Editing IPCC:AR6/SROCC/Chapter-3 (section)

==== 3.5.4.2 Linking Knowledge with Decision Making ====

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While there is a growing expectation in polar (and other) regions for a more deliberate strategy to link science with social learning and policy making about climate change (and other matters) through iterative interactions of researchers, managers and other stakeholders, meeting that expectation is confounded by several deeply rooted issues (Armitage et al., 2011 <sup>[[#fn:r2258|2258]]</sup> ; ARR, 2016; Tesar et al., 2016b <sup>[[#fn:r2259|2259]]</sup> ; Baztan et al., 2017 <sup>[[#fn:r2260|2260]]</sup> ; Forbis Jr and Hayhoe, 2018 <sup>[[#fn:r2261|2261]]</sup> ) ( ''medium confidence'' ).

In spite of the development of practices like those described above and the establishment of many co-managed arrangements in polar regions, scientists and policy makers often work in separate spheres of influence, tend to maintain different values, interests, concerns, responsibilities and perspectives, and gain limited exposure to the other’s knowledge system (see Liu et al., 2008; Armitage et al., 2011 <sup>[[#fn:r2262|2262]]</sup> ). Information exchange flows unequally, as officials struggle with information overload and proliferating institutional voices, and where local residents are mistrusting of scientists (Powledge, 2012 <sup>[[#fn:r2263|2263]]</sup> ). Inherent tensions between science-based assessment and interest-based policy, and many existing institutions often prevent direct connectivity . Further, the longstanding science mandate to remain ‘policy neutral’ typically leads to norms of constrained interaction (Neff, 2009 <sup>[[#fn:r2264|2264]]</sup> ) ( ''high confidence'' ).

Creating pathways towards greater climate resilience will therefore depend, in part, on a redefined ‘actionable science’ that creates bridges supporting better decisions through more rigorous, accessible, and engaging products, while shaping a narrative that instils public confidence (Beier et al., 2015 <sup>[[#fn:r2265|2265]]</sup> ; Fleming and Pyenson, 2017 <sup>[[#fn:r2266|2266]]</sup> ) ( ''high confidence'' ). Stakeholders of polar regions are increasingly using a suite of creative tools and practices for moving from theory to practice in resilience building by informing decision making and fostering long-term planning (Baztan et al., 2017 <sup>[[#fn:r2267|2267]]</sup> ). As noted above, these practices include participatory scenario planning, forecasting for stakeholders, and use structured decision making, solution visualisation tools and decision theatres (e.g., Schartmüller et al., 2015; Kofinas et al., 2016 <sup>[[#fn:r2268|2268]]</sup> ; Garrett et al., 2017 <sup>[[#fn:r2269|2269]]</sup> ; Holst-Andersen et al., 2017 <sup>[[#fn:r2270|2270]]</sup> ; Camus and Smit, 2019 <sup>[[#fn:r2271|2271]]</sup> ). The extent to which these practices can contribute to resilience building in the future will depend, in part, on the willingness of key actors such as scientists, to provide active decision-support services, more often than mere decision-support products (Beier et al., 2015 <sup>[[#fn:r2272|2272]]</sup> ). While progress has been made in linking science with policy, more enhanced data collaboration at every scale, more strategic social engagement, communication that both informs decisions and improves climate literacy and explicit creation of consensus documents that provide interpretive guidance about research implications and alternative choices will be important ( ''high confidence'' ).

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===== 3.5.4.2.1 Participatory scenario analysis and planning =====

Participatory scenario analysis is a quickly evolving and widely used practice in polar regions, and has proven particularly useful for supporting climate adaptation at multiple scales when it uses a social-ecological perspective (ARR, 2016; AMAP, 2017a <sup>[[#fn:r2273|2273]]</sup> ; Crépin et al., 2017 <sup>[[#fn:r2274|2274]]</sup> ; Planque et al., 2019 <sup>[[#fn:r2275|2275]]</sup> ) ( ''medium confidence).'' While there are technical dimensions in scenario analysis and planning (e.g., the building of useful simulation models that capture and communicate nuanced social-ecological system dynamics such as long-fuse big bang processes, pathological dynamics, critical thresholds, and unforeseen processes (Crépin et al., 2017), there are also creative aspects, such as the use of art to help in the visualisation of possible future (e.g., Planque et al., 2019).

Participatory scenario analysis has been applied to various problem areas related to climate change responses in the polar regions. Applications demonstrate the utility of the practice for identifying possible local futures that consider climate change or socioeconomic pathways (e.g., in Alaska, Ernst and van Riemsdijk, 2013; and in Eurasian reindeer-herding systems, van Oort et al., 2015; Nilsson et al., 2017 <sup>[[#fn:r2276|2276]]</sup> ) and interacting drivers of change (e.g., in Antarctica; Liggett et al., 2017 <sup>[[#fn:r2277|2277]]</sup> ). Scenario analysis proved helpful for stakeholders with different expertise and perspectives to jointly develop scenarios to inform ecosystem-based management strategies and adaptation options (e.g., in the Barents region; Nilsson et al., 2017 <sup>[[#fn:r2278|2278]]</sup> ; Planque et al., 2019 <sup>[[#fn:r2279|2279]]</sup> ) and to identify research needs (e.g., in Alaska; Vargas-Moreno et al., 2016 <sup>[[#fn:r2280|2280]]</sup> ), including informing and applying climate downscaling efforts (e.g., in Alaska; Ernst and van Riemsdijk, 2013 <sup>[[#fn:r2281|2281]]</sup> ).

A review of scenario analysis in the Arctic, however, found that while the practice is widespread and many are using best practice methods, less than half scenarios programs incorporated climate projections and that those utilising a backcasting approach had higher local participation than those only using forecasting (Flynn et al., 2018 <sup>[[#fn:r2282|2282]]</sup> ). It noted that integrating different knowledge systems and attention to cultural factors influence program utility and acceptance. Planque et al. (2019) <sup>[[#fn:r2283|2283]]</sup> also found that most participating stakeholders had limited experience using scenario analysis, suggesting the importance of process methods for engaging stakeholders when exploring possible, likely, and desirable futures. The long-term utility of this practice in helping stakeholders engage with each other to envision possible futures and be forward thinking in decision making will depend on the science of climate projections, further development of decision support systems to inform decision makers, attention to cultural factors and worldview, as well as refinement of processes that facilitate participants’ dialogue ( ''medium confidence'' ).

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===== 3.5.4.2.2 Structured decision making =====

Structured decision making (SDM) is an emerging practice used with stakeholders to identify alternative actions, evaluate trade-offs, and inform decisions in complex situations (Gregory et al., 2012 <sup>[[#fn:r2284|2284]]</sup> ). Few SDM processes have been undertaken in polar regions, with most as exploratory demonstration projects led by researchers. These have included indigenous residents and researchers identifying trade-offs and actions related to subsistence harvesting in a changing environment (Christie et al., 2018 <sup>[[#fn:r2285|2285]]</sup> ) stakeholder interviews to show how a ‘triage method’ can link community monitoring with community needs and wildlife management priorities (Wheeler et al., 2018 <sup>[[#fn:r2286|2286]]</sup> ), and the application of multi-criteria decision analysis to address difficult decisions related to mining opportunities in Greenland (Trump et al., 2018 <sup>[[#fn:r2287|2287]]</sup> ). The Decision Theater North at the University of Alaska is also being explored as an innovative method of decision support (Kofinas et al., 2016 <sup>[[#fn:r2288|2288]]</sup> ). SDM may have potential in creating climate resilience pathways in polar regions ( ''low confidence'' ), but there is currently limited experience with its application.

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