Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

New damage curves and multimodel analysis suggest lower optimal temperature


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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

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).

Data availability

All regional damage coefficients for the reduced-form climate change damage functions are available at 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 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

The model code and documentation of the MIMOSA model are available at, of the WITCH model at and of the REMIND model at and for the model code.


  1. Rogelj, J., McCollum, D. L., Reisinger, A., Meinshausen, M. & Riahi, K. Probabilistic cost estimates for climate change mitigation. Nature 493, 79–83 (2013).

    Article  Google Scholar 

  2. Krey, V. Global energy-climate scenarios and models: a review. Wiley Interdiscip. Rev. Energy Environ. 3, 363–383 (2014).

    Google Scholar 

  3. IPPC. Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) (Cambridge Univ. Press, 2014).

  4. van Vuuren, D. P. et al. The costs of achieving climate targets and the sources of uncertainty. Nat Clim. Change 10, 329–334 (2020).

  5. Köberle, A. C. et al. The cost of mitigation revisited. Nat. Clim. Change 11, 1035–1045 (2021).

    Article  Google Scholar 

  6. Harmsen, M. et al. Integrated assessment model diagnostics: key indicators and model evolution. Environ. Res. Lett. 16, 054046 (2021).

    Article  Google Scholar 

  7. Riahi, K. et al. Cost and attainability of meeting stringent climate targets without overshoot. Nat. Clim. Change 11, 1063–1069 (2021).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

  10. Kahn, M. E. et al. Long-term Macroeconomic Effects of Climate Change: A Cross-country Analysis Globalization Institute Working Paper 365 (Federal Reserve Bank Dallas, 2019).

  11. Howard, P. H. & Sterner, T. Few and not so far between: a meta-analysis of climate damage estimates. Environ. Resour. Econ. 68, 197–225 (2017).

    Article  Google Scholar 

  12. Bosello, F., Dasgupta, S., Parrado, R., Standardi, G. & van der Wijst, K.-I. Revisiting the Concept of Damage Functions—Deliverable for the Coacch Project - D4.3 Macroeconomic Assessment of Policy Effectiveness (COACCH Project, 2021);

  13. Tsigas, M., Frisvold, G. & Kuhn, B. in Global Trade Analysis: Modeling and Applications (ed Hertel, T.) 280–304 (Cambridge Univ. Press, 1997).

  14. Dellink, R., Lanzi, E. & Chateau, J. The sectoral and regional economic consequences of climate change to 2060. Environ. Resour. Econ. 72, 309–363 (2019).

    Article  Google Scholar 

  15. Szewczyk, W. et al. Economic Analysis of Selected Climate Impacts JRC Techinical Report (European Commission, 2020).

  16. Parrado, R. & de Cian, E. Technology spillovers embodied in international trade: intertemporal, regional and sectoral effects in a global CGE framework. Energy Econ. 41, 76–89 (2014).

    Article  Google Scholar 

  17. Eboli, F., Parrado, R. & Roson, R. Climate-change feedback on economic growth: explorations with a dynamic general equilibrium model. Environ. Dev. Econ. 15, 515–533 (2010).

    Article  Google Scholar 

  18. van der Wijst, K.-I., Hof, A. F. & van Vuuren, D. P. On the optimality of 2 °C targets and a decomposition of uncertainty. Nat. Commun. 12, 2575 (2021).

  19. Hänsel, M. C. et al. Climate economics support for the UN climate targets. Nat. Clim. Change 10, 781–789 (2020).

    Article  Google Scholar 

  20. Glanemann, N., Willner, S. N. & Levermann, A. Paris climate agreement passes the cost-benefit test. Nat. Commun. 11, 110 (2020).

    Article  CAS  Google Scholar 

  21. Rennert, K. et al. Comprehensive evidence implies a higher social cost of CO2. Nature 610, 687–692 (2022).

    Article  CAS  Google Scholar 

  22. Emmerling, J. et al. The WITCH 2016 Model - Documentation and Implementation of the Shared Socioeconomic Pathways Working Paper No. 42 (FEEM, 2016).

  23. Baumstark, L. et al. REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits. Geosci. Model Dev. 14, 6571–6603 (2021).

    Article  CAS  Google Scholar 

  24. van Vuuren, D. P. et al. A new scenario framework for climate change research: scenario matrix architecture. Clim. Change 122, 373–386 (2014).

    Article  Google Scholar 

  25. Fankhauser, S. & Tol, R. S. J. On climate change and economic growth. Resour. Energy Econ. 27, 1–17 (2005).

    Article  Google Scholar 

  26. Kikstra, J. S. et al. The social cost of carbon dioxide under climate-economy feedbacks and temperature variability. Environ. Res. Lett. 16, 094037 (2021).

    Article  CAS  Google Scholar 

  27. 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).

    Article  Google Scholar 

  28. Schinko, T. et al. Economy-wide effects of coastal flooding due to sea level rise: a multi-model simultaneous treatment of mitigation, adaptation, and residual impacts. Environ. Res Commun. 2, 015002 (2020).

    Article  Google Scholar 

  29. Leimbach, M. & Bauer, N. Capital markets and the costs of climate policies. Environ. Econ. Policy Stud. 24, 397–420 (2021).

  30. van der Wijst, K. I., Hof, A. F. & van Vuuren, D. P. Costs of avoiding net negative emissions under a carbon budget. Environ. Res. Lett. 16, 064071 (2021).

    Article  Google Scholar 

  31. Schultes, A. et al. Economic damages from on-going climate change imply deeper near-term emission cuts. Environ. Res. Lett. 16, 104053 (2021).

    Article  Google Scholar 

  32. Drupp, M. A., Freeman, M. C., Groom, B. & Nesje, F. Discounting disentangled. Am. Econ. J. Econ. Policy 10, 109–134 (2018).

    Article  Google Scholar 

  33. Stern, N. The Economics of Climate Change: The Stern Review (Cambridge Univ. Press, 2007).

  34. Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866 (InteragencyWorking Group on Social Cost of Carbon, US Government, 2010).

  35. Nordhaus, W. Estimates of the social cost of carbon: concepts and results from the DICE-2013R model and alternative approaches. J. Assoc. Environ. Resour. Econ. 1, 273–312 (2014).

    Google Scholar 

  36. Anthoff, D. & Tol, R. S. J. The Climate Framework for Uncertainty, Negotiation and Distribution (FUND), Technical Description, Version 3.9 (FUND Model, 2014).

  37. Hope, C. Critical issues for the calculation of the social cost of CO2: why the estimates from PAGE09 are higher than those from PAGE2002. Clim. Change 117, 531–543 (2013).

  38. Pindyck, R. S. The use and misuse of models for climate policy. Rev. Env. Econ. Policy 11, 100–114 (2020).

  39. Pindyck, R. S. The social cost of carbon revisited. J. Environ. Econ. Manage. 94, 140–160 (2019).

    Article  Google Scholar 

  40. Bosello, F. & Parrado, R. Macro-economic assessment of climate change impacts: methods and findings. Ekonomiaz Rev. vasca Econ. 97, 45–61 (2020).

    Google Scholar 

  41. Piontek, F. et al. Integrated perspective on translating biophysical to economic impacts of climate change. Nat. Clim. Change 11, 563–572 (2021).

    Article  Google Scholar 

  42. van den Berg, N. J. et al. Implications of various effort-sharing approaches for national carbon budgets and emission pathways. Clim. Change 162, 1805–1822 (2020).

    Article  Google Scholar 

  43. Raupach, M. R. et al. Sharing a quota on cumulative carbon emissions. Nat. Clim. Change 4, 873–879 (2014).

    Article  CAS  Google Scholar 

  44. Pan, X., Teng, F. & Wang, G. Sharing emission space at an equitable basis: allocation scheme based on the equal cumulative emission per capita principle. Appl. Energy 113, 1810–1818 (2014).

    Article  Google Scholar 

  45. Höhne, N., den Elzen, M. & Escalante, D. Regional GHG reduction targets based on effort sharing: a comparison of studies. Clim. Policy 14, 122–147 (2013).

  46. Bauer, N. et al. Quantification of an efficiency–sovereignty trade-off in climate policy. Nature 588, 261–266 (2020).

    Article  CAS  Google Scholar 

  47. Balkovič, J. et al. Pan-European crop modelling with EPIC: implementation, up-scaling and regional crop yield validation. Agric. Syst. 120, 61–75 (2013).

    Article  Google Scholar 

  48. Havlík, P. et al. Global land-use implications of first and second generation biofuel targets. Energy Policy 39, 5690–5702 (2011).

    Article  Google Scholar 

  49. Kindermann, G. et al. Global cost estimates of reducing carbon emissions through avoided deforestation. Proc. Natl Acad. Sci. USA 105, 10302–10307 (2008).

    Article  CAS  Google Scholar 

  50. Cheung, W. W. L. et al. Structural uncertainty in projecting global fisheries catches under climate change. Ecol. Modell. 325, 57–66 (2016).

    Article  CAS  Google Scholar 

  51. Blanchard, J. L. et al. Potential consequences of climate change for primary production and fish production in large marine ecosystems. Phil. Trans. R. Soc. B 367, 2979–2989 (2012).

    Article  Google Scholar 

  52. Hinkel, J. et al. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl Acad. Sci. USA 111, 3292–3297 (2014).

    Article  CAS  Google Scholar 

  53. Ward, P. J. et al. Assessing flood risk at the global scale: model setup, results, and sensitivity. Environ. Res. Lett. 8, 044019 (2013).

    Article  Google Scholar 

  54. van Ginkel, K. C. H., Dottori, F., Alfieri, L., Feyen, L. & Koks, E. E. Flood risk assessment of the European road network. Nat. Hazards Earth Syst. Sci. 21, 1011–1027 (2021).

    Article  Google Scholar 

  55. Schleypen, J. R. et al. D2.4. Impacts on Industry, Energy, Services, and Trade Deliverable of the H2020 COACCH project (COACCH Project, 2019);

  56. Dasgupta, S. et al. Effects of climate change on combined labour productivity and supply: an empirical, multi-model study. Lancet Planet Health 5, e455–e465 (2021).

    Article  Google Scholar 

  57. Lincke, D. & Hinkel, J. Economically robust protection against 21st century sea-level rise. Glob. Environ. Change 51, 67–73 (2018).

    Article  Google Scholar 

  58. den Elzen, M. G. J. & Lucas, P. L. The FAIR model: a tool to analyse environmental and costs implications of regimes of future commitments. Environ. Model. Assess. 10, 115–134 (2005).

    Article  Google Scholar 

  59. Dietz, S. & Venmans, F. Cumulative carbon emissions and economic policy: in search of general principles. J. Environ. Econ. Manage. 96, 108–129 (2019).

    Article  Google Scholar 

  60. Li, C., Held, H., Hokamp, S. & Marotzke, J. Optimal temperature overshoot profile found by limiting global sea level rise as a lower-cost climate target. Sci. Adv. 6, eaaw9490 (2020).

    Article  CAS  Google Scholar 

  61. Meinshausen, M., Wigley, T. M. L. & Raper, S. C. B. Emulating atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – part 2: applications. Atmos. Chem. Phys. 11, 1457–1471 (2011).

  62. Narayanan, G., Badri, A. A. & McDougall, R. Global Trade, Assistance, and Production: The GTAP 8 Data Base (Center for Global Trade Analysis, Purdue Univ., 2012).

  63. Visser, H., Dangendorf, S., van Vuuren, D. P., Bregman, B. & Petersen, A. C. Signal detection in global mean temperatures after ‘Paris’: an uncertainty and sensitivity analysis. Climate 14, 139–155 (2018).

    Google Scholar 

Download references


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, and from the European Commission Horizon 2020 Programme H2020/2019–2023 under Grant Agreement No. 821124 (NAVIGATE).

Author information

Authors and Affiliations



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.

Corresponding author

Correspondence to Kaj-Ivar van der Wijst.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

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.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing