Probabilistic cost estimates for climate change mitigation

  • A Corrigendum to this article was published on 15 January 2014

Abstract

For more than a decade, the target of keeping global warming below 2 °C has been a key focus of the international climate debate1. In response, the scientific community has published a number of scenario studies that estimate the costs of achieving such a target2,3,4,5. Producing these estimates remains a challenge, particularly because of relatively well known, but poorly quantified, uncertainties, and owing to limited integration of scientific knowledge across disciplines6. The integrated assessment community, on the one hand, has extensively assessed the influence of technological and socio-economic uncertainties on low-carbon scenarios and associated costs2,3,4,7. The climate modelling community, on the other hand, has spent years improving its understanding of the geophysical response of the Earth system to emissions of greenhouse gases8,9,10,11,12. This geophysical response remains a key uncertainty in the cost of mitigation scenarios but has been integrated with assessments of other uncertainties in only a rudimentary manner, that is, for equilibrium conditions6,13. Here we bridge this gap between the two research communities by generating distributions of the costs associated with limiting transient global temperature increase to below specific values, taking into account uncertainties in four factors: geophysical, technological, social and political. We find that political choices that delay mitigation have the largest effect on the cost–risk distribution, followed by geophysical uncertainties, social factors influencing future energy demand and, lastly, technological uncertainties surrounding the availability of greenhouse gas mitigation options. Our information on temperature risk and mitigation costs provides crucial information for policy-making, because it clarifies the relative importance of mitigation costs, energy demand and the timing of global action in reducing the risk of exceeding a global temperature increase of 2 °C, or other limits such as 3 °C or 1.5 °C, across a wide range of scenarios.

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Figure 1: Methodology for creating cost–risk relationships for a given temperature limit.
Figure 2: Influence of mitigation technology, energy demand and political inaction on the cost–risk distributions for staying below 2 °C.
Figure 3: Cost–risk distributions for returning global temperature increase to below 1.5 °C by 2100.

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Acknowledgements

We thank V. Krey, P. Kolp and M. Strubegger for their support in developing the model set-up and extracting the results, R. Knutti and R. Socolow for comments and feedback during the writing process and S. Hatfield-Dodds, whose review comments substantially contributed to improving our manuscript. J.R. was supported by the Swiss National Science Foundation (project 200021-135067) and the IIASA Peccei Award Grant.

Author information

All authors were involved in designing the research. J.R. performed the research in collaboration with D.L.M. All authors contributed to writing the paper.

Correspondence to Joeri Rogelj.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Text 1-3, which includes background information about our modelling framework, setup and results, Supplementary Tables 1–2, Supplementary Figures 1-10, Supplementary References. This file was replaced on 15 January 2014 and contains updated versions of Supplementary Figures 2, 3, 4, 5, 8, and 9, in line with the description in the Corrigendum 10.1038/nature12937. None of these changes affect our conclusions or discussion of results. (PDF 2632 kb)

Supplementary Data

This file contains a Cost-Risk Check Tool, which allows for interactive querying of our cost-risk distributions for 1.5, 2, 2.5, and 3°C. Different cost metrics can be selected for various energy supply and demand combinations. This file was replaced on 15 January 2014. (XLSX 204 kb)

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Rogelj, J., McCollum, D., Reisinger, A. et al. Probabilistic cost estimates for climate change mitigation. Nature 493, 79–83 (2013) doi:10.1038/nature11787

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