsdam
Fondazione
Institute for
Northwest
Netherlands
Institute for
Institute
Eni Enrico
Environmental National
Environmental
Applied
(Germany)
Mattei (Italy)
Studies (Japan) Laboratory
Assessment
Systems

(USA)
Agency
Analysis

(Austria)

Scope
Global
Global
Global
Global
Global
Global
Spatial
17
32
26
11
12
17
Resolution
geopolitical
(Regions)
regions, 384
land regions,
235
hydrologic
basins
Economic
General
Partial
Partial
General
General
General
Structure
equilibrium
equilibrium
equilibrium
equilibrium
equilibrium
equilibrium
Solution
Simulation
Simulation
Simulation
Optimization Optimization Optimization
Approach

Base (start)
2005
1975 (2015
1970
2000/2010
2005
2005
year
final
calibration
year)
Time Step
Annual
5 years
1-5 years
10 years
5 years
5 years
Time
2100
2100
2100
2110
2100
2150
Horizon
Source: Integrated Assessment Modelling Consortium, “IAMC Wiki,” 2020, at
https://www.iamcdocumentation.eu/index.php/IAMC_wiki; Joint Global Change Research Institute, “GCAM v5.3

Documentation: GCAM Model Overview,” at https://jgcri.github.io/gcam-doc/overview.html.
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Appendix B. Summary of SSPs
Table B-1. Assumptions Regarding Economy, Lifestyle, Policies, and Institutions for the
Five SSPs of the Intergovernmental Panel on Climate Change
SSP5: “Fossil-
SSP1:
SSP3:
Fueled
“Sustainability
“Regional
SSP4:
Development—
—Taking the
SSP2: “Middle
Rivalry—A
“Inequality—A Taking the

Green Road”
of the Road”
Rocky Road”
Road Divided”
Highway”
Challengesa
Low challenges to Medium challenges High challenges
Low challenges
High challenges to
mitigation and
to mitigation and
to mitigation
to mitigation,
mitigation, low
adaptation
adaptation
and adaptation
high challenges
challenges to
to adaptation
adaptation
Narrative
“The world shifts
“The world
“A resurgent
“Highly unequal
“This world places
gradually, but
fol ows a path in
nationalism,
investments in
increasing faith in
pervasively,
which social,
concerns about
human capital,
competitive
toward a more
economic, and
competitiveness
combined with
markets, innovation
sustainable path,
technological
and security,
increasing
and participatory
emphasizing
trends do not shift and regional
disparities in
societies to
more inclusive
markedly from
conflicts push
economic
produce rapid
development that historical patterns.
countries to
opportunity and
technological
respects
Development and
increasingly
political power,
progress and
perceived
income growth
focus on
lead to
development of
environmental
proceeds
domestic or, at
increasing
human capital as
boundaries.”
unevenly, with
most, regional
inequalities and
the path to
some countries
issues,”
stratification
sustainable
making relatively
both across and
development.”
good progress
within
while others fall
countries.”
short of
expectations.”
Economy and Lifestyle
Growth (per capita)
High in low-
Medium, uneven
Slow
Low in LICs,
High
income countries
medium in other
(LICs), medium-
countries
income countries
(MICs); medium
in high-income
countries (HICs)
Inequality
Reduced across
Uneven moderate
High, especial y
High, especial y
Strongly reduced,
and within
reductions across
across countries within countries
especial y across
countries
and within
countries
countries
International Trade
Moderate
Moderate
Strongly
Moderate
High, with regional
constrained
specialization in
production
Globalization
Connected
Semi-open
Deglobalizing,
Globally
Strongly globalized,
markets, regional
globalized
regional security connected elites
increasingly
production
economy
connected
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SSP5: “Fossil-
SSP1:
SSP3:
Fueled
“Sustainability
“Regional
SSP4:
Development—
—Taking the
SSP2: “Middle
Rivalry—A
“Inequality—A Taking the

Green Road”
of the Road”
Rocky Road”
Road Divided”
Highway”
Consumption and
Low growth in
Material-intensive
Material-
Elites: high
Materialism, status
Diet
material
consumption,
intensive
consumption
consumption,
consumption,
medium meat
consumption
lifestyles; rest:
tourism, mobility,
low-meat diets,
consumption
low
meat-rich diets
first in HICs
consumption,
low mobility
Policies and Institutions
International
Effective
Relatively weak
Weak, uneven
Effective for
Effective in pursuit
Cooperation
globally
of development
connected
goals, more limited
economy, not
for environmental
for vulnerable
goals
population
Environmental
Improved
Concern for local
Low priority for
Focus on local
Focus on local
Policy
management of
pol utants but only environmental
environment in
environment with
local and global
moderate success
issues
MICs, HICs;
obvious benefits to
issues; tighter
in implementation
little attention to well-being, little
regulation of
vulnerable areas
concern with global
pol utants
or global issues
problems
Policy Orientation
Toward
Weak focus on
Oriented
Toward the
Toward
sustainable
sustainability
toward security
beneﬁt of the
development, free
development
political and
markets, human
business elite
capital
Institutions
Effective at
Uneven, modest
Weak global
Effective for
Increasingly
national and
effectiveness
institutions/
political and
effective, oriented
international
national
business elite,
toward fostering
levels
governments
not for rest of
competitive
dominate
society
markets
societal
decisionmaking
Technology
Development
Rapid
Medium, uneven
Slow
Rapid in high-
Rapid
tech economies,
slow in others
Transfer
Rapid
Slow
Slow
Little transfer
Rapid
within countries
to poorer
populations
Energy Technology
Directed away
Some investment
Slow
Diversified
Directed toward
Change
from fossil fuels,
in renewables but
technological
investments
fossil fuels;
toward efficiency
continued reliance
change, directed including
alternative sources
and renewables
on fossil fuels
toward
efficiency and
not actively
domestic energy low-carbon
pursued
sources
sources
Congressional Research Service

28

Greenhouse Gas Emissions Scenarios: Background, Issues, and Policy Relevance

SSP5: “Fossil-
SSP1:
SSP3:
Fueled
“Sustainability
“Regional
SSP4:
Development—
—Taking the
SSP2: “Middle
Rivalry—A
“Inequality—A Taking the

Green Road”
of the Road”
Rocky Road”
Road Divided”
Highway”
Carbon Intensity
Low
Medium
High in regions
Low/medium
High
with large
domestic fossil
fuel resources
Energy Intensity
Low
Uneven, higher in
High
Low/medium
High
low income
countries
Environment and Natural Resources
Fossil Constraints
Preferences shift
No reluctance to
Unconventional
Anticipation of
None
away from fossil
use
resources for
constraints
fuels
unconventional
domestic supply
drives up prices
resources
with high
volatility
Environment
Improving
Continued
Serious
Highly managed
Highly engineered
conditions over
degradation
degradation
and improved
approaches,
time
near high/
successful
middle-income
management of
living areas,
local issues
degraded
otherwise
Land Use
Strong
Medium
Hardly any
Highly regulated
Medium regulations
regulations to
regulations lead to
regulation;
in MICs, HICs;
lead to slow decline
avoid
slow decline in the continued
largely
in the rate of
environmental
rate of
deforestation
unmanaged in
deforestation
tradeoffs
deforestation
due to
LICs leading to
competition
tropical
over land and
deforestation
rapid expansion
of agriculture
Agriculture
Improvements in
Medium pace of
Low technology
Agricultural
Highly managed,
agricultural
technological
development,
productivity high resource-intensive;
productivity;
change in
restricted trade
for large scale
rapid increase in
rapid diffusion of
agriculture sector;
industrial
productivity
best practices
entry barriers to
farming, low for
agriculture
small-scale
markets reduced
farming
slowly
Source: Reprinted from Brian C. O’Neil et al., “The Roads Ahead: Narratives for Shared Socioeconomic
Pathways Describing World Futures in the 21st Century,” Global Environmental Change, vol. 42 (January 2017), pp.
169-180. Narrative text from Keywan Riahi et al., “The Shared Socioeconomic Pathways and Their Energy, Land
Use, and Greenhouse Gas Emissions Implications: An Overview,” Global Environmental Change, vol. 42 (January
2017), pp. 153-168.
a. “Challenges” refers to whether societal trends in the scenario result in making climate mitigation or
adaptation harder or easier, without explicitly considering climate change itself.

Congressional Research Service

29

Greenhouse Gas Emissions Scenarios: Background, Issues, and Policy Relevance

Author Information

Michael I. Westphal

Analyst in Environmental Policy

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