Global heating increases household and industrial energy use (for cooling) *especially in Australia* further increasing household energy bills

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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. Nature508, 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. Science258, 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. Change7, 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. Change7, 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 DataTechnical 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 Agencyhttps://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).

Download references

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.

Author information

Affiliations

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.

Pledge Your Vote Now
Change language