Estimating economic damage from climate change in the United States
Costing out the effects of climate change
Episodes of severe weather in the United States, such as the present abundance ofrainfall in California, are brandished as tangible evidence of the future costs ofcurrent climate trends. Hsiang et al. collected national datadocumenting the responses in six economic sectors to short-term weather fluctuations.These data were integrated with probabilistic distributions from a set of global climatemodels and used to estimate future costs during the remainder of this century across arange of scenarios (see the Perspective by Pizer). In terms of overall effects on grossdomestic product, the authors predict negative impacts in the southern United States andpositive impacts in some parts of the Pacific Northwest and New England.
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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Supplementary Material
Summary
Materials and Methods
Figs. S1 to S18
Tables S1 to S19
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References and Notes
1
W. D. Nordhaus, Optimal greenhouse-gas reductions and tax policy in the "dice" model. Am. Econ. Rev. 83, 313–317 (1993).
2
M. L. Weitzman, On modeling and interpreting the economics of catastrophic climate change. Rev. Econ. Stat. 91, 1–19 (2009).
3
K. C. de Bruin, R. B. Dellink, R. S. Tol, AD-DICE: An implementation of adaptation in the DICE model. Clim. Change 95, 63–81 (2009).
4
A. Costinot, D. Donaldson, C. Smith, Evolving comparative advantage and the impact of climate change in agricultural markets: Evidence from 1.7 million fields around the world. J. Polit. Econ. 124, 205–248 (2016).
5
D. Lemoine, C. Traeger, Watch your step: Optimal policy in a tipping climate. Am. Econ. J. Econ. Policy 6, 137–166 (2014).
6
Y. Cai, K. L. Judd, T. M. Lenton, T. S. Lontzek, D. Narita, Environmental tipping points significantly affect the cost-benefit assessment of climate policies. Proc. Natl. Acad. Sci. U.S.A. 112, 4606–4611 (2015).
7
N. Stern, Stern Review: The Economics of Climate Change (Cambridge Univ. Press, 2006).
8
Interagency Working Group on Social Cost of Carbon, Technical support document: Social cost of carbon for regulatory impact analysis under Executive Order 12866, Tech. rep., United States Government (2010).
9
R. L. Revesz, P. H. Howard, K. Arrow, L. H. Goulder, R. E. Kopp, M. A. Livermore, M. Oppenheimer, T. Sterner, Global warming: Improve economic models of climate change. Nature 508, 173–175 (2014).
10
N. Stern, The structure of economic modeling of the potential impacts of climate change: Grafting gross underestimation of risk onto already narrow science models. J. Econ. Lit. 51, 838–859 (2013).
11
R. S. Tol, The economic effects of climate change. J. Econ. Perspect. 23, 29–51 (2009).
12
R. S. Pindyck, Climate change policy: What do the models tell us? J. Econ. Lit. 51, 860–872 (2013).
13
S. M. Hsiang, Climate econometrics. Annu. Rev. Resour. Econ. 8, 43–75 (2016).
14
T. A. Carleton, S. M. Hsiang, Social and economic impacts of climate. Science 353, aad9837 (2016).
15
R. E. Kopp, S. M. Hsiang, M. Oppenheimer, Impacts World 2013 Conference Proceedings (Potsdam Institute for Climate Impact Research, Potsdam, Germany, 2013), pp. 834–843.
16
W. Pizer, M. Adler, J. Aldy, D. Anthoff, M. Cropper, K. Gillingham, M. Greenstone, B. Murray, R. Newell, R. Richels, A. Rowell, S. Waldhoff, J. Wiener, Using and improving the social cost of carbon. Science 346, 1189–1190 (2014).
17
M. Burke, M. Craxton, C. D. Kolstad, C. Onda, H. Allcott, E. Baker, L. Barrage, R. Carson, K. Gillingham, J. Graff-Zivin, M. Greenstone, S. Hallegatte, W. M. Hanemann, G. Heal, S. Hsiang, B. Jones, D. L. Kelly, R. Kopp, M. Kotchen, R. Mendelsohn, K. Meng, G. Metcalf, J. Moreno-Cruz, R. Pindyck, S. Rose, I. Rudik, J. Stock, R. S. J. Tol, Opportunities for advances in climate change economics. Science 352, 292–293 (2016).
18
Materials and methods are available online as supplementary materials.
19
D. Rasmussen, M. Meinshausen, R. E. Kopp, Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis. J. Appl. Meteorol. Climatol. 55, 2301–2322 (2016).
20
D. P. van Vuuren, J. Edmonds, M. Kainuma, K. Riahi, A. Thomson, K. Hibbard, G. C. Hurtt, T. Kram, V. Krey, J.-F. Lamarque, T. Masui, M. Meinshausen, N. Nakicenovic, S. J. Smith, S. K. Rose, The representative concentration pathways: An overview. Clim. Change 109, 5–31 (2011).
21
K. E. Taylor, R. J. Stouffer, G. A. Meehl, An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
22
W. Schlenker, M. J. Roberts, Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl. Acad. Sci. U.S.A. 106, 15594–15598 (2009).
23
J. M. McGrath, D. B. Lobell, Regional disparities in the CO2 fertilization effect and implications for crop yields. Environ. Res. Lett. 8, 014054 (2013).
24
O. Deschênes, M. Greenstone, Climate change, mortality, and adaptation: Evidence from annual fluctuations in weather in the US. Am. Econ. J. Appl. Econ. 3, 152–185 (2011).
25
A. Barreca, K. Clay, O. Deschênes, M. Greenstone, J. S. Shapiro, Adapting to climate change: The remarkable decline in the US temperature-mortality relationship over the twentieth century. J. Polit. Econ. 124, 105–159 (2016).
26
B. Jacob, L. Lefgren, E. Moretti, The dynamics of criminal behavior. J. Hum. Resour. 42, 489–527 (2007).
27
M. Ranson, Crime, weather, and climate change. J. Environ. Econ. Manage. 67, 274–302 (2014).
28
J. Graff Zivin, M. Neidell, Temperature and the allocation of time: Implications for climate change. J. Labor Econ. 32, 1–26 (2014).
29
J. A. Rising, S. M. Hsiang, SSRN Working Paper 2458129; available at SSRN: https://ssrn.com/abstract=2458129 (2014).
30
S. M. Hsiang, M. Burke, E. Miguel, Quantifying the influence of climate on human conflict. Science 341, 1235367 (2013).
31
U.S. Energy Information Administration, The national energy modeling system: An overview, Tech. Rep. DOE/EIA-0581 (2009); www.eia.gov/outlooks/aeo/nems/overview/index.html.
32
H. Willoughby, R. Darling, M. Rahn, Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles. Mon. Weather Rev. 134, 1102–1120 (2006).
33
I. Warren, H. Bach, MIKE 21: A modelling system for estuaries, coastal waters and seas. Environ. Softw. 7, 229–240 (1992).
34
T. M. Hall, S. Jewson, Statistical modelling of North Atlantic tropical cyclone tracks. Tellus, Ser. A, Dyn. Meterol. Oceanogr. 59, 486–498 (2007).
35
K. A. Emanuel, Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl. Acad. Sci. U.S.A. 110, 12219–12224 (2013).
36
R. E. Kopp, R. M. Horton, C. M. Little, J. X. Mitrovica, M. Oppenheimer, D. J. Rasmussen, B. H. Strauss, C. Tebaldi, Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earths Futur. 2, 383–406 (2014).
37
U.S. Environmental Protection Agency, National Center for Environmental Economics, Valuing mortality risk reductions for environmental policy: A white paper, Tech. rep. (2010).
38
P. Heaton, Hidden in Plain Sight (RAND Corporation, 2010).
39
C. J. Vörösmarty, P. Green, J. Salisbury, R. B. Lammers, Global water resources: Vulnerability from climate change and population growth. Science 289, 284–288 (2000).
40
S. M. Hsiang, Essays on the social impacts of climate, Ph.D. thesis, Columbia University (2011).
41
T. Houser et al., Economic Risks of Climate Change: An American Prospectus (Columbia Univ. Press, 2015).
42
M. Burke, S. M. Hsiang, E. Miguel, Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).
43
E. Hawkins, R. Sutton, The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).
44
W. K. Viscusi, J. E. Aldy, J. Risk Uncertain. 27, 5–76 (2003).
45
C. Gollier, Pricing the Planet’s Future: The Economics of Discounting in an Uncertain World (Princeton Univ. Press, 2013).
46
M. L. Weitzman, A Review of the Stern Review on the Economics of Climate Change. J. Econ. Lit. 45, 703–724 (2007).
47
J. A. Patz, D. Campbell-Lendrum, T. Holloway, J. A. Foley, Impact of regional climate change on human health. Nature 438, 310–317 (2005).
48
S. M. Hsiang, Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. Proc. Natl. Acad. Sci. U.S.A. 107, 15367–15372 (2010).
49
G.-R. Walther, E. Post, P. Convey, A. Menzel, C. Parmesan, T. J. C. Beebee, J.-M. Fromentin, O. Hoegh-Guldberg, F. Bairlein, Ecological responses to recent climate change. Nature 416, 389–395 (2002).
50
O. Deschênes, M. Greenstone, The economic impacts of climate change: Evidence from agricultural output and random fluctuations in weather. Am. Econ. Rev. 97, 354–385 (2007).
51
M. Burke, K. Emerick, Adaptation to climate change: Evidence from US agriculture. Am. Econ. J. Econ. Policy 8, 106–140 (2016).
52
T. Deryugina, S. M. Hsiang, NBER Working Paper 20750 (NBER, 2014); www.nber.org/papers/w20750.
53
S. M. Hsiang, A. Jina, NBER Working Paper 20352 (NBER, 2014); www.nber.org/papers/w20352.
54
T. D. Mitchell, Pattern scaling: An examination of the accuracy of the technique for describing future climates. Clim. Change 60, 217–242 (2003).
55
M. Meinshausen, S. C. B. Raper, T. M. L. Wigley, Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration. Atmos. Chem. Phys. 11, 1417–1456 (2011).
56
J. Rogelj, M. Meinshausen, R. Knutti, Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat. Clim. Chang. 2, 248–253 (2012).
57
M. Collins et al., Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. Stocker et al., Eds. (Cambridge Univ. Press, Cambridge, United Kingdom and New York, NY, USA, 2013), pp. 1029–1136.
58
L. Brekke, A. Wood, T. Pruitt, Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs, Tech. Rep., USBR Tech Memo., Denver, Colorado (2014).
59
A. Arguez, I. Durre, S. Applequist, R. S. Vose, M. F. Squires, X. Yin, R. R. Heim Jr., T. W. Owen, NOAA’s 1981–2010 U.S. climate normals: An overview. Bull. Am. Meteorol. Soc. 93, 1687–1697 (2012).
60
A. W. Wood, L. R. Leung, V. Sridhar, D. P. Lettenmaier, Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim. Change 62, 189–216 (2004).
61
A. Gelman, J. B. Carlin, H. S. Stern, D. B. Rubin, Bayesian Data Analysis (Chapman & Hall/CRC, 2004).
62
U.S. Energy Information Administration, Annual Energy Outlook 2013 (US DOE/EIA, 2013).
63
U.S. Energy Information Administration, State electricity profiles (2014). Available at www.eia.gov/electricity/state/archive/2012/ on 16 June 2014.
64
U.S. Energy Information Administration, Total energy consumption, price, and expenditure estimates (2014). www.eia.gov/state/seds/data.cfm?incfile=/state/seds/sep_fuel/html/fuel_te.html&sid=US.
65
T. R. Knutson, J. J. Sirutis, G. A. Vecchi, S. Garner, M. Zhao, H.-S. Kim, M. Bender, R. E. Tuleya, I. M. Held, G. Villarini, Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J. Clim. 26, 6591–6617 (2013).
66
IMPLAN Group, 51 states totals package (2011); https://implan.com
67
F. C. Curriero, K. S. Heiner, J. M. Samet, S. L. Zeger, L. Strug, J. A. Patz, Temperature and mortality in 11 cities of the eastern United States. Am. J. Epidemiol. 155, 80–87 (2002).
68
B. G. Anderson, M. L. Bell, Weather-related mortality: How heat, cold, and heat waves affect mortality in the United States. Epidemiology 20, 205–213 (2009).
69
G. B. Anderson, M. L. Bell, Heat waves in the United States: Mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities. Environ. Health Perspect. 119, 210–218 (2011).
70
O. Seppanen, W. Fisk, Q. Lei, Lawrence Berkeley National Laboratory Technical Report (2006). https://indoor.lbl.gov/sites/all/files/lbnl-60946.pdf.
71
S. T. Berry, M. J. Roberts, W. Schlenker, NBER Working Paper 18659 (2012). http://www.nber.org/papers/w18659.
72
S. Hsiang, D. Lobell, M. Roberts, W. Schlenker, SSRN Working Paper 2977571 (2013). http://ssrn.com/abstract=2977571.
73
A. C. Fisher, W. M. Hanemann, M. J. Roberts, W. Schlenker, The economic impacts of climate change: Evidence from agricultural output and random fluctuations in weather: Comment. Am. Econ. Rev. 102, 3749–3760 (2012).
74
W. J. Sacks, D. Deryng, J. A. Foley, N. Ramankutty, Crop planting dates: An analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
75
Center for Disease Control and Prevention, Compressed Mortality File 1999-2010 on CDC WON- DER Online Database, released January 2013, Data are compiled from Compressed Mortality File 1999-2010 Series 20 No. 2P (2013). Accessed at http://wonder.cdc.gov/cmf-icd10.html on 22 March 2014.
76
M. J. Roberts, W. Schlenker, Identifying supply and demand elasticities of agricultural commodities: Implications for the US ethanol mandate. Am. Econ. Rev. 103, 2265–2295 (2013).
77
Bureau of Labor Statistics, Occupational employment statistics, http://data.bls.gov/oes/ (2014). Accessed on 20 March 2014.
78
UK Met Office, How have cooling degree days (CDD) and heating degree days (HDD) been calculated in UKCP09? (2012). Accessed at http://ukclimateprojections.metoffice.gov.uk/22715 on 16 June 2014.
79
U.S. Energy Information Administration, Residential demand module of the National Energy Modeling System: Model documentation 2013, Technical Report (U.S. Energy Information Administration, 2013).
80
U.S. Energy Information Administration, Commercial demand module of the National Energy Modeling System: Model documentation 2013, Technical Report (U.S. Energy Information Administration, 2013).
81
US Energy Information Administration, Table F30: Total energy consumption, price, and expenditure estimates, 2012 (2014). Accessed at http://www.eia.gov/state/seds/data.cfm?incfile=/state/seds/sep_fuel/html/fuel_te.html&sid=US on 16 June 2014.
82
B. R. Jarvinen, C. J. Neumann, M. A. S. Davis, NOAA Tech. Memo. (1984). Available at http://www.nhc.noaa.gov/pdf/NWS-NHC-1988-22.pdf.
83
W. D. Collins et al., Description of the NCAR community atmosphere model (CAM 3.0), Technical Reports NCAR/TN-464+STR (National Center for Atmospheric Research, Boulder, CO, 2004).
84
G. A. Grell et al., A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), Technical Reports NCAR/TN-398+STR (National Center for Atmospheric Research, Boulder, CO, 1994).
85
U.S. Bureau of Economic Analysis, GDP by industry / VA, GO, II, EMP (1997–2013, 69 industries) (2014). Accessed at http://www.bea.gov/industry/xls/GDPbyInd_VA_NAICS_1997-2013.xlsx on 8 August 2014.
86
U.S. Federal Bureau of Investigation, Crime in the United States, (2012).Accessed at www.fbi.gov/about-us/cjis/ucr/ucr-publications#Crime on 8 August 2014.
87
D. B. Diaz, Estimating global damages from sea level rise with the Coastal Impact and Adaptation Model (CIAM). Clim. Change 137, 143–156 (2016).
88
U.S. Bureau of Economic Analysis, State GDP for all industries and regions (2008–2013) (2014). Accessed at https://goo.gl/2QGaEz on 8 August 2014.
89
G. Atkinson, S. Dietz, J. Helgeson, C. Hepburn, H. Saelen, Siblings, not triplets: Social preferences for risk, inequality and time in discounting climate change. Economics: The Open-Access, Open-Assessment E-Journal 3 (2009). http://www.economics-ejournal.org/economics/journalarticles/2009-26
90
U.S. Bureau of Economic Analysis, County personal income and population (Table CA1), 2008–2013 (2014). Accessed at https://goo.gl/p44pkw on 8 August 2014.
91
J. K. Anttila-Hughes, S. M. Hsiang, SSRN Working Paper 2220501 (2012). https://ssrn.com/abstract=2220501.
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Science
Volume 356 | Issue 6345
30 June 2017
30 June 2017
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Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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Received: 21 November 2016
Accepted: 2 June 2017
Published in print: 30 June 2017
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
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
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