Editing IPCC:AR6/WGII/Chapter-17 (section)

==== 17.3.1.2 Decision-Analytic Methods Used in Decision-Making and Climate Risk Management ====

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Entities making decisions (countries, regions, organisations and individuals) select methods that best suit them in their context ( [[#Fünfgeld--2018|Fünfgeld et al., 2018]] ; [[#Shi--2019|Shi et al., 2019]] ; [[#French--2020|French, 2020]] ) ( ''high confidence'' ).

Classes of tools ( [[#Watkiss--2013|Watkiss and Hunt, 2013]] ; [[#French--2020|French, 2020]] ) include Bayesian methods, interval methods, decision-making under deep uncertainty (DMDU; see Cross-Chapter Box DEEP in this Chapter), cost–benefit analyses, multi-criteria decision analysis, elicitation and general decision support tools (Table 17.4). A summary guide for policy analysts and decision makers is presented in Table 17.4 to help identify the classes of decision-analytic methods that may be suitable for their context for managing climate risks. The table summarises how well the methods address the ''Cynefin'' context, the phase of decision-making, the types of uncertainties that exist through the decision-making process and the resources required. As terminology may vary between disciplines and research groups, suitable references to better explain the methods within the class are provided. Also, there may be overlap between the classes as individual methods are often paired with other methods to address specific requirements and approaches ( [[#Buurman--2016|Buurman and Babovic, 2016]] ; [[#Haasnoot--2019|Haasnoot et al., 2019]] ). In that respect, these methods are referred to in the next section discussing advances in the different approaches to managing climate risks.

Case studies in Table 17.4 describe the utility of classes of decision-analytic tools to facilitate decisions about climate adaptations (SM 17.2). These case studies are presented in Figure 17.8 according to the type of decision-making body and mapped according to their contribution to a decision outcome relative to the geopolitical scale of the actions being assessed. The effectiveness of these methods and tools in Table 17.4 in the context of climate change adaptation (Box 17.1) has yet to be evaluated.

Many published studies on the utility of decision-analytic methods in managing climate risks are theoretical, and therefore it is difficult to find studies on the value of analytic methods for underpinning final decisions on climate risk adaptation. Bayesian, Deep Uncertainty and elicitation methods and tools to support decision-making were the most easily located classes of methods to be used in different contexts (Figure 17.7), while the other classes were more oriented towards government processes. This result highlights a key gap at present in the need to have real-world experiences published and mapped for their utility for different tasks, thereby creating a resource for policymakers to identify suitable tools, such as in emerging communities-of-practice of decision practitioners ( [[#Watkiss--2013|Watkiss and Hunt, 2013]] ; [[#Street--2019|Street et al., 2019]] ; [[#French--2020|French, 2020]] ).

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