Sriver Ryan L, Lempert Robert J, Wikman-Svahn Per, Keller Klaus
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
RAND Corporation, Santa Monica, California, United States of America.
PLoS One. 2018 Feb 7;13(2):e0190641. doi: 10.1371/journal.pone.0190641. eCollection 2018.
Many institutions worldwide are considering how to include uncertainty about future changes in sea-levels and storm surges into their investment decisions regarding large capital infrastructures. Here we examine how to characterize deeply uncertain climate change projections to support such decisions using Robust Decision Making analysis. We address questions regarding how to confront the potential for future changes in low probability but large impact flooding events due to changes in sea-levels and storm surges. Such extreme events can affect investments in infrastructure but have proved difficult to consider in such decisions because of the deep uncertainty surrounding them. This study utilizes Robust Decision Making methods to address two questions applied to investment decisions at the Port of Los Angeles: (1) Under what future conditions would a Port of Los Angeles decision to harden its facilities against extreme flood scenarios at the next upgrade pass a cost-benefit test, and (2) Do sea-level rise projections and other information suggest such conditions are sufficiently likely to justify such an investment? We also compare and contrast the Robust Decision Making methods with a full probabilistic analysis. These two analysis frameworks result in similar investment recommendations for different idealized future sea-level projections, but provide different information to decision makers and envision different types of engagement with stakeholders. In particular, the full probabilistic analysis begins by aggregating the best scientific information into a single set of joint probability distributions, while the Robust Decision Making analysis identifies scenarios where a decision to invest in near-term response to extreme sea-level rise passes a cost-benefit test, and then assembles scientific information of differing levels of confidence to help decision makers judge whether or not these scenarios are sufficiently likely to justify making such investments. Results highlight the highly-localized and context dependent nature of applying Robust Decision Making methods to inform investment decisions.
全球许多机构都在考虑如何将未来海平面和风暴潮变化的不确定性纳入其关于大型资本基础设施的投资决策中。在此，我们研究如何利用稳健决策分析来刻画深度不确定的气候变化预测，以支持此类决策。我们探讨了一些问题，比如如何应对因海平面和风暴潮变化导致的低概率但高影响洪水事件的未来变化可能性。此类极端事件会影响基础设施投资，但由于其周围存在深度不确定性，在这类决策中很难加以考虑。本研究运用稳健决策方法来解决应用于洛杉矶港投资决策的两个问题：（1）在未来何种条件下，洛杉矶港在下一次升级时决定加固其设施以抵御极端洪水情景的决策会通过成本效益测试，以及（2）海平面上升预测和其他信息是否表明此类条件足够可能，从而证明这种投资是合理的？我们还将稳健决策方法与全概率分析进行了比较和对比。对于不同的理想化未来海平面预测，这两种分析框架得出了类似的投资建议，但为决策者提供了不同的信息，并设想了与利益相关者不同类型的互动。特别是，全概率分析首先将最佳科学信息汇总为一组单一的联合概率分布，而稳健决策分析则识别出对极端海平面上升进行近期应对的投资决策通过成本效益测试的情景，然后收集不同置信水平的科学信息，以帮助决策者判断这些情景是否足够可能，从而证明进行此类投资是合理的。结果凸显了应用稳健决策方法为投资决策提供信息时高度本地化和依赖具体情境的性质。
