Skip to main content

Thank you for visiting nature.com. 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.

Selection of climate policies under the uncertainties in the Fifth Assessment Report of the IPCC

Nature Climate Change volume 5, pages 937–940 (2015)Cite this article

Subjects

Abstract

Strategies for dealing with climate change must incorporate and quantify all the relevant uncertainties, and be designed to manage the resulting risks1. Here we employ the best available knowledge so far, summarized by the three working groups of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5; refs 2, 3, 4), to quantify the uncertainty of mitigation costs, climate change dynamics, and economic damage for alternative carbon budgets. We rank climate policies according to different decision-making criteria concerning uncertainty, risk aversion and intertemporal preferences. Our findings show that preferences over uncertainties are as important as the choice of the widely discussed time discount factor. Climate policies consistent with limiting warming to 2 °C above preindustrial levels are compatible with a subset of decision-making criteria and some model parametrizations, but not with the commonly adopted expected utility framework.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: Diagram illustrating the methodology.
Figure 2: Influence of carbon budgets, model and state uncertainties on the distribution of the gross world product (GWP) per capita in 2100.
Figure 3: Selected carbon budgets according to three decision-making criteria: relative risk aversion, consumption smoothing and pure rate of time preference.

References

  1. Kunreuther, H. et al. Risk management and climate change. Nature Clim. Change 3, 447–450 (2013).

    Article  Google Scholar 

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

    Google Scholar 

  3. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

    Google Scholar 

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

    Google Scholar 

  5. Cerreia-Vioglio, S., Maccheroni, F., Marinacci, M. & Montrucchio, L. Classical subjective expected utility. Proc. Natl Acad. Sci. USA 110, 6754–6759 (2013).

    Article  CAS  Google Scholar 

  6. Savage, L. J. The Foundations of Statistics (Dover Publications, 1954).

    Google Scholar 

  7. Heal, G. & Millner, A. Uncertainty and decision making in climate change economics. Rev. Environ. Econ. Policy 8, 120–137 (2014).

    Article  Google Scholar 

  8. Ellsberg, D. Risk, ambiguity, and the Savage axioms. Q. J. Econ. 75, 643–669 (1961).

    Article  Google Scholar 

  9. Lempert, R. J. Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis (Rand, 2003).

    Book  Google Scholar 

  10. George, E. I. & Clyde, M. Model uncertainty. Stat. Sci. 19, 81–94 (2004).

    Article  Google Scholar 

  11. Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Phil. Trans. R. Soc. A 365, 2053–2075 (2007).

    Article  Google Scholar 

  12. Knutti, R., Furrer, R., Tebaldi, C., Cermak, J. & Meehl, G. A. Challenges in combining projections from multiple climate models. J. Clim. 23, 2739–2758 (2009).

    Article  Google Scholar 

  13. Nordhaus, B. Warming the World: Economic Model of Global Warming (MIT Press, 2000).

    Book  Google Scholar 

  14. Millner, A., Dietz, S. & Heal, G. Scientific ambiguity and climate policy. Environ. Resour. Econ. 55, 21–46 (2013).

    Article  Google Scholar 

  15. Ackerman, F., Stanton, E. A. & Bueno, R. Epstein–Zin utility in dice: Is risk aversion irrelevant to climate policy? Environ. Resour. Econ. 56, 73–84 (2013).

    Article  Google Scholar 

  16. Tavoni, M. et al. Post-2020 climate agreements in the major economies assessed in the light of global models. Nature Clim. Change 5, 119–126 (2014).

    Article  Google Scholar 

  17. 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. Climatic Change 123, 353–367 (2014).

    Article  Google Scholar 

  18. Babonneau, F., Haurie, A., Loulou, R. & Vielle, M. Combining stochastic optimization and Monte Carlo simulation to deal with uncertainties in climate policy assessment. Environ. Model. Assess. 17, 51–76 (2012).

    Article  Google Scholar 

  19. 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 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Steinacher, M., Joos, F. & Stocker, T. F. Allowable carbon emissions lowered by multiple climate targets. Nature 499, 197–201 (2013).

    Article  CAS  Google Scholar 

  23. Urban, N. M. & Keller, K. Probabilistic hindcasts and projections of the coupled climate, carbon cycle and Atlantic Meridional Overturning Circulation system: A Bayesian fusion of century-scale observations with a simple model. Tellus A 62, 737–750 (2010).

    Article  Google Scholar 

  24. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  25. Ackerman, F., DeCanio, S. J., Howarth, R. B. & Sheeran, K. Limitations of integrated assessment models of climate change. Climatic Change 95, 297–315 (2009).

    Article  CAS  Google Scholar 

  26. Pindyck, R. S. Climate change policy: What do the models tell us? J. Econ. Literature 51, 860–872 (2013).

    Article  Google Scholar 

  27. Interagency Working Group on Social Cost of Carbon. Technical Update of The Social Cost of Carbon for Regulatory Impact Analysis—Under Executive Order 12866 (United States Government, 2013).

  28. Wald, A. Statistical decision functions. Ann. Math. Stat. 20, 165–205 (1949).

    Article  Google Scholar 

  29. Houser, T. et al. American Climate Prospectus: Economic Risks in The United States (Rhodium Group, 2014).

    Google Scholar 

  30. Knutti, R. & Sedláček, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Clim. Change 3, 369–373 (2013).

    Article  Google Scholar 

  31. Calvin, K. et al. The role of Asia in mitigating climate change: Results from the Asia modeling exercise. Energy Econ. 34, S251–S260 (2012).

    Article  Google Scholar 

  32. Riahi, K. et al. Locked into Copenhagen pledges—implications of short-term emission targets for the cost and feasibility of long-term climate goals. Technol. Forecast. Soc. Change 90, 8–23 (2013).

    Article  Google Scholar 

  33. Kriegler, E. et al. Roadmaps Towards Sustainable Energy Futures and Climate Protection: A Synthesis of Results from The RoSE Project 1st edn (Potsdam Institute for Climate Impact Research, 2013); http://www.rose-project.org/Content/Public/RoSE_REPORT_310513_ES.pdf

    Google Scholar 

  34. Fischer, C. & Morgenstern, R. D. Carbon abatement costs: Why the wide range of estimates? Energy J. 27, 73–86 (2006).

    Article  Google Scholar 

  35. Kriegler, E. Imprecise Probability Analysis for Integrated Assessment of Climate Change PhD thesis, Potsdam Univ. (2005)

  36. Ricciuto, D. M., Davis, K. J. & Keller, K. A Bayesian calibration of a simple carbon cycle model: The role of observations in estimating and reducing uncertainty. Glob. Biogeochem. Cycles 22, GB2030 (2008).

    Article  Google Scholar 

  37. Welch, B. L. The generalization of ‘student’s’ problem when several different population variances are involved. Biometrika 34, 28–35 (1947).

    CAS  Google Scholar 

  38. Tol, R. S. Targets for global climate policy: An overview. J. Econ. Dynam. Control 37, 911–928 (2013).

    Article  Google Scholar 

  39. Weitzman, M. Handbook of Environmental Accounting 187–207 (Edward Elgar, 2010).

    Google Scholar 

  40. Weitzman, M. L. Targets as insurance against catastrophic climate damages. J. Public Econ. Theory 14, 221–244 (2012).

    Article  Google Scholar 

  41. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    Article  CAS  Google Scholar 

  42. Epstein, L. G. & Zin, S. E. Substitution, risk aversion, and the temporal behavior of consumption and asset returns: A theoretical framework. Econometrica 57, 937–969 (1989).

    Article  Google Scholar 

  43. Gilboa, I. & Schmeidler, D. Maxmin expected utility with non-unique prior. J. Econ. Perspect. 18, 141–153 (1989).

    Google Scholar 

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

    Book  Google Scholar 

  45. Nordhaus, W. D. & Weitzman, M. L. A Review of the “Stern Review on the Economics of Climate Change”. J. Econ. Literature 45, 703–724 (2007).

    Article  Google Scholar 

  46. Vissing-Jörgensen, A. & Attanasio, O. P. Stock-market participation, intertemporal substitution, and risk-aversion. Am. Econ. Rev. 93, 383–391 (2003).

    Article  Google Scholar 

  47. Bansal, R. & Yaron, A. Risks for the long run: A potential resolution of asset pricing puzzles. J. Finance 59, 1481–1509 (2004).

    Article  Google Scholar 

Download references

Acknowledgements

We thank K. Keller from the Pennsylvania State University for supplying the climate model and his support. The paper was written while V.B. and M.T. were fellows at the Center for Advanced Studies in the Behavioural Sciences (CASBS) at Stanford University. The research leading to these results has received funding from the Italian Ministry of Education, University and Research and the Italian Ministry of Environment, Land and Sea under the GEMINA project, from the EU FP7 under grant agreement no. 308329 (ADVANCE) and from the European Research Council 336703-RISICO at IEFE, Bocconi University.

Author information

Authors and Affiliations

  1. Fondazione Eni Enrico Mattei (FEEM), Corso Magenta 63, 20123 Milan, Italy,

    L. Drouet, V. Bosetti & M. Tavoni

  2. Centro Euro-mediterraneo sui Cambiamenti Climatici (CMCC), Corso Magenta 63, 20123 Milan, Italy,

    L. Drouet, V. Bosetti & M. Tavoni

  3. Department of Economics, Bocconi University, Via Sarfatti 25, 20136 Milan, Italy,

    V. Bosetti

  4. Department of Management and Economics, Politecnico di Milano, Via Lambruschini 4/B, 20156 Milan, Italy,

    M. Tavoni

Authors
  1. L. Drouet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Bosetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Tavoni
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors were involved in designing the research and contributed equally to the writing of the manuscript. L.D. performed the scientific computing.

Corresponding author

Correspondence to L. Drouet.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Drouet, L., Bosetti, V. & Tavoni, M. Selection of climate policies under the uncertainties in the Fifth Assessment Report of the IPCC. Nature Clim Change 5, 937–940 (2015). https://doi.org/10.1038/nclimate2721

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate2721

This article is cited by

Search

Advanced search

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