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

==== 17.3.1.3 Approaches to Support Decision-Making ====

<div id="h3-19-siblings" class="h3-siblings"></div>

The common approaches presented here are not undertaken in isolation and are often combined throughout, or applied at different stages of, a decision process, as illustrated in Figure 17.7.

<div id="17.3.1.3.1" class="h4-container"></div>

<span id="role-of-informal-processes"></span>
===== 17.3.1.3.1 Role of informal processes =====

<div id="h4-5-siblings" class="h4-siblings"></div>

Informal decision-making pervades decision-making in all contexts ( ''high confidence'' ) ( [[#Orlove--2020|Orlove et al., 2020]] ); decisions relating to climate change are affected not only by rational processes but also by many informal, often behavioural responses to the situation, some of which may not require formal processes. Informal processes were officially studied in only a few of the publications contributing to Figure 17.8, but all of the studies have hints to informal decision-making that pervades all levels of governance. Although there are not many concrete studies, citing roles of study participants can lead to a perception of a disconnect between the process and the outcome that resulted (see [[#17.5.1|Section 17.5.1]] for enablers of success).

Generally, while governance requirements may define the processes of formal deliberations and decision-making, informal deliberations will carry on in parallel, supported by social media, and these informal deliberations may be used to affect the outcome of the formal processes. Stakeholders may feel excluded from the formal deliberations either by governance structures or because they do not agree with their representatives. Conflicting value systems may cause some stakeholders to feel side-lined, particularly if some of the key decision makers are perceived holding different personal views and interests or to have engaged in political horse-trading, which connect independent decisions. There may be emotional responses, driven by poor comprehension of risk and probabilistic information, and potential for group biases or insularity of participants ( [[#Engler--2019|Engler et al., 2019]] ). Well-designed decision processes recognise the informal and seek to gain information from it without introducing bias ( ''medium confidence'' ) ( [[#French--2018|French and Argyris, 2018]] ).

<div id="17.3.1.3.2" class="h4-container"></div>

<span id="stakeholder-engagement"></span>
===== 17.3.1.3.2 Stakeholder engagement =====

<div id="h4-6-siblings" class="h4-siblings"></div>

Stakeholder engagement has become increasingly part of climate-relevant decision processes ( [[#Orlove--2020|Orlove et al., 2020]] ). The degree of stakeholder engagement ranges from instructive and consultative to cooperative, which are equivalent to information exchange, influence and partners in decision-making ( [[#Sen--2000|Sen, 2000]] ; Cattino and Reckien, in press). Since the AR5, climate change adaptation and resilience literature has seen an increase in participatory approaches that deepen engagement and overcome challenges, as well as making some assessments of their effectiveness (Newton [[#Mann--2017|Mann et al., 2017]] ; [[#Wamsler--2017|Wamsler, 2017]] ; [[#Esteve--2018|Esteve et al., 2018]] ), including structured interactions among different types of stakeholders and the use of place-based boundary organisations to strengthen the interactions and heighten the awareness of the institutional context. A higher degree of public participation can lead to more transformational adaptation as well as to higher ambition for local mitigation ( ''medium confidence'' ) ( [[#17.4.4.2|Section 17.4.4.2]] ; Cattino and Reckien, in press). Challenges to stakeholder participation are access to state-of-the-art science, capacity to recognise and respond to non-reliable or false climate science information, and the removal of cognitive and other biases ( ''high confidence'' ) ( [[#Gorddard--2016|Gorddard et al., 2016]] ; [[#Engler--2019|Engler et al., 2019]] ; [[#Fulton--2021|Fulton, 2021]] ).

Participatory and elicitation approaches, where the concerns and involvement of a broader range of interest groups and stakeholders are taken into account, can improve the effectiveness of decision-making ( ''medium confidence'' ) ( [[#Gregory--2012|Gregory et al., 2012]] ; [[#Cvitanovic--2019|Cvitanovic et al., 2019]] ). Participatory planning includes a variety of co-generative strategies and approaches (e.g., qualitative scenario or adaptation pathway development) through which goals and objectives, knowledge and strategy implementation and evaluation can be decided collaboratively between practitioners, policymaking, local interests and groups, and scientists ( [[#Butler--2016|Butler et al., 2016]] ; [[#Prober--2017|Prober et al., 2017]] ; [[#Symstad--2017|Symstad et al., 2017]] ). Specifically, for climate change adaptation, these decision-making strategies can incorporate expert, Indigenous and local knowledge ( ''high confidence'' ) (Cross-Chapter Box INDIG; [[#Gustafson--2016|Gustafson et al., 2016]] ). The challenge will be to bring together these different actors, as stakeholders tend to act within rather than among systems and procedures, and it is important that platforms are developed to integrate data effectively ( [[#Rizzo--2020|Rizzo et al., 2020]] ). Furthermore, reflexive and iterative risk management may further ensure acceptance by participating groups.

Bayesian methods are increasingly used in advancing approaches for decision-making and support in climate adaptation ( [[#Sperotto--2017|Sperotto et al., 2017]] ), by being able to include stakeholder and decision-maker perceptions and biases ( [[#Dias--2018|Dias et al., 2018]] ; [[#Engler--2019|Engler et al., 2019]] ; [[#Phan--2019|Phan et al., 2019]] ; [[#Fulton--2021|Fulton, 2021]] ) in a transparent modelling environment, thereby facilitating consensus and impartiality ( ''medium confidence'' ) ( [[#Catenacci--2013|Catenacci and Giupponi, 2013]] ; [[#Gelman--2017|Gelman and Hennig, 2017]] ). Increasing computational efficiency means that these methods can enable different approaches to be addressed and different descriptive and prescriptive models to be included within a single probabilistic environment, which also can be updated in iterative processes ( ''high confidence'' ) (Table 17.4; [[#Sperotto--2017|Sperotto et al., 2017]] ; [[#Phan--2019|Phan et al., 2019]] ).

<div id="17.3.1.3.3" class="h4-container"></div>

<span id="scenario-analyses"></span>
===== 17.3.1.3.3 Scenario analyses =====

<div id="h4-7-siblings" class="h4-siblings"></div>

Scenarios are described in SR1.5 ( [[#IPCC--2018a|IPCC, 2018a]] ) and SRCCL ( [[#IPCC--2019b|IPCC, 2019b]] ) as a description of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change, prices) and relationships. Scenarios are neither predictions nor forecasts but are used to provide narratives and trajectories equipped with alternate outcomes. SR1.5 and the SRCCL describe a range of scenarios methods and how scenarios are used to guide risk management decision-making. Scenario analysis includes a range of potential future conditions from low-end and mid-range to high-end projections. Scenarios can also include a temporal component, that is, short term, medium term and long term, as defined in the SROCC ( [[#IPCC--2019c|IPCC, 2019c]] ).

Scenarios and pathways, combined with elicitation methods, are becoming widely used to assess adaptation and resilience strategies ( ''high confidence'' ) ( [[#Butler--2016|Butler et al., 2016]] ; [[#Prober--2017|Prober et al., 2017]] ; [[#Symstad--2017|Symstad et al., 2017]] ; [[#Lawrence--2019|Lawrence et al., 2019]] ; [[#Phan--2019|Phan et al., 2019]] ; [[#Sperotto--2019|Sperotto et al., 2019]] ; [[#Haasnoot--2020a|Haasnoot et al., 2020a]] ). They can support the consideration of a wide range of alternative possible futures ( [[#Catenacci--2013|Catenacci and Giupponi, 2013]] ; [[#Jäger--2018|Jäger et al., 2018]] ), enabling identification of potential path dependencies caused by adaptation options ( ''high confidence'' ) ( [[#Pretorius--2017|Pretorius, 2017]] ; [[#Haasnoot--2020a|Haasnoot et al., 2020a]] ). They can also increase the willingness of stakeholders to consider costly actions, by placing them within broader sequences of action ( ''limited evidence'' ) ( [[#Barnett--2014|Barnett et al., 2014]] ). The development, consideration and understanding of scenarios can be enhanced by using visualisation tools to better display storylines, enabling the discussion of alternative futures by participants in decision-making processes ( ''limited evidence'' ) ( [[#Winters--2016|Winters et al., 2016]] ).

<div id="17.3.1.3.4" class="h4-container"></div>

<span id="evaluating-trade-offs-robust-decision-making-and-deep-uncertainty"></span>
===== 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>
===== 17.3.1.3.5 Adaptive feedback management =====

<div id="h4-9-siblings" class="h4-siblings"></div>

Iterative decision-making requires that the implementation of adaptations be reviewed to determine whether the adaptation effectively achieved the objectives, and whether adjustments or additional actions were required ( [[#17.5|Section 17.5]] ). Adaptive feedback management is an approach to managing dynamic climate risks by designing a field monitoring programme to provide data to an assessment procedure which in turn advises on what adjustments need to be made to a ‘control action’, all of which are part of the adaptation to be implemented ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ; Figure 17.7). Adaptive feedback management is more able to account for the dynamic nature of risk and the future emergence of unforeseen risks because of the active design of how to adjust the management approach ( [[#Dickey-Collas--2014|Dickey-Collas, 2014]] ).

Adaptive feedback management is important for managing climate risks that fall within the ''Cynefin'' context of chaos, relying on observations and indicators to learn about the system and to trigger actions ( ''medium confidence'' ) ( [[#Helmrich--2020|Helmrich and Chester, 2020]] ). It has been a valued approach for managing wildfish fisheries in many oceans ( ''high confidence'' ) ( [[#Fulton--2019|Fulton et al., 2019]] ; [[#Hollowed--2020|Hollowed et al., 2020]] ; [[#Bahri--2021|Bahri et al., 2021]] ) and is important for responding to the challenges of climate change ( ''high confidence'' ) ( [[#Holsman--2019|Holsman et al., 2019]] ; [[#Hollowed--2020|Hollowed et al., 2020]] ; [[#Bahri--2021|Bahri et al., 2021]] ).

While the benefits of investment in data and assessments can outweigh the costs of implementation ( ''low confidence'' ) ( [[#Fulton--2019|Fulton et al., 2019]] ), the implementation may take time when resources are limited, particularly in developing nations, where low-cost approaches will be needed for deciding on pathways for adaptation ( [[#Bhave--2016|Bhave et al., 2016]] ; [[#Shortridge--2016|Shortridge et al., 2016]] ).

Iterative decision-making and adaptive feedback management meet when the feedback management procedure is reviewed in total for its effectiveness in one of the review and adjustment iterations. At present, a common approach for assessing different adaptation options and their interaction is using, for example, scenarios in dynamic models ( [[#Adam--2014|Adam et al., 2014]] ; [[#Girard--2015|Girard et al., 2015]] ). An emerging field in adapting fisheries to climate change is to embed the decision-making system in the scenario models in order to assess the capability of feedback management (decision-making, monitoring and capacity for adjustment of the options over time) to achieve satisfactory trade-offs among the objectives of the different stakeholders ( ''medium confidence'' ) ( [[#Melbourne-Thomas--2017|Melbourne-Thomas et al., 2017]] ; [[#Holsman--2019|Holsman et al., 2019]] ; [[#Hollowed--2020|Hollowed et al., 2020]] ). This method can enable prospective evaluation of future whole-of-management scenarios described in this chapter.

<div id="17.3.2" class="h2-container"></div>

<span id="integration-across-portfolios-of-adaptation-responses"></span>