Research Article

Estimating economic damage from climate change in the United States

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Science  30 Jun 2017:
Vol. 356, Issue 6345, pp. 1362-1369
DOI: 10.1126/science.aal4369
  • 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.

  • 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)].

  • 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.

  • 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.

  • 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.

Supplementary Materials

  • Estimating economic damage from climate change in the United States

    Solomon Hsiang, Robert Kopp, Amir Jina, James Rising, Michael Delgado, Shashank Mohan, D. J. Rasmussen, Robert Muir-Wood, Paul Wilson, Michael Oppenheimer, Kate Larsen, Trevor Houser

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Figs. S1 to S18
    • Tables S1 to S18
    • References

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