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Article
Comprehensive evidence implies a higher
social cost of CO2
Kevin Rennert1, Frank Errickson2,15 , Brian C. Prest1,15, Lisa Rennels3,15, Richard G. Newell1,
William Pizer1, Cora Kingdon3, Jordan Wingenroth1, Roger Cooke1, Bryan Parthum4,
David Smith4, Kevin Cromar5,6, Delavane Diaz7, Frances C. Moore8, Ulrich K. Müller9,
Richard J. Plevin10, Adrian E. Raftery11, Hana Ševčíková12, Hannah Sheets13, James H. Stock14,
Tammy Tan4, Mark Watson9, Tony E. Wong13 & David Anthoff3 ✉
The social cost of carbon dioxide (SC-CO2) measures the monetized value of the
damages to society caused by an incremental metric tonne of CO2 emissions and is a
key metric informing climate policy. Used by governments and other decision-makers
in benet–cost analysis for over a decade, SC-CO2 estimates draw on climate science,
economics, demography and other disciplines. However, a 2017 report by the US
National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted
that current SC-CO2 estimates no longer reect the latest research. The report
provided a series of recommendations for improving the scientic basis, transparency
and uncertainty characterization of SC-CO2 estimates. Here we show that improved
probabilistic socioeconomic projections, climate models, damage functions, and
discounting methods that collectively reect theoretically consistent valuation of
risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2
estimate is $185 per tonne of CO2 ($44–$413 per tCO2: 5%–95% range, 2020 US dollars)
at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US
government’s current value of $51 per tCO2. Our estimates incorporate updated
scientic understanding throughout all components of SC-CO2 estimation in the new
open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully
responsive to the near-term NASEM recommendations. Our higher SC-CO2 values,
compared with estimates currently used in policy evaluation, substantially increase
the estimated benets of greenhouse gas mitigation and thereby increase the
expected net benets of more stringent climate policies.
Policies to mitigate greenhouse gas emissions are often evaluated in
terms of their net benefits to society. The net benefit of a climate policy
is the difference between the economic cost of the emission reduction
(the mitigation costs), and the value of the damages that are prevented
by that emission reduction (climate benefits, among others). In regula
-
tory impact analysis the climate benefits of CO
2
emission reductions
are typically computed by multiplying the change in CO
2
emissions
caused by the policy with an estimate of the SC-CO
2
. This makes the
SC-CO2 a highly influential metric, informing analysis of a wide range
of climate policies worldwide.
For more than a decade, the US government has used the SC-CO
2
to measure the benefits of reducing carbon dioxide emissions in its
required regulatory analysis of more than 60 finalized, economically
significant regulations, including standards for appliance energy effi-
ciency and vehicle and power plant emissions
2
. In the USA, the SC-CO
2
has also been used as the basis for federal tax credits for carbon cap-
ture and storage; proposed federal carbon tax legislation; state-level
zero-emission credit payments for nuclear generators and power sec-
tor planning; among other applications
3
. The SC-CO
2
also supports
decision-making by government environmental agencies in other
countries (for example, Germany, Canada and Mexico), and is used in
standardized corporate environmental and sustainability accounting
4
.
The SC-CO
2
is estimated using integrated assessment models (IAMs)
that couple simplified representations of the climate system and global
economy to estimate the economic effects of an incremental pulse of
CO
2
emissions. These models generally follow a four-step process in
which (1) projections of population and gross domestic product (GDP)
inform a CO
2
emissions pathway; (2) the CO
2
emissions path drives
a climate model that projects atmospheric greenhouse gas concen-
trations, temperature changes and other physical variables such as
https://doi.org/10.1038/s41586-022-05224-9
Received: 23 December 2021
Accepted: 11 August 2022
Published online: 1 September 2022
Open access
1Resources for the Future, Washington, DC, USA. 2School of Public and International Affairs, Princeton University, Princeton, NJ, USA. 3Energy and Resources Group, University of California,
Berkeley, CA, USA. 4Environmental Protection Agency, Washington, DC, USA. 5Marron Institute of Urban Management, New York University, Brooklyn, NY, USA. 6NYU Grossman School of
Medicine, New York, NY, USA. 7EPRI, Palo Alto, CA, USA. 8Department of Environmental Science and Policy, University of California, Davis, CA, USA. 9Department of Economics, Princeton
University, Princeton, NJ, USA. 10Independent researcher, Portland, OR, USA. 11Departments of Statistics and Sociology, University of Washington, Seattle, WA, USA. 12Center for Statistics and
the Social Sciences, University of Washington, Seattle, WA, USA. 13School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA. 14Department of Economics,
Harvard University, Cambridge, MA, USA. 15These authors contributed equally: Frank Errickson, Brian C. Prest, Lisa Rennels. ✉e-mail: anthoff@berkeley.edu
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688 | Nature | Vol 610 | 27 October 2022
Article
sea level rise; (3) the resulting climate change impacts are monetized
and aggregated as economic damages; and (4) economic discounting
combines all future damages into a single present value.
In 2017, a NASEM report assessing the SC-CO
2
estimation methodol-
ogy used by the US federal government found that the leading IAMs
used for estimating the SC-CO
2
have not kept pace with recent advances
in climate, economic and demographic science1. The NASEM report
offered near-term recommendations for improving each step of the
SC-CO2 estimation process to improve the scientific basis, charac-
terization of uncertainty, and transparency of the SC-CO
2
. Recently,
Executive Order 13990 re-established the US Interagency Working
Group on the Social Cost of Greenhouse Gases (IWG) to update the
federal government’s official SC-CO
2
estimates, and to consider these
NASEM recommendations in the process. Others have also criticized
the models supporting the past federal SC-CO
2
estimates for problems
including damages representations that do not reflect recent science,
outdated climate system models, and imperfect characterization of the
compounding uncertainties affecting SC-CO2 estimates5–7.
Here we provide probabilistic SC-CO
2
estimates from the Green-
house Gas Impact Value Estimator (GIVE), a newly created integrated
assessment model designed for quantifying the benefits of emission
reductions. The model is built on the Mimi.jl platform, an open-source
package for constructing modular integrated assessment models
8
.
By using novel components for each step of the SC-CO
2
estimation
process, GIVE incorporates recent scientific advances that are unac-
counted for by the previous generation of IAMs used in regulatory
analysis. Crucially, GIVE quantifies uncertainties in each component
and propagates these compounding uncertainties through the entire
computation, thus allowing for a theoretically consistent valuation of
the risk associated with a marginal emission of CO2.
Each individual component in GIVE is based on recent peer-reviewed
research on socioeconomic projections, climate modelling, climate
impact assessments and economic discounting. We implement GIVE
with a set of internally consistent, probabilistic projections of popula-
tion
9
, per capita economic growth
3,10
, and CO
2
, CH
4
and N
2
O emissions
3
generated using a combination of statistical modelling and expert
elicitation, collectively referred to as the Resources for the Future Socio-
economic Projections3 (RFF-SPs). Many existing IAMs use outdated
climate models and have been shown to produce temperature dynamics
inconsistent with more sophisticated Earth system models
1,11
. Further,
damage functions supporting previous SC-CO2 estimates are, to a large
extent, based on studies from several decades ago
1
. A vast literature
since then has expanded and improved our scientific understanding
of how changes in climate are likely to affect human wellbeing12. To
address these shortcomings, we combine socioeconomic uncertainty
with probabilistic models for the climate system and damage functions
(defined as functions that relate changes in climate outcomes such as
temperature to economic impacts in dollars). The GIVE model employs
0
5
10
15
20
Global population (billion)
a
−2%
−1%
0%
1%
2%
3%
4%
5%
Average per capita GDP growth rate
(2020 to year)
b
−20
0
20
40
60
Global CO2 emissions (GtCO2)
c
2050 2100 2150 2200 2250
0
500
1,000
1,500
Atmospheric CO2 concentrations (ppm)
d
2050 2100 2150 2200 2250
Year YearYear
0
2
4
6
8
Global surface temperature change
relative to 1850–1900 (°C)
e
2050 2100 2150 2200 2250
2050 2100 2150 2200 2250 2050 2100 2150 2200 2250
Year YearYear
2050 2100 2150 2200 2250
0
2
4
6
8
Global mean sea-level change
relative to 1900 (m)
f
Fig. 1 | RFF-S P socioeco nomic scen arios and the r esulting cl imate system
projections. a–c, Probabilistic socioeconomic projections for global
population (a), per capita GDP g rowth rates (b), and c arbon dioxide emi ssion
levels (c) from the RFF-SP scenar ios. d–f, Correspo nding climate sys tem
project ions that accou nt for parametric u ncertain ty in FaIR and BRI CK for
atmospheric carbon dioxide concentrations (d), global sur face tempera ture
changes rel ative to the 1850 –1900 mean (e), and global mean se a-level changes
relative to 1900 (f). In all p anels, solid ce ntre lines depic t the median ou tcome,
with darker sh ading spanning th e 25%–75% quantile range and lig hter shading
spanning the 5%–95% quantile range.
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Nature | Vol 610 | 27 October 2022 | 689
the FaIR v1.6.2 climate model
13,14
, the BRICK sea-level model
15–17
, and
updated damage function components representing the latest empiri-
cal research for the impacts of climate on agriculture
18
, mortality
19
,
energy consumption20 and sea-level rise21.
Recent important contributions to the SC-CO2 literature have
generated improvements to various components used by IAMs
22–27
(see Supplementary Information sectionSI.3 for an overview of this
literature). The GIVE model’s key contribution to this literature is the
holistic implementation of recent advances in probabilistic socio-
economics accounting for policy uncertainty, fully quantified scien-
tific uncertainty including climate tail risk and sea-level rise, addition
of non-market sectoral damages (that is, costs not included in GDP
accounting, such as mortality risk), and economic discounting tied to
uncertain economic growth. These advances enable a full valuation of
the risk resulting from those compounding uncertainties on the basis
of improved scientific, economic and demographic evidence3, which
have previously been unavailable. The GIVE model’s implementation
of this comprehensive set of scientific improvements affirms a key
result from recent work on the SC-CO222–27, namely that improved sci-
entific understanding of the components of SC-CO2 calculation leads
to a higher SC-CO
2
than has been previously used in US policymaking;
moreover, our approach demonstrates this using a more robust meth-
odology that reflects the current state of the literature. GIVE’s inputs
and outputs are spatially resolved at the level of 184 countries for popu-
lation, income and damages (except for agriculture damage outputs,
which are resolved at 16 regions). Climate change has the potential
to exacerbate existing economic inequities6,28,29, and our work would
allow future consideration of this issue through equity weighting30.
We calculate the SC-CO2 as the discounted sum of additional damages
per incremental tonne of CO
2
produced by an emissions pulse in 2020
along an uncertain emissions trajectory derived via formal expert elici-
tation that reflects continued technology and policy evolution. We use
an empirically calibrated stochastic discounting framework consistent
with the observed behaviour of interest rates and economic growth31.
We provide 10,000 SC-CO2 values using a Monte Carlo approach that
samples interrelated socioeconomic, climate, and damage function
uncertainties (Extended Data Table2). The GIVE model can also be used
to compute the social cost of other greenhouse gases (for example,
CH4, N2O and hydrofluorocarbons).
We illustrate the relative importance of our updated model com-
ponents by comparing them to outputs from the well known DICE
model32. We also assess the sensitivity of our SC-CO2 estimates to our
choice of sectoral, regionally disaggregated damage functions by
comparing them to two aggregate, global damage functions based
on meta-analyses of the broader damages literature32,33.
Socioeconomic projections of economic growth, population and
greenhouse gas emissions represent important sources of uncertainty
in the SC-CO
2
. In previous models, this uncertainty has been poorly
characterized
1,34,35
. Population and growth scenarios based upon the
Shared Socioeconomic Pathway (SSP)
36
narratives, which were promi-
nently featured in the Intergovernmental Panel on Climate Change
(IPCC) Sixth Assessment Report (AR6)
14
, do not typically come with
associated probabilities, though there have been efforts to assign such
probabilities a posteriori on the basis of expert surveys37. The small
number of SSPs precludes sampling the large and continuous space of
possibilities that characterizes future socioeconomics and emissions.
A strength of scenario-based analysis is in the qualitative exploration
of uncertainty, for example through the use of bounding scenarios,
including scenarios accounting for outcomes well outside the range
of historical experience that become increasingly possible over very
long time horizons. Such an approach does not, however, facilitate the
quantitative evaluation of uncertainty and the calculation of expected
values, a common requirement for policy analysis. In some cases,
a lack of quantification of relative probabilities can lead to disagree-
ments over what scenarios constitute a plausible reference case38–40.
A holistic, probabilistic approach to accounting for these uncertainties
was recently introduced
41,42
. Building on this approach, we sample the
RFF-SPs, comprising multi-century probabilistic projections of popula-
tion9 and GDP per capita10 at the country level as well as a distribution
of projections of global CO
2
, CH
4
and N
2
O emissions derived from a
combination of statistical and expert-based approaches.
The RFF-SPs complement the scenario-based approach by provid-
ing an alternative approach that characterizes the joint uncertainty
across annual GDP, population and greenhouse gas emissions for the
multi-century timespan required for climate damage estimation. They
also leverage expert knowledge to account for potential future changes
in policy and technology. The RFF-SPs project that (Fig.1): median
world population peaks at 11 billion around 2130 and subsequently
declines to 7.3 billion in 2300, (2.8 billion–21 billion: 5%–95% range);
median global per capita annualized economic growth declines slowly
to reach a cumulative time-average rate of 0.88% between 2020 and
2300 (0.17%–2.7%: 5%–95% range); median net global CO2 emissions
decline to 17 GtCO2 in 2100, which isroughly 40% of today’s levels
(−7GtCO2to62GtCO2: 5%–95% range), with slower declines thereaf-
ter (see Supplementary Information sectionSI.1 for more detail on
the RFF-SPs).
Our mean SC-CO
2
estimate using the preferred discounting scheme
is $185 per tCO
2
($44–$413 per tCO
2
: 5%–95% range, in 2020 US dollars,
as are all dollar results in this study) (Fig.2). This is 3.6 times greater
than the US government’s current, most commonly cited mean value
of $51 per tCO
2
using a 3% constant discount rate
43
. We report mean
SC-CO2 values throughout this paper to align our results with the stand-
ard expected net benefit framework that is routinely used for policy
analysis44 and supported by standard economic theory45,46.
SC-CO2 estimates are well known to be highly sensitive to the discount
rate
32
because the long residence time of CO
2
in the atmosphere means a
CO
2
emissions pulse continues to cause damages long after it was emit-
ted. Our preferred discounting scheme uses a 2% near-term risk-free
discount rate, which reflects the recent literature on real interest
rates
47–49
, which have declined substantially over recent decades
50,51
, as
$308
$118
$
185
$80
$
30
8
$308
$
118
$118
$185
$
80
$80
0 200 400 600 800 1,000
SC-CO2 (US$ per tonne of CO2)
3.0%
2.5%
2.0%
1.5%
Near-term discount rate
Fig. 2 | SC-C O2 distributions vary with the choice of near-term discount
rates. Distributions of the SC-CO2 based on R FF-SP scenario s amples,
a stochast ic, growth-linke d discountin g framework, unc ertainty i n the FaIR
climate and B RICK sea-level m odels, and unce rtainty in c limate damage
parameters. Colours correspond to near-term average discount rates of 3.0%
(blue), 2.5% (ora nge), 2.0% (red, our preferred s pecific ation) and 1.5 % (teal).
Dashed ver tical lines hi ghlight mean S C-CO2 values. Box a nd whisker plots
along the bo ttom of the fi gure depict th e median of each S C-CO2 distribution
(centre white lin e), 25%–75% quantile r ange (box width), and 5%–95% qu antile
range (coloured h orizontal lin es) values. All SC- CO2 values are expres sed in
2020 US dolla rs per metric to nne of CO2.
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690 | Nature | Vol 610 | 27 October 2022
Article
well as the central tendency from a survey of academic economists52.
Our discount rate is related to stochastic consumption growth in a
Ramsey-like equation, which is the commonly used approach to value
marginal impacts amid uncertainty in future payoffs and consump-
tion levels
53,54
. In this way, the parameterization of the discount rate
captures risk preferences using the risk aversion parameters discussed
in Methods.
We also assess (Extended Data Fig.1 and Table1) the sensitivity of
our SC-CO2 estimates to discounting by also using near-term rates of
3% ($80 per tCO
2
mean, $12–$197 per tCO
2
: 5%–95% range), to facilitate
comparison with the US government’s current, most commonly cited
$51 per tCO2 figure, as well as 2.5% ($118 per tCO2 mean, $23–$280 per
tCO2: 5%–95% range) and 1.5% ($308 per tCO2 mean, $94–$626 per
tCO2: 5%–95% range). We additionally show (Extended Data Fig.2) the
temporal evolution of the discounted marginal damages by year based
upon the preferred 2% near-term discount rate case.
Our SC-CO
2
estimates are based on regionally disaggregated damage
functions for four sectors. As a sensitivity analysis, we replace the sec-
toral damage functions in GIVE with two distinct, globally aggregated
damage functions that are based on meta-analyses of the climate impact
literature
32,33
. Under a 2% near-term discount rate, these sensitivity
runs yield relatively similar SC-CO
2
distributions with mean values
that differ by −18% to +11% (Extended Data Table1) from our preferred
SC-CO2 estimate (Extended Data Fig.1).
The single largest contributor to the overall increase in the SC-CO2
relative to the widely used DICE model is the use of a lower near-term
discount rate, and updated damage functions are the second largest
contributor. We disaggregate impacts of the changes to the near-term
discount rate, the sectoral damage functions, and the remaining GIVE
components (the RFF-SPs and FaIR) in Table1. We start by running
DICE-2016R, which uses none of our updated components and uses
DICE’s default discounting approach, yielding an SC-CO
2
estimate of
$44 per tCO
2
. Updating the climate modelling, the socioeconomic
scenarios, and the discounting approach reflecting a 3% near-term
discount rate but retaining the DICE-2016R damage function increases
the mean SC-CO2 by 34% to $59 per tCO2. Incorporating our sectoral
damage functions in place of the DICE-2016R damage function further
increases the estimate to $80 per tCO
2
, or a total increase of 81%. Finally,
using a lower 2% near-term discount rate has the largest effect, increas-
ing the mean SC-CO
2
estimate to this study’s value of $185 per tCO
2
,
a 321% increase relative to $44 per tCO
2
, and a 3.6-fold increase relative
to the widely cited US government value of $51 per tCO2.
The four climate damage sectors represented in the model vary sub-
stantially in their respective contributions to the overall magnitude and
uncertainty of the SC-CO
2
(Fig.3). Temperature mortality impacts are
the largest driver of the SC-CO
2
, contributing a mean partial SC-CO
2
(defined as the SC-CO2 estimated for an individual impact sector) of
$90 per tCO2 ($39–$165 per tCO2: 5%–95% range) to the $185 per tCO2
total using a near-term 2% discount rate. Agricultural impacts have a
similar mean contribution of $84 per tCO2, but greater uncertainty, with
a 5%–95% partial SC-CO
2
range spanning −$23 to $263 per tCO
2
. This
large range, which includes the potential for beneficial effects of higher
temperatures and CO2 concentrations on agriculture, arises owing to
compounding uncertainty in the relationship between CO2, tempera-
ture and crop yields, and how these factors interact with the economic
system to affect human welfare
18
. We sample uncertain parameters
for mortality and agriculture (seeMethods), the damage sectors for
which parameter uncertainty is quantified in the underlying studies.
The relatively small contribution of sea-level rise, which includes
both coastal damages and adaptation costs, to the total SC-CO
2
(mean
partial SC-CO
2
of $2 per tCO
2,
$0–$4 per tCO
2
: 5%–95% range) is attribut-
able in part to the inertia in the physical system connecting CO2 emis-
sions and sea-level rise and in part to the optimal regional adaptation
response allowed by the Coastal Impact and Adaptation Model (CIAM)
that we incorporate into GIVE21. Such optimal, forward-looking adap-
tation responses can substantially reduce estimated coastal damages
relative to a static scenario assuming no response to evolving coastal
risks55,56. Future research could improve the characterization of plau-
sible versus optimal coastal adaptation responses. The relatively slow
pace of sea-level rise also causes the greatest damages to occur far in
the future when discounting effects are strongest. Energy costs for
residential and commercial buildings (based on a previous work)
20
also make a relatively small contribution to the overall SC-CO2 (mean
partial SC-CO2 of $9 per tCO2, $4–$15 per tCO2: 5%–95% range), owing
to increased energy demand from cooling being offset by decreased
heating demand and future technological progress; these results are
broadly consistent with other recent empirical work57.
We quantify the impact on four critical, globally significant damage
sectors that are often considered to contribute the most to the SC-CO21,58
and for which studies exist that can be readily incorporated into SC-CO
2
0 100 200 300 400
SC-CO
2
(US$ per tonne of CO
2
)
Agriculture
Energy
Mortality
Sea-level ris
e
Total
$9
$84
$185
$90
$2
Fig. 3 | Par tial SC-CO2 estimate s and uncertainty levels strongly differ
across th e four climate da mage sect ors. Box and whi sker plots for the clima te
damage sec tors included i n the GIVE mod el, based on par tial SC-CO2 estimates
for each sec tor. The figure d epicts the me dian (centre white li ne), 25%–75%
quantile ran ge (box width), and 5%–95% quan tile range (coloured h orizontal
lines) parti al SC-CO2 values. B lack diamonds hi ghlight each s ector’s mean
partial SC-CO2, with t he numeric value w ritten dire ctly above. All S C-CO2 values
are express ed in 2020 US dollar s per metric to nne of CO2.
Table 1 | Evolution of mean SC-CO2 from DICE-2016R to this
study
Row Scenario Mean SC-CO2
($ per tCO2)
Incremental change
($ per tCO2) Share
of total
change (%)
aDICE-2016R 44
bGIVE with DICE
damage function,
3% near-term
discount rate
59 15 11
cGIVE with sectoral
damages, 3%
near-term
discount rate
80 21 15
dThis study: GIVE
with sectoral
damages, 2%
near-term
discount rate
185 105 74
All SC-CO2 values are expressed in 2020 US dollars per metric tonne of CO2. Row a represents
the SC-CO2 using base DICE-2016R deterministic. The mean SC-CO2 of $44per tCO2 is similar
to the value previously estimated from IWG DICE-2010 of $46per tCO2 at a 3% discount rate,
after converting to 2020 dollars65. Row b then retains the DICE-2016R damage function but
otherwise deploys GIVE under discounting parameters of ρ=0.8%, η=1.57, which are consistent
with a 3% near-term discount rate (seeMethods section ‘Discounting’ for descriptions of ρ
and η). Row c replaces the DICE-2016R damage function with our sectoral damage functions,
and row d then uses our preferred discounting parameters from this study of ρ=0.2%, η=1.24,
which are consistent with a 2% near-term discount rate. The inal row represents the preferred
mean value from this study.
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Nature | Vol 610 | 27 October 2022 | 691
estimation owing to their global coverage, regional disaggregation
and monetization. A limitation of this study is that other categories
of climate damages—including additional non-market damages other
than human mortality—remain unaccounted for. The inclusion of addi-
tional damage sectors such as biodiversity59, labour productivity60,61,
conflict
62
and migration
63
in future work would further improve our
estimates. Current evidence strongly suggests that including these
sectors would raise the estimates of the SC-CO2, although accounting
for adaptation responses could potentially counteract some of that
effect. Other costs of climate change, including the loss of cultural
heritage, particular ways of life, or valued ecosystems, may never be
fully valued in economic terms but would also probably raise the SC-CO
2
beyond the estimates presented here. The addition of alternate studies
covering the same sectors to incorporate additional independent lines
of evidence is also a promising area for continued work to improve the
SC-CO2. The modular structure of the Mimi.jl framework facilitates
such addition of new damage sectors with ease, providing a flexible
basis for future scientific improvement of the SC-CO2.
Although we approximate the effects of a rapid Antarctic ice sheet
disintegration tipping point within the BRICK sea-level component,
incorporating additional potential discontinuities in the climate sys-
tem would further improve our SC-CO
2
estimates
64
. We expect that,
in total, the future inclusion of additional damage sectors and tipping
elements will probably raise the estimates of the SC-CO
2
, and therefore
that the estimates from the present study are probably best viewed as
conservative. Similarly, accounting for different climate model struc-
tures, as the recent IPCC AR6 report does in chapter 714, would further
strengthen the robustness of our SC-CO
2
estimates and their associated
uncertainty levels. For example, that chapter (see cross-chapter box 7.1
and table 2)14 shows that the MAGICC climate model projects slightly
higher temperature increases than the FaIR model.
The methods used in this study reflect the culmination of several
important advances: development of fully probabilistic very-long-run
socioeconomic inputs that natively incorporate uncertainty over
future climate policy; incorporation of state-of-the-science repre-
sentations of the climate system and sectoral damage functions; and
an empirically calibrated discounting approach that accounts for
uncertainty in future economic growth. These advances collectively
allow for the full characterization of uncertainties, and their com-
pounding interactions, throughout all steps of SC-CO2 estimation,
including sectoral market and nonmarket damages to human health.
Their implementation on Mimi.jl8, an open-source, modular compu-
tational platform for assembling IAMs, improves the scientific basis
and transparency of the resulting estimates and is responsive to the
NASEM near-term recommendations. The methodology also provides
a straightforward means with which to calculate SC-CO2 results for
other years and estimate the social cost of other greenhouse gases
(for example, CH4, N2O and hydrofluorocarbons). Our higher SC-CO2
values, compared to estimates currently used in policy evaluation,
substantially increase the estimated benefits of greenhouse gas mitiga-
tion, and thereby increase the expected net benefits of more stringent
climate change policies.
Online content
Any methods, additional references, Nature Research reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41586-022-05224-9.
1. National Academies of Sciences, Engineering, and Medicine. Valuing Climate Damages:
Updating Estimation of the Social Cost of Carbon Dioxide (The National Academies Press,
2017).
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Methods
Socioeconomic projections
The RFF-SPs3 used in this study were designed to address the require-
ments for socioeconomic projections posed by SC-CO
2
estimation: (1)
The roughly 300-year time horizon required to account for the vast
majority of discounted future damages; (2) the need for geographically
disaggregated estimates of GDP and population to support damages at
a regional scale; (3) uncertainty accounting for expected future changes
in both technology and policy (the SC-CO
2
is measured against the best
estimate of future emissions, inclusive of future mitigation policies
except the one under analysis); and (4) the interdependence of future
population, GDP and greenhouse gas emissions trajectories1.
The RFF-SPs address key shortcomings identified in the approach
to socioeconomic projections originally developed by the US IWG
in 2010
66
and used consistently through the current US interim esti-
mates
43
. The IWG used five socioeconomic scenarios to 2100, drawn
from the Energy Modeling Forum 22 modelling exercise
67
, one of which
represented future climate policy. The IWG scenarios were critiqued for
not spanning the true uncertainty in GDP, population and emissions,
nor reflecting the broader scenario literature overall34,68. The RFF-SPs
used here improve on those scenarios by explicitly characterizing
uncertainty in the demographic, economic and emissions projections.
The multi-century time horizon required for the projections is long
relative to the length of the historical record available to estimate
country-level statistical models of population and economic growth.
Accounting for uncertainty in future emissions over that time horizon
requires assessing the potential for structural changes in technology
and policies that are out of the range of historical experience. To address
these challenges, the RFF-SPs were generated based upon a combina-
tion of statistical and expert-based approaches.
We generated probabilistic, country-level population projections
through 2300
9
by extending the fully probabilistic statistical approach
used by the United Nations for its official population forecasts to 2100.
We further incorporated feedback and improvements suggested by a
panel of nine leading demographic experts convened to review pre-
liminary results.
Our trajectories of country-level GDP per capita from 2018 to 2300
come from a multifactor Bayesian dynamic model, in which each country’s
GDP per capita is based on a global frontier of developed economies
and country-specific deviations from that frontier10. We reweight the
probabilities of the Bayesian model trajectories using results from the
RFF Economic Growth Survey, a formal expert elicitation focused on
quantifying uncertainty in long-run economic growth3.
The resulting probabilistic socioeconomic trajectories represent an
alternative to existing scenario-based approaches, such as those based
on the Shared Socioeconomic Pathways narratives. Such scenarios do
not typically come with associated probabilities, though there have
been efforts to assign such probabilities to the SSPs aposteriori on the
basis of expert surveys37. The use of non-probabilistic scenarios have
been criticized in the literature for being overconfident and failing to
reflect uncertainty
69
. Indeed, multi-century socioeconomic projec-
tions are deeply uncertain, as illustrated by the wide 5%–95% ranges
that we consider (see Fig.1). The scenarios based on the SSP narratives
and their commonly used extensions beyond 210063,70–72 fail to span
that uncertainty3.
We also generate multi-century distributions of global CO2, CH4
and N2O emissions through RFF’s Future Emissions Survey, which
elicited experts in socioeconomic projections and climate policy3.
Experts provided uncertainty ranges for future fossil fuel and
process-related CO
2
emissions as well as changes in natural CO
2
stocks
and negative-emissions technologies, incorporating their own uncer-
tainty around future mitigation policy. They also quantified the sen-
sitivity of emissions projections to future economic growth, thereby
allowing for the development of a joint set of projections of emissions
and economic growth. The experts additionally provided uncertainty
ranges for trajectories of CH4 emissions, N2O emissions, and net CO2
emissions from other sources of CO2 emissions and sinks.
Climate models
FAIR. We represent the global climate system and carbon cycle dynamics
using version 1.6.2 of the Finite Amplitude Impulse Response (FaIR)
model73–75. FaIR is an emissions-based simple climate model with a
carbon cycle that depends on background warming levels and cumu-
lative carbon uptake by land and ocean sinks. This state-dependency
enables FaIR to replicate the equilibrium and impulse-response
behaviours found in more sophisticated Earth system models, which
is important for producing scientifically grounded SC-CO
2
estimates.
These features are not found in the previous climate models used for
SC-CO2 calculations, which lack carbon cycle feedback and have been
shown to respond too slowly to changes in radiative forcing
1,11
. We run
FaIR with randomly sampled CO
2
, CH
4
and N
2
O emissions time series
from the RFF-SPs and represent other greenhouse gases and short-lived
climate forcers using the SSP2-4.5 scenario76, which is the scenario
that most closely matches the median RFF-SP emissions trajectories.
We account for climate model uncertainties by randomly sampling a
calibrated 2,237-member ensemble of parameters that was produced
using FaIR as part of the IPCC AR674. See Supplementary Information
sectionSI.2 for more detail on the FaIR model.
BRICK. We make probabilistic projections of regional changes in sea
level using the Building blocks for Relevant Ice and Climate Knowledge
(BRICK) model. BRICK represents individual contributions to sea level
from the Greenland and Antarctic ice sheets, glaciers and small ice caps,
thermal expansion, and land water storage and has been thoroughly
described in prior studies15. BRICK downscales changes in global sea
level to regional changes using maps of time-invariant scaling fac-
tors15,77. The Antarctic ice sheet model component also accounts for a
potential tipping point where rapid ice sheet disintegration can occur
when annual mean Antarctic surface temperatures cross an uncertain
threshold16.
We closely follow past work and calibrate BRICK to the historic
sea-level record over the period 1850–2017 with a Bayesian frame-
work
15,17,78,79
. This calibration process uses observational constraints
on global mean sea-level changes
80
in addition to individual contribu-
tions from glaciers and small ice caps
81
, the Greenland ice sheet
82,83
,
the Antarctic ice sheet84 and trends in thermal expansion85. It further
statistically accounts for measurement error estimates provided with
each observational time-series dataset86. We select physically informed
prior distributions for BRICK’s uncertain parameters that are consist-
ent with previous model calibration studies
15,17
. For the Antarctic ice
sheet model component, we select prior distributions based on a
paeleoclimate calibration that uses independent sea-level data from
240,000 years before the current era to the present16. We use our cali-
bration framework to create a Markov chain of ten million representa-
tive samples from BRICK’s joint posterior parameter distribution and
assess convergence based on graphical diagnostics and Gelman–Rubin
potential scale reduction factors that are less than 1.187,88. We discard
the first one million samples for the initial burn-in period and select
a random subset of 10,000 samples from the remaining chain for our
final sea-level parameter values. The distributions of the uncertain
parameters in BRICK are shown in Supplementary Information Table4.
Damage functions
Sea-level rise. The sea-level rise damage calculations are based on
a previous work
21
that presents the Coastal Impacts and Adaptation
Model (CIAM). CIAM is an optimization model that assesses the costs of
various adaptation strategies against flooding damages and potential
impacts from regional changes in sea level. It chooses the least-cost
strategy for each of over 12,000 coastal segments across the globe in the
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Article
Dynamic Interactive Vulnerability Assessment (DIVA) database
89
after
taking into account local physical and socioeconomic characteristics.
CIAM’s potential adaptation strategies are specified as a combination of
(1) a choice on retreating inland from the coastline, protecting coastal
communities and infrastructure, or remaining in place without taking
any adaptive actions and (2) a choice on the degree of investment in
coastal defence against several different storm surge return periods
conditional on protection being decided on. The DIVA database pro-
vides generalized extreme value distributions that define these return
periods for each individual segment.
CIAM is a deterministic model. All uncertainty in coastal damages is
therefore the result of uncertain sea-level projections that arise owing
to GIVE’s probabilistic emission scenarios and climate and sea-level
model parametric uncertainties that we sample.
Building energy expenditures. The energy demand damage function
is based on the results of Clarke etal. 2018
20
, a study that used the
Global Change Analysis Model (GCAM)
90,91
to project how climate
change affects regional building energy demand through 2100. GIVE’s
damage functions relate each degree of global temperature rise to a
change in regional energy expenditures, expressed as a proportion
of that region’s GDP. We derive these damage functions using output
data provided by the authors ofref. 20. That output includes, for each
of the 12 GCAM regions, the net change in regional energy expendi-
tures as a proportion of regional GDP at various temperature levels
(varying over both time and scenario). Reference
20
notes that this
relationship is approximately linear in temperature. For each of the
12 GCAM regions, we fit a linear function to these datapoints by
regressing the net change in energy expenditures as a proportion of
GDP on global temperature rise relative to the preindustrial period.
We assume the intercept is zero to ensure the resulting function yields
no change in energy expenditures at zero temperature rise. This yields
a coefficient for each region, denoted βj
E
(see Supplementary Infor-
mation Table2 for these values). Energy damages for each country i
located in region j are then calculated using the corresponding coef-
ficient, as
β
Change in energy expenditures as a proportion of GDP
=×(Temperature rise) .(1
)
it
j
Et
,
We multiply this energy expenditure share by country-level GDP to
generate damages in dollars.
Reference
20
did not feature any explicit consideration of uncertainty,
so we do not include uncertainty in this damage function. Uncertainty
in energy-related damages remain, however, owing to GIVE’s uncertain
temperature projections and GDP trajectories.
Temperature-related mortality. The mortality damage functions are
based on the results of Cromar etal. 202219, in which a panel of health
experts was convened to conduct a meta-analysis of peer-reviewed
research studying the impacts of temperature on all-cause mortality
risk, which includes human health risks related to a broad set of health
outcomes including cardiovascular, respiratory and infectious disease
categories. The meta-analysis combined studies to produce region-
ally disaggregated estimates of the effects on all-cause mortality of
each degree of warming across a broad range of baseline tempera-
tures, including both increased mortality risk at high temperatures
and reduced risk at cooler temperatures. This produced, for each
of 10 regions, a point estimate (and its standard error) representing
the net change in all-cause mortality risk per degree Celsius of glob-
ally averaged surface temperatures (see Supplementary Information
Table1).
To reflect uncertainty in these estimates, we sample these parameters
βj
M
for region j from a normal distribution centred on the point estimate
and set the standard deviation equal to the reported standard error.
We then compute temperature-induced excess deaths in country i in
region j as
β
(Temperature-induced excess deaths)
=×(Temperature rise) ×(Baseline mortality), (2)
it
j
M
ti
t
,
,
where we calculate baseline mortality as the regional population level
times its baseline mortality rate from the RFF-SPs,
(Baseline mortality)
=Population×(Baseline mortality rate). (3
)
it
it
it
,
,
,
We monetize these excess deaths using the value of a statistical life
(VSL) as follows:
(Monetized excess mortality)
=VSL ×(Temperature−induced excess deaths). (4)
it
it it
,
, ,
The baseline VSL value for 2020 for the USA (denoted
V
SL
US,2020
base
) is
derived using EPA’s 1990 Guidance value of $4.8 million and adjusted
for income growth and inflation, resulting in a 2020 US VSL of $10.05
million in 2020 dollars44 (see data explainer notebook in thereplication
code for this paperfor the full derivation). We then base the VSL for
country i in year t on the EPA’s baseline VSL for 2020, adjusted for country i’s
GDP per capita in year t, as
VSL=VSL×
GDP per capita
GDP per capita ,(5
)
it it
ε
,US,2020
base ,
US,2020
where ε = 1 represents the income elasticity of the VSL. The primary
function of ε is to adjust the US VSL to other countries and at uncertain
future income levels. We use a unit elasticity, which is in line with the
central tendency of values recommended in the literature for such
cases92–95.
Agriculture. The agricultural damage function is based on Moore etal.
2017
18
, which estimated damages in two steps using: (1) a meta-analysis
of published studies of the effects of temperature, rainfall and CO2 on
crop yields that builds on previous work
96,97
, and (2) a computable gen-
eral equilibrium model to estimate the economic welfare consequences
of these yield shocks while accounting for trade patterns and supply
and demand adjustments in agricultural markets across 16 regions.
Reference 18 presents results in the form of damage functions that
directly relate global mean surface temperature increase to welfare
change in economic terms. Their study presents three different param-
eterizations of these damage functions to characterize uncertainty: a
central, low and high estimate.
They estimated each of these three parameterizations for 1, 2 and
3 degrees Celsius of temperature increase, resulting in three piece-
wise linear damage functions for each region (see Supplementary
Information Fig.1). To address uncertainty as part of our Monte Carlo
sampling framework, we sampled a value from a triangular distribu-
tion with lower bound 0, mode 0.5 and upper bound 1 for each draw.
Assigning the low, central and high damage functions to each of these
values respectively, the two nearest functions were linearly interpo-
lated to produce the damage function for that draw, also interpolating
linearly between the resultant 1-degree Celsius value and the origin,
since damages at zero temperature increase can be assumed to be
zero. Importantly, this uncertainty sampling scheme preserves the
covariance between regions arising through connections in the global
trade network.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Lastly, we incorporated their results into our model via the equation
σf
TAgPctCost =GDPper capita
GDPper capita (),
it iit
iit
,
,
,1990
−ϵ
ag share
where AgPctCost
i,t
is the damage in the agricultural sector as a propor
-
tion of GDP in region i at time t; σ
i
is the share of agriculture in GDP
in 1990 in region i; ϵ = 0.31 is the income elasticity of the agriculture
share in GDP98; Tt is global average surface temperature increase;
and f
i
is the piecewise linear function for region i resulting from the
steps described above.
Discounting
Our discounting approach directly follows from NASEM recommen-
dations as developed in a previous work1,31. Given the long residence
time of CO2 in the atmosphere, the damages from CO2 emitted today
persist for centuries. These future damages must be converted to
present dollar equivalents using an appropriate discount rate. The
climate economics literature typically uses Ramsey-style discounting
that links the discount rate to future economic growth
99
. This linkage
leads to the Ramsey-like equation for the discount rate over time,
denoted r
t
: r
t
= ρ + ηg
t
, where ρ is the rate of pure time preference, g
t
is the average rate of consumption growth from the year of the emis-
sions pulse (described in the next section) to year t, and ηg
t
reflects the
extent to which society discounts damages because future individuals
are relatively wealthier. More specifically, η reflects how much the
marginal value of consumption declines as consumption increases
(a 1% increase in consumption corresponds with a η% decline in the
marginal value of a dollar).
We evaluate the stochastic discount rate for each realized path of
uncertain consumption growth (r
t
= ρ + ηg
t
), explicitly and structurally
modelling the uncertainty in discount rates that is often summarized
by a declining term structure100. This uncertainty in the discount rate
leads to a stochastic discount factor (SDF
t
) used to discount future
marginal climate damages. The SDF
t
can also be written equivalently
in terms of relative consumption levels54,101 as
ρ
c
c
SDF= 1
(1 +) .(6
)
ttt
η
−20202020
−
Here c
t
is world average per capita consumption in year t.We use this
SDFt to discount marginal climate damages (MDt) to a present value.
Whereas the climate economics literature routinely uses a
Ramsey-like approach to discounting
32,54,101–105
, prior estimates by
the US IWG disconnected discounting and future economic growth
by using a constant, deterministic discount rate. That approach
implicitly assumes that η = 0, corresponding to no linkage between
consumption growth and discounting as well as zero aversion to risk.
Our approach re-establishes the Ramsey-like link between growth
and discount rates. We use ρ and η values that were empirically
calibrated
3
to be consistent with the RFF-SPs and evidence on the
observed behaviour of interest rates
48
. This procedure also produces
near-term risk-free discount rates (defined as the average risk-free
discount rate over the first decade of the time horizon) consistent
with the desired values, such as those reported in Fig.1. Our preferred
SC-CO
2
estimate corresponds to a near-term 2% rate, which is consist-
ent with real risk-free interest rates over the last 30 years, and uses
ρ = 0.2% and η = 1.24 (refs.
3,31
). The (ρ, η) values corresponding to
the alternative near-term rates of 1.5%, 2.5% and 3% are (0.01%, 1.02),
(0.5%, 1.42) and (0.8%, 1.57), respectively.
The Ramsey-like form for the discount rate is a standard approach to
value marginal impacts and account for their risk amid uncertainty in
future payoffs and consumption levels in the discounted expected utility
framework
53,54
. In that framework, the value of the η parameter reflects
the degree of risk aversion as well as the inverse of the intertemporal
elasticity of substitution. That framework is also used for benefit–cost
analysis of policy and regulatory analysis under uncertainty, as it quan-
tifies the risk premium associated with uncertainty and risk aversion
in the valuation of a marginal emission of CO2. Although the Ramsey
framework is widely used, other considerations for decision-making
under uncertainty in the context of climate change, such as the role of
epistemic uncertainty and alternative preference structures including
ambiguity aversion, have also been proposed
106
. We use the discounted
expected utility framework because it is the most established and widely
used framework for regulatory and policy analysis107,108.
Estimating the SC-CO2
We estimate the SC-CO2 in a three-step calculation process. In the first
step, we run the GIVE model out to the year 2300 for two separate cases:
a ‘baseline’ case and a ‘perturbed’ case that adds an extra 0.1 MtC pulse
of CO
2
emissions in the year 2020 and is otherwise identical. In the
second step, we calculate marginal climate damages in year t as the
difference in modelled damages per tonne between the pulse and
baseline runs as
∑∑
MD
=(Damages with pulse −Baseline damages),(7
)
t
dr
R
td
rt
dr
=1
4
=1
,, ,,
d
where we aggregate over each of the four damage sectors d at their
respective geographic resolutions (that is, countries or regions) r.
In the third and final step, we calculate the SC-CO2 by discounting
these marginal damages using the stochastic discount factors SDFt
from equation(5) above and then aggregate them over time into a
single present value
∑
SC−CO= SDF×MD .(8
)
t
tt2
=2020
2300
For our preferred results, we calculate 10,000 unique SC-CO
2
esti-
mates. For each estimate, we sample the RFF-SP scenarios to account
for uncertainties in global CO
2
, CH
4
and N
2
O emission trajectories in
addition to country-level population and GDP growth levels. We also
sample parametric uncertainties in the FaIR and BRICK models as well
as the agricultural and temperature-related mortality damage functions
(Extended Data Table2). As described above, our preferred SC-CO
2
estimate uses discounting parameters of ρ = 0.2% and η = 1.24 for a
near-term rate of 2%.
When we report partial SC-CO
2
estimates for a given damage sector,
we follow the estimation procedure outlined above, but only include
the impacts from that individual sector when calculating marginal
damages in equations(7), (8). We normalize our estimates on the basis
of emission pulse size and report all results throughout the paper in
units of 2020 US dollars per metric tonne of CO
2
. We use the implicit
GDP price deflator from the US Bureau of Economic Analysis to convert
values to 2020 dollars.
We typically summarize the distribution of our 10,000 SC-CO2 esti-
mates by its mean, that is, E[SC-CO2], where the expectation operator is
taken jointly over all uncertain parameters determining marginal dam-
ages (MDt) and the stochastic discount factor (SDFt). This calculation
is consistent with economic theory for pricing investments and other
actions with uncertain payoffs, and therefore properly accounts for
the risk premium in the valuation of a marginal emission of CO
2
owing
to the many compounding uncertainties we model46.
Software
All our results are computed using open-source software tools. We use
the Julia programming language for the entire replication code of this
paper
109
. All models used in this study are implemented on the Mimi.jl
computational platform for integrated assessment models8.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Article
Data availability
Data for this paper are available at https://doi.org/10.5281/zenodo.
6932028.
Code availability
The replication code for this paper is available at https://doi.org/
10.5281/zenodo.6932028, including instructions on how to rerun the
entire analysis for this paper.
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Acknowledgements The views expressed in this paper are those of the authors and do not
necessarily relect the views or policies of the US Environmental Protection Agency. This work
was supported by the Alfred P. Sloan Foundation, the Hewlett Foundation, and individual
donations to RFF’s Social Cost of Carbon Initiative. The work of A.E.R. and H.Š. was supported
by NIH grant R01 HD070936.
Author contributions All authors contributed to the analytical methods underlying the model.
D.A., R.C., K.C., D.D., F.E., C.K., F.C.M., U.K.M., R.G.N., W.P., B.C.P., A.E.R., L.R., K.R., H.Š., H.S.,
J.H.S., M.W. and T.E.W. contributed to the research underlying the four individual modules of
the model. D.A., F.E., C.K., B.P., R.J.P., L.R., D.S., T.T. and J.W. programmed the integrated model
and performed the computations. D.A., F.E., R.G.N., B.C.P., L.R., K.R. and J.W. evaluated the
results and wrote the paper with input from all authors.
Competing interests D.A., F.E., B.C.P., L.R., K.R. and J.W. received support from ICF with
funding from the US Environmental Protection Agency during part of the time this paper
was developed; that funding was not affected by this study’s results. D.D. is employed at
EPRI, a non-proit public interest research institute supported by a combination of
funding from industry, governments and foundations that could be affected by the
results of this research, both positively and negatively. R.G.N. is a member of the NASEM
Board on Environmental Change and Society, which oversaw the NASEM consensus
study that guided this research, and which he also co-chaired. W.P. was also a member
of that NASEM consensus study committee when he was on the faculty at Duke
University. R.G.N. has also been a member of the National Petroleum Council since 2016,
a federally chartered advisory committee to the US Secretary of Energy, who appoints
its members.
Additional information
Supplementary information The online version contains supplementary material available at
https://doi.org/10.1038/s41586-022-05224-9.
Correspondence and requests for materials should be addressed to David Anthoff.
Peer review information Nature thanks Joeri Rogelj, Massimo Tavoni and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at http://www.nature.com/reprints.
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a: 3.0% near-term discount rate
b: 2.5% near-term discount rate
c: 2.0% near-term discount rate
−100 0100 200300 400 500 600 700 800 900 1,00
0
SC-CO
2
(US$ per tonne of CO
2
)
d: 1.5% near-term discount rate
DICE-2016R
GIVE
Howard & Sterner
Damage function
Extende d Data Fig. 1 | SC- CO2 distri butions are ro bust to dif ferent dama ge
functi on specif ication s ($ per tCO2). Distributions of the SC-CO2 using the
damage func tions from GI VE (orange, our prefer red specif ication), DICE-
2016R32 (bl ue), and Howard & Sterner33 (red) for ne ar-term discount ra tes of
1.5%, 2 .0%, 2. 5% and 3.0%. A ll results use th e RFF-SP scenar ios, a stochas tic
growth-lin ked discountin g framework, and s ample uncer tain climate, se a-level
and damage fu nction param eters, includi ng for DICE-2016R and Howard &
Sterner33 damage func tions. The D ICE-2016R damage funct ion is based on ref. 32
(see page 2 of that wo rk’ssupporting info rmation)32. The H oward & Sterner
damage func tion is base d on the base co effic ient in their t able 2, spec ificati on (8).
All SC-CO2 values a re expressed in 2 020 US dollars pe r metric tonne of C O2.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Article
2020 2040 2060 2080 2100 2120 2140 2160 2180 2200 2220 2240 2260 2280
Year
−0.5
0.0
0.5
1.0
1.5
2.0
SC-CO2 (US$ per tonne of CO2)
Extende d Data Fig. 2 | Di scounted ma rginal dam ages by year, preferre d 2%
near-term discount rate case. Solid line repre sents mean d iscounted
marginal da mages for a one-tonn e CO2 emissions p ulse in 2020, dott ed line
represen ts the median, w ith darker shading s panning the 25 %–75% quantile
range and ligh ter shading span ning the 5%–95% quant ile range. All SC- CO2
values are expre ssed in 2020 U S dollars per met ric tonne of CO2.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Extended Data Table 1 | Mean SC-CO2 values (with 5th–95th quantile ranges), by damage function and discount rate ($ per tCO2)
Near-term discount rate
Dama
g
e function 1.5% 2% 2.5% 3%
GIVE sectoral $308
(
$94–$626
)
$185
(
$44–$413
)
$118
(
$23–$280
)
$80
(
$12–$197
)
DICE-2016R $275
($35–$690)
$152
($20–$390)
$91
($12–$233)
$59
($8–$149)
Howard & Sterner$370
(
$106–$828
)
$205
(
$56–$468
)
$123
(
$33–$286
)
$80
(
$22–$183
)
Our preferred estimates correspond to the GIVE sectoral damage functions at a 2% near-term discount rate, shown in bold. All results use the RFF-SP scenarios, a stochastic growth-linked
discounting framework, and sample uncertain climate, sea level, and damage function parameters, including for DICE-2016R and Howard & Sterner33 damage functions. The DICE-2016R
damage function is based on Nordhaus 2016 (see page 2 of that work’s supporting information)32. The Howard & Sterner damage function is based on the base coeficient in their table 2,
speciication (8). All SC-CO2 values are expressed in 2020 US dollars per metric tonne of CO2.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Article
Extended Data Table 2 | Sources of SC-CO2 uncertainty
Model ComponentUncertaint
y
Source
Global CO2, CH4, and N2O emission tra
j
ectories RFF-SPs3
Countr
y
-level GDP
g
rowth ratesRFF-SPs3,10
Countr
y
-level population RFF-SPs9
FaIR climate-carbon cycle model 2,237-member constrained ensemble of the uncertain parameters (sampled with replacement)
from IPCC AR6 report74
BRICK sea-level model10,000-member ensemble of the uncertain parameters derived from a Bayesian calibration
framework15,16
A
g
riculture dama
g
e function Uncertain dama
g
e coefficient distributions based on Moore et al.18
Temperature-related mortalit
y
dama
g
e function Uncertain dama
g
e coefficient distributions based on Cromar et al.19
The left column shows the inputs and components of the GIVE model that contribute to uncertainty in the SC-CO2. The right column briely describes these uncertainties and their sources.
Refs. 3,9,10,15,16,18,19,74.
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1.
2.
3.
4.
5.
6.
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