Bhattacharya Ashmita, Papakonstantinou Konstantinos G, Warn Gordon P, McPhillips Lauren, Bilec Melissa M, Forest Chris E, Hasan Rahaf, Chavda Digant
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Nat Commun. 2025 Jan 27;16(1):1076. doi: 10.1038/s41467-024-55679-9.
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. The framework is developed for coastal adaptation applications, considering a wide variety of possible action types, including various forms of nature-based infrastructure. Related environmental impacts of carbon emissions and uptake are also incorporated, and social cost of carbon implications are discussed, together with several future directions and supported features.
与气候变化相关的风险缓解通常采用成本效益分析来解决，该分析针对广泛的模拟情景评估缓解策略，并确定要实施的静态政策，而不考虑未来的观测结果。由于气候预测中存在大量固有的不确定性，就随时间演变的实际气候轨迹而言，这种确定的政策可能不是最优的。因此，在这项工作中，我们将气候风险管理表述为基于马尔可夫决策过程（MDP）和部分可观测马尔可夫决策过程（POMDP）的动态决策问题，考虑实时数据以评估不断变化的条件和相关模型不确定性，以便及时选择最佳的生命周期行动，并为所制定的优化问题提供全局最优保证。该框架是为沿海适应应用而开发的，考虑了各种可能的行动类型，包括各种形式的基于自然的基础设施。还纳入了碳排放和吸收的相关环境影响，并讨论了碳影响的社会成本，以及几个未来方向和支持特性。
