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Managing Catastrophic Climate Risks Under Model Uncertainty Aversion

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  • Loïc Berger

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Johannes Emmerling
  • Massimo Tavoni

Abstract

We propose a robust risk management approach to deal with the problem of catastrophic climate change that incorporates both risk and model uncertainty. Using an analytical model of abatement, we show how aversion to model uncertainty influences the optimal level of mitigation. We disentangle the role of preferences from the structure of model uncertainty, which we define by means of a simple measure of disagreement across models. With data from an expert elicitation about climate change catastrophes, we quantify the relative importance of these two effects and calibrate a numerical integrated assessment model of climate change. The results indicate that the structure of model uncertainty, and specifically how model disagreement varies in abatement, is the key driver of optimal abatement and that model uncertainty warrants a higher level of climate change mitigation.
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Suggested Citation

  • Loïc Berger & Johannes Emmerling & Massimo Tavoni, 2017. "Managing Catastrophic Climate Risks Under Model Uncertainty Aversion," Post-Print hal-01744501, HAL.
    • Handle: RePEc:hal:journl:hal-01744501
    DOI: 10.1287/mnsc.2015.2365
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