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

===== 17.3.1.3.2 Stakeholder engagement =====

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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]] ).

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