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Global non-linear effect of temperature on economic production

Nature 527, 235–239 (12 November 2015) | Download Citation

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Abstract

Growing evidence demonstrates that climatic conditions can have a profound impact on the functioning of modern human societies1,2, but effects on economic activity appear inconsistent. Fundamental productive elements of modern economies, such as workers and crops, exhibit highly non-linear responses to local temperature even in wealthy countries3,4. In contrast, aggregate macroeconomic productivity of entire wealthy countries is reported not to respond to temperature5, while poor countries respond only linearly5,6. Resolving this conflict between micro and macro observations is critical to understanding the role of wealth in coupled human–natural systems7,8 and to anticipating the global impact of climate change9,10. Here we unify these seemingly contradictory results by accounting for non-linearity at the macro scale. We show that overall economic productivity is non-linear in temperature for all countries, with productivity peaking at an annual average temperature of 13 °C and declining strongly at higher temperatures. The relationship is globally generalizable, unchanged since 1960, and apparent for agricultural and non-agricultural activity in both rich and poor countries. These results provide the first evidence that economic activity in all regions is coupled to the global climate and establish a new empirical foundation for modelling economic loss in response to climate change11,12, with important implications. If future adaptation mimics past adaptation, unmitigated warming is expected to reshape the global economy by reducing average global incomes roughly 23% by 2100 and widening global income inequality, relative to scenarios without climate change. In contrast to prior estimates, expected global losses are approximately linear in global mean temperature, with median losses many times larger than leading models indicate.

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Acknowledgements

We thank D. Anthoff, M. Auffhammer, V. Bosetti, M. P. Burke, T. Carleton, M. Dell, L. Goulder, S. Heft-Neal, B. Jones, R. Kopp, D. Lobell, F. Moore, J. Rising, M. Tavoni, and seminar participants at Berkeley, Harvard, Princeton, Stanford universities, Institute for the Study of Labor, and the World Bank for useful comments.

Author information

    • Marshall Burke
    •  & Solomon M. Hsiang

    These authors contributed equally to this work.

Affiliations

  1. Department of Earth System Science, Stanford University, California 94305, USA

    • Marshall Burke

  2. Center on Food Security and the Environment, Stanford University, California 94305, USA

    • Marshall Burke

  3. Goldman School of Public Policy, University of California, Berkeley, California 94720, USA

    • Solomon M. Hsiang

  4. National Bureau of Economic Research

    • Solomon M. Hsiang
    •  & Edward Miguel

  5. Department of Economics, University of California, Berkeley, California, 94720, USA

    • Edward Miguel

Authors

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Contributions

M.B. and S.M.H. conceived of and designed the study; M.B. and S.M.H. collected and analysed the data; M.B., S.M.H. and E.M. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Marshall Burke.

Replication data have been deposited at the Stanford Digital Repository (http://purl.stanford.edu/wb587wt4560).

Extended data

Extended data figures

  1. 1.

    Understanding the non-linear response function.

  2. 2.

    Growth versus level effects, and comparison of rich and poor responses.

  3. 3.

    Comparison of our results and those of Dell, Jones and Olken5.

  4. 4.

    Projected impact of climate change (RCP8.5, SSP5) on regional per capita GDP by 2100, relative to a world without climate change, under the four alternative historical response functions.

  5. 5.

    Projected impact of climate change (RCP8.5) by 2100 relative to a world without climate change, for different historical response functions and different future socioeconomic scenarios.

  6. 6.

    Estimated damages at different levels of temperature increase by socioeconomic scenario and assumed response function, and comparison of these results to damage functions in IAMs.

Extended data tables

  1. 1.

    Regression estimates for global sample, main estimate and robustness

  2. 2.

    Comparing temperature effects on per-capita growth in rich versus poor countries

  3. 3.

    Projected impacts of climate change on global GDP per capita by 2100 under RCP8.5, relative to a world without climate change

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Text and Data, Supplementary Tables 1-3 and additional references (see Page 1 for more details).

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DOI

https://doi.org/10.1038/nature15725

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