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The costs of achieving climate targets and the sources of uncertainty


Effective climate policy requires information from various scientific disciplines. Here, we construct a metamodel from climate and integrated assessment models that assesses the emissions budget, costs and uncertainty sources of achieving temperature targets. By calibrating to the model-based literature range, the metamodel goes beyond the parametric uncertainty of individual models. The resulting median estimates for the cumulative abatement costs (at 5% discount rate) for 2 °C and 1.5 °C targets are around US$15 trillion and US$30 trillion, but estimates vary over a wide range (US$10–100 trillion for the 1.5 °C target). The sources determining this uncertainty depend on the climate target stringency. Climate system uncertainty dominates at high warming levels, but uncertainty in emissions reductions costs dominates for the Paris Agreement targets. In fact, costs differences between different socio-economic development paths can be larger than the difference in median estimates for the 2 °C and 1.5 °C targets. This simple metamodel helps to explore implications of scenario uncertainty and identify research priorities.

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Fig. 1: Key relationships in the literature relevant to the costs of achieving climate targets.
Fig. 2: Application of the metamodel.
Fig. 3: The relative contribution of single factors in the overall variance in mitigation costs.

Data availability

The data used to assess the impact of non-CO2 emissions and the mitigation costs that support the findings of this study are publicly available online at the scenario databases hosted by IIASA: (ref. 4), (ref. 2) and (ref. 18). The data for each figure are available at the PBL/IMAGE website.

Code availability

Model codes are available at (


  1. 1.

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

  2. 2.

    Clarke, L. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 414–510 (Cambridge Univ. Press, 2014).

  3. 3.

    Edenhofer, O. et al. The economics of low stabilization: model comparison of mitigation strategies and costs. Energy J. 31, 11–48 (2010).

    Google Scholar 

  4. 4.

    Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environ. Chang. 42, 153–168 (2017).

    Article  Google Scholar 

  5. 5.

    Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8, 325–332 (2018).

    CAS  Article  Google Scholar 

  6. 6.

    Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).

    Article  Google Scholar 

  7. 7.

    Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (Cambridge Univ. Press, 2013).

  8. 8.

    Millar, R. J. et al. Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nat. Geosci. 10, 741–747 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Schweizer, V. A few scenarios still do not fit all. Nat. Clim. Change 8, 361–362 (2018).

    Article  Google Scholar 

  10. 10.

    Rozenberg, J., Guivarch, C., Lempert, R. & Hallegatte, S. Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation. Clim. Change 122, 509–522 (2014).

    Article  Google Scholar 

  11. 11.

    Guivarch, C., Lempert, R. & Trutnevyte, E. Scenario techniques for energy and environmental research: an overview of recent developments to broaden the capacity to deal with complexity and uncertainty. Environ. Modell. Softw. 97, 201–210 (2017).

    Article  Google Scholar 

  12. 12.

    Van Vuuren, D. P. et al. Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Clim. Change 81, 119–159 (2007).

    CAS  Article  Google Scholar 

  13. 13.

    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 

  14. 14.

    Meinshausen, M. et al. Greenhouse-gas emission targets for limiting global warming to 2°C. Nature 458, 1158–1162 (2009).

    CAS  Article  Google Scholar 

  15. 15.

    Gillett, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).

    Article  Google Scholar 

  16. 16.

    Booth, B. B. B. et al. High sensitivity of future global warming to land carbon cycle processes. Environ. Res. Lett. 7, 024002 (2012).

    Article  Google Scholar 

  17. 17.

    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. Clim. Past 14, 139–155 (2018).

    Article  Google Scholar 

  18. 18.

    IPCC. Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

  19. 19.

    van Vuuren, D. P. et al. Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials. Energy Policy 37, 5125–5139 (2009).

    Article  Google Scholar 

  20. 20.

    Pollitt, H. & Mercure, J. F. The role of money and the financial sector in energy-economy models used for assessing climate and energy policy. Clim. Policy 18, 184–197 (2017).

    Article  Google Scholar 

  21. 21.

    Kriegler, E. et al. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Clim. Change 123, 353–367 (2014).

    Article  Google Scholar 

  22. 22.

    Luderer, G. et al. Residual fossil CO2 emissions in 1.5–2 °C pathways. Nat. Clim. Change 8, 626–633 (2018).

    CAS  Article  Google Scholar 

  23. 23.

    Millar, R. J. & Friedlingstein, P. The utility of the historical record for assessing the transient climate response to cumulative emissions. Phil. Trans. R. Soc. A 376, 20160449 (2018).

    Article  Google Scholar 

  24. 24.

    Butler, M. P., Reed, P. M., Fisher-Vanden, K., Keller, K. & Wagener, T. Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis. Environ. Modell. Softw. 59, 10–29 (2014).

    Article  Google Scholar 

  25. 25.

    Lemoine, D. & McJeon, H. C. Trapped between two tails: trading off scientific uncertainties via climate targets. Environ. Res. Lett. 8, 034019 (2013).

  26. 26.

    Lamontagne, J. R., Reed, P. M., Marangoni, G., Keller, K. & Garner, G. G. Robust abatement pathways to tolerable climate futures require immediate global action. Nat. Clim. Change 9, 290–294 (2019).

    Article  Google Scholar 

  27. 27.

    Gillingham, K. et al. Modeling uncertainty in integrated assessment of climate change: a multimodel comparison. J. Assoc. Environ. Resour. Econ. 5, 791–826 (2018).

    Google Scholar 

  28. 28.

    Sutton, R. T. ESD ideas: a simple proposal to improve the contribution of IPCC WGI to the assessment and communication of climate change risks. Earth Syst. Dynam. 9, 1155–1158 (2018).

    Article  Google Scholar 

  29. 29.

    IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  30. 30.

    Rogelj, J. et al. Differences between carbon budget estimates unravelled. Nat. Clim. Change 6, 245–252 (2016).

    Article  Google Scholar 

  31. 31.

    Vose, D. Risk Analysis: A Quantitative Guide (John Wiley & Sons, 2008).

  32. 32.

    Meinshausen, M., Raper, S. C. B. & Wigley, T. M. L. Emulating coupled atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: model description and calibration. Atmos. Chem. Phys. 11, 1417–1456 (2011).

    CAS  Article  Google Scholar 

  33. 33.

    The common Integrated Assessment Model (IAM) documentation. IAMC (2018).

  34. 34.

    Wijst, K.-I. v. d. Optimal Policy for Carbon Pricing: Challenging the Hotelling Rule and Dissecting Mitigation Cost Uncertainties. Master of Mathematical Sciences thesis, Utrecht Univ. (2018).

  35. 35.

    Sobol, I. M. On sensitivity estimation for nonlinear mathematical models. Matem. Mod. 2, 112–118 (1990).

    Google Scholar 

  36. 36.

    Saltelli, A. Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun. 145, 280–297 (2002).

    CAS  Article  Google Scholar 

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The research presented in this paper benefitted from funding under the European Union’s Horizon 2020 Framework Programme for Research and Innovation under grant agreement nos. 82112 (NAVIGATE) and 641816 (CRESCENDO). In addition, C.J. was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (grant no. GA01101).

Author information




D.P.v.V., K.-I.v.d.W. and S.M. designed the metamodel and the experiments. All authors contributed to the elaboration of the model and the writing of the article.

Corresponding author

Correspondence to D. P. van Vuuren.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks James Glynn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Discussions 1–4, Figs. 1–14 and Table 1.

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van Vuuren, D.P., van der Wijst, KI., Marsman, S. et al. The costs of achieving climate targets and the sources of uncertainty. Nat. Clim. Chang. 10, 329–334 (2020).

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