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| http://dx.doi.org/10.1021/acscentsci.4c00695 | DOI Listing |
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Department of Engineering Physics, Tsinghua University, Beijing, China.
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Climate change-related risk mitigation is typically addressed using cost-benefit analysis that evaluates mitigation strategies against a wide range of simulated scenarios and identifies a static policy to be implemented, without considering future observations. Due to the substantial uncertainties inherent in climate projections, this identified policy will likely be sub-optimal with respect to the actual climate trajectory that evolves in time. In this work, we thus formulate climate risk management as a dynamic decision-making problem based on Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), taking real-time data into account for evaluating the evolving conditions and related model uncertainties, in order to select the best possible life-cycle actions in time, with global optimality guarantees for the formulated optimization problem.
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