CATASTROPHIC THRESHOLDS, BAYESIAN LEARNING AND THE ROBUSTNESS OF CLIMATE POLICY RECOMMENDATIONS

Wonjun Chang () and Thomas F. Rutherford
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Wonjun Chang: Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD 20740, USA
Thomas F. Rutherford: #x2020;Department of Agricultural and Applied Economics, University of Wisconsin–Madison, Madison WI 53706, USA‡Optimization Theme, Wisconsin Institutes for Discovery, Madison, WI 53715, USA

Climate Change Economics (CCE), 2017, vol. 08, issue 04, 1-23

Abstract: How does risk and uncertainty in climate thresholds impact optimal short-run mitigation? This paper contrasts the near-term mitigation consequences of using an expected value, stochastic programming, and stochastic control model to capture the policy effects of uncertain climate thresholds. The risk of threshold outcomes increases expected climate damages. The passive learning associated with stochastic programming creates an extra incentive to mitigate promptly by reducing the damages from remaining threshold hazards. The active learning associated with stochastic control creates yet another incentive to do near-term mitigation, through the delaying of potential threshold effects.

Keywords: Uncertainty effect; stochastic programming; act-then-learn; integrated assessment models; climate tipping; economics of climate change; Bayesian learning (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S2010007817500142

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