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Global Warming and a Potential Tipping Point in the Atlantic Thermohaline Circulation: The Role of Risk Aversion

Author

Listed:
  • Mariia Belaia

    (University of Hamburg
    The International Max Planck Research School on Earth System Modelling)

  • Michael Funke

    (University of Hamburg
    CESifo)

  • Nicole Glanemann

    (Potsdam Institute for Climate Impact Research
    WHU - Otto Beisheim School of Management)

Abstract

The risk of catastrophes is one of the greatest threats of climate change. Yet, conventional assumptions shared by many integrated assessment models such as DICE lead to the counterintuitive result that higher concern about climate change risks does not lead to stronger near-term abatement efforts. This paper examines whether this result still holds in a refined DICE model that employs the Epstein–Zin utility specification and that is fully coupled with a dynamic tipping point model describing the evolution of the Atlantic thermohaline circulation (THC). Risk is captured by the possibility of a future collapse of the circulation and it is nourished by fat-tailed uncertainty about climate sensitivity. This uncertainty is assumed to resolve in the middle of the second half of this century and the near-term abatement efforts, which are undertaken before that point of time, can be adjusted afterwards. These modelling choices allow posing the question of whether aversion to this specific tipping point risk has a significant effect on near-term policy efforts. The simulations, however, provide evidence that it has little effect. For the more likely climate sensitivity values, a collapse of the circulation would occur in the more distant future. In this case, acting after learning can prevent the catastrophe, implying the remarkable insensitivity of the near-term policy to risk aversion. For the rather unlikely and high climate sensitivity values, the expected damage costs are not great enough to justify taking very costly measures to safeguard the THC. Our simulations also provide some indication that risk aversion might have some effect on near-term policy, if inertia limiting the speed of decarbonisation is accounted for. As it is highly uncertain how restrictive this kind of inertia will be, future research might investigate the effects of risk aversion if additional uncertainty about inertia is considered.

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  • Mariia Belaia & Michael Funke & Nicole Glanemann, 2017. "Global Warming and a Potential Tipping Point in the Atlantic Thermohaline Circulation: The Role of Risk Aversion," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(1), pages 93-125, May.
  • Handle: RePEc:kap:enreec:v:67:y:2017:i:1:d:10.1007_s10640-015-9978-x
    DOI: 10.1007/s10640-015-9978-x
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    6. Nicolas Taconet & Céline Guivarch & Antonin Pottier, 2019. "Social Cost of Carbon under stochastic tipping points: when does risk play a role?," CIRED Working Papers hal-02408904, HAL.
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    8. Brock, W. & Xepapadeas, A., 2017. "Climate change policy under polar amplification," European Economic Review, Elsevier, vol. 99(C), pages 93-112.
    9. Samuel Jovan Okullo, 2020. "Determining the Social Cost of Carbon: Under Damage and Climate Sensitivity Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(1), pages 79-103, January.
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    More about this item

    Keywords

    Integrated assessment modeling; Risk aversion; Epstein–Zin utility; DICE; Thermohaline circulation; Climate sensitivity; Uncertainty;
    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
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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