Documentation for
Data-driven Spatial Climate Impact Model (DSCIM)
Version092023-EPA
The Climate Impact Lab
September 2023

Contents
1 Background1

2 Socioeconomics and Emissions Module2

2.1

Historical income data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

2.2

Future income, population, and emissions projections . . . . . . . . . . . . . . . . . . . . . . .

33 Climate Module
3.1

3.2

4

High resolution data and simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

3.1.1

Historical climate data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

3.1.2

Future climate simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5SC-GHG simulations using the FaIR climate model emulator . . . . . . . . . . . . . . . . . .

74 Damages Module
4.1

10

Dose response function estimation and projection . . . . . . . . . . . . . . . . . . . . . . . . .

10

4.1.1

Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

4.1.2

Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

4.2

Damage function estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

4.3

Calculation of probabilistic damage streams using FaIR climate outputs . . . . . . . . . . . .

17

5 Discounting Module & Valuation

19

5.1

Discounting Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19

5.2

The Weitzman parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20i

6 Putting it all together: the SC-GHG calculation

20

6.1

Step by step process to SC-GHGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21

6.2

DSCIM-EPA user options and defaults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

A Appendix: Accounting for risk aversion

26

A.1 The certainty equivalent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

26

A.2 Illustration of risk averse damage function . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

26

B Appendix: Damage Function Emulation

29

C Appendix: Sector-specific Damages Details

31

C.1 Mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31

C.2 Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

32

C.3 Labor Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34

C.4 Agricultural Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35

C.5 Coastal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38

Version history• Version092022-EPA: Initial version.
• Version092023-EPA: Updated to include additional reference material related to socioeconomic extrapolation in the damages and discounting modules.

iiAbbreviations
ADM1First Administrative Division Level (e.g., state/province)

AEO

Annual Energy Outlook

BCSD

Bias Correction and Spatial Disaggregation

BEST

Berkeley Earth Surface Temperature

CE

Certainty Equivalent

CIAM

Coastal Impact and Adaptation Model

CIL

Climate Impact Lab

CMIP

Coupled Model Intercomparison Project

DSCIM

Data-driven Spatial Climate Impact Model

ECS

Equilibrium Climate Sensitivity

EPA

Environmental Protection Agency

ESL

Extreme Sea Level

FACTS

Framework for Assessing Changes to Sea-level

FaIR

Finite Amplitude Impulse Response model

GCM

Global Climate Model

GDDP

Global Daily Downscaled Projections

GDP

Gross Domestic Product

GHG

Greenhouse Gases

GMFD

Global Meteorological Forcing Dataset

GMST

Global Mean Surface Temperature

GMSL

Global Mean Sea Level

IAM

Integrated Assessment Model

IAV

Impacts, Adaptation, and Vulnerability

IEA

International Energy Agency

IIASA

International Institute for Applied Systems Analysis

IPCC

Intergovernmental Panel on Climate Change

LMSL

Local Mean Sea Level

NASEM

National Academies of Sciences, Engineering, and Medicine

NEX

NASA Earth Exchange

OECD

Organization for Economic Cooperation and Development

PPP

Purchasing Power Parity

PWT

Penn World Tables

RCP

Representative Concentration Pathwayiii

Abbreviations contd.
RFF

Resources for the Future

RIA

Regulatory Impact Analysis

SCC

Social Cost of Carbon

SC-GHG

Social Cost of Greenhouse Gases

SESL

Semi-Empirical Sea Level

SLR

Sea level Rise

SMME

Surrogate Model Mixed Ensemble

SP

Müller-Stock-Watson-RFF Socioeconomic Projections

SSP

Shared Socioeconomic Pathway

TCR

Transient Climate Response

UDEL

University of Delaware

VSL

Value of a Statistical Lifeiv

1

Background

Rapid advancement in both climate science and economics over the last decade means there is an urgent
need to update the social cost of carbon and other greenhouse gases (collectively referred to as SC-GHGs),
as called for in both the 2017 National Academies of Sciences, Engineering, and Medicine (NASEM) report,“Valuing Climate Damages,” (1 ) and the January 2021 Executive Order 13990, “Protecting Public Healthand the Environment and Restoring Science to Tackle the Climate Crisis.” Updating U.S. SC-GHG estimates
based on the recommendations of NASEM would return them to the frontier of understanding about therisks from climate change (2 ).

The Climate Impact Lab (CIL) has developed the Data-driven Spatial Climate Impact
Model (DSCIM), a robust, empirically-based model for estimating SC-GHGs that is groundedin the best available science and economics and is consistent with recommendations set out
by the NASEM (1 ). The theory, framework, and implementation of the CIL’s complete approach has been peer-reviewed and is published in Nature (3 ) and The Quarterly Journal of
Economics (4 ), with many technical elements, including the construction of empirical damage
functions and valuation of uncertain and unequal local impacts, published in our earlier 2017
Science article (5 ). In this user manual, we provide an overview of the key components ofan implementation of DSCIM, referred to as DSCIM-EPA, for the U.S. Environmental Protection Agency’s (EPA) September 2022 draft technical report, "Report on the Social Cost ofGreenhouse Gases: Estimates Incorporating Recent Scientific Advances."
DSCIM-EPA contains four major components, each of which is based on best-available science and has
been detailed in our peer-reviewed publications (3 , 4 , 5 ):1. Socioeconomics and emissions projections DSCIM-EPA produces SC-GHGs that are consistent
with the NASEM recommendations, employing the Resources for the Future Socioeconomic Projections(RFF-SPs). These provide probabilistic, country-level projections of population, GDP, and greenhouse gas
emissions, while allowing for some dependencies among individual scenario draws in each dimension. Further
detail can be found in Section 2.
2. Climate System Representation DSCIM-EPA incorporates the Finite Amplitude Impulse Response(FaIR) model, which was identified by the NASEM as a simple climate model capable of meeting key criteria
related to the current scientific understanding of the timescales on which the climate system responds to CO2emissions and of climate-carbon cycle feedbacks (1 ). In Section 3, we outline our implementation of FaIR,
to project the impact that an additional ton of CO2 or other GHG has on global mean surface temperature(GMST).
1

3. Damage Functions In Section 4, we outline our approach to generating damage functions that are
empirically derived and plausibly causal, capture local-level nonlinearities for the entire global population,
and are inclusive of both adaptation costs and benefits.
4. Discounting In Section 5, we outline DSCIM-EPA’s approach to discounting, supported by the latest
empirical evidence and able to capture uncertainty in future growth, as well as the insurance value of climate
mitigation that protects against lower probability, higher damage scenarios.
DSCIM will be an evolving model, continuously updated with new sectors and the latest advances inscience and economics. The DSCIM-EPA codebase for download and replication of SC-GHG values used by
EPA is available online at: https://github.com/ClimateImpactLab/dscim-epa.

2

Socioeconomics and Emissions Module

2.1

Historical income data

One of the most important measures of vulnerability to weather and climate in our analyses is the wealthor incomes of those affected. This means that, in order to capture the differential vulnerability to climatechange in our empirical estimates, we need detailed subnational measures of income. In order to obtain
income data for each subnational region in the empirical analyses detailed in Section 4, we draw subnational
incomes from three main sources, using a combination of subnational gross domestic product (GDP) datasets
as well as globally-comprehensive national GDP data.
• Penn World Tables (PWT) national GDP.1 This dataset provides national-level incomes from1950 to 2014 for most countries in the world. We use Penn World Tables version 9.0 to obtain nationallevel income for all countries in our analyses.
• European Union subnational GDP.2 This dataset provides national and subnational level income
data for the European Union (EU) countries in our datasets. We use this dataset to obtain subnational
income at the Nomencalture of Territorial Units for Statistics (NUTS2) level of aggregation.3• First administrative division (ADM1) subnational GDP from national sources (6 ). Thisdataset provides national and subnational income data for 1,503 administrative regions from 83 countries. We use this dataset to obtain subnational level income data for all countries outside the EU.
Using these data, we construct a consistent multi-country panel of subnational incomes at the NUTS2
level for EU countries and ADM1 level for non-EU countries. To do so, we use the Eurostat (7 ) and1 Penn World Tables (PWT) database: https://www.rug.nl/ggdc/productivity/pwt/.
2 Eurostat database: http://ec.europa.eu/eurostat/data/database.
3 These data are used only in the mortality analysis detailed in Section C.1.

2

Gennaioli et al. (6 ) to downscale the PWT national-level incomes. We prefer this approach to using thesubnational data directly, as there are known inconsistencies in measurement of subnational GDP acrosscountries. Thus, we make the assumption that the within-country distributions of GDP in the Eurostat and
Gennaioli et al. are accurate, but the exact levels may not be. We rely on the PWT data as a consistentmeasure of GDP levels for all countries; thus, our subnational GDP estimates sum to national GDP fromPWT for all countries in the sample. All subnational income data are in constant 2005 dollars purchasing
power parity (PPP).
Subnational data collected by reference (6 ) are drawn from disparate sources, often using census data,
which are typically not annual, leading to an unbalanced panel. To construct annual values of income percapita using these data, we linearly interpolate between years, before constructing the Bartlett kernel4 andtaking averages across all years. Full details of our construction of these data are provided in reference (4 ).

2.2

Future income, population, and emissions projections

In order to understand the economic impact of climate change in the future, we need to understand what
the economy will be like in the future. A standard approach to date has been to project future incomes andpopulations in multiple different scenarios or pathways, for example in the widely-used shared socioeconomic
pathways (SSPs) (8 ). However, this often ignores the probability of the realization of any scenario, and thisprobability of economic outcomes is essential for characterizing the uncertainty of SC-GHGs. To remedy
the challenges of such an approach, RFF have produced a set of 10,000 probabilistic projections of GDP,
population, and greenhouse gas emissions scenarios up to year 2300 that we employ in this analysis. Thesesocioeconomic projections (RFF-SPs) are intended to fully meet the criteria of the NASEM report, whichnotes that purely statistical approaches may not fully inform future growth and should be combined withexpert judgement (1 ).
One major advantage of the RFF-SPs is that they establish a set of dependencies across GDP, population,and emissions projections. For example, in a high economic growth projection, this may be associated withtechnological improvements that could lower emissions intensity of GDP, and therefore be associated witha lower emissions scenario. This correspondence is described more below. The data in the RFF-SPs areprovided in 5-year increments. To construct annual estimates, we linearly interpolate between the time-series
data in the RFF-SPs in log space for population and GDP at the global-level.
Future projections of GDP. For projections of GDP, RFF combine an econometric approach describedin Müller, Stock, and Watson (9 ) with expert elicitation from 10 notable scholars of growth. This approach
is fully described in Rennert et al. (10 ). The Müller, Stock, and Watson projections calculate values for
113 countries, and RFF imputes values for a further 71 countries from these in order to have country-levelprojections for every country represented in the SSP database. The resulting median of GDP per capita
4 The Bartlett kernel is a method of weighting the time series such that greater weight is given to more recent years.

3

growth rates varies between approximately 1-1.5 percent annually from 2020 to 2300, with a 1st and 99th
range of approximately 0-4 percent.5
Future projections of national populations. Population projections build upon the standard methodology employed by the United Nations for population projections up to 2100. In the RFF-SPs, RFF extend
this time-horizon to 2300 and combine the statistical methods with expert judgement from 9 leading demographers. Methods and results are fully described in Raftery and Ševčíková (11 ). Median projections
generate a population of approximately 11 billion in 2100 and 7.5 billion in 2300.
Future projections of CO2 emissions A standard approach to date to model future climate based on
greenhouse gas emissions has been to use emissions pathways called Representative Concentration Pathways(RCPs) developed for the Coupled Model Intercomparison Projects (CMIP). Rather than follow a purely
scenario-based approach like that of the RCPs, RFF produced a distribution of future possible emissions
trajectories based on expert elicitation (10 ). These values are then used to establish distributions of future
global emissions conditional on five different scenarios of GDP growth. In order to pair socioeconomic
simulations with emissions simulations, each randomly drawn economic simulation is associated with arandomly drawn percentile of the emissions distribution for that level of GDP. Across all emissions scenarios,
the median trajectory represents a 50 percent decrease in CO2 emissions in 2100 from today’s level.

3

Climate Module

We aim to represent the climate system at the spatial and temporal resolution required to estimate empiricaldamage functions, allowing for a baseline climate under some assumed trajectory of greenhouse gas emissions
to be established, as well as representation of a response to a marginal emission of a greenhouse gas. Because
the CIL approach derives global estimates of climate damages by calculating high-resolution, local impact
projections, the climate module must be broken into two separate but related components: (i) highly spatiallyand temporally resolved historical climate data, and future climate projections from an ensemble of global
climate models (GCMs) combined with surrogate climate models under a range of emissions pathways,
which can be used to estimate spatially resolved damages and construct empirically derived global damage
functions; and (ii) projections of GMST and global mean sea level (GMSL) under a baseline climate andin response to a marginal emission of GHG, including representation of the full probability distribution ofclimate uncertainty with a large ensemble of simulations.6
5 The RFF-SP GDP is in units of $2011. We convert to $2019 using 1.1346350337995368, which was obtained from the
FRED implicit GDP deflator, downloaded circa July 2020.
6 In principle, one could compute a SC-GHG estimate by perturbing each GCM in the suite with a marginal emission of thegreenhouse gas and projecting damages for each location in both the original and perturbed simulations. However, in practice,such a procedure would prevent the calculation of an SC-GHG for any climate trajectory that did not exactly coincide with
one of the GCMs, and would also be prohibitively costly from a computational standpoint. Instead, we rely on a probabilistic,
reduced-complexity climate-carbon cycle model, in combination with our empirically derived damage functions, to construct

4

3.1

High resolution data and simulations

To create damage estimates at a highly resolved spatial and temporal resolution, historical climate data andfuture climate data for multiple emissions scenarios at a high spatial and temporal resolution are required.
Historical climate data are used to estimate the relationship between temperature and impacts (called “doseresponse" functions and described in Section 4.1) within a sector (e.g. the mortality-temperature relationship), and projected future climate data are used to produce climate change damage estimates under several
possible emissions trajectories. In the next two sections, historical climate data sources are outlined, followed
by a description of the climate change projection data used and how probabilistic weights are assigned toeach future climate projection from each GCM in the surrogate model mixed ensemble (SMME), whose
method is described in (12 ).
3.1.1

Historical climate data

The primary historical climate data product used is the Global Meteorological Forcing Dataset (GMFD,(13 )), which includes daily, global, gridded estimates of meteorological variables at the surface. GMFD
was derived from a weather forecasting reanalysis product (NCEP/NCAR), allowing for global coverage
where observations do not exist and corrected to observational weather data where available. GMFD data
is available at a 1/4 degree (about 25 km) horizontal resolution on a daily timescale from 1948-2010. Daily
surface temperature and daily total precipitation were used from GMFD. Several other historical climate
data sources were used on a sector-by-sector basis to investigate and ensure the consistency of estimatedresponse surfaces across differing climate datasets (Appendix C and references therein).
3.1.2

Future climate simulations

A global high-resolution (1/4 degree, daily) dataset of bias-corrected future climate projections produced by
NASA’s Earth Exchange program (NEX) is used, called the Global Daily Downscaled Projections (GDDP,(14 )). This dataset includes projections that are bias corrected and downscaled from the output of 21
GCMs that are part of the Phase 5 CMIP (CMIP5) archive (15 ). The NEX-GDDP dataset was adjusted
by NASA using the Bias Correction Spatial Disaggregation (BCSD) statistical downscaling method, whichuses as a reference daily maximum and minimum temperature and daily precipitation data from GMFD
(1950-2005) and a traditional quantile mapping bias correction approach (16 , 14 ). Quantile mapping isused to adjust the GCM outputs for historical and future time periods, and then the bias-corrected output
is spatially downscaled to a 1/4-degree resolution using a delta method that interpolates the daily bias
corrected changes relative to the GMFD climatology. For each GCM, three datasets are used: 1) simulated
response to historical climate forcing from 1850-2005, 2) simulated response to climate forcing through 2100
under RCP4.5 projected emissions from CMIP5, and 3) under RCP8.5 projected emissions from CMIP5.
SC-GHG estimates, consistent with the recommendations of the NASEM.

5

Each sector requires a different climate variable transformation, but all are derived from daily maximum
and minimum temperature and/or daily total precipitation. When necessary, daily average temperature istaken as the mean of daily maximum and minimum temperatures.
Because the CMIP5 ensemble of GCMs is not representative of a probabilistic distribution of climate
uncertainty but instead is an “ensemble of opportunity”, it fails to adequately represent tail outcomes.
Consequently, we use the SMME method (12 ) to assign probabilistic weights to the GCM climate projections
and synthesize “surrogate" GCMs to improve representation of the tails of the distribution. To accomplish
this, the SMME approach uses a weighting scheme that is based on projections of GMST from a reducedcomplexity climate model and a form of linear pattern scaling to construct model surrogates to fill in the tailsof the probability distribution that are not fully represented by the CMIP5 GCMs. The SMME approachprovides an additional 12 surrogate models (Figure 1) to the existing 21 NEX-GDDP GCMs. For more on
this, see Appendix B.2.3 of (4 ).

Figure 1: Future climate projections from the surrogate model mixed ensemble (SMME) Figure shows
the 21 climate models (outlined maps) and 12 model surrogates (maps without outlines) that are weighted such thatthe weighted distribution of the 2080 to 2099 global mean surface temperature anomaly (∆GMST) exhibited by the
33 total models matches the probability distribution of estimated ∆GMST responses (blue-grey line) under RCP8.5.
For this construction, the anomaly is relative to the 1986-2005 average.

6

3.2

SC-GHG simulations using the FaIR climate model emulator

The climate module of an SC-GHG calculation is meant to represent the climate response to the outputsof the socioeconomic module, namely, emissions of greenhouse gases and other forcing agents, such thata baseline climate and response to a marginal emission (or, “pulse") of GHG emissions – CO2 , methane(CH4 ), and nitrous oxide (N2 O) in DSCIM-EPA – can be established. The steps for producing simulations
necessary for SC-GHG calculations, described in this section, are identical across gases except for the gas
species and size of the pulse (see Table 1). To represent the climate response with and without a pulseof emissions, the module must convert the GHG emissions to atmospheric GHG concentrations, and inturn to radiative forcing (how much the energy imbalance is perturbed by the additional greenhouse gas),and finally a global average temperature response to that radiative forcing. The Finite Amplitude Impulse
Response (FaIR) reduced-complexity climate model(17 , 18 ) used extensively in the IPCC’s 6th Assessment
Report (AR6)(19 ), satisfies key criteria for such a module, including those outlined by NASEM (1 ), andwas identified by NASEM as an exemplar of a simple climate model meeting those criteria. These criteria
include transparency, simplicity, and ability to accurately and probabilistically represent climate and carbon
cycle systems and their uncertainties in a manner consistent with IPCC assessments and insights from more
complex Earth system models.
We use FaIR version 1.6.27 to be consistent with the version used in AR6 Working Group I (WG1)and Working Group III (WG3) reports. To represent the possible set of future climate change, we sample
from the set of 10,000 probabilistic emissions pathways from Resources for the Future that, together withprobabilistic trajectories of GDP and population, are referred to here as RFF-SPs (10 ) and described in
Section 2. Each RFF-SP represents a possible future trajectory of GDP, population, and emissions of CO2 ,CH4 , and N2 O, representing a wide range of emissions and climatic pathways (see the top row of Figure 2).
Following the method used in previous estimates of SC-GHGs (1 ), projections start in the current period(here defined as 2020) and run through the year 2300.
The SC-GHGs are estimates of the cost to society of emitting a marginal tonne of the GHG. The RFFSPs represent a probabilistic set of baseline, or "control", climate change scenarios, whereas to represent a
set of "pulse" scenarios, a pulse of a GHG emission is added to each of the RFF-SPs in the specified pulse
year. The difference between the pulse and control simulations characterizes the climate response to a pulseof GHG emissions. Table 1 lists the pulse sizes for each GHG estimated in DSCIM-EPA – CO2 , CH4 , andN2 O – in gas species units and as entered into the FaIR model emissions array8 . Pulse years evaluated are:2020, 2030, 2040, 2050, 2060, 2070, and 2080.
7 https://github.com/OMS-NetZero/FAIR/tree/v1.6.2
8 FaIR expects an emissions array of 39 different species, 3 of which are included in the RFF-SPs. Emissions for othergases and for the time range outside of 2020-2300 are taken from the CMIP6 SSP2-4.5 emissions pathway as provided by the
Reduced Complexity Model Intercomparison Project (www.rcmip.org), which includes historical emissions for past years. Theseemissions are concatenated with the RFF-SPs in order to run FaIR from 1750-2500 but are otherwise trimmed to 2020-2300 for
the SC-GHG calculations.
9 https://www.rcmip.org/

7

GHG

Pulse size (species units)

Pulse size (FaIR input)

CO2

3.66 Gt CO2

1.0 GtC

CH4

40.0 Mt CH4

40.0 Mt CH4

N2 O

1.57 Mt N2 O

1.0 Mt N2

Table 1: Pulse sizes were chosen to be approximately 10% of year 2020 emissions for each gas. The reference 2020
emissions were taken from the globally aggregated SSP3-7.0 emissions produced for the Reduced Complexity Model
Intercomparison Project (RCMIP)9 . SSP3-7.0 is a standard emissions pathway from CMIP6. “Mt‘ is Megatonne,“Gt" is Gigatonne, “GtC" is Gigatonne of Carbon.

Figure 2: Baseline and difference between the pulse and baseline for key climate variables in the
FaIR simple climate model: Each panel shows the temporal trajectory of key variables in FaIR that underlie
our calculation of the SC-GHGs. All panels show percentiles (1-99 range, light gray shading; 5-95 range, darkgray shading; median, dashed curve) across the 10,000 RFF-SP-FaIR climate parameter draws. Row one) RFFSP emissions (column one) and the response to the RFF-SP emissions. These constitute the baseline (“control")simulations. The RFF-SPs include CO2 , CH4 , and N2 O emissions trajectories, but only CO2 baseline emissions inunits of gigatonnes of carbon (GtC) are shown here (top row, first column). The values for total radiative forcing,global mean surface temperature, and sea level rise are shown as anomalies from the 2001-2010 average, while emissions
and CO2 concentration are absolute values. Row two) As Row one, but showing the difference between the 2020
CO2 “pulse" simulations, in which a pulse of carbon emissions in year 2020 is added to the baseline carbon emissions
for each RFF-SP draw, and the control simulations for each climate variable. The pulse size is identical across all
RFF-SP draws and thus, there is no uncertainty in column one. Row three) As Row two, but for a pulse of Methane(CH4 ). Row four) As Row two but for a pulse of Nitrous Oxide (N2 O). All GHG baseline and pulse emissions incolumn one are shown in the units in which they are entered into FaIR. See Table 1.

A key criterion for the climate module is the ability to probabilistically represent the uncertainty inthe carbon-climate system in line with the latest scientific evidence. To satisfy this, we run FaIR with8

a set of calibrated input parameters that were developed for use in the AR6 and determine the global
mean temperature response to emissions (see (19 ) Box 7.1 and Chapter 7 Supplementary Material 7.SM.2
for additional details). These parameters are the result of constraining a 1 million-member ensemble ofemissions-driven FaIR simulations over 1750-2019, as described in Ch. 7 of AR6 WG1 and its Supplementary
Material (Section 7SM.2). The input parameters sample uncertainty in effective radiative forcing (ERF), theclimate response (surface and deep ocean effective heat capacities, efficacy of ocean heat uptake, heat transfer
coefficient between the surface and deep ocean layers, and climate feedback parameter) and the carbon cycle(airborne fraction of CO2 , and change in airborne fraction of CO2 ). The constrained parameter set includes
2,237 members (20 )10 that satisfy criteria for matching the trend in historical global average temperature, theassessed historical ocean heat uptake, 2014 atmospheric CO2 concentrations, and airborne fraction of CO2
concentrations in transient CO2 increase simulations. These parameters yield climate simulations that areconsistent with the assessed ranges of equilibrium climate sensitivity (ECS) and transient climate response(TCR), and the ranges of GMST change for the AR6 emissions scenarios (see Ch 7 Cross-Chapter Box 7.1
of (19 )).
A full representation of the emissions uncertainty combined with the climate uncertainty, even in alightweight climate model such as FaIR, is infeasible as it requires over 22 million simulations (10,000 RFFSPs x 2,237 climate parameters) for each pulse gas and pulse year. Instead, the 10,000 RFF-SPs were jointly
sampled with the 2,237 climate parameters 10,000 times with replacement. The result is a set of 10,000 drawsof an RFF-SP emissions pathway paired with one set of FaIR climate parameters. The final ensemble of
RFF-SP-FaIR-climate-parameter draws yields 6,282 unique RFF-SPs, 2,208 unique climate parameters, and9,997 unique pairs. The RFF-SP to FaIR input parameter pairings were selected to ensure consistency acrosssocioeconomics and climate modules used within the EPA report. The climate response to the combined
RFF-SP emissions and climate uncertainty is shown in Figure 2.
For the coastal sector, we also must represent GMSL with and without a pulse, as we do GMST, in order
to compute marginal coastal damages conditional on GMSL, due to a pulse of GHG emissions. Because
GMSL is not an output of the FaIR model, a process for deriving time series of GMSL from GMST timeseries is developed and involves using the Semi-Empirical Sea Level (SESL) model to calculate GMSLs from
GMSTs with and without a pulse. These derived GMSLs are differenced to produce a pulse “delta", which isthen added to probabilistic baseline GMSL trajectories modeled using the Framework for Assessing Changes
to Sea-level (FACTS), used in the IPCC’s AR6(21 ). We use a quantile mapping approach to align thepulse deltas with the baseline GMSL time series to produce GMSL trajectories with and without a pulse.
Lastly, because the FACTS-based baselines are limited to the CMIP6 emissions pathways and do not include
RFF-SP emissions, we approximate baseline GMSL time series for each RFF-SP-FaIR climate parameterdraw using an emulation approach. Details of the development of the GMSL time series are described inAppendix C.5.
10 The v1.0 parameter set was downloaded from https://doi.org/10.5281/zenodo.5513022.

9

These probabilistic simulations of global mean surface temperature and global mean sea level are used
to derive probabilistic damage streams due to the baseline emissions and to a marginal pulse of emissions.

4

Damages Module

Here we first describe our approach to creating global “damage functions” that are reflective of the emissions,
population, and economy represented in each of the RFF-SP simulations, and that relate changes in GMST
(and GMSL for coastal damages) to changes in global damages11 . This process involves estimating highresolution local dose response functions (Section 4.1.1) for each economic sector that we model12 , projecting
those local dose response functions into the future to estimate local damages in the future (Section 4.1.2), andaggregating local damages to the globe and estimating global damage functions (Section 4.2), with sectorspecific details provided in Appendix C. We then describe the application of the global damage functions toprobabilistic FaIR GMST and GMSL trajectories to arrive at streams of damages representative of RFF-SP
socioeconomics and emissions uncertainty and climate uncertainty (Section 4.3). Note that the location- andsector-specific outputs are consistent across DSCIM-EPA user options and pre-computed, due to the large
compute footprint associated with producing these outputs.

4.1

Dose response function estimation and projection

4.1.1

Estimation

Our approach to estimating damages begins with collecting globally representative, high-resolution datasetsof socioeconomic outcomes, such as death rates and electricity demand, matched to the high-resolution
historical weather data described in section 3.1.1. We analyze these data using sophisticated econometrictechniques, which account for the many geographic, institutional, and cultural differences between regions aswell as trends over time, in order to estimate causal (rather than correlative) relationships between weather
and our outcomes of interest. The result of this analysis is a location-specific “dose response” function: adescription of the nonlinear effect of changes in temperature (and other weather variables for some sectors)
on an outcome of interest. To capture the potential for adaptation and dynamic vulnerability, we include
climate and socioeconomics in this estimation process, allowing the dose response function to vary withincome levels and climatic adaptation.
The exact form of the dose response function varies across sectors but has the same purpose - estimating
how climate variables like temperature affect some outcome (e.g. mortality risk, energy consumption), whileaccounting for adaptation based on access to resources (i.e. income) and the long-run climate. In general, alocation-specific annual damage outcome takes the form:
11 At the end of this Section, we also describe damage functions that include only direct damages within the territorial U.S.
12 These are mortality, energy, labor, agriculture. We also model the coastal sector, but it relies on a process-based modeland does not estimate dose response functions.

10

Dscit =fsc (Tit , LON GRU N CLIMi , log(GDP pc)i ) + qsc (OT HERCLIMit ) + αsci + γsct + ϵscit(1)

where s indicates economic sector, c indicates category of damage outcome (e.g. age group for mortality, fuelcategory for energy) with c varying by sector, i denotes the spatial regions of analysis (e.g. county, state,country, which varies by sector), and t indicates the time period (e.g. day, year, which varies by sector). Thus,
Dscit is the sector-specific, category-specific damage outcome in spatial unit i at time period t. Dscit is afunction of the temperature vector that captures the nonlinear transformations of daily grid-cell temperature(Tit ), the long-run climate variable (LON GRU N CLIMi ), and the natural logarithm of annual per capita
GDP (GDP pci ).13 The long-run climate and annual per capita GDP together determine how sensitive theoutcome in a given location is to daily weather variations. Dscit may also be a function of transformationsof other climate variables like precipitation, noted generally here as qscs (OT HERCLIMit ).14
Our econometric specifications for each sector and category include some form of location-specific andtime period-specific intercepts (αsci and γsct , respectively), referred to as “fixed effects".15 Location fixed
effects flexibly account for all permanent differences in the outcome between locations (for example, due togeography or history), while time period fixed effects flexibly account for trends in the outcome that arecommon across locations (for example, due to macroeconomic fluctuations or technological innovations). The
use of fixed effects purges our estimates of confounding variation, enabling us to identify a plausibly causal
effect of temperature variation on the outcome. Finally, ϵscit denotes the stochastic error term.
The parameters that describe the dose-response function are estimated with uncertainty, which must be
accounted for when using them to project the impacts of future climate change (see below). We accountfor this statistical uncertainty by resampling parameter values from their joint probability distribution. For
greater detail on the econometric estimation in the mortality, energy, labor, and agriculture sectors, see (4 )Section IV, (3 ) Methods: Econometric estimation of energy–temperature responses, (22 ) Section 4, and(23 ) Methods section, respectively, with summaries provided in Appendix C.
4.1.2

Projections

Next, we apply this dose-response function to future projections of local temperatures and socioeconomics.
While we use the RFF-SPs for SC-GHG calculations, their use in global climate models (GCMs) is infeasible,
since the time required to compute climate changes at the resolution of the models necessitates using far
fewer scenarios of emissions. The climate science community has centered their analyses of climate change
in the GCMs on the RCP scenarios from CMIP5. These RCPs trace out possible emissions scenarios that
range from cases with ambitious mitigation of CO2 to ones in which emissions continue to grow for much of
13 The exact parameterization of the long-run climate varies by sector.
14 The exact parameterization of other climate variables varies by sector.
15 The exact parameterization of the fixed effects varies by sector.

11this century. In our estimates of climate damages, we use climate projection data from two RCPs (RCP4.5
and RCP8.5) from CMIP5, as discussed in Section 3.1.2, in order to fill out the probable space of future
warming.
A similar logic leads us to calculate damages using a more limited set of socioeconomic projections thanthe RFF-SPs. The shared socioeconomic pathways (SSPs) database provides these projections across five
separate scenarios for the economic future of the planet. The SSPs propose a set of plausible scenariosof socioeconomic development over the 21st century in the absence of climate impacts and policy for use
by the Integrated Assessment Modeling (IAM) and Impacts, Adaptation, and Vulnerability (IAV) scientificcommunities (8 ). Projections of national incomes under the SSP scenarios are provided by the Organization
for Economic Co-operation and Development (OECD) Env-Growth model (24 ) and the International Institute for Applied Systems Analysis (IIASA) GDP model (25 ). The estimates of climate damages in DSCIM
incorporate the SSP2, SSP3, and SSP4 scenarios under each of these two models, in combination with thetwo emissions pathways described above.
For our projections we apply the dose-response functions to the CMIP5 projected weather data, for each
GCM. We perform projections across the multiple drivers of uncertainty in future projections, consisting of:(1) different emissions scenarios (across RCPs), (2) different modeled changes in climate (across GCMs), (3)different socioeconomic scenarios (across SSPs), (4) different modeled country-level incomes (across economic
models), and (5) different values of the statistical uncertainty describing the dose-response functions.
To isolate the impacts of climate change from changes in vulnerability driven by socioeconomics, we
also apply the dose-response functions to a scenario in which climate remains in its historical state while
economies continue to develop. The final impacts of climate change are the difference between the full projected outcomes, including all future changes, and the projected outcomes accounting only for socioeconomicchanges. Formally, the impact of climate change (Impact) in impact region r16 , at year y for economic sectors and damage category c takes the form:

Impactscry,lpjd = fsc,d (Try,lp , LON GRU N CLIMry,lp , log(GDP pc)ry,j )|{z}(A) dose-response function applied to climate-change scenario(2)− fsc,d (Try0 , LON GRU N CLIMry0 , log(GDP pc)ry,j ) .
|{z}(B) dose-response function applied to historical climate scenario

To capture multiple drivers of uncertainty, projections are separately carried out under each GCM l, RCP p,
SSP j 17 , and resampled estimate d of dose-response function parameters. The impact described in Equation
2 represents the change in a given outcome due to a shift in the temperature distribution under climate
16 There are 24,378 approximately county-sized impact regions globally, each of which is agglomerated from second administrative units based on maintaining approximately similar population across regions and approximately similar average climate
within each region. Details can be found in (4 ) and (3 )17 Note that a subscript for growth model has been omitted to simplify notation.

12

change, accounting for the evolution of the dose-response function as locations warm and incomes increase.
This construct isolates the additional impact of climate change net of other factors (for example, income) thatwill change in the future. The two objects being differenced, (A) and (B), are identical in every way, except
for the climate.18 Thus, we evaluate (B) using future levels of income but use T and LON GRU N CLIM
values from a historical baseline y0 .19 20
The resulting impacts are typically in physical units, such as changes in death rates and agricultural yields.
To translate these into economic damages, we apply a monetary value per physical unit. For example, in themortality sector, the value of a statistical life (VSL)21 represents how much individuals are willing to pay
to reduce mortality risk in their own life and has the units of dollars per death. By multiplying projectedchanges in death rates from climate change with population (in each age category), we estimate total changes
in premature deaths, which we then multiply with the VSL and sum across age categories to estimate total
economic losses in dollar terms (26 ).22 The VSL is used for valuation of heat and cold-related deaths in themortality sector and for flood-related deaths in the coastal sector (the coastal sector also includes valuationof abandoned property, inundated land, flood-related property damages, protective measure construction,
relocation, and wetland loss). The translation of impacts from physical units to monetary damages is specificto each sector and is described in detail in Appendix C. We also estimate the costs of adaptation, based on
the observed changes in vulnerability that are represented in the dose-response functions. The total economic
losses combine direct losses from climate change (expressed in Equation 2) and indirect costs of adaptation
to avoid further climate change losses.
This procedure provides us with total economic losses in each location, year, and sector under each GCM,
RCP, and SSP, for multiple draws capturing uncertainty in dose-response function parameter estimates. One
way to handle such uncertainty is to simply calculate a mean over these multiple draws for each sector, before
combining all sectors and aggregating to the globe. However, this fails to account for the fact that individuals
are risk-averse and are willing to pay a risk premium to avoid potentially severe damages. For DSCIM-EPA
results provided here, we calculate a value from the multiple draws that incorporates this risk premium bycomputing a certainty equivalent (CE) over the draws, referred to as the "risk averse" result (in contrast
to the "risk neutral" result). The calculation is performed using a constant relative risk aversion (CRRA)
utility function for various values of the elasticity of marginal utility of consumption, (denoted η)23 as follows(see Appendix A for additional details)24 . Relative to historical climatology (h), for each sector s, SSP j,
18 All fixed effects and other controls included in Equation 1 cancel out and are therefore omitted.
19 Note that the agriculture sector projects precipitation variables in addition to temperature, and they are also from thehistorical baseline.
20 We define the baseline period y as having the climatology of 1981-2005.
0
21 We use the U.S. EPA’s VSL for 2020 amounting to $9,926,524.56 (2019$). Additional details are in Appendix section C.1
22 It should be noted that the VSL does not represent the value of an individual life. Rather it represents willingness-to-pay
for a collective risk reduction of one “statistical life". For instance, if, on average, each individual is willing to pay $100 per
year to reduce his/her probability of dying by 0.00001, then collectively a group of 100,000 people would be willing-to-pay $10
million per year to avoid the loss of one “statistical life".
23 Eta values are η = [1.016010255, 1.244459066, 1.421158116].
24 To execute this calculation, we set a subsistence value for consumption equal to the minimum baseline income projectedacross all year, region, SSP combinations, which is equal to $39.39. For each region-year-SSP, we constrain the maximum

13

GCM l, RCP p and in each impact region r and year y, we compute damages per capita accounting for therisk premium as:
noCC
CC
Damages Per Capitasrjlpy = CEsrjlhy− CEsrjlpy
1! 1−η X 1−η 
Csrjdlpy
1CC
where CEsrjlpy =∗ (1 − η)
K
1−η

(3)

d∈K

and Csrjdlpy = GDPpcrjy − Damagessrjdlpy
CE CC represents the CE of consumption over dose response function uncertainty draws under climatechange and is a function of η and local per capita consumption, C, in each of the draws, which we define as
local GDPpc less damages in each draw. We similarly calculate the CE of consumption under a no-climatechange counterfactual scenario (CE noCC ), where uncertainty in local consumption exists due to variability
in the local climate around its historical mean, and subtract it from the CE CC to obtain the change in CEdamages due to changes in climate25 . We thus obtain a single measure that captures total economic losses(including the risk premium) in each location and year, within each GCM, RCP, and SSP combination andfor each sector and η value.26 .
Note that our treatment of coastal damages from sea level rise uses a different modeling framework, duelargely to the fact that historical sea level rise has been relatively minimal in most locations. This leaves
little opportunity for robust empirical estimation of an economic response function that would be globally
representative and applicable to future sea level rise. Instead, we employ a parameterized optimization
model (pyCIAM, (27 )) that calculates adaptation strategies and projects resulting future damages for 9,096
coastal segments using a least-cost optimization framework. Damages represented in this framework include(a) the cost of permanent inundation of immobile capital or land; extreme sea level-related (b) damages tocapital and (c) mortality; (d) expenditures on protection (i.e. infrastructure construction); (e) relocation
costs; and (f) wetland loss. Because of the use of this process-based approach, no empirical estimation stepusing historical outcomes is used within the coastal sector, and the projections for the coastal sector stemfrom a more process-based methodology.
damage draw such that consumption does not fall below the subsistence level.
25 If we ignore this baseline uncertainty, we would overstate the risk premium by conflating weather variation with climate
uncertainty. We therefore treat consumption uncertainty due to variability in the historical baseline climate in a similarmanner as we treat uncertain damages due to climate change. Specifically, we calculate local no-climate-change consumption
for each impact region in each Monte Carlo draw as GDP per capita minus climate “damages" due to historical climate
variability (i.e., Csrjdlhy = GDPpcrjy − “Damages"srjdlhy , where the subscript h denotes the historical climatology instead
noCC =of climate change under RCP p. We then take the certainty equivalent of consumption across these draws (i.e., CEsrjlhy
! 1


1−η
1−η
Csrjdlhy
1 P∗ (1 − η)
).
d∈K
K
1−η
26 Note that accounting for the risk premium across climate uncertainty is handled by stochastic Ramsey discounting on thedamages derived from probabilistic outputs from the FaIR reduce complexity climate model after global damage functions havebeen estimated. This is described in Section 5.1

14

4.2

Damage function estimation

The valued losses including the risk premium described in the previous section are used to estimate a global“risk averse damage function” for each sector and for the combined sectors. The damage function describes
the globally aggregated economic losses that result from a given change in the global average climate. The"risk averse" damage function is fit to globally aggregated impact region level damages where a CE acrossdose response function draws (also referred to as Monte Carlo draws) has been taken in each impact region,
collapsing the damages in each impact region as shown in Equation 3 before summation to the globe for each
GCM × RCP × SSP combination. In contrast, a "risk neutral" damage function would be fit to globally
aggregated impact region level damages where a mean across dose response function draws has been taken.
To construct such a risk averse damage function, we first index projected global economic losses in eachyear from 2020-2099, under each GCM × RCP pairing, to the change in GMST (∆GMST) projected in thefive-year window centered around that year under that pairing.27 For each sector s28 , SSP j, and year y 29 ,we then estimate a damage function that is quadratic in ∆GMST, fit to points from 33 GCMs (l) across the
two RCP emissions scenarios (p)30 . This takes the form:

2
Damagesslpyj = β1syj ∆GM STylp + β2syj ∆GM STylp+ ϵslpyj(4)

where ϵslpyj denotes the stochastic error term. The coefficients31 β1syj and β2syj are estimated by ordinary
least squares. For all sectors except coastal, damage functions of the same functional form are estimatedon ∆GMST variables. However, because coastal damages are a function of the change in GMSL (∆GMSL)
rather than ∆GMST, a damage function of the same form is estimated on ∆GMSL and ∆GMSL2 , where the
GMSL values are as described in Appendix C.5. The damage function representing the combined 5-sector
damages is a concatenation of the estimated damage function on (agriculture + mortality + energy + labor)
with the damage function estimated on coastal, such that the damage function has four coefficients, two of
which act on ∆GMST variables and two of which act on ∆GMSL variables. Thus damages under a given∆GMST and ∆GMSL pair are obtained by summing damages from coastal (conditional on ∆GMSL) anddamages for the combined 4 sectors of agriculture, mortality, energy, and labor (conditional on ∆GMST).
This results in a set of damage functions that spans the space of the GMSTs and GMSLs and global
damages that are based on the three SSP socioeconomic pathways32 and two RCP emissions pathways. To
27 ∆GMST is relative to the 2001-2010 average.
28 Sectors include agriculture, coastal, energy, labor, mortality, and their combination. The five-sector combination consists ofsumming damages across sectors at the impact region level for each GCM and dose response function draw, before computing
either the mean (risk neutral) or the CE (risk averse) in that impact region, and aggregating to the globe.
29 The damage function also depends on η but we leave out a subscript for parsimony.
30 In actuality, the damage functions are fit to points inclusive of a 5-year centered window around y.
31 Damagefunction coefficients are installed during DSCIM-EPA installation and setup,
after which
they reside inside of "/PATH/TO/dscim-epa/input/damage_functions/".
DSCIM-EPA is available athttps://github.com/ClimateImpactLab/dscim-epa.
32 Note the damage functions are also spanning two growth models, IIASA and OECD. In total there are 6 risk averse damage

15

Figure 3: RFF-SP Risk Averse Global Damage Functions: Risk averse global damage functions for all 10,000
RFF-SP draws (this maps to 6,282 unique damage functions due to the sampling with replacement described in
Section 3.2) are shown for the combined sectors conditional on ∆GMST (agriculture + mortality + energy + labor;
left panel) and for coastal, conditional on ∆GMSL (right panel) for the year 2099. The draw-specific combinationof these two sets of damage functions makes up the combined five-sector damage functions used in the combined
SC-GHG calculation.

construct damage functions that are based on the RFF-SPs (10 ), which are used to calculate the SC-GHGs,
we use these SSP-based damage functions to emulate global damage functions for an arbitrary draw of an
RFF-SP. The same approach is used for emulating damage functions that are conditional on ∆GMST and
those conditional on ∆GMSL. The emulation procedure is as follows:
1. We denominate our estimated annual SSP-based damage functions as a share of annual global GDP
from the SSP.
2. We calculate a set of weights, constrained to sum to unity, that, when used to take a weighted average ofglobal GDP across SSP-growth models, most closely recovers the global GDP in the RFF-SP simulation
run that we wish to emulate.
3. We apply these weights to take a weighted average of the redenominated damage functions from Step1.
4. Finally, we convert this new, emulated RFF-SP-specific damage function into an economy-wide measureof total dollar damages by multiplying by RFF-SP-specific global GDP. This results in a damage
function denominated in total dollar damages.
This emulation procedure provides us with damage functions for each of the RFF-SP draws k which arethen used in SC-GHG calculations. Full details about the emulator can be found in Appendix B. Figure 3
shows the combined-sector GMST-based global risk-averse damage functions on the left and global coastal
functions – 3 SSPs x 2 growth models – each of which is fit on GMST points from 2 RCPs (RCP4.5 and RCP8.5 from CMIP5).
Because GMSL is not an output of the GCM data we use, a different method to compute GMSL points is developed anddescribed in Appendix C.5.

16risk-averse damage functions on the right for all RFF-SP draws. These damage functions are independentof pulse gas, pulse size, pulse year, FaIR climate draw33 , Weitzman parameter (see Section 5.2), discounting
type, and pure rate of time preference ρ.
Lastly, because the GCM simulations and the SSPs end in 2099 but SC-GHG calculations depend ondamages out to 2300, an extrapolation of these time-varying damage functions must be performed. To
calculate the damage function in years y > 2099, we extrapolate the 2099 damage function as follows: Let
β1s,y,k and β2s,y,k denote the coefficients of the sector s, year y emulated damage function under RFF-SP
simulation run k. To obtain coefficients for years y > 2099, the 2099 damage function coefficients are
multiplied by the ratio of RFF-SP simulation k global GDP in year y to its 2099 global GDP, i.e.:s,y,ks,2099,kβm= βm
×

RFFGDPky
RFFGDPk,2099(5)

for m = {1, 2}. 34
A set of SC-GHGs limited to the direct damages occurring within the territorial United States is alsoavailable. These are computed using the same method that has just been described above, except only
impact regions within the United States border are included in the aggregation step. This is only a partial
accounting of the cost of climate change to US citizens and residents because it excludes internationaltransmission mechanisms, like trade, cross-border investment and migration, damage to the assets of U.S.
citizens and residents outside the United States, or consideration of how GHG emission reduction activity
within the United States impacts emissions in other countries.

4.3

Calculation of probabilistic damage streams using FaIR climate outputs

Once a damage function is estimated, it can be applied to the probabilistic ∆GMST trajectories produced by
FaIR and ∆GMSL trajectories emulated with SESL and FACTS (as described in Section 3.2). The procedure
to produce damage streams is as follows:
For each sector s, year y, and for each RFF-SP draw k (RFF-SP paired with a climate parameter set)35 :1. Calculate damages due to climate change using the emulated RFF-SP specific damage function coefficients. We refer to these damages as Damagesnopulse , since they are computed using ∆ GMST fromthe FaIR model run with RFF-SP k baseline emissions, without an additional pulse of greenhouse gas
33 The damage functions are independent of FaIR climate draw, except for the fact that the climate parameter is paired withan RFF-SP draw
34 This damage function extrapolation is the same as our approach to extrapolating SSP-based damage functions in otherwork (22 , 23 ), and is consistent with extrapolation of SSP global GDP and population needed for endogenous discounting or
certainty equivalent calculations in that work. There, in each SSP, the annual per capita global GDP growth rate in every year
post-2099 is equal to the average annual growth rate from 2095-2099 and the level of global population is held fixed at theyear 2099 level. This extrapolation of SSP global GDP and population is not needed here but we note it for interested readersfamiliar with these other works.
35 These calculations also vary by eta value (η) because the damage function coefficients vary by η. This is due to the η in
the CRRA utility function used within each impact region to calculate the damages including the risk premium to avoid severe
damages (See Equation 3), before aggregation to the globe for damage function estimation. We leave out a subscript for eta forparsimony.

17emissions36 :)2 .
+ β2syk (∆GMSTnopulse
Damagesnopulse= β1syk ∆GMSTnopulse
yk
syk
yk

(6)

2. Calculate global consumption under climate change by subtracting these baseline damages from RFFSP GDP. In DSCIM-EPA this object is referred to as “global consumption no pulse".
GlobalCnopulse= RFFGDPyk − Damagesnopulse
syk
syk

(7)

Then, for each pulse year u and greenhouse gas o:
3. Calculate damages due to climate change under the RFF-SP emissions plus a “pulse" of emissions in
the pulse year as in Equation 637 . The pulse of emissions in year u causes damages in all years y >= u.
syk
pulse
syk
pulse 2
Damagespulse
sykuo = β1 ∆GMSTykuo + β2 (∆GMSTykuo ) .

(8)

4. Calculate global consumption under climate change with the pulse by subtracting these pulse damages
from RFF-SP GDP. In DSCIM-EPA this object is referred to as "global consumption pulse".
pulse
GlobalCpulse
sykuo = RFFGDPyk − Damagessykuo(9)

5. Calculate marginal damages by subtracting damages without the pulse from damages with a pulse.
nopulseMarginalDamagesskyuo = Damagespulse
sykuo − Damagessyk(10)This results in a set of 10,000 probabilistic time series of marginal damages due to a pulse of GHG
emissions, as well as global consumption under climate change with and without an extra pulse of emissions,for each sector, GHG, and pulse year. Marginal damages can then be discounted as described in Section 5
using discount factors calculated from the global consumption, GlobalCnopulse , paths. The final SC-GHG
calculation is described in detail in Section 6 after a summary of steps leading up to the final calculation.
36 When computing the coastal partial SC-GHG and the coastal component of the combined 5-sector SC-GHG, the same
approach is used except ∆GMSLnopulse
is used in place of the ∆GMSTnopulse
variables.
ykyk
37 When computing the coastal partial SC-GHG and the coastal component of the combined 5-sector SC-GHG, the same
pulse
approach is used except ∆GMSLpulse
ykuo is used in place of the ∆GMSTykuo variables.

18

5

Discounting Module & Valuation

5.1

Discounting Procedure

In the DSCIM-EPA SC-GHG calculation, we implement continuous time Ramsey discounting to discountdamages back to the pulse year u.38 Specifically, we use the Ramsey discount rate consistent with the rate of
consumption growth in each RFF-SP draw k, inclusive of climate damages. The stochastic discount factor
for damages in RFF-SP draw k and in year y > u is set as follows:
SDFky =yYce−(ρ+ηg̃kτ )(11)

τ =u+1ckτ
c. In eachwhere the rate of consumption growth inclusive of climate damages, g̃kτ
, is given by ln ck,τ−1
year τ , ckτ 39 is given by Equation 9— baseline global GDP40 in the given RFF-SP draw minus the damages
associated with the GMST anomaly from the RFF-SP FaIR climate parameter draw41 . The parameter ρ
measures the pure rate of time preference, while η is the elasticity of marginal utility of consumption.
Note that since marginal damages in each draw are discounted at the stochastic discount rate consistent
with that draw,42 this discounting approach adjusts for all dimensions of the intertemporal tradeoff: the rateof baseline economic growth, the severity of climate change, and the correlation between payoffs from climatechange mitigation and the future state of the world. Each dollar of future losses is thus valued according to
the Ramsey formula’s comparison of future and present marginal utility appropriate to that particular draw.
We implement this discounting approach using three alternative sets of {η, ρ} combinations obtained
from (10 ) to match near-term rates of 1.5 percent, 2.0 percent, and 2.5 percent43 .
Lastly, the expected SC-GHG is computed by taking the net present value of discounted marginal damages
in each RFF-SP draw, applying an adjustment to account for consumption uncertainty before the pulse year,
and then averaging the adjusted net present values across RFF-SP draws. This process is described in detailin Step 8. of the next section, Section 6.
38 In Section 6.1 Step 8, we describe an additional procedure to discount damages back to the present, accounting for uncertainty between the present and the pulse year u.
39 For compactness, we use the variable symbol “c " instead of “GlobalC nopulse ", as in the previous section, to representkyglobal consumption no pulse.
40 Note that Ramsey discounting performed on SSP-based damages, as is done in other work (3 , 23 ), requires extrapolationof global GDP and population from 2100 to 2300. There, in each SSP, the annual per capita global GDP growth rate in every
year post-2099 is equal to the average annual growth rate from 2095-2099 and the level of global population is held fixed at theyear 2099 level. This extrapolation of SSP global GDP and population is not needed for this RFF-based calculation here but
we note it for interested readers familiar with these other works.
41 As described in 3.2, the RFF-SPs and FaIR climate parameters are sampled jointly such that an RFF-SP is paired 1:1 witha set of FaIR climate parameters.
42 Specifically, marginal damages in a given year under a given RFF-SP draw are discounted by multiplying by the SDF forthat year and draw.
43 These η and ρ combinations are η, ρ = [1.016010255, 9.149608e-05], [1.244459066, 0.00197263997], [1.421158116,
0.00461878399] matching 1.5%, 2.0%, and 2.5% near-term rates, respectively.

19

5.2

The Weitzman parameterTo confront issues related to climate uncertainty where damages can exceed global GDP causing mathematically undefined stochastic discount factors, we introduce an additional parameter inspired by the work ofMartin Weitzman (e.g. (28 )) that caps global damages due to climate change. We define the “Weitzman
parameter", ω, to be a scalar fraction from 0-1 that is used to calculate the “Weitzman threshold". The
Weitzman threshold, C ky , is the value of the share of contemporaneous GDP in each year and RFF-SP
that serves to top-code marginal utility and constrain global damages such that the Ramsey discounting
calculation is always defined.
Formally, for unadjusted consumption cky (baseline global GDP, RFFGDPky , minus the damage draw)
and Weitzman threshold C ky , we define an adjusted consumption value, ĉky , as follows:ĉky =cky if cky ≥ C ky

!

−1

U (C ky ) − U ′ (C ky ) × (C ky − cky ) if cky < C ky
U

(12)

where
C ky = ω ∗ RFFGDPky(13)

Implemented with a CRRA utility function with utility curvature parameter η, ĉky becomes:cky if cky ≥ C ky
1# 1−ηĉky = "

−η
1−η
 C

− (1 − η) ∗ (C ky )(C ky − cky )if cky < C ky

(14)

The intuition of this equation is that the Weitzman parameter top codes the marginal utility of consumption at the value of C, allowing each dollar of losses below C to have the same marginal utility as thatat the level of C. Total utility is then calculated including the utility costs below the bottom coded value,
and then converted back into an equivalent consumption value, ĉky , by inverting the utility function. Thenĉky enters directly into the Ramsey discounting calculation in a standard way, replacing cky . In this way,
the choice of the Weitzman parameter, ω and by proxy the Weitzman threshold, C ky , governs how quickly
global consumption declines under bad climate outcomes.

6

Putting it all together: the SC-GHG calculation

The previous sections describe the DSCIM-EPA implementation of each of the SC-GHG component modules
following recommendations by the NASEM(1 ). Next we summarize how DSCIM-EPA uses these modules
in a step-by-step process for calculating sector-specific and combined SC-GHGs for a range of pulse years.

20

6.1

Step by step process to SC-GHGs

1. Local Climate Damage Projections: This step is described in detail in Section 4.1.2 and Appendix
C.
We generate Monte Carlo draws of local climate damages across dose response function uncertainty
for the five sectors – agriculture, coastal, energy, labor, and mortality – using projections for 24,378
approximately U.S. county-sized “impact regions” spanning the world in each future year. These damage
projections are pre-computed for CMIP5 RCP4.5 and RCP8.5 emissions pathways paired with SSP2,
3, and 4 using GDP growth from IIASA and from OECD growth models, resulting in 12 projection
simulations (each with Monte Carlo uncertainty) for each of 33 GCMs, for the combined five sectors,
and for each of the five individual sectors.
This step varies by the following DSCIM-EPA user options: sector.
2. Calculation of the Local Welfare Costs of Climate Change: This step is described in detail in
Section 4.1.2.
Next, for each of the 12 outputs in Step 1, we calculate the local welfare loss from the range of projectedclimate damages in each impact region in each GCM, for each sector and for the total damages acrossthe five sectors44 . DSCIM-EPA presents SC-GHGs using a calculation that incorporates the local risk
premium to avoid potentially severe harm arising from econometric uncertainty.
This calculation proceeds as follows. For each damage realization, we calculate local consumption foreach impact region in each Monte Carlo draw as GDP per capita minus climate damages. We thentake the CE of consumption across Monte Carlo draws to calculate expected welfare with climatechange, accounting for risk aversion to the uncertainty in the damage estimates.4546 We similarly
calculate expected welfare under a no-climate-change counterfactual scenario, where uncertainty inlocal consumption exists due to variability in the local climate around its historical mean.47 The
damage caused by climate change, including the risk premium, is then calculated as expected welfare
under a no-climate-change counterfactual scenario minus expected welfare with climate change.
This step varies by the following DSCIM-EPA user options: η, sector
3. Climate Damage Aggregation: This step is described in Section 4.2.
44 Damages are totalled by summing the sectors for each Monte Carlo draw.
45 We use the constant relative risk aversion (CRRA) utility for this calculation with three different values of the elasticity of
marginal utility of consumption (η = [1.016010255, 1.244459066, 1.421158116]).
46 To execute this calculation, we set a subsistence value for local consumption equal to the minimum baseline income projectedacross all year, region, SSP combinations. We constrain the maximum damage draw such that consumption does not fall below
the subsistence level.
47 If we ignore this baseline uncertainty, we would overstate the risk premium. We therefore treat consumption uncertainty
due to variability in the historical baseline climate in a similar manner as we treat uncertain damages due to climate change.
Specifically, we calculate local no-climate-change consumption for each impact region in each Monte Carlo draw as GDP per
capita minus climate “damages" due to historical climate variability. Taking the certainty equivalent of consumption acrossthese draws yields expected welfare in the no-climate-change counterfactual scenario.

21

Once we calculate local damages inclusive of the risk premium over damage uncertainty in Step 2, weaggregate local climate damages to the global level48 for each GCM, RCP, SSP-growth model, andfuture year to estimate a damage function for each future year and SSP-growth model scenario.
This step varies by the following DSCIM-EPA user options: η, “global" versus “territorial U.S.", sector
4. Damage Function Estimation in Each Future Year: This step is described in Section 4.2.
The aggregated damages from Step 3 span the range of GMST over which to estimate sector damage
functions (except coastal), as described in Section 4 and Appendix C. Probabilistic sea level rise projections from LocalizeSL span the range of GMSL over which to estimate the coastal damage function,
as described in Appendix C.5. Damage functions are estimated on globally (or U.S.) aggregated points
from all GCMs under CMIP5 RCP4.5 and RCP8.5 emissions such that a greater range of GMST and
GMSL are supported. These damage functions are quadratic in GMST and GMSL and have zerointercept49 . As described in 4.2 and Appendix B, these six SSP-based damage functions are thenused to emulate a damage function for each of the 10,000 RFF-SP draws to emulate what aggregated damages may have looked like if a fully resolved spatial projection of each RFF-SP emissions
and socioeconomics pathway could be executed. The result is a set of 10,000 RFF-SP-based damage
functions50 .
This step varies by the following DSCIM-EPA user options: η, “global" versus “territorial U.S.", sector.
For efficiency and ease of replication for the EPA report, DSCIM-EPA begins computations from precomputed RFF-SP damage function coefficients.
5. FaIR Model Projections of Changes in GMST and GMSL: This step is described in detail in
Section 3.2.
We use the FaIR reduced-complexity climate model to simulate GMST trajectories based on the 10,000
draws of RFF-SP and climate parameter pairings representing the probabilistic distribution of thechange in GMST under emissions and climate uncertainty, with and without the marginal pulse of
GHG emissions. We use a Semi-Empirical Sea Level model to obtain probabilistic GMSL estimatesconsistent with each GMST Monte Carlo draw, as described in Section C.5. One set of 10,000 control(or baseline) simulations are produced, and 21 sets of 10,000 pulse simulations are produced – one foreach of 3 gases (CO2 , CH4 , and N2 O) × 7 pulse years (2020, 2030, 2040, 2050, 2060, 2070, and 2080)
48 Or we aggregate local climate damages to the U.S. level in the case of SC-GHGs that include only the direct damages
within the territorial U.S.
49 When combining damage functions from coastal and non-coastal sectors, we estimate 1-dimensional damage functions (i.e.
vs. GMST and GMSL, respectively) separately and then combine the outputs. While this approach will not capture the risk
premium associated with the simultaneous experience of coastal and non-coastal losses, it avoids large coefficient biases thatcan be introduced when estimating a 2-dimensional damage function (i.e. vs. GMST and GMSL, simultaneously) that isconstrained to pass through the origin.
50 There are 6,282 unique RFF-SPs in the 10,000 RFF-SP draws as described in 3.2, however we produce a set of damage
function coefficients for all 10,000 RFF-SP draws even though some are duplicate for ease of computation. Damage functions
do not vary by FaIR climate parameter.

22for both GMST and GMSL variables. These FaIR simulations are pre-computed and as such pulse size
is predetermined (see Table 1) and not modifiable, and pulse year and gas can only be selected fromthe above lists.51
This step varies by the following DSCIM-EPA user options: GHG, pulse year
6. Calculation of Undiscounted Marginal Damages Associated with a Pulse of Emissions:This step is described in Section 4.3.
We use the 10,000 RFF-SP FaIR climate paramter draws for each GHG and pulse year from Step 5 to
calculate the damage caused by the climate change baseline (emissions from the RFF-SP draw), andby a pulse of GHG emissions added to the baseline in the pulse year. Damage functions estimated in
Step 4 are applied to the GMST temperature outputs (or GMSL in the case of the coastal sector andcombined five sectors) from Step 5 to obtain damages in each future year with and without a pulseof emissions. Marginal damages are equal to damages with a pulse of emissions minus the damages
without a pulse. Marginal damages are produced for each of the 10,000 RFF-SP draws, for each η.
This step varies by the following DSCIM-EPA user options: η, “global" versus “territorial U.S.", sector,
GHG, pulse year
7. Discount the Stream of Future Damages: This step is described in detail in Section 5.1.
We discount, using stochastic Ramsey discounting, each of the streams of marginal damages caused by
the pulse of emissions from Step 6 by taking into account future GDP after removing climate damages.
Discount factors are a function of “global consumption no pulse" after top-coding the marginal utility
based on the Weitzman parameter.
This step varies by the following DSCIM-EPA user options: η, the pure rate of time preference ρ52 ,“global" versus “territorial U.S.", sector, GHG, pulse year 53
8. Calculate the Cost Per Ton of GHG
The discounted streams of marginal damages for each RFF-SP draw from Step 7 represent the distribution of present values in each year from the pulse year to 2300. To compute the SC-GHG for each of
the 10,000 RFF-SP draws k, we calculate the net present value of marginal damages in the draw (i.e.,
by summing present values from pulse year u to 2300) and divide this value by the number of tons of
GHG in the pulse. This results in a distribution over k of the social cost per ton of greenhouse gas
51 One could in principle generate new GMST and GMSL paths in response to RFF-SP emissions baselines – using FaIR orother simple climate and sea level models – that represent different GHGs, pulse years, or pulse sizes. These could then be set
as DSCIM-EPA inputs, provided the file and data structure were identical to existing data inputs.
52 η and ρ can only be selected in pre-determined pairs that represent near-term discount rates of 1.5%, 2.0%, 2.5% from (10 )53 Note that for the SC-GHG calculations that include only direct damages occurring within the territorial U.S., the stochastic
discount factor is still computed using global GDP less global damages, since U.S. damages would be occurring in a world with
global damages. In this way this SC-GHG is discounted using the same rate and can be directly compared to the global
SC-GHG.

23

emissions for each sector s, GHG o, and pulse year u.
PDVDamagesskuo =2300
X

SDFsky ∗ MarginalDamagesskyuo

(15)

y=u

PDVDamagesskuo
Tonspulse

SC-GHGskuo =(16)

These SC-GHGs do not account for the uncertainty in growth occurring before the pulse year whenmarginal damages are zero. To account for this, an adjustment factor is computed that adjusts SCGHGskuo , such that for pulse year u (adopting the notation for global consumption no pulse, csky from
Section 5):

csk,y=u1)−η ∗RFFPopk,y=u
E[csk,y=u ](17)

CESC-GHGskuo = AdjustmentFactorsku ∗ SC-GHGskuo(18)

AdjustmentFactorsk,y=u = (In this way, the CESC-GHG, or “certainty equivalent" SC-GHG, is the social cost of greenhouse gasthat has been adjusted to include the risk premium for consumption uncertainty occurring before thepulse year. See the EPA’s Report on the Social Cost of Greenhouse Gases for a detailed explanationof the motivation and derivation of this approach.
The final expected SC-GHG, ExpectedSC-GHG, is the mean over all 10,000 CESC-GHGs54 and converted from 2019$ to 2020$55 :10000

ExpectedSC-GHGsuo =X
1
CESC-GHGskuo
10000

(19)

k=1

This step varies by the following DSCIM-EPA user options: η, the pure rate of time preference ρ,
54 This procedure adjusts for all dimensions of the intertemporal tradeoff.

Taking the mean over draw-specific Ramsey
discounted SC-GHGs is in fact equivalent to calculating the difference between certainty equivalents (over RFF-SP draws) ofglobal consumption without a pulse and with a pulse, and then applying annual stochastic discount factors that reflect thegrowth rate of the (no pulse) global consumption certainty equivalent.
55 We report SC-GHGs in 2020$ using the 2019 to 2020 price level deflator 113.648/112.294, obtained fromhttps://apps.bea.gov/iTable/iTable.cfm?reqid=19step=3isuri=1selecta lly ears = 0nipat ablel ist = 13series = af irsty ear =2005lasty ear = 2020scale = −99categories = surveythetable =24

“global" versus “territorial U.S.", sector, GHG, pulse year.

6.2

DSCIM-EPA user options and defaults
User Option

Options

Default

Pulse year

2020, 2030, 2040, 2050
2060, 2070, 2080
combined (CAMEL)

All pulse years

Sectorcombined

Domaincoastalagriculture
mortality
energylabor[1.016010255, 9.149608e-05][1.244459066, 0.00197263997][1.421158116, 0.00461878399]
Global, Territorial U.S.

Settings

Value

GHG
pulse_size

CO2 , CH4 , N2 O
1 GtC
40 MtCH4
1 MtN20.5“uncollapsed"η, ρweitzman_parameter
fair_aggregation

all rates
GlobalAll GHGs

Notes

5-sector combined SC-GHG isinternally referred to as CAMEL

1.5% near-term rate
2.0% near-term rate
2.5% near-term rate
Scope of damages
included in the SC-GHGs,
global or direct damages
within territorial U.S.
Notes
CO2 pulse size
CH4 pulse size
N2 O pulse size
50% of RFFGDP
draw-specific discounting
produces distributionof SC-GHGs

Table 2: This table describes the DSCIM-EPA user options (top table), and settings for other options that are notmodifiable (bottom table).

25

A

Appendix: Accounting for risk aversion

A.1

The certainty equivalent

Figure 4 illustrates the concept of risk aversion via a heuristic example in which uncertain consumption
under climate change takes a value of either C CC1 or C CC2 with equal probability. Mean damages fromclimate change (i.e., “risk-neutral damages”) are equal to the difference between the no climate change levelof consumption (C noCC ) and the average level of consumption under climate change (CCC). The certainty

equivalent level of consumption under climate change (CE CC ) is defined as the level of consumption thatprovides utility equal to the expected utility of consumption across the 2 probabilistic possible states of theworld. The difference between the no climate change level of consumption and the certainty equivalent levelof consumption under climate change represents the “risk-averse damages” from climate change, reflecting
the additional amount that risk-averse individuals are willing to pay over and above the average loss fromclimate change in order to avoid the chance of especially bad outcomes.

Figure 4: CE cartoon Here is a schematic that describes the CE calculation.

A.2

Illustration of risk averse damage function

As described in the main text (Section 4.1.2), dose response function uncertainty is sampled within each
impact region and GCM resulting in uncertain damages depending on how sensitive the damage is to local

26

weather. In order to account for the risk-averse agent who is willing to pay to avoid the possibility ofsevere harm, a CE across damage uncertainty within impact region and GCM is calculated. Alternatively,
to represent an agent that is risk neutral and unwilling to pay for such certainty, a mean across damage
uncertainty within impact region and GCM is taken.
Once damages representing dose response function uncertainty are calculated for each impact region, they
are aggregated across all regions of the world to the global level separately for each global climate model ineach future year. Each climate model projects a different change in global mean surface temperature relative
to 2001-2010 (∆GM ST ) in each emissions scenario, allowing us to estimate how global damages in a given
year vary with ∆GM ST in that year. The type of global damage function is determined by whether the
local damage uncertainty was collapsed with a CE (risk averse) or a mean (risk neutral). Figure 5 illustrates
such damage functions for the end-of-century.

27

Figure 5: Illustrative damage functions with and without risk aversion Illustration of a damage function
estimation with and without risk aversion in underlying spatially aggregated damages. Each data point represents
globally aggregated damages from a single GCM-RCP-SSP-growthmodel for a 5 year period at the end of the century,
plotted against that GCM-RCP’s global average temperature at the end of the century. The green curve is estimatedusing the green dots, each of which represents a GCM realization of globally-aggregated risk-averse damages. The
blue curve is estimated through the blue dots, each of which represents a GCM realization of globally-aggregatedrisk-neutral damages.

28

B

Appendix: Damage Function Emulation

Climate damages are initially calculated for a small number of socioeconomic scenarios, consisting of SSP
trajectories for SSP2, SSP3, and SSP4, as produced by the OECD Env-Growth model and the IIASA GDP
model (6 scenarios total). To evaluate damages across the full range of RFF-SPs, we apply an emulation
scheme. Conceptually, this scheme interpolates between the SSPs in order to match the country-level GDPs
designated by a given RFF-SP.
The emulation scheme is estimated and applied separately for each 5-year period, t, of each RFF-SP, k.
For target period t, we have a snapshot consisting of GDP and population levels for each country according
to the RFF-SP and each SSP. Within this period, we describe the RFF-SP country-level incomes levels as aunit simplex interpolation of SSP income levels in the same period. That is, we want to select values αjkt
for which
RFFLogIncomekit ≈X

αjkt SSPLogIncomejitjfor all countries i, across SSP j, such thatj αjkt = 1 for each period of each RFF-SP. The natural log

P

of GDP per capita for each country is used, since this is the form of income used in the raw projections.
Specifically,
RFFLogIncomekit = log(RFFGDPkit/RFFPopulationkit )SSPLogIncomejit = log(SSPGDPjit/SSPPopulationjit )using the country-level GDP and population from the RFF-SPs and SSPs.
Since an exact solution is generally not possible, we minimize a weighted sum of country-level errors
according to the following optimization problem for each RFF-SP scenario and period:minXsuch thatX{αjkt }


X
wkit RFFLogIncomekit − 
αjkt SSPLogIncomejit ijαjkt = 1,jαjkt ≥ 0∀j

The weighting values, wkit , are set equal to the RFF-SP GDP level for the given country. This allows the
optimization to produce a result closer to global GDP. The optimization is solved with linear programming
using the Gurobi optimizer (29 ).
Recall that the αjkt are in 5-year increments. To construct the estimate for each year, y, we linearlyinterpolate αjkt between years to get αjky . Yearly weights are then divided by the sum of weights across29

SSP, i.e.,αjky
α̃jky = Pj αjkyto make sure thatj α̃jky = 1. Next, we apply the coefficients from the unit simplex interpolation to globalP

damage functions, which we estimated for each SSP within each year, to construct an interpolated damage
function. The SSP damage functions are normalized by the global SSP GDP in this step, and then thesefractional damages are multiplied by global RFF GDP (interpolated between years) to recover level damages.
As a result, total damages for RFF-SPs beyond the range of the SSPs are extrapolated linearly. That is,given the SSP-specific damage functions, djy (∆GM ST ), providing dollar damages in year y as a function ofclimatic temperature, we calculated′ky (∆GMST) = RFFGlobalGDPky

X

α̃jky djy (∆GMST)/SSPGlobalGDPjyjwhere SSPGlobalGDPjy =i SSPGDPjiy and RFFGlobalGDPky =P

30

i RFFGDPkiy .

PCAppendix: Sector-specific Damages Details

Each sector has different data and method requirements for dose response estimation and projection, anddamage function estimation. A description of sector-specific details is included in the following sub-sections.

C.1

Mortality

Figure 6: Empirically derived mortality-only damage functions Both panels show damage functions relating
empirically derived total global mortality damages to anomalies in global mean surface temperature (∆GMST) under socioeconomic scenario SSP3. In panel A, each point (red = RCP8.5, blue = RCP4.5) indicates the value of the full mortality riskof climate change in a single year (ranging from 2095 to 2100) for a single simulation of a single climate model, accounting
for both costs and benefits of adaptation. The black line is the quadratic damage function estimated through these points.
The distribution of temperature anomalies at end of century (2095-2100) under two emissions scenarios across our 33 climate
models is in the bottom panel. In panel B, the end-of-century damage function is repeated. Damage functions are shown indark blue for every 10 years pre-2100, each of which is estimated analogously to the end-of-century damage function and isshown covering the support of ∆GMST values observed in the SMME climate models for the associated year. Our projection
results generate mortality damages only through 2100, due to limited availability of climate and socioeconomic projections foryears beyond that date. To capture impacts after 2100, we extrapolate observed changes in damages over the 21st century to
generate time-varying damage functions through 2300. The resulting damage functions are shown in light grey for every 50
years post-2100, each of which is extrapolated. The distribution of temperature anomalies around 2200 (2181-2200) under twoemissions scenarios using the FaIR simple climate model is in the bottom panel. To value lives lost or saved, in both panels we
use the age-varying U.S. EPA VSL and an income elasticity of one applied to all impact regions.

• We use comprehensive historical mortality records to quantify how death rates across the globe havebeen affected by observed climate changes. Specifically, we compile the largest sub-national vitalstatistics database in the world, detailing 399 million deaths across 41 countries accounting for 55
percent of the global population. These mortality records are combined with decades of detailed daily
and local temperature observations.
• We econometrically estimate the relationship that extreme cold and extreme heat have on death rates
31

in the historical data. We find that the mortality-temperature relationship is strongly modified by
the climate and income levels of the affected population, demonstrating that adaptation affects thevulnerability of a population to extreme temperatures.
• We use these empirical mortality-temperature relationships to generate projections of the future impacts of climate change on mortality rates for areas across the globe, dividing the world into 24,378
distinct regions (each containing roughly 300,000 people, about the size of a U.S. county) up to 2100.
These projections account for the benefits of adaptation by allowing mortality-temperature relationships to evolve into the future as climates warm and incomes rise.
• We develop a technique to estimate the cost of adaptive behaviors and technologies (4 ). When combined with projections of mortality effects of climate change including adaptation benefits, these projections capture the full mortality risk of climate change, for the first time accounting for both adaptation
benefits and costs, in addition to direct mortality impacts.
• Projected impacts of the full mortality risk of climate change are then monetized and used to determine
the costs of excess mortality risk in a given year. This monetization uses the U.S. EPA value of astatistical life (VSL), and applies an income elasticity of one to scale the VSL across the globe, using
country-level incomes.56• These projections include five scenarios for future income and population growth, two trajectories of
future greenhouse gas emissions, and simulations from 33 climate models, allowing for an assessmentof the uncertainty surrounding any particular projection. The full estimates also reflect statisticaluncertainty related to the underlying economic and health data.
• For a detailed description of the methods and results summarized above, please see Carleton et al(2022) (4 ).

C.2

Energy• We match globally representative, longitudinal data on energy consumption with 0.25◦ × 0.25◦ globally harmonized historical climate data. Energy consumption data are derived from International
Energy Agency (IEA) data files that describe electricity and direct fuel consumption across residential,commercial, industrial, and agricultural end-uses (excluding transportation) in 146 countries during
1971-2010.
56 The U.S. EPA’s VSL for 2020 amounts to $9,926,524.56 (2019$).

Starting from the original published VSL (1990 VSL =$4.8mil (1990$)) and following the methodology described in the 2010 EPA Guidelines, the 1990 VSL was adjusted for inflation
to the dollar year of DSCIM (2019$) using the annual GDP deflator and adjusted over time to a 2020 EPA VSL using an IEVSL= 0.4 and income data (real GDP per capita) from the most recent annual FRED series.

32

Figure 7: Empirically derived global energy damage functions Total global electricity consumption impacts(left) and other fuels consumption impacts (right) at end-of-century, indexed against ∆GMST realized in each climate model
simulation (blue dots=RCP 4.5; red dots=RCP 8.5). Black lines represent end-of-century quadratic damage functions, which
are estimated through the points shown. Shaded areas indicate the range between 5th and 95th percentiles. Probability density
functions display the distribution of ∆GMST at end-of-century in each emissions scenario.

• We econometrically estimate the effect of historical temperature distributions on national annual
per capita energy consumption using random year-to-year variation, and measure how this energytemperature response differs across energy types (electricity and other fuels), income levels, and climate
zones.
• We project impacts of climate change in 24,378 globally comprehensive geographic regions (roughly thesize of US counties) through 2099 by combining the econometric results with a probabilistic ensemble ofdownscaled climate projections based on CMIP5 models. When projecting these impacts, we accountfor how the energy-temperature response will evolve as populations become richer and exposed towarmer climates.
• We monetize and pool the empirically derived impact projections, aggregating damages across locations
and indexing them against the global mean surface temperature anomaly (∆GMST) expressed in eachclimate model realization. Damages are projected under five scenarios for future income and population
growth, two trajectories of future greenhouse gas emissions, and simulations from 33 climate models,
allowing for an assessment of the uncertainty surrounding any particular projection. The full estimatesalso reflect statistical uncertainty related to the underlying energy-temperature response estimates.
• To monetize the projected impacts of climate change on energy consumption, we apply country-specificreal prices for electricity and other fuels to the projected quantity impacts, thus reflecting differential
costs across geographies and fuels.

33• Impacts on electricity consumption are valued using an average cost of electricity generation, which the
IEA’s World Energy Outlook 2017 provides globally as of 2016 at the country or world region level.57
Impacts on other fuels consumption are valued using residential and non-residential end-user prices
excluding taxes, which the IEA’s Energy Prices and Taxes dataset provides for coal, oil, and natural
gas fuels in 55 countries as of 2012.58 Countries that lack price data for a given fuel are assigned theglobal average price for that fuel. To obtain a price for the pooled, multi-fuel “other fuels” category,
we weight the prices of the individual fuels according to their shares in a country’s overall “other fuels”
consumption as of 2012 (the most recent year for which consumption data are available).59 Thus, eachcountry receives unique prices at which its impacts on other fuels consumption are valued.60• To extrapolate prices into the future, we use US average annual price growth rates for electricity andother fuels between 2020 and 2050, as projected by the US Energy Information Administration’s Annual
Energy Outlook 2021 (AEO). We apply these growth rates up to the year 2099 to the baseline country
× fuel prices described above. Specifically, based on AEO projections, we allow electricity prices todecline by 0.27% per year and other fuels prices to rise by 0.82% per year.61• For a detailed description of the methods and results summarized above, please see Rode et al. (2021)(3 ), which is published at Nature.

C.3

Labor Supply• We harmonize daily and weekly worker-level labor supply data from time use and labor force surveys
in seven countries (USA, Mexico, Brazil, France, UK, Spain, and India) and combine these data withdetailed daily and local temperature observations.
• We systematically evaluate the nonlinear response of labor supply to daily temperature separately forworkers in “high-risk" sectors (i.e., weather-exposed sectors– agriculture, mining, construction, andmanufacturing) and low-risk sectors (all others).
57 Costs are specified for the following geographies: Japan, European Union, Korea, Brazil, Australia, Mexico, Southeast
Asia, Middle East, India, Africa, United States, China, Canada, Russia. When a cost is not available specific to a particulargeography we extend these costs based on UN world region classifications: Oceania receives the Australia cost, N., S., andW. Europe receive the EU cost, E. Europe receives the Russia cost, Central America/Caribbean receive the Mexico cost, S.
America receives the Brazil cost, N. Africa receives the Middle East cost, and S. Asia receives the India cost.
58 We take a weighted average of residential and non-residential prices, with a weight of 16% on residential and 84% on
non-residential. These weights are determined based on the average share of consumption in these two sectors in the set of 55
countries where a sectoral breakdown is available.
59 Although our consumption data do cover fuels besides coal, oil, and natural gas (including solar, geothermal, and biofuels),no price data are available for fuels other than coal, oil, and natural gas. We therefore extend the weighted average price to theremaining fuels.
60 Even countries that are assigned the global average price for the individual fuels will differ in the shares of each fuel in theirother fuels consumption mix.
61 The electricity price growth rate is of a consumption-weighted average electricity price across residential, commercial, andindustrial sectors. The other fuels price growth rate is of a consumption-weighted average price across multiple fuels (i.e.,natural gas, metallurgical coal, other coal, coal to liquids, distillate fuel oil, residual fuel oil, propane, and motor gasoline) inresidential, commercial, and industrial sectors.

34• We apply these labor supply-temperature responses over novel probabilistic climate change projections
to project the effects of climate change on the labor supply of high- and low-risk workers in each of
24,378 impact regions. Importantly, in characterizing total impacts in each region, we take into accountthe changing composition of its workforce over time across high- and low-risk workers. Future shares ofhigh- and low-risk workers are predicted based on present-day empirical estimates of how these sharesvary as a function of income and average climate, together with high-resolution data on future incomes
and climates.
• Interpreting labor supply impacts of climate change through a simple theoretical framework, we monetize the implied disutility to workers of a warmer climate, following the method in Rode et al. (2021)(22 ). This monetized value is calculated in terms of the compensating wage increase needed to offset
the implied disutility due to warming, which is revealed by the labor supply response of workers towarmer temperatures.
• We aggregate projected monetized disutility costs (i.e., damages) across all workers in all locations
globally to obtain total global damages. Damages are projected under five scenarios for future income
and population growth, two trajectories of future greenhouse gas emissions, and simulations from 33
climate models, allowing for an assessment of the uncertainty surrounding any particular projection.
The full estimates also reflect statistical uncertainty related to the underlying dose-response function
estimates.
• For a detailed description of the methods and results summarized above, please see Rode et al (2022)(22 ).

C.4

Agricultural Productivity• We assemble one of the largest datasets of subnational crop production available to researchers, constructing a dataset for six crops in 12,658 locations (41,186 location-crop pairs) in 55 countries spanning
up to 137 years. We cover the staple crops maize, soybean, rice, wheat, cassava, and sorghum; representing two thirds of global crop caloric production. These data are drawn from individual country
sources for each country and span varying numbers of years. Full details can be found in (23 ).
• A key issue is determining what weather variables are important for future crop yields, as a wide rangeof candidate weather measures has been proposed in the literature, but debate has continued overwhich are of first-order importance. These include, for example, vapor pressure deficit, degree days,
minimum temperature, drought, the count of rainy days, and the total amount of extreme rainfall.
We adapt cross-validation, a machine learning technique, to a causal inference context, enabling us tosystematically assess a rich set of channels through which weather shocks might causally affect yields
35

Figure 8: Empirically derived labor supply damage function The damage function above relates empiricallyderived total global labor disutility damages to anomalies in global mean surface temperature (∆GMST) at end-of-century.
Each point (red = RCP8.5, blue = RCP4.5) indicates the global labor disutility costs of climate change in a single year
(ranging from 2095 to 2099) for a single simulation of a single climate model, accounting for changes to workforce composition
as incomes grow and the climate warms. The black line represents the end-of-century quadratic damage function, which isestimated through the points shown. Shaded areas indicate the range between 5th and 95th percentiles. Probability density
functions display the distribution of ∆GMST at end-of-century in each emissions scenario.

in each of our crops, while also systematically assessing the extent to which farmers might use accessto income, irrigation, or expectations of long-run climate to mitigate these yield losses.
• We account for adaptation (30 ) by observing the real world trade-offs that farmers make as they
are confronted with different climates under different socioeconomic conditions, and estimate implied
adaptation costs (4 ) assuming a profit-maximizing farmer.
• Following (4 ), we use our empirically based, adaptation-adjusted yield-temperature and yield-precipitation
relationships to generate projections of the future impacts of climate change on crop yields for areas
across the globe, dividing the world into 24,378 distinct regions (each containing roughly 300,000
people, about the size of a U.S. county) up to the end of the 21st century.
• We develop an approach to value changes in future global yields under climate change. To do this,
we assign country-specific average prices to total calorie production, allowing producers to intensify or
reduce production in order to meet demand in their market at increasing marginal cost. In general,aggregate yield losses under climate change harm consumers as caloric consumption is lower and prices
are higher; producers are also harmed by production losses, but this harm may be offset by profit gains
36

Figure 9: Empirically derived agriculture damage function Empirical damage function describing calories (kCal)
lost as a quadratic function of the global mean surface temperature anomaly (∆ GMST). Each point represents a single climatemodel-by-Monte-Carlo run for RCP 4.5 (blue) or RCP 8.5 (red) in 2093-2097. Grey bands indicate quantiles 10-90 and 25-75,
conditional on ∆ GMST. The bottom panel shows the distribution of warming under each RCP, boxplots to the right show
the distribution of end-of-century damages by RCP. The right axis describes end-of-century caloric losses normalized by 2015
global caloric production of the six crops studied here (maize, soybean, rice, wheat, cassava, and sorghum).

from higher prices. All estimates and projections of yield impacts account for within-crop varietal
switching. In monetizing these yield impacts, we additionally account for costly changes in the total
amount of agricultural land (along a long-run supply curve) in response to price signals caused by net
changes to the overall supply of staple crops under climate change. This movement along the long-run
supply curve is in response to the price signal from slow moving (and therefore expected) changes insupply in-line with climate change; annual weather deviations from the long-run trend are unexpected
and occur after planting decisions have been made, so farmers cannot respond to this component of
the price signal. We apply a simple shrinkage estimator to the variance in prices to account for the
price and quantity effects of storage. Consumers demand calories with perfect substitution between
crops, with the distribution of cropped locations and crop types held fixed. Trade is assumed to be
frictionless within set markets, and zero between these markets.
• As a last step in the valuation process, the results are multiplied by 0.45 to account for crop switching
and global trade protective effects, based on an average of the estimates in prior research documenting
these quantities (e.g., (31 ); (32 ); (33 ); (34 )). The DSCIM-EPA results also account for the benefits of
CO2 fertilization on crop yields based on established estimates in the literature (Moore et al., 2017).
37

Based on the various scenarios presented in (23 ), CO2 fertilization is generally found to affect crop
yields by 5.0-9.5% and monetized damages by less than 5%.
• For a detailed description of the methods and results summarized above, please see (23 ).

C.5

CoastalOur sea level rise impact projections rely on outputs from pyCIAM, a model derived from the Coastal
Impact and Adaptation Model (CIAM, (35 )) and modified with updated input data, improved processrepresentation, and increased scalability (27 ). The functionality of that model is described in (27 ), thoughinputs used in that manuscript differ slightly from those used in this version of DSCIM, due to the needto ensure consistency in input data across sectors. Below, we briefly describe the data inputs used in thisversion:• We create a global model of coastal dryland and wetland area, historical population, and physicalcapital by coastline segment and 10cm elevation bins. We do so by aggregating best-available highresolution global datasets describing capital distribution (LitPop v1.2, (36 )), population (Landscan
2011, (37 )), coastal elevation (CoastalDEM v1.1, (38 )), and wetland/mangrove extent (GLOBCOVER
v2.3, (39 ); Global Mangrove Watch 2016 (40 )).
• We augment these data with estimates of Local Mean Sea Level (LMSL, AVISO+ Mean Dynamic
Topography, (41 )) and extreme sea level (ESL) distributions (CoDEC, (42 )). These data combined
allow for the representation of historical flood exposure.
• To model future exposure consistent with the SSPs, we estimate local SLR using the LocalizeSL model(v3.2, (43 , 44 ))62 and population and income using the downscaled SSP income dataset following
the common approach used across sectors (e.g. (4 , 3 )). Furthermore, we leverage the capital growthmodel contained within the OECD Env-Growth model (24 ) to project country-level trends in physicalcapital. These country-level trends are used to scale segment-by-elevation capital stock data over time.
• Current and projected future representations of exposure (land area, physical capital, and population)and hazard (SLR and extreme sea levels) are aggregated into a single dataset - the Sea Level Impacts
Input Dataset by Elevation, Region, and Scenarios (SLIIDERS) dataset (27 ).63 Dryland and wetland
values are estimated from country-level income and local population density (for dryland value), basedon the corresponding equations in (27 ) (derived from (35 )). The fraction of each country’s capital stockthat is mobile is estimated from capital class categorizations in Penn World Table 10.0 (45 ). Countrylevel construction costs are taken from the 2021 World Bank International Comparison Project (ICP)
62 Available at: https://github.com/bobkopp/LocalizeSL
63 The dataset used in the DSCIM model described here differs slightly from the dataset released with this manuscript, toensure consistency in socioeconomic data across all DSCIM sectors

38

(46 ) and augmented with data from (47 ) for countries not appearing in the ICP. A country-level
resilience factor used in CIAM is updated based on downscaled income. Country-level VSL values areused internally within the optimal adaptation model to value extreme sea level-driven mortality when
determining an optimal adaptation pathway. Country-level VSL is also used when valuing mortality
impacts along that optimal pathway.
• pyCIAM estimates protection and retreat strategies, as well as incurred costs, for a set of mutually
exclusive global coastal segments. We use a set of 9,096 segments (19,714 unique intersections ofcoastal segment and impact region) based on the locations at which extreme sea levels are estimatedin the CoDEC input dataset. Costs are divided into those arising from managed and unmanaged
retreat, dryland lost to inundation, wetland lost to inundation and protective barriers, construction
and maintenance of those protections, and life and property lost due to storm surge. The specificationof internal functions used to estimate these various cost types and to determine optimal adaptation
strategy by segment are described in detail in (27 ).
• We run pyCIAM on the three SSP scenarios (using the two growth models to project capital stock andincome)64 ; 11 SLR scenarios based on the CMIP5 RCP emissions pathways65 and various assumptionsof sea level dynamics; and 10,000 Monte Carlo samples to represent the distribution of possible local
mean sea level (LMSL) values conditional on each of those scenarios. This results in 660 thousand
unique simulations of coastal costs and adaptation under different future trajectories of hazard andexposure (110,000 SLR trajectories and 6 socioeconomic trajectories). We additionally run the modelfor 10,000 Monte Carlo samples representing background LMSL change in the absence of climatechange. This background change includes factors such as Glacial Isostatic Adjustment (GIA), tectonics,
and subsidence. The difference in projected average annual loss (AAL) between each “with climatechange” sample and the corresponding “without climate change” sample defines the sea level riseattributable damages for that Monte Carlo draw.
• We then group the 110,000 SLR realizations based on their GMSL value in each year to create aconditional distribution of possible coastal damages as a function of year and GMSL, for each choice ofsocioeconomic trajectory. These estimates are then used to construct coastal damage functions relating
damages to GMSL, identically to how damage functions relating damages to GMST are constructed
for other sectors.
• To model damages from a GHG pulse, we need to model the impact of that pulse on GMSL. To dothis, the NASEM “Valuing Climate Damages” report suggested the use of semi-empirical models to
64 (27 ) incorporate 5 SSP scenarios, however due to cross-sectoral consistency, only 3 are used here.
65 CMIP5 RCP emissions pathways are used in the LocalizeSL modeling,

however the remainder of the DSCIM coastal
sector relies on CMIP6 RCP emissions pathways. Unless otherwise specified, "RCP" in this section refers to CMIP6 emissions
scenarios. This is meant to avoid confusion between the naming of the SSP socioeconomic pathways and the "SSP-RCP" (e.g.
"SSP3-7.0") nomenclature used in CMIP6 representative concentration pathway emissions pathways.

39

define the statistical relationship between GMST and GMSL (1 ). We use the model shown as an
example in that report, appropriately titled the Semi-Empirical Sea Level (SESL) model (48 ), whichis calibrated to geological evidence about the relationship between GMST and GMSL over the last two
thousand years. We run this model on the FaIR GMST outputs both with and without a GHG pulse to
generate an equivalent number of GMSL realizations. We subtract the runs without the pulse from theruns with the pulse to determine the impact of the pulse on GMSL. We then add this distribution of
GMSL pulse differences to baseline probabilistic projections of GMSL, modeled using the Framework
for Assessing Changes to Sea-level (FACTS) as described in the IPCC’s 2021 Sixth Assessment Report(AR6) (21 , 49 ). We align this distribution of baseline GMSL with that of the GHG pulse-induced
marginal SLR by quantile mapping the SESL outputs under the control experiment (i.e. no pulse)with the FACTS-based distribution in each year and for each RCP. For example, the pulse associatedwith the 60th percentile of GMSL in 2050 for RCP7.066 under the control scenario is added to the 60th
percentile of RCP7.0 projections in 2050 from FACTS. This quantile mapping is defined mathematicallyas:h̃base,r,y,p = ppf (hFACTS,r,y , cdf (hSESLbase ,r,y , hSESLbase ,r,y,p ))(20)h̃pulse,r,y,p = h̃base,r,y,p + hSESLpulse ,r,y,p − hSESLbase ,r,y,p

(21)

where
– h̃base/pulse,r,y,p are the bias-corrected GMSL projections for scenarios without (base) and with(pulse) a GHG pulse, for RCP scenario r, in year y, using draw p from the distributions of climate
and sea level parameters characterizing FaIR and SESL, respectively;– hSESLbase ,r,y,p is the SESL-based GMSL projection for the same indices of RCP, year, and parameter draw;
hm,r,y,1 hm,r,y,2  is the full distribution of GMSL projections for model m (either FACTS or– hm,r,y =  ... 
hm,r,y,np
SESLbase ) across all np climate and sea level parameter draws;– cdf(d, v) is the empirical cumulative distribution function for distribution d and value v; and– ppf(d, q) is the percent point function (inverse cdf) for distribution d and quantile q
The bias correction of SESL-based GMSL values to the distribution seen in the IPCC AR6 ensuresconsistency with the best available assessment of projected future sea-level rise. Notably, however,66 We use "RCP7.0" to replace the CMIP6 nomenclature of "SSP3-7.0" to avoid confusion with the SSP socioeconomic
pathways.

40

our use of the FACTS AR6 projections differs from the presentation in AR6 in a few respects thatmakes overall GMSL projections more conservative (both slightly lower and narrower). First, AR6presents ‘p-boxes’ that characterize imprecise probabilities, produced by combining multiple probability
distributions of sea-level change for the same forcing. Such p-boxes take the outer limits of all the
probability distributions considered. For example, the likely range in AR6 has at least a 66% probabilityof containing the true value. Such imprecise probabilities are ill-suited for benefit-cost analyses, andso instead we combine the contributing probability distributions using equal weights. This will leadthe resulting distributions to be narrower than those presented in AR6. Second, while AR6 focusesupon medium confidence projections that consider only those ice-sheet processes for which there is atleast a medium level of evidence with medium agreement, they also consider, in parallel, projections
that incorporate processes in which there is low confidence (limited evidence and/or low agreement)
regarding magnitude, rate, timing and thresholds (see AR6 WG1, Box 9.4). Our projections are
based upon the AR6 medium confidence projections exclusively, and so do not consider, for example,
potential contributions from Marine Ice Cliff Instability. Finally, like our projections, AR6’s medium
confidence projections from 2100 to 2150 are based upon the assumption on no additional ice-sheet
acceleration after 2100; however, beyond 2150, there is low confidence in all projections, and so AR6
combines multiple additional methods to project GMSL rise in 2300. Our projections through 2300
are consistent with the approach adopted by AR6 through 2150 and do not incorporate these alternate
methods, leading our 2300 projections under SSP5-8.5 to be lower and narrower than those emphasizedin AR6 (e.g. in AR6 WG1, Fig. SPM.8).
• The FACTS-based baseline GMSL trajectories are only available for the CMIP6 RCP emissions trajectories. To approximate baselines for each of the 10,000 RFF-SP GMSL scenarios (10,000 joint drawsof RFF-SP and FaIR/SESL climate parameters), we employ a 3-step process:1. For each year, y, of the projection, we search for the two “bounding” RCP scenarios, rlow and rhi .
To do this, we first identify the FAIR/SESL parameter draw, ps , associated with this RFF-SP
draw, s, and find the corresponding SESL-based GMSL projection, hSESLbase ,RFF-SPs ,y,ps . We
then find the SESL-based GMSL projections associated with each of the RCPs for the given year
and parameter draw, hSESLbase ,r,y,ps . We find the two RCPs, rlow and rhi , with GMSLs that mostclosely bound that of the RFF-SP draw, conditional on the selection of ps . We assign weights tothese RCPs according to their relative distance from the RFF-SP. For example, suppose RFFSP10 has a projected GMSL of 0.78 m in 2100. Using FAIR/SESL parameter set p10 , if RCP4.5
and RCP7.0 are the closest bounding scenarios with projections of 0.7m and 0.8m in 2100, we
would assign wRCP 4.5 = 0.2 and wRCP 7.0 = 0.8 for RFF-SP10 in 2100. See Figure 10a.67
67 When the SESL-based GMSL for a given y and p falls outside of the bounds of GMSL across all RCP, we simply assign
unit weight to the nearest RCP.

41

2. For both rlow and rhi we perform a quantile mapping from the SESL-based and FACTS-based
GMSL distributions using Equation 20, with a lookup value defined by the SESL-based GMSL
projection for the given RFF-SP scenario in the given year and with the given parameter draw
(0.78 m in the previous example). This yields us two bias-corrected representations of this value,each derived from one of the bounding RCPs. See Figure 10b.
3. We take the weighted average of the two bias-corrected GMSL estimates, using the weights from
Step 1. See Figure 10c.

Figure 10: Schematic of GMSL Projection Approach This figure illustrates the process to bias-correct SemiEmpirical Sea Level projections for the RFF-SPs. In (a), we determine the bounding SSP-RCPs for a given RFF-SP, year, and
FAIR/SESL parameter draw. The figure uses 10 as an example RFF-SP draw index, matched to a FAIR/SESL parameter draw
with the same index. Only three SSP-RCPs are shown for illustration. In (b), we quantile map from the SESL-based GMSL
projections to the FACTS-based GMSL projections for each of the two bounding SSP-RCPs. In (c), we take the weighted
average of the two quantile mapped projections.

42

Figure 11: Model-derived coastal damage function Damage function describing aggregate economic losses due
to sea level rise as a function of the global mean sea level anomaly (∆ GMSL). Each point represents the average annual
loss calculated from a single realization of local mean sea levels (LMSLs) consistent with the associated GMSL value in the2085-2105 period (centered on 2095). These GMSL values are relative to the GMSL in 1990-2010. The black line indicates thedamage function defined by Ordinary Least Squares (OLS) regression, while grey bands display quantile regression results forquantiles 1-99, 10-90, and 25-75. Damages arise from land value loss, storm surge-driven mortality and capital stock losses,
domestic migration, construction of local protections, and wetland loss. They are estimated separately for 9,500 unique coastal
segments using the CIAM model. The bottom panel shows the distribution under SSP2-4.5, SSP5-8.5, and a low-likelihood,high-consequence scenario involving plausible ice sheet dynamics that lead to high GMSL values when coupled with the SSP58.5 emissions trajectory.

43

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