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On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."
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.
The abstract from the study is as follows:
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).
Some of the key findings are pictorially shown below.
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.
The abstract from the study is as follows:
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).
Some of the key findings are pictorially shown below.
Spatial distributions of projected damages
Probabilistic national aggregate damage functions by sector.
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)].
(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)].
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 CO2effect.
(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.
(B) Without CO2effect.
(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.