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Probabilistic Stabilization Targets

Author

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  • Luke G. Fitzpatrick

    (Department of Economics, University of Miami)

  • David L. Kelly

    (Department of Economics, University of Miami)

Abstract

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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    Cited by:

    1. Dominika Czyz & Karolina Safarzynska, 2023. "Catastrophic Damages and the Optimal Carbon Tax Under Loss Aversion," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 85(2), pages 303-340, June.
    2. Frederick Ploeg, 2018. "The safe carbon budget," Climatic Change, Springer, vol. 147(1), pages 47-59, March.
    3. Donovan, Pierce & Springborn, Michael, 2022. "Balancing conservation and commerce: A shadow value viability approach for governing bycatch," Journal of Environmental Economics and Management, Elsevier, vol. 114(C).
    4. Agliardi, Elettra & Xepapadeas, Anastasios, 2022. "Temperature targets, deep uncertainty and extreme events in the design of optimal climate policy," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    5. In Chang Hwang & Richard S. J. Tol & Marjan W. Hofkes, 2019. "Active Learning and Optimal Climate Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1237-1264, August.
    6. Lemoine, Derek & Traeger, Christian P., 2016. "Ambiguous tipping points," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 5-18.
    7. Hwang, In Chang & Reynès, Frédéric & Tol, Richard S.J., 2017. "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Elsevier, vol. 48(C), pages 1-18.
    8. Ahlvik, Lassi & Iho, Antti, 2018. "Optimal geoengineering experiments," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 148-168.

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    More about this item

    Keywords

    Climate Change; Stabilization Targets; Probabilistic Stabilization Targets; Uncertainty; Learning Publication Status: Under Review;
    All these keywords.

    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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