Probabilistic cost estimates for climate change mitigation (original) (raw)

Nature volume 493, pages 79–83 (2013)Cite this article

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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.

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Figure 2: Influence of mitigation technology, energy demand and political inaction on the cost–risk distributions for staying below 2 °C.

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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

Authors and Affiliations

  1. Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, Zürich, CH-8092, Switzerland
    Joeri Rogelj
  2. International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361, Laxenburg, Austria
    Joeri Rogelj, David L. McCollum & Keywan Riahi
  3. New Zealand Agricultural Greenhouse Gas Research Centre, Private Bag 11008, Palmerston North 4442, New Zealand ,
    Andy Reisinger
  4. School of Earth Sciences, University of Melbourne, Victoria, 3010, Australia
    Malte Meinshausen
  5. PRIMAP Group, Potsdam Institute for Climate Impact Research, PO Box 60 12 03, 14412 Potsdam, Germany ,
    Malte Meinshausen
  6. Graz University of Technology, Inffeldgasse, Graz, A-8010, Austria
    Keywan Riahi

Authors

  1. Joeri Rogelj
  2. David L. McCollum
  3. Andy Reisinger
  4. Malte Meinshausen
  5. Keywan Riahi

Contributions

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.

Corresponding author

Correspondence toJoeri Rogelj.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information (download PDF )

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 (download XLSX )

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). https://doi.org/10.1038/nature11787

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  1. Norman Rogers 2 January 2013, 18:21
    Yes, the target keeping global warming below 2 degrees is a popular target. It might be 1.83 degrees if it was based on anything except popular appeal. Yes, the climate modeling community has spent years improving their models. That doesn?t prove that the models are any good, but having spent years and billions, nobody is going to be frank about that. All the estimates of cost depend on the ability of the science of economics to predict the future. That speaks for itself. When you combine non-linear models that contain internal non-linearities and parametrizations used to force the models to fit history, errors are being multiplied by errors as happens when their are non-linearities. Complex models are often forced to fit the past but give completely wrong results about the future because there are so many free parameters that they can fit anything.

Editorial Summary

Climate mitigation: political indecision costs dear

Uncertainties in the costs of climate change mitigation are underpinned by uncertainties in geophysics, technology, social systems and politics. Usually the geophysical uncertainties are assessed separately from the other three, making an overall assessment of the main uncertainties difficult. Here, Joeri Rogelj and colleagues use an integrated modelling approach to calculate the mitigation costs of staying below a certain global warming threshold, such as the much-discussed 2 °C, as affected by the four main uncertainties. They find that political uncertainties have by far the largest impact on the cost distribution. From their results the authors conclude that we would have to adopt a high-efficiency, low-energy-demand course well before 2020, as well as mitigation efforts, if the 2 °C objective were to become a reality.

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