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HomeScienceVol. 356, No. 6345Estimating economic damage from climate change in
the United States
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ESTIMATING ECONOMIC DAMAGE FROM CLIMATE CHANGE IN THE UNITED STATES
Solomon Hsiang https://orcid.org/0000-0002-2074-0829 shsiang@berkeley.edu,
Robert Kopp https://orcid.org/0000-0003-4016-9428 shsiang@berkeley.edu, [...] ,
Amir Jina https://orcid.org/0000-0003-3446-7883, James Rising
https://orcid.org/0000-0001-8514-4748, [...] , Michael Delgado
https://orcid.org/0000-0002-2414-045X, Shashank Mohan
https://orcid.org/0000-0002-8216-5161, D. J. Rasmussen
https://orcid.org/0000-0003-4668-5749, Robert Muir-Wood
https://orcid.org/0000-0002-3706-1478, Paul Wilson
https://orcid.org/0000-0002-1565-0108, [...] , Michael Oppenheimer
https://orcid.org/0000-0002-9708-5914, Kate Larsen
https://orcid.org/0000-0002-1833-9008, and Trevor Houser
https://orcid.org/0000-0002-0514-7058+9 authors +7 authors +2 authors
fewerAuthors Info & Affiliations
Science
30 Jun 2017
Vol 356, Issue 6345
pp. 1362-1369
DOI: 10.1126/science.aal4369
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* Contents
* Costing out the effects of climate change
* Abstract
* System architecture
* Distribution of costs and benefits
* Nationally aggregated sectoral impacts
* Uncertainty
* Nationally aggregated total damage
* Risk and inequality of total local damages
* Discussion
* Acknowledgments
* Supplementary Material
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COSTING OUT THE EFFECTS OF CLIMATE CHANGE
Episodes of severe weather in the United States, such as the present abundance
of rainfall in California, are brandished as tangible evidence of the future
costs of current climate trends. Hsiang et al. collected national data
documenting the responses in six economic sectors to short-term weather
fluctuations. These data were integrated with probabilistic distributions from a
set of global climate models and used to estimate future costs during the
remainder of this century across a range of scenarios (see the Perspective by
Pizer). In terms of overall effects on gross domestic product, the authors
predict negative impacts in the southern United States and positive impacts in
some parts of the Pacific Northwest and New England.
Science, this issue p. 1362; see also p. 1330
ABSTRACT
Estimates of climate change damage are central to the design of climate
policies. Here, we develop a flexible architecture for computing damages that
integrates climate science, econometric analyses, and process models. We use
this approach to construct spatially explicit, probabilistic, and empirically
derived estimates of economic damage in the United States from climate change.
The combined value of market and nonmarket damage across analyzed
sectors—agriculture, crime, coastal storms, energy, human mortality, and
labor—increases quadratically in global mean temperature, costing roughly 1.2%
of gross domestic product per +1°C on average. Importantly, risk is distributed
unequally across locations, generating a large transfer of value northward and
westward that increases economic inequality. By the late 21st century, the
poorest third of counties are projected to experience damages between 2 and 20%
of county income (90% chance) under business-as-usual emissions (Representative
Concentration Pathway 8.5).
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Economically rational management of the global climate requires that the costs
of reducing greenhouse gas emissions be weighed against the benefits of doing so
(or, conversely, the costs of not doing so). A vast literature has considered
this problem, developing, among other insights, our understanding of the optimal
timing of investments (1), the role of uncertainty (2), the importance of future
adaptation (3), the role of trade (4), and the potentially large impact of
unanticipated tipping points (5, 6). Integrated assessment models that value the
benefits of greenhouse gas abatement are used by governments to estimate the
social cost of climate change (7, 8), which in turn informs the design of
greenhouse gas policies. However, the estimated benefits of greenhouse gas
abatement—or conversely, the “damages” from climate change—are conceptually and
computationally challenging to construct. Because of this difficulty, previous
analyses have relied on rough estimates, theorized effects, or limited process
modeling at continental scales or larger (9–11), with no systematic calibration
to observed human-climate linkages (12). Since the original development of these
models, methodological innovations (13) coupled with data availability and
computing power have fueled rapid growth in a spatially resolved, empirical
understanding of these relationships (14). Yet integrated assessments of climate
change and their calculation of the social cost of carbon do not reflect these
advances (15–17).
Here, we develop an integrated architecture to compute potential economic
damages from climate change based on empirical evidence, which we apply to the
United States. Our risk-based approach is grounded in empirical longitudinal
analyses of nonlinear, sector-specific impacts, supplemented with detailed
energy system, inundation, and cyclone models. Built upon a calibrated
distribution of downscaled climate models, this approach is probabilistic and
highly resolved across geographic space while taking into account the spatial
and sectoral covariance of impacts in each possible future. Our framework is
designed to continuously integrate new empirical findings and new climate model
projections as the supporting subfields of research advance in the future. When
applied to the U.S. economy, this approach provides a probabilistic and
empirically derived “damage function,” linking global mean surface temperature
(GMST) to market and nonmarket costs in the United States, built up from
empirical analyses using micro-level data.
SYSTEM ARCHITECTURE
We developed the Spatial Empirical Adaptive Global-to-Local Assessment System
(SEAGLAS) to dynamically integrate and synthesize research outputs across
multiple fields in near-real time. We use SEAGLAS to construct probabilistic,
county-level impact estimates that are benchmarked to GMST changes. [See section
A of the supplementary materials (SM) for additional details (18).]
County-level projections of daily temperature and precipitation are constructed
and sampled following a three-step process that simultaneously captures the
probability distribution of climate responses to forcing, spatiotemporal
structures within each climate realization, and spatiotemporal autocorrelation
of weather (19): (i) For each forcing pathway considered [Representative
Concentration Pathways (RCPs) 2.6, 4.5, and 8.5] (20), a probability
distribution for GMST change is constructed based on an estimated distribution
of equilibrium climate sensitivity, historical observations, and a simple
climate model (SCM) (19). (ii) The joint spatiotemporal distribution of monthly
temperature and precipitation is constructed from a broad range of global
climate models (GCMs), statistically downscaled from the Coupled Model
Intercomparison Project 5 (CMIP5) archive (21) and assigned a probability of
realization such that the distribution of 21st-century GMST change mirrors the
distribution from the SCM. Tails of the distribution beyond the range present in
the CMIP5 archive are represented by “model surrogates” constructed by scaling
patterns from CMIP5 models using the GMST projections from the SCM. Together, we
refer to the union of monthly resolution GCM and model surrogate output as the
set of climate realizations that are each weighted to reflect a single
probability distribution (Fig. 1A). These weights are used when we compute
damage probability distributions for specific RCP scenarios. (iii) We then
construct a set of 10 daily projections for each climate realization by
superimposing daily weather residuals relative to monthly climatologies that are
resampled in yearly blocks from the period 1981 to 2010 (Fig. 1B).
Fig. 1 Recombining previous research results as composite inputs to SEAGLAS.
(A) Forty-four climate models (outlined maps) and model surrogates (dimmed maps)
are weighted so that the distribution of the 2080 to 2099 GMST anomaly exhibited
by weighted models matches the probability distribution of estimated GMST
responses (blue-gray line) under RCP8.5. Analogous display for precipitation in
fig. S1. (B) Example of 10 months of daily residuals in New York City, block
resampled from historical observations at the same location and superimposed on
monthly mean projections for a single model (GFDL-CM3) and scenario (RCP8.5)
drawn from (A). (C to H) Examples of composite (posterior) county- level
dose-response functions derived from nonlinear Bayesian meta-analysis of
empirical studies based on selection criteria in (30). Median estimate is black,
central 95% credible interval is blue-gray. To construct probabilistic impact
projections, responses for each category are independently resampled from each
distribution of possible response functions and combined with resampled climate
realizations, as in (A), and weather realizations, as in (B). [(C) and (D)]
Estimated causal effect of (C) 24 hours temperature and (D) seasonal rainfall on
maize yields. (E) Daily average temperature on all-cause mortality for the 45-
to 64-year-old population. (F) Daily maximum temperature on daily labor supply
in high-risk industries exposed to outdoor temperatures. [(G) and (H)] Daily
maximum temperature on (G) monthly violent crime rates and (H) annual
residential electricity demand. All sources are detailed in SM section B.
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A distribution of empirically grounded economic impacts is computed for each
joint realization of county-level daily temperature and precipitation: (iv)
Econometrically derived dose-response functions (13) estimating the nonlinear
effects of temperature, rainfall, and CO2 on agriculture (22, 23), mortality
(24, 25), crime (26, 27), labor (28), and energy demand (24) are constructed via
Bayesian meta-analysis (29) (e.g., Fig. 1, C to H, and SM sections B and C).
Following the approach and criteria laid out in (30), we only employ studies
that are nationally representative, spatially disaggregated, and account for
temporal displacement and unobserved heterogeneity across locations, along with
the additional criterion that studies statistically identify marginal
distortions in the distribution of experienced daily temperatures (13, 14). (v)
Econometric uncertainty is accounted for by resampling from the 26 posterior
functions in (iv) (fig. S4). (vi) County-level daily projections from (iii) are
mapped onto the distribution of possible responses from (v) to construct 3143
county-level joint distributions for 15 impacts across 29,000 possible states of
the world during 2000 to 2099 (SM sections D and E), although for display
purposes we primarily summarize 2080 to 2099 impacts here.
A parallel approach is necessary to estimate energy demand changes and coastal
impacts: (vii) Energy demand estimated in (iv) is used as a partial calibration
for the National Energy Modeling System (NEMS) (31) (SM section G). NEMS is then
run with different weather realizations to estimate energy supply costs. (viii)
Cyclone exposure is simulated via analytical wind field models (32) that force a
storm surge model (33), with cyclogenesis and storm tracks generated via either
(i) semiparametrically resampling historical activity (34) or (ii) resampling
from projected storm tracks and intensities (35) (SM section H). (ix) Inundation
from localized probabilistic sea level rise projections (36) interacting with
storm surge and wind exposure in (viii) are mapped onto a database of all
coastal properties maintained by Risk Management Solutions, where engineering
models predict damage (SM section H).
Finally, economic impacts are aggregated and indexed against the GMST in their
corresponding climate realization to construct multidimensional probabilistic
damage functions suitable for application in integrated assessment modeling: (x)
Direct impacts from (vi), (vii), and (ix) are aggregated across space or time
within each sector. Monetizing the value of nonmarket impacts (deaths and crime)
using willingness-to-pay or accounting estimates (37, 38), impacts across all
sectors are aggregated to compute total damages (SM sections I and J).
Importantly, for clarity, our approach holds the scale and spatial distribution
of the U.S. population and economy fixed at values observed in 2012, since
current values are well understood and widely agreed on. Various previous
analyses [e.g., (39)] note that natural demographic change and economic growth
may dominate climate change effects in overall magnitude, although such
comparisons are not our focus here. Because we compute impacts using scale-free
intensive measures (e.g., percentage changes), future expansion of the economy
or population does not affect our county-level estimates, and our aggregate
results will be unbiased as long as this expansion is balanced across space. If
such expansion is not balanced across space, then our aggregated results will
require a second-order adjustment with a sign that depends on the spatial
covariance of changes in climate exposure and changes in economic or population
structure, as shown in (40). In previous work (41), we demonstrated how results
for some direct impacts might change if future rates of adaptation to climate
mirror historical patterns and rates. The paucity of existing quantitative
studies on adaptation prevents us from currently applying this approach to all
sectors, although such additions are expected in future work.
DISTRIBUTION OF COSTS AND BENEFITS
Standard approaches to valuing climate damage describe average impacts for large
regions (e.g., North America) or the entire globe as a whole. Yet examining
county-level impacts reveals major redistributive impacts of climate change on
some sectors that are not captured by regional or global averages. Figure 2 and
fig. S2 display the median average impact during the period 2080 to 2099 due to
climate changes in RCP8.5, a trajectory consistent with fossil-fuel–intensive
economic growth, for each county. In cases where responses to temperature are
nonlinear (e.g., Fig. 1, C, E, and H), the current climate of counties affects
whether additional warming generates benefits, has limited effect, or imposes
costs. For example, warming reduces mortality in cold northern counties and
elevates it in hot southern counties (Fig. 2B). Sectors with roughly linear
responses, such as violent crime (Fig. 1G), have more uniform effects across
locations (Fig. 2H). Atlantic coast counties suffer the largest losses from
cyclone intensification and mean sea level (MSL) rise (Fig. 2F and fig. S10). In
general (except for crime and some coastal damages), Southern and Midwestern
populations suffer the largest losses, while Northern and Western populations
have smaller or even negative damages, the latter amounting to net gains from
projected climate changes.
Fig. 2 Spatial distributions of projected damages.
County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are
changes relative to counterfactual “no additional climate change” trajectories.
Color indicates magnitude of impact in median projection; outline color
indicates level of agreement across projections (thin white outline, inner 66%
of projections disagree in sign; no outline, ≥83% of projections agree in sign;
black outline, ≥95% agree in sign; thick white outline, state borders; maps
without outlines shown in fig. S2). Negative damages indicate economic gains.
(A) Percent change in yields, area-weighted average for maize, wheat, soybeans,
and cotton. (B) Change in all-cause mortality rates, across all age groups. (C)
Change in electricity demand. (D) Change in labor supply of full-time-equivalent
workers for low-risk jobs where workers are minimally exposed to outdoor
temperature. (E) Same as (D), except for high-risk jobs where workers are
heavily exposed to outdoor temperatures. (F) Change in damages from coastal
storms. (G) Change in property-crime rates. (H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].
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Combining impacts across sectors reveals that warming causes a net transfer of
value from Southern, Central, and Mid-Atlantic regions toward the Pacific
Northwest, the Great Lakes region, and New England (Fig. 2I). In some counties,
median losses exceed 20% of gross county product (GCP), while median gains
sometimes exceed 10% of GCP. Because losses are largest in regions that are
already poorer on average, climate change tends to increase preexisting
inequality in the United States. Nationally averaged effects, used in previous
assessments, do not capture this subnational restructuring of the U.S. economy.
NATIONALLY AGGREGATED SECTORAL IMPACTS
We recover sector-specific damages as a function of GMST change by nationally
aggregating county-level impacts within each state of the world defined by an
RCP scenario, climate realization, resampled weather, and econometrically
derived parameter estimate (SM sections D and E). The distribution of sectoral
impacts is compared with GMST change in each realization in Fig. 3 (SM section
J). Although several sectors exhibit micro-level responses that are highly
nonlinear with respect to county temperature (e.g., Fig. 1C), aggregated damages
exhibit less-extreme curvature with respect to GMST change, as was hypothesized
and derived in (42).
Fig. 3 Probabilistic national aggregate damage functions by sector.
Dot-whiskers indicate the distribution of direct damages in 2080 to 2099
(averaged) for multiple realizations of each combination of climate models and
scenario projection (dot, median; dark line, inner 66% credible interval; medium
line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from
RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis
according to GMST change realized in each model-scenario combination (blue axis
is change relative to preindustrial). Black lines are restricted cubic spline
regressions through median values, and gray shaded regions are bounded (above
and below) by restricted cubic spline regressions through the 5th and 95th
quantiles of each distribution, all of which are restricted to intercept the
origin. (A) Total agricultural impact accounting for temperature, rainfall, and
CO2 fertilization (CO2 concentration is uniform within each RCP, causing
discontinuities across scenarios). (B) Without CO2 effect. (C) All-cause
mortality for all ages. (D) Electricity demand used in process model, which does
not resample statistical uncertainty (SM section G). (E and F) Labor supply for
(E) low-risk and (F) high-risk worker groups. (G) Property-crime rates. (H)
Violent-crime rates.
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Average yields in agriculture decline with rising GMST, but higher CO2
concentrations offset much of the loss for the coolest climate realizations in
each of the three RCP scenarios. Accounting for estimated effects of CO2
fertilization (SM section B) and precipitation, warming still dominates,
reducing national yields ∼9.1 (±0.6 SEM) % per °C (Fig. 3A). Because effects of
CO2 are highly uncertain and not derived using the same criteria as other
effects, we evaluate the sensitivity of these projections by computing losses
without CO2 fertilization (Fig. 3B) and find that temperature and rainfall
changes alone would be expected to reduce yields ∼12.1 (±0.7) % per °C (see also
figs. S11 and S12 and tables S10 and S11).
Rising mortality in hot locations more than offsets reductions in cool regions,
so annual national mortality rates rise ∼5.4 (±0.5) deaths per 100,000 per °C
(Fig. 3C). For lower GMST changes, this is driven by mortality between ages 1
and 44 and by infant mortality and ages ≥45 for larger GMST increases (fig. S13
and table S12).
Electricity demand rises on net for all GMST changes, roughly 5.3 (± 0.14) % per
°C, because rising demand from hot days more than offsets falling demand on cool
days (Fig. 3D and table S13). Because total costs in the energy sector are
computed using NEMS, demand is not statistically resampled as other sectors are
(SM section G).
Total hours of labor supplied declines ∼0.11 (±0.004) % per °C in GMST for
low-risk workers, who are predominantly not exposed to outdoor temperatures, and
0.53 (±0.01) % per °C for high-risk workers who are exposed (∼23% of all
employed workers, in sectors such as construction, mining, agriculture, and
manufacturing) (Fig. 3, E and F, and table S14).
Property crime increases as the number of cold days—which suppress property
crime rates (fig. S4)—falls but then flattens for higher levels of warming
because hot days do not affect property crime rates. Violent crime rates
increase linearly at a relatively precise 0.88 (±0.04) % per °C in GMST (Fig. 3,
G and H, and table S15).
Coastal impacts are driven by the amplification of tropical cyclone and
extratropical cyclone storm tides by local MSL rise and by the alteration of the
frequency, distribution, and intensity of these cyclones (SM section H). Rising
MSL increases the storm tide height and floodplain during cyclones: Fig. 4, A to
D, illustrates how 1-in-100-year floodplains evolve over time due to MSL rise
(RCP8.5) with and without projected changes in cyclones for two major coastal
cities. Coastal impacts are distributed highly unequally, with acute impacts for
eastern coastal states with topographically low cities; MSL rise alone raises
expected direct annual economic damage 0.6 to 1.3% of state gross domestic
product (GDP) for South Carolina, Louisiana, and Florida in the median case, and
0.7 to 2.3% for the 95th percentile of MSL rise (Fig. 4E) (RCP8.5). Nationally,
MSL rise would increase annual expected storm damages roughly 0.0014% GDP per cm
if capital and storm frequency remain fixed (Fig. 4F). Accounting for the
projected alteration of the TC distribution roughly doubles the damage from MSL
rise, the two combined costing an estimated additional 0.5 (±0.2) % of GDP
annually in 2100 when aggregated nationally (Fig. 4G).
Fig. 4 Economic costs of sea level rise interacting with cyclones.
(A) Example 100-year floodplain in Miami, Florida, under median sea level rise
for RCP8.5, assuming no change in tropical cyclone activity. (B) Same, but
accounting for projected changes in tropical cyclone activity. (C) Same as (A),
but for New York, New York. (D) Same as (B), but for New York, New York. (E)
Annual average direct property damages from tropical cyclones and extratropical
cyclones in the five most-affected states, assuming that installed
infrastructure and cyclone activity is held fixed at current levels. Bars
indicate capital losses under current sea level, median, 95th-percentile and
99th-percentile sea level rise in RCP8.5 in 2100. (F) Nationally aggregated
additional annual damages above historical versus global mean sea level rise
holding storm frequency fixed. (G) Annual average direct property damages
nationally aggregated in RCP8.5, incorporating mean sea level rise and either
historical or projected tropical cyclone activity. Historical storm damage is
the dashed line.
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UNCERTAINTY
At the county level, conditional upon RCP, uncertainty in direct damages is
driven by climate uncertainty (both in GMST and in the expected spatiotemporal
distribution of changes conditional on GMST), by within-month weather exposure,
and by statistical assumptions and sampling used to derive dose-response
functions, as well as by uncertainty generated by the interaction of these
factors. Figure 2 displays county-level uncertainty in the impact on each sector
by indicating the level of agreement among 11,000 projections on the overall
sign of impacts in each county. Notably, process models (e.g., NEMS) and other
variables, such as baseline work hours or the VSL, contain uncertainty that
remains uncharacterized.
Aggregating results nationally, we decompose uncertainty into contributions from
climate, within-month weather, and dose-response relationships by resampling
each individually while holding the others fixed (43), recovering how these
variances combine to produce the total variance across projections (figs. S6 and
S7). In general, climate uncertainty dominates, contributing 41 to 104% of the
total variance by end of century, with econometric uncertainty in low-risk labor
(88% of total variance) being the only exception. Within-month weather
uncertainty has a negligible effect on 20-year averages. The interaction between
climate and dose-response uncertainty also contributes to the total variance
(negatively in some cases), because impact functions are nonlinear (SM section
F).
NATIONALLY AGGREGATED TOTAL DAMAGE
Impacts across sectors can be aggregated into a single measure of overall
economic damage if suitable values can be assigned to each impact category. For
nonmarket costs, we use current U.S. Environmental Protection Agency values for
the value of a statistical life (37) and published estimates for the cost of
crime (38), which we combine with current average market valuations of market
impacts (SM section I). Summing across impacts, we estimate the conditional
distribution of total direct damages as a function of GMST change (Fig. 5A),
finding that expected annual losses increase by ∼0.6% GDP per 1°C at +1°C of
GMST warming (relative to 1981 to 2010) to 1.7% GDP per 1°C at +5°C GMST (SM
section J). This response is well approximated by a quadratic function (fig.
S14) that is highly statistically significant for changes above 1°C (P < 0.001)
(table S16). Combined uncertainty in aggregate impacts grows with warming, so
the very likely (5th to 95th percentile) range of losses at 1.5°C of warming is
−0.1 to 1.7% GDP, at 4°C of warming is 1.5 to 5.6% GDP, and at 8°C warming is
6.4 to 15.7% GDP annually (gray band, Fig. 5A and table S17). Approximating this
damage function with a linear form suggests losses of ∼1.2% GDP per 1°C on
average in our sample of scenarios (table S16).
Fig. 5 Estimates of total direct economic damage from climate change.
(A) Total direct damage to U.S. economy, summed across all assessed sectors, as
a function of global mean temperature change. Dot-whisker markers as in Fig. 3.
The black line is quadratic regression through all simulations (damage = 0.283
ΔGMST + 0.146 ΔGMST2); the shaded region is bounded by quantile regressions
through the 5th and 95th percentiles. Alternative polynomial forms and
statistical uncertainty are reported in fig. S14 and tables S16 and S17. (B)
Contributions to median estimate of aggregate damage by impact category.
(Coastal impacts do not scale with temperature.) (C) Probability distribution
damage in each of 3143 U.S. counties as a fraction of county income, ordered by
current county income. Dots, median; dark whiskers, inner 66% credible interval;
light whiskers, inner 90%. (D) Distributions of GDP loss compared with direct
damages when a CGE model is forced by direct damages each period. Black line,
median (labeled); boxes, interquartile range; dots, outliers. Energy, Ag.,
Labor, and Mortality indicate comparisons when the model is forced by damages
only in the specified sector and GDP losses are compared with direct damages in
that sector under the same forcing. CGE mortality only affects GDP through lost
earnings, but direct mortality damages in (A) to (C) account for nonmarket VSL.
“All” indicates the ratio of total costs (excluding mortality for consistency)
in complete simulations where all sectors in the CGE model are forced by direct
damages simultaneously.
Expand for more
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The greatest direct cost for GMST changes larger than 2.5°C is the burden of
excess mortality, with sizable but smaller contributions from changes in labor
supply, energy demand, and agricultural production (Fig. 5B). Coastal storm
impacts are also sizable but do not scale strongly with GMST because projections
of global MSL are dependent on RCP but are not explicitly calculated as
functions of GMST (36), causing the coastal storm contribution to the slope of
the damage function to be relatively muted. It is possible to use alternative
approaches to valuing mortality in which the loss of lives for older and/or
low-income individuals are assigned lower value than those of younger and/or
high-income individuals (44), an adjustment that would alter damages differently
for different levels of warming based on the age and income profile of affected
individuals (e.g., fig. S13). Here, we focus on the approach legally adopted by
the U.S. government for environmental cost-benefit analysis, in which the lives
of all individuals are valued equally (37). Because the VSL parameter is
influential, challenging to measure empirically, and may evolve in the future,
its influence on damages is an important area for future investigation.
RISK AND INEQUALITY OF TOTAL LOCAL DAMAGES
Climate change increases the unpredictability and between-county inequality of
future economic outcomes, effects that may alter the valuation of climate
damages beyond their nationally averaged expected costs (45). Figure 5C displays
the probability distribution of damage under RCP8.5 as a fraction of county
income, ordering counties by their current income per capita. Median damages are
systematically larger in low-income counties, increasing by 0.93% of county
income (95% confidence interval = 0.85 to 1.01%) on average for each reduction
in current income decile. In the richest third of counties, the average very
likely range (90% credible interval, determined as the average of 5th and 95th
percentile values across counties) for damages is −1.2 to 6.8% of county income
(negative damages are benefits), whereas for the poorest third of counties, the
average range is 2.0 to 19.6% of county income. These differences are more
extreme for the richest 5% and poorest 5% of counties, with average intervals
for damage of −1.1 to 4.2% and 5.5 to 27.8%, respectively.
We note that it is possible to adjust the aggregate damage function in Fig. 5A
to capture societal aversion to both the risk and inequality in Fig. 5C. In SM
section K, we demonstrate one approach to constructing such inequality-neutral,
certainty-equivalent damage functions. Depending on the parameters used to value
risk and inequality, accounting for these factors may dramatically influence
society’s valuation of damages in a manner similar to the large influence of
discount rates on the valuation of future damages (46). This finding highlights
risk and inequality valuation as critical areas for future research.
DISCUSSION
Our results provide a probabilistic, national damage function based on spatially
disaggregated, empirical, longitudinal analyses of climate impacts and available
global climate models, but it will not be the last estimate. Because we use
stringent selection criteria for empirical studies, there are multiple known
sectors of the U.S. economy for which no suitable studies exist and were thus
omitted from this analysis [e.g., effects of morbidity (47), worker productivity
(48), or biodiversity loss (49)]. The SEAGLAS architecture is constructed around
the idea that rigorous future studies will quantify climate impacts in these
“missing sectors” and thus should be included in future assessments. Our
approach therefore allows for updating based on new econometric results or
climate model projections, and our results should be interpreted as current best
estimates that will be dynamically adjusted as research in the community
advances.
We stress that the results presented here are projections relative to a
counterfactual baseline economic trajectory that is unknown and will evolve
based on numerous factors unrelated to climate change. As constructed, knowledge
of this baseline trajectory is not essential to estimating the relative
first-order impact imposed by climate change.
We should expect that populations will adapt to climate change in numerous ways
(14). Some actions, such as use of air conditioning (25), likely limit the
impact of climatic exposure, whereas other actions, such as social conflict
(30), likely exacerbate impacts. Because the empirical results that we use
describe how populations have actually responded to climatic conditions in the
past, our damage estimates capture numerous forms of adaptation to the extent
that populations have previously employed them (50). For example, if farmers
have been adjusting their planting conditions based on observable rainfall, the
effect of these adjustments will be captured by our results. Although, if there
are trends in adaptive behaviors, previously unobserved adaptation “tipping
points,” or qualitative gains in adaptation-related technologies, then our
findings may require adjustment. In previous work, we demonstrated how to employ
empirical approaches to project trends in adaptive behaviors and recomputed
impacts in some sectors (41), but sufficient data do not yet exist to estimate
these effects in all the sectors we cover here. Yet in cases where sufficient
data do exist to simulate these adaptations, the net effect of this correction
is small in magnitude relative to the large uncertainty that is introduced by
such adjustments (41), a result of the high uncertainty in current estimates for
trends in adaptation (25, 51).
As mentioned above, populations may move across space in response to altered
climate conditions. This response will not alter our local projections, but it
will cause our estimates to over- or underpredict nationally aggregated impacts,
depending on the spatial covariance between population changes and local
economic losses caused by climate change. This adjustment will tend to be second
order relative to the direct effect of climate change (13); nonetheless,
accounting for this adjustment is an area for future investigation.
Another possible adjustment that may occur in response to climate damages is for
the economy to reallocate nonlabor resources, partially shifting the locations
of economic activity, to cope with these changes. We consider the extent to
which this response might alter the direct economic damages that we characterize
above by developing a computable general equilibrium (CGE) model that
reallocates capital across locations and industries in response to the capital
and productivity losses described above during each period of a century-long
integration (SM section L). Theoretically, it is possible for these
reallocations to reduce damages, as production migrates away from adverse
climates, or for them to increase damages, as losses in one location alter
economic decisions in other locations and/or later periods by influencing
markets through prices. We simulate the trajectory of the future economy under
each RCP8.5 climate realization, imposing our computed direct damages each
period. When direct damages are imposed on only one sector at a time, the total
end-of-century economic loss may be larger or smaller than the corresponding
direct damages estimate, depending on the sector and climate realization (Fig.
5D). Market costs of mortality computed with this approach are dramatically
lower than nonmarket costs described above because the foregone earnings in the
market equilibrium are much smaller than the VSL used to compute direct damages.
Overall, in a complete simulation where national markets are simultaneously
forced by direct damages in all sectors, net market losses in general
equilibrium tend to be larger than direct damages by ∼50% (mortality is excluded
from both). These simulations are relatively coarse approximations of the
complex national economy and do not capture international trade effects, but
they suggest that the spatial reallocation of economic activity within the
United States may not easily mitigate the economic damage from climate change.
Our results are “bottom-up” micro-founded estimates of U.S. damages, although
parallel analyses have employed “top-down” macro-level approaches that estimate
how overall productivity measures (such as GDP) directly respond to temperature
or cyclone changes without knowledge of the underlying mechanisms generating
those losses. This alternative approach can be compared to our estimates of
market losses only, as they will not account for nonmarket valuations. Our
market estimates are for a 1.0 to 3.0% loss of annual national average GDP under
RCP8.5 at the end of the century. Previous top-down county-level analysis of
productivity estimates that national output would decline 1.2 to 3.1% after 20
years of exposure to RCP8.5 temperatures at the end of the century (52). In
top-down global analyses of all countries, the 10.3% intensification of average
U.S. tropical cyclone exposure in emissions scenario A1B (roughly comparable to
RCP8.5) (35) is estimated to reduce GDP ∼0.09% per year (53) (not accounting for
MSL rise), and the cumulative effect of linear national warming by an additional
1°C over 75 years is estimated to reduce GDP ∼2.9% (2080 to 2099 average) (42).
In comparison, we estimate that losses to cyclone intensification are ∼0.07% of
annual GDP per 1°C in global mean temperature change and that economy-wide
direct damages are ∼1.2% of annual GDP per year per 1°C. Overall, such
comparisons suggest that top-down and bottom-up empirical estimates are
beginning to converge, although important differences—in accounting procedures
as well as recovered magnitudes and temporal structure—remain. Future
investigation should reconcile these differences.
We have focused on the U.S. economy, although the bulk of the economic damage
from climate change will be borne outside of the United States (42), and impacts
outside the United States will have indirect effects on the United States
through trade, migration, and possibly other channels. In ongoing work, we are
expanding SEAGLAS to cover the global economy and to account for additional
sectors, such as social conflict (30), in order to construct a global damage
function that is essential to estimating the global social cost of carbon and
designing rational global climate policies (7, 9).
ACKNOWLEDGMENTS
This research was funded by grants from the National Science Foundation, the
U.S. Department of Energy, Skoll Global Threats Fund, and by a nonpartisan grant
awarded jointly by Bloomberg Philanthropies, the Office of Hank Paulson, and
Next Generation. The methodology and results presented represent the views of
the authors and are fully independent of the granting organizations. We thank M.
Auffhammer, M. Meinshausen, K. Emanuel, J. Graff Zivin, O. Deschênes, J.
McGrath, L. Lefgren, M. Neidell, M. Ranson, M. Roberts, A. Norris, K. Chadha, A.
Dobbin, A. Guerrero, L. Schick, and W. Schlenker for providing data and
additional analysis; M. Burke, W. Fisk, N. Stern, W. Nordhaus, T. Broccoli, M.
Huber, T. Rutherford, J. Buzan, K. Fisher-Vanden, M. Light, D. Lobell, M.
Greenstone, K. Hayhoe, G. Heal, D. Holtz-Eakin, J. Samet, A. Schreiber, W.
Schlenker, J. Shapiro, M. Spence, L. Linden, L. Mearns, S. Ringstead, G. Yohe,
and seminar participants at Duke, MIT, Stanford, the University of Chicago, and
the National Bureau of Economic Research (NBER) for important discussions and
advice; and J. Delgado and S. Shevtchenko for invaluable technical assistance.
Rhodium Group is a private economic research company that conducts independent
research for clients in the public, private, and philanthropic sectors. Risk
Management Solutions is a catastrophe risk modeling company that provides hazard
modeling services to financial institutions and public agencies. The analysis
contained in this research article was conducted independently of any commercial
work and was not influenced by clients of either organization. Data and code
used in this analysis can be obtained at
https://zenodo.org/communities/economic-damage-from-climate-change-usa/. S.H.
and R.K. conceived of the study. All authors designed the analysis. R.K. and
D.J.R. developed climate projections. A.J. and S.H. gathered and reanalyzed
econometric results. J.R. developed the meta-analysis system. S.H., R.K., A.J.,
J.R., M.D., and T.H. designed the economic projection systems, and J.R.
developed it with support from M.D. and A.J. T.H., M.D., and S.M. developed the
energy modeling system, with econometric support from A.J. R.M.-W., P.W., S.H.,
R.K., and T.H. designed the approach for analyzing cyclone losses; P.W. and
R.M.-W. conducted modeling; and M.D. and A.J. analyzed results. M.D., S.M., and
T.H. implemented general equilibrium modeling; R.K., S.H., A.J., and J.R.
contributed to its design; and M.D., A.J., and S.H. analyzed the output. J.R.
developed and implemented the approach for analyzing uncertainty. A.J. conducted
analysis and construction of aggregate damage functions. R.K., S.H., and A.J.
developed and implemented the approach for valuing risk and inequality of
damages. S.H., R.K., A.J., J.R., M.D., D.J.R., K.L., and T.H. designed the
figures; D.J.R. and A.J. constructed Fig. 1; M.D. and T.H. constructed Fig. 4;
and A.J. constructed Figs. 2, 3, and 5. All authors wrote the manuscript.
SUPPLEMENTARY MATERIAL
SUMMARY
Materials and Methods
Figs. S1 to S18
Tables S1 to S19
References (54–91)
RESOURCES
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30 June 2017
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ACKNOWLEDGMENTS
This research was funded by grants from the National Science Foundation, the
U.S. Department of Energy, Skoll Global Threats Fund, and by a nonpartisan grant
awarded jointly by Bloomberg Philanthropies, the Office of Hank Paulson, and
Next Generation. The methodology and results presented represent the views of
the authors and are fully independent of the granting organizations. We thank M.
Auffhammer, M. Meinshausen, K. Emanuel, J. Graff Zivin, O. Deschênes, J.
McGrath, L. Lefgren, M. Neidell, M. Ranson, M. Roberts, A. Norris, K. Chadha, A.
Dobbin, A. Guerrero, L. Schick, and W. Schlenker for providing data and
additional analysis; M. Burke, W. Fisk, N. Stern, W. Nordhaus, T. Broccoli, M.
Huber, T. Rutherford, J. Buzan, K. Fisher-Vanden, M. Light, D. Lobell, M.
Greenstone, K. Hayhoe, G. Heal, D. Holtz-Eakin, J. Samet, A. Schreiber, W.
Schlenker, J. Shapiro, M. Spence, L. Linden, L. Mearns, S. Ringstead, G. Yohe,
and seminar participants at Duke, MIT, Stanford, the University of Chicago, and
the National Bureau of Economic Research (NBER) for important discussions and
advice; and J. Delgado and S. Shevtchenko for invaluable technical assistance.
Rhodium Group is a private economic research company that conducts independent
research for clients in the public, private, and philanthropic sectors. Risk
Management Solutions is a catastrophe risk modeling company that provides hazard
modeling services to financial institutions and public agencies. The analysis
contained in this research article was conducted independently of any commercial
work and was not influenced by clients of either organization. Data and code
used in this analysis can be obtained at
https://zenodo.org/communities/economic-damage-from-climate-change-usa/. S.H.
and R.K. conceived of the study. All authors designed the analysis. R.K. and
D.J.R. developed climate projections. A.J. and S.H. gathered and reanalyzed
econometric results. J.R. developed the meta-analysis system. S.H., R.K., A.J.,
J.R., M.D., and T.H. designed the economic projection systems, and J.R.
developed it with support from M.D. and A.J. T.H., M.D., and S.M. developed the
energy modeling system, with econometric support from A.J. R.M.-W., P.W., S.H.,
R.K., and T.H. designed the approach for analyzing cyclone losses; P.W. and
R.M.-W. conducted modeling; and M.D. and A.J. analyzed results. M.D., S.M., and
T.H. implemented general equilibrium modeling; R.K., S.H., A.J., and J.R.
contributed to its design; and M.D., A.J., and S.H. analyzed the output. J.R.
developed and implemented the approach for analyzing uncertainty. A.J. conducted
analysis and construction of aggregate damage functions. R.K., S.H., and A.J.
developed and implemented the approach for valuing risk and inequality of
damages. S.H., R.K., A.J., J.R., M.D., D.J.R., K.L., and T.H. designed the
figures; D.J.R. and A.J. constructed Fig. 1; M.D. and T.H. constructed Fig. 4;
and A.J. constructed Figs. 2, 3, and 5. All authors wrote the manuscript.
AUTHORS
AFFILIATIONSEXPAND ALL
SOLOMON HSIANG*,† HTTPS://ORCID.ORG/0000-0002-2074-0829 SHSIANG@BERKELEY.EDU
Global Policy Laboratory, Goldman School of Public Policy, University of
California, Berkeley, CA, USA.
National Bureau of Economic Research, Cambridge, MA, USA.
View all articles by this author
ROBERT KOPP*,† HTTPS://ORCID.ORG/0000-0003-4016-9428 SHSIANG@BERKELEY.EDU
Department of Earth and Planetary Sciences and Institute of Earth, Ocean, and
Atmospheric Sciences, Rutgers University, New Brunswick, NJ, USA.
View all articles by this author
AMIR JINA† HTTPS://ORCID.ORG/0000-0003-3446-7883
Department of Economics and Harris School of Public Policy, University of
Chicago, Chicago, IL, USA.
View all articles by this author
JAMES RISING† HTTPS://ORCID.ORG/0000-0001-8514-4748
Global Policy Laboratory, Goldman School of Public Policy, University of
California, Berkeley, CA, USA.
Energy Resource Group, University of California, Berkeley, CA, USA.
View all articles by this author
MICHAEL DELGADO HTTPS://ORCID.ORG/0000-0002-2414-045X
Rhodium Group, New York, NY, USA.
View all articles by this author
SHASHANK MOHAN HTTPS://ORCID.ORG/0000-0002-8216-5161
Rhodium Group, New York, NY, USA.
View all articles by this author
D. J. RASMUSSEN HTTPS://ORCID.ORG/0000-0003-4668-5749
Woodrow Wilson School of Public and International Affairs, Princeton University,
Princeton, NJ, USA.
View all articles by this author
ROBERT MUIR-WOOD HTTPS://ORCID.ORG/0000-0002-3706-1478
Risk Management Solutions, Newark, CA, USA.
View all articles by this author
PAUL WILSON HTTPS://ORCID.ORG/0000-0002-1565-0108
Risk Management Solutions, Newark, CA, USA.
View all articles by this author
MICHAEL OPPENHEIMER HTTPS://ORCID.ORG/0000-0002-9708-5914
Woodrow Wilson School of Public and International Affairs, Princeton University,
Princeton, NJ, USA.
Department of Geosciences, Princeton University, Princeton, NJ, USA.
View all articles by this author
KATE LARSEN HTTPS://ORCID.ORG/0000-0002-1833-9008
Rhodium Group, New York, NY, USA.
View all articles by this author
TREVOR HOUSER HTTPS://ORCID.ORG/0000-0002-0514-7058
Rhodium Group, New York, NY, USA.
View all articles by this author
FUNDING INFORMATION
National Science Foundation: award313443, SES 1463644
U.S. Department of Energy: award314050, DE-BP0004706
Bloomberg Philanthropies: award308002
Office of Hank Paulson: award308003
Skoll Global Threats Fund: award308005
Next Generation: award308004
NOTES
*
Corresponding author. Email: shsiang@berkeley.edu (S.H.);
robert.kopp@rutgers.edu (R.K.)
†
These authors contributed equally to this work.
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* Solomon Hsiang et al.
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Estimating economic damage from climate change in the United
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FiguresMultimedia
FIGURES
Fig. 1 Recombining previous research results as composite inputs to SEAGLAS.
(A) Forty-four climate models (outlined maps) and model surrogates (dimmed maps)
are weighted so that the distribution of the 2080 to 2099 GMST anomaly exhibited
by weighted models matches the probability distribution of estimated GMST
responses (blue-gray line) under RCP8.5. Analogous display for precipitation in
fig. S1. (B) Example of 10 months of daily residuals in New York City, block
resampled from historical observations at the same location and superimposed on
monthly mean projections for a single model (GFDL-CM3) and scenario (RCP8.5)
drawn from (A). (C to H) Examples of composite (posterior) county- level
dose-response functions derived from nonlinear Bayesian meta-analysis of
empirical studies based on selection criteria in (30). Median estimate is black,
central 95% credible interval is blue-gray. To construct probabilistic impact
projections, responses for each category are independently resampled from each
distribution of possible response functions and combined with resampled climate
realizations, as in (A), and weather realizations, as in (B). [(C) and (D)]
Estimated causal effect of (C) 24 hours temperature and (D) seasonal rainfall on
maize yields. (E) Daily average temperature on all-cause mortality for the 45-
to 64-year-old population. (F) Daily maximum temperature on daily labor supply
in high-risk industries exposed to outdoor temperatures. [(G) and (H)] Daily
maximum temperature on (G) monthly violent crime rates and (H) annual
residential electricity demand. All sources are detailed in SM section B.
GO TO FIGUREOPEN IN VIEWER
Fig. 2 Spatial distributions of projected damages.
County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are
changes relative to counterfactual “no additional climate change” trajectories.
Color indicates magnitude of impact in median projection; outline color
indicates level of agreement across projections (thin white outline, inner 66%
of projections disagree in sign; no outline, ≥83% of projections agree in sign;
black outline, ≥95% agree in sign; thick white outline, state borders; maps
without outlines shown in fig. S2). Negative damages indicate economic gains.
(A) Percent change in yields, area-weighted average for maize, wheat, soybeans,
and cotton. (B) Change in all-cause mortality rates, across all age groups. (C)
Change in electricity demand. (D) Change in labor supply of full-time-equivalent
workers for low-risk jobs where workers are minimally exposed to outdoor
temperature. (E) Same as (D), except for high-risk jobs where workers are
heavily exposed to outdoor temperatures. (F) Change in damages from coastal
storms. (G) Change in property-crime rates. (H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].
GO TO FIGUREOPEN IN VIEWER
Fig. 3 Probabilistic national aggregate damage functions by sector.
Dot-whiskers indicate the distribution of direct damages in 2080 to 2099
(averaged) for multiple realizations of each combination of climate models and
scenario projection (dot, median; dark line, inner 66% credible interval; medium
line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from
RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis
according to GMST change realized in each model-scenario combination (blue axis
is change relative to preindustrial). Black lines are restricted cubic spline
regressions through median values, and gray shaded regions are bounded (above
and below) by restricted cubic spline regressions through the 5th and 95th
quantiles of each distribution, all of which are restricted to intercept the
origin. (A) Total agricultural impact accounting for temperature, rainfall, and
CO2 fertilization (CO2 concentration is uniform within each RCP, causing
discontinuities across scenarios). (B) Without CO2 effect. (C) All-cause
mortality for all ages. (D) Electricity demand used in process model, which does
not resample statistical uncertainty (SM section G). (E and F) Labor supply for
(E) low-risk and (F) high-risk worker groups. (G) Property-crime rates. (H)
Violent-crime rates.
GO TO FIGUREOPEN IN VIEWER
Fig. 4 Economic costs of sea level rise interacting with cyclones.
(A) Example 100-year floodplain in Miami, Florida, under median sea level rise
for RCP8.5, assuming no change in tropical cyclone activity. (B) Same, but
accounting for projected changes in tropical cyclone activity. (C) Same as (A),
but for New York, New York. (D) Same as (B), but for New York, New York. (E)
Annual average direct property damages from tropical cyclones and extratropical
cyclones in the five most-affected states, assuming that installed
infrastructure and cyclone activity is held fixed at current levels. Bars
indicate capital losses under current sea level, median, 95th-percentile and
99th-percentile sea level rise in RCP8.5 in 2100. (F) Nationally aggregated
additional annual damages above historical versus global mean sea level rise
holding storm frequency fixed. (G) Annual average direct property damages
nationally aggregated in RCP8.5, incorporating mean sea level rise and either
historical or projected tropical cyclone activity. Historical storm damage is
the dashed line.
GO TO FIGUREOPEN IN VIEWER
Fig. 5 Estimates of total direct economic damage from climate change.
(A) Total direct damage to U.S. economy, summed across all assessed sectors, as
a function of global mean temperature change. Dot-whisker markers as in Fig. 3.
The black line is quadratic regression through all simulations (damage = 0.283
ΔGMST + 0.146 ΔGMST2); the shaded region is bounded by quantile regressions
through the 5th and 95th percentiles. Alternative polynomial forms and
statistical uncertainty are reported in fig. S14 and tables S16 and S17. (B)
Contributions to median estimate of aggregate damage by impact category.
(Coastal impacts do not scale with temperature.) (C) Probability distribution
damage in each of 3143 U.S. counties as a fraction of county income, ordered by
current county income. Dots, median; dark whiskers, inner 66% credible interval;
light whiskers, inner 90%. (D) Distributions of GDP loss compared with direct
damages when a CGE model is forced by direct damages each period. Black line,
median (labeled); boxes, interquartile range; dots, outliers. Energy, Ag.,
Labor, and Mortality indicate comparisons when the model is forced by damages
only in the specified sector and GDP losses are compared with direct damages in
that sector under the same forcing. CGE mortality only affects GDP through lost
earnings, but direct mortality damages in (A) to (C) account for nonmarket VSL.
“All” indicates the ratio of total costs (excluding mortality for consistency)
in complete simulations where all sectors in the CGE model are forced by direct
damages simultaneously.
GO TO FIGUREOPEN IN VIEWER
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FiguresTables
View figure
Fig. 1
Fig. 1 Recombining previous research results as composite inputs to SEAGLAS.
(A) Forty-four climate models (outlined maps) and model surrogates (dimmed maps)
are weighted so that the distribution of the 2080 to 2099 GMST anomaly exhibited
by weighted models matches the probability distribution of estimated GMST
responses (blue-gray line) under RCP8.5. Analogous display for precipitation in
fig. S1. (B) Example of 10 months of daily residuals in New York City, block
resampled from historical observations at the same location and superimposed on
monthly mean projections for a single model (GFDL-CM3) and scenario (RCP8.5)
drawn from (A). (C to H) Examples of composite (posterior) county- level
dose-response functions derived from nonlinear Bayesian meta-analysis of
empirical studies based on selection criteria in (30). Median estimate is black,
central 95% credible interval is blue-gray. To construct probabilistic impact
projections, responses for each category are independently resampled from each
distribution of possible response functions and combined with resampled climate
realizations, as in (A), and weather realizations, as in (B). [(C) and (D)]
Estimated causal effect of (C) 24 hours temperature and (D) seasonal rainfall on
maize yields. (E) Daily average temperature on all-cause mortality for the 45-
to 64-year-old population. (F) Daily maximum temperature on daily labor supply
in high-risk industries exposed to outdoor temperatures. [(G) and (H)] Daily
maximum temperature on (G) monthly violent crime rates and (H) annual
residential electricity demand. All sources are detailed in SM section B.
View figure
Fig. 2
Fig. 2 Spatial distributions of projected damages.
County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are
changes relative to counterfactual “no additional climate change” trajectories.
Color indicates magnitude of impact in median projection; outline color
indicates level of agreement across projections (thin white outline, inner 66%
of projections disagree in sign; no outline, ≥83% of projections agree in sign;
black outline, ≥95% agree in sign; thick white outline, state borders; maps
without outlines shown in fig. S2). Negative damages indicate economic gains.
(A) Percent change in yields, area-weighted average for maize, wheat, soybeans,
and cotton. (B) Change in all-cause mortality rates, across all age groups. (C)
Change in electricity demand. (D) Change in labor supply of full-time-equivalent
workers for low-risk jobs where workers are minimally exposed to outdoor
temperature. (E) Same as (D), except for high-risk jobs where workers are
heavily exposed to outdoor temperatures. (F) Change in damages from coastal
storms. (G) Change in property-crime rates. (H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].
View figure
Fig. 3
Fig. 3 Probabilistic national aggregate damage functions by sector.
Dot-whiskers indicate the distribution of direct damages in 2080 to 2099
(averaged) for multiple realizations of each combination of climate models and
scenario projection (dot, median; dark line, inner 66% credible interval; medium
line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from
RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis
according to GMST change realized in each model-scenario combination (blue axis
is change relative to preindustrial). Black lines are restricted cubic spline
regressions through median values, and gray shaded regions are bounded (above
and below) by restricted cubic spline regressions through the 5th and 95th
quantiles of each distribution, all of which are restricted to intercept the
origin. (A) Total agricultural impact accounting for temperature, rainfall, and
CO2 fertilization (CO2 concentration is uniform within each RCP, causing
discontinuities across scenarios). (B) Without CO2 effect. (C) All-cause
mortality for all ages. (D) Electricity demand used in process model, which does
not resample statistical uncertainty (SM section G). (E and F) Labor supply for
(E) low-risk and (F) high-risk worker groups. (G) Property-crime rates. (H)
Violent-crime rates.
View figure
Fig. 4
Fig. 4 Economic costs of sea level rise interacting with cyclones.
(A) Example 100-year floodplain in Miami, Florida, under median sea level rise
for RCP8.5, assuming no change in tropical cyclone activity. (B) Same, but
accounting for projected changes in tropical cyclone activity. (C) Same as (A),
but for New York, New York. (D) Same as (B), but for New York, New York. (E)
Annual average direct property damages from tropical cyclones and extratropical
cyclones in the five most-affected states, assuming that installed
infrastructure and cyclone activity is held fixed at current levels. Bars
indicate capital losses under current sea level, median, 95th-percentile and
99th-percentile sea level rise in RCP8.5 in 2100. (F) Nationally aggregated
additional annual damages above historical versus global mean sea level rise
holding storm frequency fixed. (G) Annual average direct property damages
nationally aggregated in RCP8.5, incorporating mean sea level rise and either
historical or projected tropical cyclone activity. Historical storm damage is
the dashed line.
View figure
Fig. 5
Fig. 5 Estimates of total direct economic damage from climate change.
(A) Total direct damage to U.S. economy, summed across all assessed sectors, as
a function of global mean temperature change. Dot-whisker markers as in Fig. 3.
The black line is quadratic regression through all simulations (damage = 0.283
ΔGMST + 0.146 ΔGMST2); the shaded region is bounded by quantile regressions
through the 5th and 95th percentiles. Alternative polynomial forms and
statistical uncertainty are reported in fig. S14 and tables S16 and S17. (B)
Contributions to median estimate of aggregate damage by impact category.
(Coastal impacts do not scale with temperature.) (C) Probability distribution
damage in each of 3143 U.S. counties as a fraction of county income, ordered by
current county income. Dots, median; dark whiskers, inner 66% credible interval;
light whiskers, inner 90%. (D) Distributions of GDP loss compared with direct
damages when a CGE model is forced by direct damages each period. Black line,
median (labeled); boxes, interquartile range; dots, outliers. Energy, Ag.,
Labor, and Mortality indicate comparisons when the model is forced by damages
only in the specified sector and GDP losses are compared with direct damages in
that sector under the same forcing. CGE mortality only affects GDP through lost
earnings, but direct mortality damages in (A) to (C) account for nonmarket VSL.
“All” indicates the ratio of total costs (excluding mortality for consistency)
in complete simulations where all sectors in the CGE model are forced by direct
damages simultaneously.
Reference #1
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