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Probabilistic Stabilization Targets


  • Luke G. Fitzpatrick

    (Department of Economics, University of Miami)

  • David L. Kelly

    (Department of Economics, University of Miami)


We study stabilization targets: common environmental policy recommendations that specify a maximum probability of an environmental variable exceeding a fixed target (e.g. limit climate change to at most 2°C above preindustrial). Previous work generally considers stabilization targets under certainty equivalence. Using an integrated assessment model with uncertainty about the sensitivity of the temperature to greenhouse gas (GHG) concentrations (the climate sensitivity), learning, and random weather shocks, we calculate the optimal GHG emissions policy with and without stabilization targets. We characterize the range of feasible targets and show that in general, climate change has too much uncertainty and inertia to be controlled with the precision implied by stabilization targets. We find that uncertainty exacerbates the welfare cost of stabilization targets. First, the targets are inflexible and do not adjust to new information about the climate system. Second, the target forces the emissions policy to overreact to transient shocks. These effects are present only in a model with uncertainty. Total welfare costs in the baseline model are 4.7%, which is 66% higher than the welfare cost under certainty.

Suggested Citation

  • Luke G. Fitzpatrick & David L. Kelly, 2015. "Probabilistic Stabilization Targets," Working Papers 2015-03, University of Miami, Department of Economics.
  • Handle: RePEc:mia:wpaper:2015-03

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    References listed on IDEAS

    1. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    2. Matthias Schmidt & Alexander Lorenz & Hermann Held & Elmar Kriegler, 2011. "Climate targets under uncertainty: challenges and remedies," Climatic Change, Springer, vol. 104(3), pages 783-791, February.
    3. Derek Lemoine & Christian Traeger, 2014. "Watch Your Step: Optimal Policy in a Tipping Climate," American Economic Journal: Economic Policy, American Economic Association, vol. 6(1), pages 137-166, February.
    4. Kelly, David L & Kolstad, Charles D, 2001. "Solving Infinite Horizon Growth Models with an Environmental Sector," Computational Economics, Springer;Society for Computational Economics, vol. 18(2), pages 217-231, October.
    5. repec:aea:aecrev:v:107:y:2017:i:10:p:2947-57 is not listed on IDEAS
    6. Kelly, David L. & Tan, Zhuo, 2015. "Learning and climate feedbacks: Optimal climate insurance and fat tails," Journal of Environmental Economics and Management, Elsevier, vol. 72(C), pages 98-122.
    Full references (including those not matched with items on IDEAS)


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    Cited by:

    1. Lemoine, Derek & Traeger, Christian P., 2016. "Ambiguous tipping points," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 5-18.
    2. repec:spr:climat:v:147:y:2018:i:1:d:10.1007_s10584-017-2132-8 is not listed on IDEAS
    3. repec:eee:jeeman:v:92:y:2018:i:c:p:148-168 is not listed on IDEAS
    4. Frederick Ploeg, 2018. "The safe carbon budget," Climatic Change, Springer, vol. 147(1), pages 47-59, March.

    More about this item


    Climate Change; Stabilization Targets; Probabilistic Stabilization Targets; Uncertainty; Learning Publication Status: Under Review;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • O44 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Environment and Growth

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