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

===== 17.3.1.3.4 Evaluating trade-offs, robust decision-making and deep uncertainty =====

<div id="h4-8-siblings" class="h4-siblings"></div>

Trade-offs are pervasive in decision-making for climate change adaptation, including between adaptation and mitigation, economic/social and environmental cost including distributional/equity considerations, affordability and risk reduction, short- and long-term consequences, and spatial variations ( [[#Borgomeo--2016|Borgomeo et al., 2016]] ; [[#Hudson--2016|Hudson et al., 2016]] ; [[#Gil--2018|Gil et al., 2018]] ; [[#Landauer--2019|Landauer et al., 2019]] ).

Trade-offs are often directly compared in cost–benefit analyses which require rigorous estimation of the monetised costs and benefits, where monetisation is feasible and values uncontested (such as for infrastructure) ( ''high confidence'' ) ( [[#de%20Ruig--2019|de Ruig et al., 2019]] ; Table 17.4). Other tools can be employed, such as cost-effectiveness analysis and multi-criteria analysis in order to draw stakeholders into the process ( [[#Posner--2004|Posner, 2004]] ; [[#Matheny--2007|Matheny, 2007]] ; [[#Mechler--2016|Mechler and Schinko, 2016]] ). Stakeholder participation in measuring costs and benefits and in the modelling can aid the process ( [[#Doukas--2020|Doukas and Nikas, 2020]] ).

Logic trees include a range of decision protocols and multi-criteria rules, either based on quantitative or qualitative categories ( [[#Roncoli--2016|Roncoli et al., 2016]] ), often termed multi-criteria analyses. The concept of the logic tree has been increasingly applied in climate risk decision-making contexts ( [[#Nikas--2018|Nikas et al., 2018]] ).

Since the AR5, robust decision-making methods are increasingly used to account for deep uncertainty in many climate-related risks ( ''high confidence'' ) ( [[#Marchau--2019|Marchau et al., 2019]] ; Table 17.4), particularly when decisions need to be made well in advance of when the adaptations need to be implemented (Cross-Chapter Box.5 in SROCC Chapter 1; Cross-Chapter Box DEEP in this Chapter). Reducing risk and building resilience under the context of these types of wicked problems require asking ‘what if’ questions about the future, remaining flexible in the face of uncertainty and seeking out policies that provide good outcomes no matter what the future climate might bring ( ''high confidence'' ) ( [[#17.6|Section 17.6]] ; e.g., [[#Larson--2015|Larson et al., 2015]] ; [[#Bhave--2016|Bhave et al., 2016]] ; [[#Bhave--2018|Bhave et al., 2018]] ). In these cases, trade-offs can be assessed and options can be prioritised through iterative decision-making processes, such as multi-criteria decision-making, robust decision-making and dynamic adaptation pathway planning ( ''high confidence'' ) (Table 17.4; [[#Kwakkel--2014|Kwakkel et al., 2014]] ; [[#Kwakkel--2016|Kwakkel et al., 2016]] ; [[#Shortridge--2016|Shortridge et al., 2016]] ; [[#Lawrence--2017|Lawrence and Haasnoot, 2017]] ; [[#Haasnoot--2019|Haasnoot et al., 2019]] ; [[#Lempert--2019|Lempert, 2019]] ; [[#Roelich--2019|Roelich and Giesekam, 2019]] ; [[#Haasnoot--2020a|Haasnoot et al., 2020a]] ). They can address limitations of data-intensive robust decision-making in developing countries ( [[#Daron--2015|Daron, 2015]] ), use proxy data to enable the use of robust decisions in data-scarce contexts ( [[#Shortridge--2016|Shortridge and Guikema, 2016]] ; [[#Ahmad--2019|Ahmad et al., 2019]] ), incorporate multiple-objectives into robust decision-making ( [[#Singh--2015|Singh et al., 2015]] ), and supplement pathway development with real options analysis ( [[#Buurman--2016|Buurman and Babovic, 2016]] ; [[#Smet--2017|Smet, 2017]] ; [[#Haasnoot--2019|Haasnoot et al., 2019]] ; [[#Lawrence--2019|Lawrence et al., 2019]] ). Often, there are close synergies between the application of these methods and using scenario analyses ( [[#Workman--2021|Workman et al., 2021]] ).

<div id="17.3.1.3.5" class="h4-container"></div>

<span id="adaptive-feedback-management"></span>