Robust decision-making for climate change adaptation under deep uncertainty is a common challenge faced by policymakers and scientists in the world. In recent years, the limitations of risk assessment and adaptation planning based on the prediction results of climate models have become increasingly acknowledged. The uncertainties embodied in the model specifications, model parameterizations, prediction results, decision implementation process, and context changes bring significant risks to decision-making far beyond the general recognition of uncertainty drawn from historical data. Such combined and future-looking uncertainty is termed deep uncertainty. Researchers have applied robust decision-making (RDM) theory, adaptation pathways (AP) method, and engineering options analysis (EOA) method to climate change adaptation decision-making research. These theories and techniques can help scientists and policy makers to couple systematic analysis with thoughtful deliberation, typically in coordination with stakeholders, to consider the implications of choices under likely and unlikely future conditions, and thus to better grasp the impact of deep uncertainty on climate change decision-making. The literature on RDM, AP, and EOA is huge and in this review paper, we focus on their advancements and applications in the field of coastal flood control, and review the main strengths and interconnections of these three methods. The RDM method combines the traditional control theory with modern management decision-making theory, focuses on future scenario generation, cost-benefit comparisons of multiple adaptation measures under different future scenarios, and conducts the comparative static screening of the large numbers of scenarios-adaptation measure combinations. The AP method pays more attention to the most plausible scenario-adaptation measure combinations as time goes towards the future, rather than giving the full consideration of all future scenarios, and promotes the consideration of solutions that are adaptable through time. The EOA method is based on the framework of the AP method, with a focus on the specific engineering design. As a consequence, it may lack consideration for larger-scale application and implications. This paper considers how these three methods can be integrated to serve the decision-making process which is characterized by multiple decision goals, large number of plausible future scenarios, and large number of feasible mitigation and adaptation measures. A demonstrative case study of Shanghai is presented for this purpose. This case study proposes a framework to serve the mission of dynamic adaptation planning and engineering designs by combining the strengths of RDM, AP and EOA. We expect that the integration can help researchers to identify low-regret and even no-regret solutions that are beneficial over a broad set of potential future situations. Because the integration must depend on the continuous and effective dialogues among scientists, policymakers, local experts, and other stakeholders, it is bound to be a co-production process of the decision-making knowledge and such a process has the potential to improve public confidence on the proposed policies and solutions. Finally, we argue that the integrated application of these three methods and the process of knowledge co-creation in the integration process are the key direction for future development in the field of climate change adaptation decision-making under the context of deep uncertainty.
Tian, Z; Wu, WX; Liu, JG; Sun, LX
A review of decision-making methods for climate change adaptation under deep uncertainty: With a focus on flooding control in coastal cities
Chinese Science Bulletin-Chinese