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New damage curves and multimodel analysis suggest lower optimal temperature

Nature Climate Change volume 13, pages 434–441 (2023)Cite this article

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Abstract

Economic analyses of global climate change have been criticized for their poor representation of climate change damages. Here we develop and apply aggregate damage functions in three economic Integrated Assessment Models (IAMs) with different degrees of complexity. The damage functions encompass a wide but still incomplete set of climate change impacts based on physical impact models. We show that with medium estimates for damage functions, global damages are in the range of 10% to 12% of GDP by 2100 in a baseline scenario with 3 °C temperature change, and about 2% in a well-below 2 °C scenario. These damages are much higher than previous estimates in benefit-cost studies, resulting in optimal temperatures below 2 °C with central estimates of damages and discount rates. Moreover, we find a benefit-cost ratio of 1.5 to 3.9, even without considering damages that could not be accounted for, such as biodiversity losses, health and tipping points.

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Fig. 1: Overview of the creation and use of the damage functions.
Fig. 2: End-of-century damages for the five macro-regions for two scenarios.
Fig. 3: Sensitivity analysis of the global damage costs.
Fig. 4: Emission pathways, damage costs and climate policy costs in CBA setting.
Fig. 5: Optimal temperature in 2100 in CBA for different levels of discounting and SLR adaptation assumptions.
Fig. 6: BCR for the CBA scenario using the medium damage function (50th percentile).

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Data availability

All regional damage coefficients for the reduced-form climate change damage functions are available at https://zenodo.org/record/5546264#.YlWeBehBw2w. This includes the sea-level rise, non-sea-level rise and combined damage functions for all used damage quantiles. All scenario data from the three models are available at https://doi.org/10.5281/zenodo.7627679. Source data are provided with this paper.

Code availability

The calculations and the figures used in this paper and the scripts required to reproduce them are available at https://doi.org/10.5281/zenodo.7627679.

The model code and documentation of the MIMOSA model are available at https://github.com/kvanderwijst/Project-MIMOSA/, of the WITCH model at https://www.witchmodel.org/ and of the REMIND model at https://rse.pik-potsdam.de/doc/remind/2.1.0/ and https://github.com/remindmodel/remind for the model code.

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Acknowledgements

The research presented in this paper and all authors benefitted from funding under the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No. 776479 for the project CO-designing the Assessment of Climate Change costs (COACCH, https://www.coacch.eu) and from the European Commission Horizon 2020 Programme H2020/2019–2023 under Grant Agreement No. 821124 (NAVIGATE).

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Authors and Affiliations

  1. PBL Netherlands Environmental Assessment Agency, Den Haag, the Netherlands

    Kaj-Ivar van der Wijst, Andries Hof & Detlef van Vuuren

  2. Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands

    Kaj-Ivar van der Wijst, Andries Hof & Detlef van Vuuren

  3. RFF-CMCC European Institute on Economics and the Environment (EIEE), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Milan, Italy

    Francesco Bosello, Shouro Dasgupta, Laurent Drouet, Johannes Emmerling, Ramiro Parrado & Gabriele Standardi

  4. CMCC@Ca’Foscari Centro Euro-Mediterraneo sui Cambiamenti Climatici, Università Ca’Foscari, Venice, Italy

    Francesco Bosello, Shouro Dasgupta, Ramiro Parrado & Gabriele Standardi

  5. Department of Environmental Sciences, Informatics and Statistics, Ca’Foscari University of Venice, Venice, Italy

    Francesco Bosello

  6. Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science (LSE), London, UK

    Shouro Dasgupta

  7. Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

    Marian Leimbach & Franziska Piontek

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Contributions

All authors contributed to the manuscript, the development of the idea and set up of the study. F.B., R.P., G.S., S.D. and K.-I.v.d.W. developed the damage functions. F.B., L.D., J.E., A.H., M.L., F.P., D.v.V. and K.-I.v.d.W. developed and ran the CBA scenarios. K.-I.v.d.W. performed the multimodel analysis.

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Correspondence to Kaj-Ivar van der Wijst.

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Nature Climate Change thanks Elisa Lanzi, Jarmo Kikstra and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Calculation of the costs and the benefits (avoided damages) for the Benefit-Cost-Ratio analysis.

First, the direct policy and residual damage costs are scaled to include the indirect costs (remaining difference with a baseline run without damages). The scaled residual damages are subtracted from the total damages from a no-policy run.

Extended Data Table 1 Impacts categories included in the estimation of the reduced-form climate change damage functions and implementation for their economic assessment
Full size table

Supplementary information

Supplementary Information

Extra figures, description of how the damage functions were created, and more information on the updates of the MIMOSA and the WITCH models.

Source data

Source Data Fig. 1

All data points of the damage function subplot.

Source Data Fig. 2

All data points.

Source Data Fig. 3

All data points.

Source Data Fig. 4

All data points.

Source Data Fig. 5

All data points.

Source Data Fig. 6

All data points.

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van der Wijst, KI., Bosello, F., Dasgupta, S. et al. New damage curves and multimodel analysis suggest lower optimal temperature. Nat. Clim. Chang. 13, 434–441 (2023). https://doi.org/10.1038/s41558-023-01636-1

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