Letter | Published:

Stochastic integrated assessment of climate tipping points indicates the need for strict climate policy

Nature Climate Change volume 5, pages 441444 (2015) | Download Citation


Perhaps the most ‘dangerous’ aspect of future climate change is the possibility that human activities will push parts of the climate system past tipping points, leading to irreversible impacts1. The likelihood of such large-scale singular events2 is expected to increase with global warming1,2,3, but is fundamentally uncertain4. A key question is how should the uncertainty surrounding tipping events1,5 affect climate policy? We address this using a stochastic integrated assessment model6, based on the widely used deterministic DICE model7. The temperature-dependent likelihood of tipping is calibrated using expert opinions3, which we find to be internally consistent. The irreversible impacts of tipping events are assumed to accumulate steadily over time (rather than instantaneously8,9,10,11), consistent with scientific understanding1,5. Even with conservative assumptions about the rate and impacts of a stochastic tipping event, today’s optimal carbon tax is increased by 50%. For a plausibly rapid, high-impact tipping event, today’s optimal carbon tax is increased by >200%. The additional carbon tax to delay climate tipping grows at only about half the rate of the baseline carbon tax. This implies that the effective discount rate for the costs of stochastic climate tipping is much lower than the discount rate7,12,13 for deterministic climate damages. Our results support recent suggestions that the costs of carbon emission used to inform policy12,13 are being underestimated14,15,16, and that uncertain future climate damages should be discounted at a low rate17,18,19,20.

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We thank K. Arrow, B. Brock, L. Goulder and participants of the 2014 Workshop on the Economics of Complex Systems at the Beijer Institute of Ecological Economics for comments. Y.C., K.L.J. and T.S.L. were supported by NSF (SES-0951576). T.S.L. was also supported by the Züricher Universitätsverein, the University of Zurich and the Ecosciencia Foundation. T.M.L. was supported by a Royal Society Wolfson Research Merit Award and the European Commission (ENV.2013.6.1-3) HELIX Project. Part of this study was done while T.S.L. was visiting the Hoover Institution. Supercomputer support was provided by Blue Waters (NSF awards OCI-0725070 and ACI-1238993, and the state of Illinois) and by NIH (1S10OD018495-01).

Author information


  1. Department of Quantitative Business Administration, University of Zurich, CH 8008, Zürich, Switzerland

    • Thomas S. Lontzek
  2. Hoover Institution, Stanford University, Stanford, California 94305, USA

    • Yongyang Cai
    •  & Kenneth L. Judd
  3. Becker Friedman Institute, University of Chicago, Chicago, Illinois 60636, USA

    • Yongyang Cai
  4. National Bureau of Economic Research, Cambridge, Massachusetts 02138, USA

    • Kenneth L. Judd
  5. Earth System Science Group, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QE, UK

    • Timothy M. Lenton


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Y.C., K.L.J. and T.S.L. developed the model with input from T.M.L. Y.C. and K.L.J. developed the computational method and Y.C. developed the code. All authors analysed the results. T.S.L. and T.M.L. took the lead in writing the paper with inputs from Y.C. and K.L.J.

Competing interests

The authors declare no competing financial interests.

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Correspondence to Thomas S. Lontzek or Timothy M. Lenton.

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