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Endogenous Risks and Learning in Climate Change Decision Analysis

In: Coping with Uncertainty

Author

Listed:
  • B. O’Neill

    (Institute for Applied Systems Analysis)

  • Y. Ermoliev

    (Institute for Applied Systems Analysis)

  • T. Ermolieva

    (Institute for Applied Systems Analysis)

Abstract

We analyze the effects of risks and learning on climate change decisions. Using a new two-stage, dynamic, climate change stabilization model with random time horizons, we show that the explicit incorporation of ex-post learning and safety constraints induces risk aversion in ex-ante decisions. This risk aversion takes the form in linear models of VaR- and CVaR-type risk measures. We also analyze extensions of the model that account for the possibility of nonlinear costs, limited emissions abatement capacity, and partial learning. We find that in all cases, even in linear models, any conclusion about the effect of learning can be reversed. Namely, learning may lead to either less- or more restrictive ex-ante emission reductions depending on model assumptions regarding costs, the distributions describing uncertainties, and assumptions about what might be learned. We analyze stylized elements of the model in order to identify the key factors driving outcomes and conclude that, unlike in most previous models, the quantiles of probability distributions play a critical role in solutions.

Suggested Citation

  • B. O’Neill & Y. Ermoliev & T. Ermolieva, 2006. "Endogenous Risks and Learning in Climate Change Decision Analysis," Lecture Notes in Economics and Mathematical Systems, in: Coping with Uncertainty, pages 283-300, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-35262-4_16
    DOI: 10.1007/3-540-35262-7_16
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    Citations

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    Cited by:

    1. 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.
    2. Jo-Ting Huang-Lachmann & Edeltraud Guenther, 2020. "From Dichotomy to an Integrated Approach: Cities’ Benefits of Integrating Climate Change Adaptation and Mitigation," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    3. Emilio L. Cano & Javier M. Moguerza & Tatiana Ermolieva & Yurii Yermoliev, 2017. "A strategic decision support system framework for energy-efficient technology investments," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 249-270, July.
    4. Tatiana Ermolieva & Petr Havlik & Yuri Ermoliev & Nikolay Khabarov & Michael Obersteiner, 2021. "Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model," Sustainability, MDPI, vol. 13(2), pages 1-16, January.

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