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Estimating a social cost of carbon for global energy consumption

Abstract

Estimates of global economic damage caused by carbon dioxide (CO2) emissions can inform climate policy1,2,3. The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO2 emissions today impact future economic outcomes through altering the climate4,5,6. Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC7,8, but they rely on models9,10,11 that are spatially coarse and not tightly linked to data2,3,6,7,12,13. Here we show that the release of one ton of CO2 today is projected to reduce total future energy expenditures, with most estimates valued between −US$3 and −US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annual global electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in global mean surface temperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in GMST. Our finding of net savings contradicts previous research7,8, because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight14.

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Fig. 1: Estimated effect of temperature on energy consumption is mediated by income and climate.
Fig. 2: Projected impact of climate change on energy consumption in the twenty-first century.
Fig. 3: Economic costs from energy consumption impact of climate change.
Fig. 4: Social cost of carbon for global energy consumption.

Data availability

The data for replicating the findings of this study are available on Zenodo at https://doi.org/10.5281/zenodo.5099834.

Code availability

The code for replicating the findings of this study is available on GitHub at https://github.com/ClimateImpactLab/energy-code-release-2020/.

References

  1. 1.

    Interagency Working Group on Socal Cost of Carbon Social Cost of Carbon for Regulatory Impact Analysis—under Executive Order 12866 Technical Report (United States Government, 2010).

  2. 2.

    Revesz, R. L. et al. Global warming: improve economic models of climate change. Nature 508, 173–175 (2014).

    Google Scholar 

  3. 3.

    Pizer, W. et al. Using and improving the social cost of carbon. Science 346, 1189–1190 (2014).

    ADS  CAS  Google Scholar 

  4. 4.

    Nordhaus, W. D. An optimal transition path for controlling greenhouse gases. Science 258, 1315–1319 (1992).

    ADS  CAS  Google Scholar 

  5. 5.

    Greenstone, M., Kopits, E. & Wolverton, A. Developing a social cost of carbon for US regulatory analysis: a methodology and interpretation. Rev. Environ. Econ. Policy 7, 23–46 (2013).

    Google Scholar 

  6. 6.

    National Academies of Sciences, Engineering, and Medicine Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide (The National Academies Press, 2017).

  7. 7.

    Diaz, D. & Moore, F. Quantifying the economic risks of climate change. Nat. Clim. Change 7, 774–782 (2017).

    ADS  Google Scholar 

  8. 8.

    Anthoff, D. & Tol, R. S. The uncertainty about the social cost of carbon: a decomposition analysis using FUND. Climatic Change 117, 515–530 (2013).

    ADS  Google Scholar 

  9. 9.

    Stern, N. Stern Review Report on the Economics of Climate Change (HM Treasury, 2006).

  10. 10.

    Waldhoff, S., Anthoff, D., Rose, S. & Tol, R. S. The marginal damage costs of different greenhouse gases: an application of FUND. Economics 8, 1–33 (2014).

  11. 11.

    Nordhaus, W. D. Estimates of the Social Cost of Carbon: Background and Results from the Rice-2011 Model Technical Report (National Bureau of Economic Research, 2011).

  12. 12.

    Pindyck, R. S. Climate change policy: what do the models tell us? J. Econ. Lit. 51, 860–872 (2013).

    Google Scholar 

  13. 13.

    Burke, M. et al. Opportunities for advances in climate change economics. Science 352, 292–293 (2016).

    ADS  CAS  Google Scholar 

  14. 14.

    Adler, M. et al. Priority for the worse-off and the social cost of carbon. Nat. Clim. Change 7, 443–449 (2017).

    ADS  Google Scholar 

  15. 15.

    Moore, F. C., Baldos, U., Hertel, T. & Diaz, D. New science of climate change impacts on agriculture implies higher social cost of carbon. Nat. Commun. 8, 1607 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Diaz, D. B. Evaluating the Key Drivers of the US Government’s Social Cost of Carbon: A Model Diagnostic and Inter-Comparison Study of Climate Impacts in DICE, FUND, and PAGE (Stanford University Policy and Economics Research Roundtable, 2014).

  17. 17.

    Carleton, T. A. et al. Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits Working Paper 27599 (National Bureau of Economic Research, 2020); http://www.nber.org/papers/w27599

  18. 18.

    Hsiang, S. et al. Estimating economic damage from climate change in the United States. Science 356, 1362–1369 (2017).

    ADS  CAS  Google Scholar 

  19. 19.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    ADS  Google Scholar 

  20. 20.

    Auffhammer, M., Hsiang, S. M., Schlenker, W. & Sobel, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy 7, 181–198 (2013).

    Google Scholar 

  21. 21.

    Kopp, R., Hsiang, S. & Oppenheimer, M. Empirically calibrating damage functions and considering stochasticity when integrated assessment models are used as decision tools. In Impacts World 2013 Conference Proc. 834–843 (Potsdam Institute for Climate Impact Research, 2013).

  22. 22.

    O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122, 387–400 (2014).

    ADS  Google Scholar 

  23. 23.

    Rasmussen, D. J. & Kopp, R. E. in Economic Risks of Climate Change: An American Prospectus 219–248 (Columbia Univ. Press, 2015); https://cup.columbia.edu/book/economic-risks-of-climate-change/9780231174565

  24. 24.

    Hsiang, S. Climate econometrics. Annu. Rev. Resour. Econ. 8, 43–75 (2016).

    Google Scholar 

  25. 25.

    Smith, C. J. et al. FAIR v1. 3: a simple emissions-based impulse response and carbon cycle model. Geosci. Model Dev. 11, 2273–2297 (2018).

    ADS  CAS  Google Scholar 

  26. 26.

    Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. 4, 66–95 (2012).

    Google Scholar 

  27. 27.

    Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).

    ADS  CAS  Google Scholar 

  28. 28.

    Moore, F. C. & Diaz, D. B. Temperature impacts on economic growth warrant stringent mitigation policy. Nat. Clim. Change 5, 127–131 (2015).

    ADS  Google Scholar 

  29. 29.

    Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8, 895–900 (2018).

    ADS  CAS  Google Scholar 

  30. 30.

    Deschênes, O. & Greenstone, M. Climate change, mortality, and adaptation: evidence from annual fluctuations in weather in the US. Am. Econ. J. Appl. Econ. 3, 152–185 (2011).

    Google Scholar 

  31. 31.

    Davis, L. W. & Gertler, P. J. Contribution of air conditioning adoption to future energy use under global warming. Proc. Natl Acad. Sci. USA 112, 5962–5967 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Auffhammer, M., Baylis, P. & Hausman, C. H. Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States. Proc. Natl Acad. Sci. USA 114, 1886–1891 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Wenz, L., Levermann, A. & Auffhammer, M. North–south polarization of European electricity consumption under future warming. Proc. Natl Acad. Sci. USA 114, E7910–E7918 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Auffhammer, M. Climate Adaptive Response Estimation: Short and Long Run Impacts of Climate Change on Residential Electricity and Natural Gas Consumption using Big Data Technical Report (National Bureau of Economic Research, 2018).

  35. 35.

    Hadley, S. W., Erickson, D. J., Hernandez, J. L., Broniak, C. T. & Blasing, T. Responses of energy use to climate change: a climate modeling study. Geophys. Res. Lett. 33, L17703 (2006).

    ADS  Google Scholar 

  36. 36.

    Zhou, Y., Eom, J. & Clarke, L. The effect of global climate change, population distribution, and climate mitigation on building energy use in the US and China. Climatic Change 119, 979–992 (2013).

    ADS  Google Scholar 

  37. 37.

    Isaac, M. & Van Vuuren, D. P. Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy 37, 507–521 (2009).

    Google Scholar 

  38. 38.

    Clarke, L. et al. Effects of long-term climate change on global building energy expenditures. Energy Econ. 72, 667–677 (2018).

    Google Scholar 

  39. 39.

    Gollier, C. & Hammitt, J. K. The long-run discount rate controversy. Annu. Rev. Resour. Econ. 6, 273–295 (2014).

    Google Scholar 

  40. 40.

    Bauer, M. & Rudebusch, G. D. The Rising Cost of Climate Change: Evidence from the Bond Market (Federal Reserve Bank of San Francisco, 2020).

  41. 41.

    Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).

    ADS  Google Scholar 

  42. 42.

    World Energy Balances (Edition 2017) International Energy Agency, 2018); https://www.oecd-ilibrary.org/content/data/9ddec1c1-en

  43. 43.

    Rasmussen, D. J., Meinshausen, M. & Kopp, R. E. Probability-weighted ensembles of US county-level climate projections for climate risk analysis. J. Appl. Meteorol. Climatol. 55, 2301–2322 (2016).

    ADS  Google Scholar 

  44. 44.

    McNeil, M. A. & Letschert, V. E. Modeling diffusion of electrical appliances in the residential sector. Energy Build. 42, 783–790 (2010).

    Google Scholar 

  45. 45.

    Legros, G. et al. The Energy Access Situation in Developing Countries: A Review Focusing on the Least Developed Countries and Sub-Saharan Africa (World Health Organization, 2009).

  46. 46.

    Almond, D., Chen, Y., Greenstone, M. & Li, H. Winter heating or clean air? Unintended impacts of China’s Huai River policy. Am. Econ. Rev. 99, 184–190 (2009).

    Google Scholar 

  47. 47.

    Ramsey, F. P. A mathematical theory of saving. Econ. J. 38, 543–559 (1928).

    Google Scholar 

  48. 48.

    Tong, D. et al. Committed emissions from existing energy infrastructure jeopardize 1.5 °C climate target. Nature 572, 373–377 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Woodard, D. L., Davis, S. J. & Randerson, J. T. Economic carbon cycle feedbacks may offset additional warming from natural feedbacks. Proc. Natl Acad. Sci. USA 116, 759–764 (2019).

    ADS  CAS  Google Scholar 

  50. 50.

    Global Administrative Areas GADM Database of Global Administrative Areas, Version 2.0 (University of California, Berkeley, Museum of Vertebrate Zoology, International Rice Research Institute, University of California, Davis, 2012); www.gadm.org/data.html

  51. 51.

    Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. Technical note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).

    ADS  Google Scholar 

  52. 52.

    Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Climatic Change 109, 33–57 (2011).

    ADS  CAS  Google Scholar 

  53. 53.

    Thomson, A. M. et al. RCP 4.5: a pathway for stabilization of radiative forcing by 2100. Climatic Change 109, 77 (2011).

    ADS  CAS  Google Scholar 

  54. 54.

    Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5 (2011).

    ADS  Google Scholar 

  55. 55.

    Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Phil. Trans. R. Soc. Lond. A 365, 2053–2075 (2007).

    ADS  MathSciNet  Google Scholar 

  56. 56.

    Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Google Scholar 

  57. 57.

    Samir, K. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).

    Google Scholar 

  58. 58.

    Cuaresma, J. C. Income projections for climate change research: a framework based on human capital dynamics. Glob. Environ. Change 42, 226–236 (2017).

    Google Scholar 

  59. 59.

    Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the shared socioeconomic pathways. Glob. Environ. Change 42, 200–214 (2017).

    Google Scholar 

  60. 60.

    IIASA Energy Program SSP Database, Version 1.1 Data set Technical Report (National Bureau of Economic Research, 2016); https://tntcat.iiasa.ac.at/SspDb

  61. 61.

    Bright, E. A., Coleman, P. R., Rose, A. N. & Urban, M. L. LandScan 2011 (2012); https://web.ornl.gov/sci/landscan/index.shtml

  62. 62.

    Jiang, L. & O’Neill, B. C. Global urbanization projections for the shared socioeconomic pathways. Glob. Environ. Change 42, 193–199 (2017).

    Google Scholar 

  63. 63.

    Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the shared socioeconomic pathways. Environ. Res. Lett. 11, 084003 (2016).

    ADS  Google Scholar 

  64. 64.

    Huppmann, D. et al. IAMC 1.5 °C Scenario Explorer and Data hosted by IIASA. (Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis, 2018).

  65. 65.

    Carleton, T. A. & Hsiang, S. M. Social and economic impacts of climate. Science 353, aad9837 (2016).

    Google Scholar 

  66. 66.

    Auffhammer, M. & Aroonruengsawat, A. Simulating the impacts of climate change, prices and population on California’s residential electricity consumption. Climatic Change 109, 191–210 (2011).

    ADS  Google Scholar 

  67. 67.

    Graff Zivin, J. & Neidell, M. Temperature and the allocation of time: implications for climate change. J. Labor Econ. 32, 1–26 (2014).

    Google Scholar 

  68. 68.

    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2002).

  70. 70.

    Millar, R. J., Nicholls, Z. R., Friedlingstein, P. & Allen, M. R. A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmos. Chem. Phys. 17, 7213–7228 (2017).

    ADS  CAS  Google Scholar 

  71. 71.

    Board of Governors of the US Federal Reserve System 10-year Treasury Inflation-indexed Security, Constant Maturity (DFII10) Technical Report (FRED, Federal Reserve Bank of St. Louis, 2020); https://fred.stlouisfed.org/series/DFII10

  72. 72.

    Carleton, T. & Greenstone, M. Updating the United States Government’s Social Cost of Carbon Working Paper (Univ. Chicago, Becker Friedman Institute for Economics, 2021).

  73. 73.

    Nordhaus, W. A Question of Balance: Weighing the Options on Global Warming Policies (Yale Univ. Press, 2014).

  74. 74.

    Arrow, K. J. Global climate change: a challenge to policy. The Economists’ Voice 4, 1–5 (2007).

  75. 75.

    Dasgupta, P. The Stern review’s economics of climate change. Natl Inst. Econ. Rev. 199, 4–7 (2007).

    Google Scholar 

  76. 76.

    Dasgupta, P. Discounting climate change. J. Risk Uncertain. 37, 141–169 (2008).

    MATH  Google Scholar 

  77. 77.

    Hall, R. E. Reconciling cyclical movements in the marginal value of time and the marginal product of labor. J. Polit. Econ. 117, 281–323 (2009).

    Google Scholar 

  78. 78.

    Weitzman, M. L. A review of the Stern review on the economics of climate change. J. Econ. Lit. 45, 703–724 (2007).

    Google Scholar 

  79. 79.

    Weitzman, M. L. On modeling and interpreting the economics of catastrophic climate change. Rev. Econ. Stat. 91, 1–19 (2009).

    Google Scholar 

  80. 80.

    McGrath, G. Natural gas-fired electricity conversion efficiency grows as coal remains stable. Today in Energy https://www.eia.gov/todayinenergy/detail.php?id=32572 (2017).

  81. 81.

    Emission factors for greenhouse gas inventories.US Environmental Protection Agency https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf (2018).

  82. 82.

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).

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Acknowledgements

This project is an output of the Climate Impact Lab consortium that gratefully acknowledges funding from the Carnegie Corporation, Energy Policy Institute of Chicago (EPIC), the International Growth Centre, the National Science Foundation (SES1463644), the Sloan Foundation and the Tata Center for Development. T.C. acknowledges funding from the US Environmental Protection Agency Science To Achieve Results Fellowship (FP91780401). J.R. acknowledges funding from the H2020-MSCA-RISE project GEMCLIME-2020 GA number 681228. We thank L. Alcocer, T. Bearpark, T. Chong, Z. Delgerjargal, G. Dobbels, D. Gergel, R. Goyal, S. Greenhill, I. Higuera-Mendieta, D. Hogan, A. Hussain, T. Kulczycki, R. Li, B. Malevich, M. Norman, O. Nwabuikwu, S. Annan-Phan, C. Schwarz, N. Sharma, J. Simcock, Y. Song, E. Tenezakis, J. Wang and J. Yang for research assistance during all stages of this project, and we thank S. Anderson, J. Chang, M. Landín and T. Mayer for project management. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for the CMIP, and we thank the climate modelling groups (listed in Extended Data Fig. 2b) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank seminar participants at the UC Berkeley Energy Camp, the University of Chicago EPIC Lunch Series and the Mansueto Institute Lunch Colloquium, LSE Workshop in Environmental Economics, International Energy Workshop, International Workshop on Empirical Methods in Energy Economics, the University of Michigan Sustainability and Development Conference, the Berkeley/Harvard/Yale Environmental and Energy Economics Seminar, the NBER EEE Summer Institute, the UCLA Luskin Center for Innovation Climate Adaptation Research Symposium and the Federal Reserve Bank of Richmond Climate Change Economics Workshop for helpful comments.

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Authors

Contributions

A.R., S.H., M.G., R.K., T.H., A.J., J.R., M.D. and T.C. conceived and planned the study. J.Y., K.E.M., M.D., R.K., A.R., A.J. and T.C. prepared the historical climate data. J.Y., K.E.M., M.D. and R.K. created and prepared the climate projection data. A.R., T.C., A.H., M.D., I.N. and T.H. prepared the energy data. A.R., T.C., A.H., I.N., A.J., J.R., S.H. and MG estimated energy–climate response functions. J.R. and A.R. computed projected impacts of future climate change. A.R., M.D., T.C., K.E.M., A.H., I.N., A.J., J.R., S.H. and M.G. constructed damage functions and computed partial social cost of carbon. A.R., S.H. and M.G. wrote the main text, A.R., T.C., A.J., M.D., J.R., J.Y. and K.M. wrote the supplementary materials, and all authors edited.

Corresponding authors

Correspondence to Ashwin Rode or Solomon Hsiang.

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Competing interests

A.R., T.C., A.H., A.J., R.K., I.N., J.R. and J.Y. declare no competing interests. M.D., T.H., K.E.M.: The Rhodium Group provides independent research and analysis on a range of global economic topics to clients in the public and private sectors. This includes analysis of global energy market trends. Although some of those clients could potentially be impacted by the results of this research, both positively and negatively, Rhodium staff contributions to this research were conducted completely independently. M.G., S.H. and R.K.: we received no financial or in-kind support for the research conducted in this paper, nor have we received funding or in-kind support from any ‘interested’ parties. As part of a diversified portfolio, we each hold more than US$10,000 in stocks and bonds of various companies including those within the energy sector. No other parties have the right to review the paper before its circulation. M.G. holds the position of director at the Energy Policy Institute at the University of Chicago and the Environment and Energy Lab at University of Chicago Labs, and is faculty director at the E2e Lab. From July 2015 to January 2017, M.G. was a member of the US Department of Energy’s Secretary of Energy Advisory Board. S.H. is the director of the Global Policy Laboratory at the University of California, Berkeley. R.K. previously served as a consultant to Rhodium Group, which provides independent research and analysis on a range of global economic topics to clients in the public and private sectors, including analysis of global energy market trends. R.K. currently serves as director of the Rutgers Institute of Earth, Ocean, and Atmospheric Sciences. R.K. provided technical support to and M.G. was a co-chair of the US Federal Government Interagency Working Group on Social Cost of Carbon between September 2009 and March 2010. R.K. was an author of the National Academies of Sciences, Engineering, and Medicine report Valuing Climate Damages Updating Estimation of the Social Cost of Carbon Dioxide published in 2017.

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Peer review information Nature thanks Katrina Jessoe, William Pizer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Map of the 24,378 “impact regions” for which location-specific projections are calculated.

Map is produced with R software, ggplot2 package, using Global Administrative Region dataset (GADM) basemap50. A clustering algorithm17 is used to form these impact regions from the full set of GADM administrative regions50, such that they are roughly similar in total population, and so that they are approximately internally homogenous with respect to mean temperature, diurnal temperature range, and mean precipitation.

Extended Data Fig. 2 Future climate projections used in generating probabilistic, empirically-based climate change impact projections.

Panel (a) shows local climate distributions under the 21 climate models (outlined maps) and 12 model surrogates (dimmed maps) (‘Data assembly’ in Methods, Supplementary Sections A.2.2, A.2.3) that are weighted in climate change impact projections so that the weighted distribution of the 2080 to 2099 global mean surface temperature anomaly (ΔGMST) exhibited by the 33 total models matches the probability distribution of estimated ΔGMST responses (blue-grey line) under a high (RCP8.5) emissions scenario. For this construction, the anomaly is relative to values in 1986–2005. Maps are produced with Python programming language, using data from ref. 43 and Global Administrative Region dataset (GADM) basemap79. Panel (b) lists all 33 models and model surrogates, and their corresponding model weights for both high (RCP8.5) and moderate (RCP4.5) emissions scenarios43. These are used to capture climate model uncertainty when generating climate change impact projections under a given emissions scenario (Supplementary Section B.5).

Extended Data Fig. 3 Sample overlap between present and future.

The density plots demonstrate the overlap in the joint income × long-run climate distributions at 2010 and 2090. Long-run climate is measured by heating degree days (a) and cooling degree days (b). Distributions are for 24,378 impact regions, in 2010 (grey-black) and 2090 under the RCP8.5 emissions scenario and SSP3 socioeconomic scenario (red-orange). All impact regions within a country are assigned the national per capita income. Although the future distribution is shifted towards higher incomes, greater cooling degree days, and fewer heating degree days, the substantial overlap in the two distributions allows for credible extrapolation of energy-temperature responses into the future (‘Projecting the impacts of climate change’ in Methods).

Extended Data Fig. 4 Climate-change induced changes in total energy expenditures at end-of-century, by present-day income deciles.

The bar chart above depicts annual climate-change induced changes in total energy expenditures at 2099 under a high emissions scenario (RCP8.5) and the SSP3 socioeconomic scenario, separately for each decile of 2012 national per capita income. Income deciles are calculated across all countries at 2012; representative countries in selected deciles are indicated. Expenditures are calculated under a 1.4% annual price growth scenario and are expressed in 2019 USD per capita based on each decile’s projected 2099 population. Bars represent mean estimates across an ensemble of 33 climate models. Intervals indicate 5th–95th percentiles of projected distributions, accounting for climate model and econometric uncertainty (Supplementary Section B.5). The chart demonstrates that heterogeneity in expenditure changes at end-of-century (Fig. 2a) is systematically correlated with present-day national income per capita. Over the upper half of the present-day income distribution, we find that countries with higher incomes today are generally projected to experience larger overall net savings at end-of-century. This partly reflects the fact that today’s richest countries tend to be in temperate climates, where energy savings from fewer cold days will more than offset increases in costs from more hot days. The smallest savings at end-of-century are projected to occur in middle deciles of the present-day income distribution, which is consistent with many of these countries being situated in the tropics and also attaining sufficiently high income levels at end-of-century to increase electricity consumption due to more hot days. The positive correlation between present-day income and net savings at end-of-century does not hold in the lower ranges of today’s income distribution. Net savings in today’s poorest deciles (i.e. first and second) are actually higher than in the third and fourth deciles, as many of the poorest countries are projected to remain too poor at end-of-century to increase electricity consumption on hot days.

Extended Data Fig. 5 The impacts of climate change on energy expenditures.

Time series of changes in total global energy expenditures under the SSP3 socioeconomic scenario for moderate (RCP4.5; Panel a) and high (RCP8.5; Panel b) emissions scenarios, assuming various energy price trajectories. Three of these trajectories are based on direct extrapolation of present-day price statistics at either moderate (1.4%), stagnant (0%), or high (3%) annual growth rates (Supplementary Section C.1), while five are based on price projections from integrated assessment models (Supplementary Section C.2) named in the legend. Expenditure changes in a given year are expressed as a percent of global GDP in that year. Aggregate global expenditure changes are obtained by monetizing and summing over the spatially disaggregated impacts across both electricity and other fuels. Regardless of the emissions scenario or assumed price trajectory, end-of-century changes (i.e. net savings) represent a minute fraction of the US $ 353 trillion end-of-century global GDP projected under SSP3.

Extended Data Fig. 6 Recovering local temperature-energy consumption relationships using aggregate energy consumption data.

An illustration demonstrating how the effect of local temperature on local per capita energy consumption can be recovered from observations of local temperatures and national per capita energy consumption. a: Let a hypothetical, linear response of daily temperature and energy consumption exist at a local (i.e. grid cell) level, depicted by the diagonal grey line. Let E denote baseline daily energy consumption on a 20 °C day. Average per capita energy consumption is observed on day d in countries i (blue circle) and j (pink circle), respectively consisting of 8 and 5 equally populated grid cells experiencing different temperatures. While the temperature is observed in each grid cell, only the national average per capita energy consumption is observed. b: Height of each bar represents unobserved energy consumption on day d within each grid cell. Pink bars are grid cells in country j and blue bars are grid cells in country i. Energy consumption within each grid cell responds to temperature within that grid cell. Averaging temperature and per capita energy consumption across grid cells within each country produces the country-level observations in Panel a. A regression using these observations recovers the grid cell-level response. Note that this illustration depicts a linear energy-temperature response for illustrative purposes, however a nonlinear temperature-energy consumption response can be recovered as well, if nonlinear transformations of temperature are computed at the grid-cell-level before being aggregated to the national level (‘Econometric estimation of energy–temperature responses’ in Methods, Equation 2).

Extended Data Table 1 Social cost of energy consumption due to climate change under alternative future price scenarios
Extended Data Table 2 Social cost of energy consumption due to climate change under alternative future price scenarios
Extended Data Table 3 Feedback effects of climate change-induced energy consumption on CO2 emissions

Supplementary information

Supplementary Information

This file contains raw datasets, numerical methods, intermediate results and further supplementary information. See page 1 for list of contents.

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Rode, A., Carleton, T., Delgado, M. et al. Estimating a social cost of carbon for global energy consumption. Nature 598, 308–314 (2021). https://doi.org/10.1038/s41586-021-03883-8

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