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

== 17.3 Decision-Making Processes of Risk Management and Adaptation ==

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AR5 ( [[#Chambwera--2014|Chambwera et al., 2014]] ; [[#Jones--2014|Jones et al., 2014]] ; [[#Klein--2014|Klein et al., 2014]] ; Kunreuther et al., 2014; [[#Mimura--2014|Mimura et al., 2014]] ) represented a significant step forward in focusing attention on how decision-making may facilitate effective and robust responses to climate risks remaining after mitigation measures have been taken, following recognition of these needs in the IPCC 4th Assessment Report (AR4), including the diverse contexts that face decision makers ( [[#Klein--2007|Klein et al., 2007]] ).

AR5 ( [[#Jones--2014|Jones et al., 2014]] ; Kunreuther et al., 2014) recognised that the decision-making procedures are as important to consider in managing risks as are the options for responding to climate change, mostly because the procedures can themselves constrain the choices of actions, which could, in turn, lead to constrained pathways which are undesirable. The importance of iterative risk management is emphasised because risk and adaptation are dynamic. It also identified that (i) risk assessments, decision-support tools, early-warning systems, accounting for uncertainty and delivering no-regret options by examining trade-offs are important, (ii) integration across different governance portfolios is needed due to potential conflict of different actions between portfolios, and (iii) planning, implementation and decision-making, including the use of methods, are dependent on local context.

Since AR5, the IPCC special reports have provided the value of integrated assessment processes for assessing trade-offs and synergies ( [[#IPCC--2018a|IPCC, 2018a]] ), adaptive management and governance, the roles of formal and informal decision-making ( [[#IPCC--2019b|IPCC, 2019b]] ) and the importance of developing policy and governance options for risk management, including managing disasters, enhancing resilience, addressing decision-relevant uncertainties and being prepared for abrupt change and extreme events ( [[#IPCC--2019c|IPCC, 2019c]] )

[[IPCC:Wg2:Chapter:Chapter-16|Chapter 16]] has shown that climate risks vary greatly from small to large, local to regional, uncertain to deeply uncertain. The plethora of risks means there are many types of decisions, and many forms of analyses and processes that may be drawn on. Decisions can differ according to whether they are strategic, tactical or operational; whether there are one or many decision makers, from a domestic setting to national governments; the level of uncertainty present; the time available to take the decision; and many more factors (Chapter 1; [[#17.1|Section 17.1]] ).

The pathway to a decision may not be linear, depending on when and in what detail the decision-making or consultative group may need to be understanding the climate risk and its real-world context ( ''sense-making'' , ''modelling'' ), has sufficient background to analyse and explore options for ameliorating the risk ( ''analysis'' , ''exploration'' ), or is ready for interpreting the analyses and deciding on the requirements and strategies for implementing a chosen strategy ( ''interpretation–implementation'' ) ( ''high confidence'' ) (Figure 17.7; [[#French--2020|French et al., 2020]] ). The development of decision-support tools for climate risk management ( [[#Palutikof--2019a|Palutikof et al., 2019a]] ; [[#Palutikof--2019b|Palutikof et al., 2019b]] ) and more generally ( [[#Papathanasiou--2016|Papathanasiou et al., 2016]] ), along with archives of experiences from practitioners ( [[#Watkiss--2013|Watkiss and Hunt, 2013]] ; [[#17.5|Section 17.5]] ; [[#Bowyer--2014|Bowyer et al., 2014]] ; [[#French--2020|French, 2020]] ), means that some aspects of the decision-making process can be circumvented or at least streamlined as that experience is re-used ( ''high confidence'' ).

No single approach to decision-making best suits an individual climate risk across any adaptation context ( [[#Richards--2013|Richards et al., 2013]] ), although there is now a greater awareness of the methods and approaches that are available and their requirements for best practice ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ) ( ''high confidence'' ). This section aims, firstly, to assess the factors that people responsible for organising and facilitating decision-making may wish to consider in choosing the methods and approach for them to make decisions in their context. It also assesses existing experience in analysing the utility of methods for climate risk decision-making. The second part then assesses progress in integrating decision-making across a portfolio of risks.

Processes and methods to facilitate decision-making, from problem recognition to implementing a solution, have evolved in many contexts, disciplines and applications over the last century ( ''high confidence'' ). As a result, decision-making terminology has a vast number of synonyms that are not compiled here. For clarity, the term ‘decision-analytic methods’ refers to procedures or tools that may be used by decision makers to help develop, analyse and contrast alternative actions/adaptations; ‘approaches’ refers to processes that may be undertaken by decision makers to facilitate the development of proposed actions/adaptations; ‘decision-support tools’ refers to software or procedures that facilitate the use of knowledge and data ( [[#Papathanasiou--2016|Papathanasiou et al., 2016]] ).

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'''Cross-Chapter Box LOSS | Loss and Damage'''
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Authors: Reinhard Mechler (Austria/Germany), Adelle Thomas (Bahamas), Christian Huggel (Switzerland), Emily Boyd (Sweden), Veruska Muccione (Italy), Ivo Wallimann-Helmer (Switzerland), Laurens Bouwer (the Netherlands), Sirkku Juhola (Finland), Chandni Singh (India), Carolina Adler (Switzerland/Chile/Australia), Kris Ebi (USA), Patricia Pinho (Brazil), Rawshan Ara Begum (Malaysia/Australia/Bangladesh), Adugna Gemeda (Ethiopia), Johanna Nalau (Australia/Finland), Katja Frieler (Germany), Richard Jones (UK), Riyanti Djalante (Japan), Rosa Perez (Philippines), Tabea Lissner (Germany), Anita Wreford (New Zealand), Mark Pelling (UK), Francois Gemenne (Belgium), Nick Simpson (Zimbabwe/South Africa), Doreen Stabinsky (USA)

'''An intensifying dialogue'''
This Cross-Chapter Box offers an assessment of the growing literature on Loss and Damage. Capitalised letter ‘Loss and Damage’ (L&amp;D) has been used to refer to negotiations under the UNFCCC. Research has used lowercase ‘losses and damages’ for residual effects from (observed) impacts and (projected) risks (see Glossary, Annex II).

Dialogue around L&amp;D issues started with a proposal for insurance and compensation by the Alliance of Small Island States (AOSIS) ( [[#INC--1991|INC, 1991]] ) and has intensified over recent years with suggestions made to consider complements to adaptation in order to manage residual impacts and risks ‘beyond adaptation’ in vulnerable developing countries ( [[IPCC:Wg2:Chapter:Chapter-1#1.4|Section 1.4.5]] ). L&amp;D was formally recognised in 2013 at the 19th meeting of the Conference of the Parties (COP19) through the ''Warsaw International Mechanism on Loss and Damage'' ( [[#UNFCCC--2013|UNFCCC, 2013]] ), governed by an Executive Committee (ExCom), to advance knowledge, foster dialogue and enhance action and support. Article 8 of the Paris Agreement provided a permanent legal basis for the Warsaw International Mechanism (WIM) ( [[#UN--2015|UN, 2015]] ).

IPCC’s first assessment of L&amp;D in 2018 found residual risks to rise with further global warming leading to soft and hard adaptation limits in some natural and human systems (e.g., coral reefs, human health, coastal livelihoods) ( [[#Roy--2018|Roy et al., 2018]] ). Sections 8.4.5.6, 16.4 and 17.2 corroborate these findings concluding that, depending on mitigation and adaptation pathways, residual risks in key systems in many regions will create potential for negative impacts beyond adaptation limits ( ''medium confidence'' ). The assessment in 2018 also noted that there is ‘not one definition of L&amp;D’. This ambiguity has persisted, and a policy space for L&amp;D has not clearly been delimited ( ''high confidence'' ). There is, however, coalescence in dialogue among academia, civil society and policy around a distinct set of themes as identified by stakeholder surveys as well as literature, methods and evidence reviews ( [[#Vanhala--2016|Vanhala and Hestbaek, 2016]] ; [[#Boyd--2017|Boyd et al., 2017]] ; [[#Mechler--2018|Mechler et al., 2018]] ; [[#Calliari--2019|Calliari, 2019]] ; [[#McNamara--2019|McNamara and Jackson, 2019]] ): risk management, limits to adaptation, existential risk, finance and support, including liability, compensation and litigation (Sections 8.3, 16.4; ''medium confidence'' ; Figure Cross-Chapter Box LOSS.1). Various advisory groups have been set up with participation of policy and experts from research, civil society and practice to help inform the implementation of WIM workplans ( [[#UN--2015|UN, 2015]] ; [[#UN--2019|UN, 2019]] ).

'''Risk management'''
An increasing body of research has focused on the role of climate risk management (Sections 8.3, 16.4 and 17.2; ''high confidence'' ) ( [[#Birkmann--2015|Birkmann and Welle, 2015]] ; [[#Gall--2015|Gall, 2015]] ; [[#van%20der%20Geest--2015|van der Geest and Warner, 2015]] ; [[#Mechler--2016|Mechler and Schinko, 2016]] ; [[#Boyd--2017|Boyd et al., 2017]] ; [[#IPCC--2018b|IPCC, 2018b]] ; [[#IPCC--2019b|IPCC, 2019b]] ; [[#Boda--2020|Boda et al., 2020]] ; [[#Broberg--2020|Broberg and Romera, 2020]] ). A technical expert group on comprehensive risk management (TEG CRM) advises the WIM ExCom, while other expert groups focus on slow-onset events and non-economic L&amp;D ( [[#UNFCCC--2019a|UNFCCC, 2019a]] ).

There is evidence that, without strong risk management and adaptation, losses and damages will continue to affect the poorest vulnerable populations, potentially creating poverty traps ( ''high confidence'' ) (Sections 8.3, 8.4.5.6 and Tables 8.7, 17.2; [[#Serdeczny--2019|Serdeczny, 2019]] ; [[#Tschakert--2019|Tschakert et al., 2019]] ; [[#Thomas--2020|Thomas et al., 2020]] ). Research has started to develop global inventories on losses and damages, including on intangible effects ( [[#Tschakert--2019|Tschakert et al., 2019]] ; [[#Otto--2020|Otto et al., 2020]] ), and engaged with the practice community for data collection. Practice has provided guidance to report on losses and damages in countries (I)NDCs (WWF &amp; Practical Action, 2020). Yet, systematic risk assessments of climate-related losses and damages including adaptation limits (see, e.g., Leal [[#Filho--2018|Filho and Nalau, 2018]] ; [[#Robinson--2018|Robinson, 2018]] ) have remained scarce ( [[IPCC:Wg2:Chapter:Chapter-16#16.4|Section 16.4]] ; ''high confidence'' ). Thus, many vulnerable countries lack comprehensive data at scale of risk management including on economic (e.g., loss of livelihood assets and infrastructure) and non-economic losses and damages (e.g., culture, health, biodiversity), thus hampering effective risk management (Thomas and Benjamin, 2018; Martyr-Koller et al., 2021; Singh et al. 2021). Van den Homberg and McQuistan (2019) propose a losses and damages inventory also to be used to monitor how technologies may shape risks as well as adaptation limits. While early warning and other risk reduction options as well as risk retention considerations are being discussed, L&amp;D dialogue has strongly focused on risk finance for residual risks, particularly through the donor-supported provision of public insurance systems ( [[#Linnerooth-Bayer--2019|Linnerooth-Bayer et al., 2019]] ; [[#Schäfer--2019|Schäfer et al., 2019]] ; [[#Broberg--2020|Broberg and Romera, 2020]] ; [[#Nordlander--2020|Nordlander et al., 2020]] ).

[[File:3afd6e680edf84f41008e148bd1e5868 IPCC_AR6_WGII_Figure_17_Box_Cross-Chapter_Box_LOSS_1.png]]

'''Figure Box Cross-Chapter Box LOSS.1 |''' '''Charting out the L&amp;D discursive and policy space.''' The figure shows key discursive strands relevant for L&amp;D, including their inter-relationships with and distinction from adaptation. The figure also identifies expert groups set up under the WIM and showcases the scale of responses discussed, a focus on ''ex ante'' risk management and ''ex post'' attention to losses and damages as well as contributions by climate change and other stresses for the themes. Adapted from [[#Boyd--2017|Boyd et al. (2017)]] and building on [[#Vanhala--2016|Vanhala and Hestbaek (2016)]] , Mechler et al. (2018), [[#McNamara--2019|McNamara and Jackson (2019)]] and Calliari (2019).

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Cross-Chapter Box LOSS

'''Transformation'''
The role of transformation in risk management for overcoming any soft limits to adaptation is seeing emerging attention ( ''medium confidence'' , ''limited evidence'' ), and the TEG CRM has also been tasked to consider transformation. Relocation and retreat of assets and communities, where ''in situ'' adaptation is considered impossible, is increasingly being debated in research and practice, including in terms of finance and L&amp;D implications ( [[IPCC:Wg2:Chapter:Chapter-8#8.4.4|Section 8.4.4]] ; [[#Boston--2021|Boston et al., 2021]] ; [[#Desai--2021|Desai et al., 2021]] ; [[#Mach--2021|Mach and Siders, 2021]] ; [[#van%20der%20Geest--2021|van der Geest and van den Berg, 2021]] ; [[#Zickgraf--2021|Zickgraf, 2021]] ). Livelihood transformation occurs where current livelihoods become unfeasible in the face of multiple climatic and non-climatic stressors ( [[IPCC:Wg2:Chapter:Chapter-8#8.3.4.1|Section 8.3.4.1]] ) requiring change within sectors (such as switching from cropping to livestock rearing ( [[#Escarcha--2020|Escarcha et al., 2020]] ) or across sectors, when farming households relocate to offer labour elsewhere ( [[IPCC:Wg2:Chapter:Chapter-9#9.1|Section 9.1]] ; [[#Rasel--2013|Rasel et al., 2013]] ). [[#Biermann--2017|Biermann and Boas (2017)]] suggest revamping global governance systems to effectively address the protection and voluntary resettlement of those displaced by climate variability and change. A WIM taskforce on displacement is tasked to further advise on human mobility, including migration, displacement and planned relocation ( [[#UNFCCC--2019a|UNFCCC, 2019a]] ).

'''The existential dimension'''

There has been less and often implicit discussion on the existential dimension of climate-related risk as pertaining to L&amp;D ( ''medium confidence'' ). [[#McNamara--2019|McNamara and Jackson (2019)]] infer an existential dimension from notions of inevitability and irreversibility associated with migration and relocation of communities ( [[#Eckersley--2015|Eckersley, 2015]] ; [[#Mayer--2017|Mayer, 2017]] ; [[#McNamara--2018|McNamara et al., 2018]] ), socio-cultural impacts linked to glacial retreat ( [[#Jurt--2015|Jurt et al., 2015]] ) and adverse psychological and inter-subjective effects ( [[#Herington--2017|Herington, 2017]] ; [[#Adams--2021|Adams et al., 2021]] ). Many SIDS in their NDCs refer to sea level rise in particular posing existential threats, and call for enhanced international support for L&amp;D (Thomas and [[#Benjamin--2017|Benjamin, 2017]] ).

'''Finance and support'''
International support and finance, including compensation for losses and damages, have been in the spotlight from the beginning of the dialogue ( ''high confidence'' ), starting with AOSIS’ proposal ( [[#INC--1991|INC, 1991]] ). Recent work has focused on ''finance sources'' , such as solidarity-based donor and other support for experienced losses and damages and climate-induced displacement as well as questions of compensation and litigation ( [[#Roberts--2017|Roberts et al., 2017]] ; [[#Gewirtzman--2018|Gewirtzman et al., 2018]] ; [[#Mechler--2021|Mechler and Deubelli, 2021]] ; [[#Robinson--2021|Robinson et al., 2021]] ). A selection of finance ''options'' has also been explored such as donor-supported insurance systems with built-in risk reduction provisions ( [[#Gewirtzman--2018|Gewirtzman et al., 2018]] ) as well as roles for social protection ( [[#Aleksandrova--2021|Aleksandrova and Costella, 2021]] ). International policy and donors have provided technical assistance for insurance-related options ( [[#Insuresilience%20Global%20Partnership--2018|Insuresilience Global Partnership, 2018]] ).

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Cross-Chapter Box LOSS

As national and donor-related funding for impacts and risk management remains limited ( [[#Schäfer--2019|Schäfer and Künzel, 2019]] ; 17.2; [[#Serdeczny--2019|Serdeczny, 2019]] ) even at current global warming, many highly exposed developing countries remain financially constrained in their capacity to attend to residual impacts and risk management needs ( [[#Linnerooth-Bayer--2015|Linnerooth-Bayer and Hochrainer-Stigler, 2015]] ; [[#Roberts--2017|Roberts et al., 2017]] ; [[#UNEP--2021a|UNEP, 2021a]] ) ( ''high confidence'' ). Discussion on options for the risk retention layer ‘beyond adaptation’ are likely to see further attention as the dialogue proceeds.

Although there is no explicit mandate regarding L&amp;D, about a quarter of the Green Climate Fund’s approved projects explicitly refer to L&amp;D, while 16% of projects have thematic links to L&amp;D across their main project activities ( [[#Kempa--2021|Kempa et al., 2021]] ). Any estimate of L&amp;D finance needs and spending, however, remains highly speculative, as long as its exact remit including in relation to adaptation has not been clarified politically ( ''medium evidence'' , ''high agreement'' ) ( [[#Markandya--2019|Markandya and González-Eguino, 2019]] ).

Liability and compensation, implying legally defined reimbursement of losses and damages attributable to climate change, remain contentious in L&amp;D dialogue ( ''high confidence'' ). Yet, in half of the academic and grey literature surveyed by [[#McNamara--2019|McNamara and Jackson (2019)]] , compensation is mentioned. Studies have laid out responsibility principles, such as historical responsibility based on the polluter pays principle, beneficiary pays and ability to pay. Discussions on compensation are closely linked to justice and equity scholarship which has studied compensatory, distributive and procedural equity considerations for burden sharing ( [[#Roser--2015|Roser et al., 2015]] ; [[#Wallimann-Helmer--2015|Wallimann-Helmer, 2015]] ; [[#Huggel--2016|Huggel et al., 2016]] ; [[#Boran--2017|Boran, 2017]] ; [[#Page--2017|Page and Heyward, 2017]] ; [[#Roberts--2017|Roberts et al., 2017]] ; [[#Shockley--2017|Shockley and Hourdequin, 2017]] ; [[#Wallimann-Helmer--2019|Wallimann-Helmer et al., 2019]] ; [[#Garcia-Portela--2020|Garcia-Portela, 2020]] ).

Litigation and liability are linked, and a growing research body has examined the role of litigation and international law for the L&amp;D context finding that litigation risks for governments and business may increase as the science, particularly on attribution, matures further ( [[#Mayer--2016|Mayer, 2016]] ; [[#Banda--2017|Banda and Fulton, 2017]] ; WGI CWGB Attribution, 8.2.1.2); [[#Marjanac--2018|Marjanac and Patton, 2018]] ; [[#James--2019|James et al., 2019]] ; [[#Simlinger--2019|Simlinger and Mayer, 2019]] ; [[#Wewerinke-Singh--2019|Wewerinke-Singh and Salili, 2019]] ; [[#Toussaint--2020|Toussaint and Martinez Blanco, 2020]] ) ( ''high agreement'' , ''medium evidence'' ).

'''Outlook'''
The WIM has been reviewed twice as to its delivery on its key functions. As an outcome of the second review in 2019, an expert group on Action and Support has been set up to further discuss issues pertaining to finance, technology and capacity building and a Santiago Network for Technical Assistance will be established to consider providing technical support directly to developing countries ( [[#UNFCCC--2019b|UNFCCC, 2019b]] ). Overall, the L&amp;D dialogue under the WIM supported by an increasing body of research has made important advances with regard to the two functions of knowledge generation and coordination, yet less so on action and support ( ''medium confidence'' ) ( [[#Calliari--2020|Calliari et al., 2020]] ). Resolution on the last item will need additional attention as, despite the coalescence of themes, the L&amp;D dialogue continues to proceed across interlinked yet contested discussion strands.

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=== 17.3.1 Decision-Analytic Methods and Approaches ===

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Different classes of decision-analytic methods have been variously presented in IPCC reports since AR4 but without a summary assessment of their capacity to deal with different contexts of the decision maker. ‘Communities-of-practice’ are developing tool boxes to support analysing and making of decisions generally ( [[#French--2020|French, 2020]] ). These communities of decision analysts can act like broad-based statisticians to advise on matching methods to the climate risk and its context, before individual decision specialists are consulted. Some scientific literature is presenting guides for choosing different methods, tools and approaches ( [[#Shi--2019|Shi et al., 2019]] ). This sub-subsection provides a summary guide for policy analysts and decision makers to help identify the classes of decision-analytic methods that may be suitable for their context for managing climate risks. It focuses on decision-analytic methods, noting that decision-support tools will underpin many of these methods by organising information ( [[#Bourne--2016|Bourne et al., 2016]] ; [[#Papathanasiou--2016|Papathanasiou et al., 2016]] ; [[#Ceccato--2018|Ceccato et al., 2018]] ; [[#Haße--2018|Haße and Kind, 2018]] ) or support modelling ( [[#Papathanasiou--2016|Papathanasiou et al., 2016]] ; [[#Kwakkel--2017|Kwakkel, 2017]] ; [[#Gardiner--2018|Gardiner et al., 2018]] ), sometimes with a particular decision-analytic process in mind ( [[#Hadka--2015|Hadka et al., 2015]] ; [[#Torresan--2016|Torresan et al., 2016]] ; [[#Tonmoy--2018|Tonmoy et al., 2018]] ).

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==== 17.3.1.1 Factors to Consider in Selecting Methods to Facilitate Decision-Making ====

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The choice of methods and approaches to decision-making for climate risks (next section) will depend on (i) the cognitive needs of the deliberations, otherwise considered to be the phase in developing a decision, (ii) the types of models and modelling available to facilitate the deliberations, (iii) the degree of uncertainty surrounding the choices and (iv) the context of a choice ( ''high confidence'' ) ( [[#Richards--2013|Richards et al., 2013]] ; [[#Jones--2014|Jones et al., 2014]] ; [[#Shi--2019|Shi et al., 2019]] ; [[#French--2021|French, 2021]] ).

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===== 17.3.1.1.1 Cognitive phases of decision-making =====

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The decision process often involves overlapping and iterative development of the components leading towards a decision, resulting in the blurring of stages but involving different phases of cognitive activity (Figure 17.7; [[#Holtzman--1989|Holtzman, 1989]] ; [[#French--2015|French, 2015]] ; [[#French--2020|French, 2020]] ). Framing the problem ( [[#Orlove--2020|Orlove et al., 2020]] ), by modelling its relationships with the human and natural systems and eliciting objectives, values and scope of the problem from stakeholders, is a precursor to analyses of options but may be returned to whenever a phase of ‘ ''sense-making and modelling'' ’ is required ( ''high confidence'' ) ( [[#Ackermann--2012|Ackermann, 2012]] ; [[#Keeney--2012|Keeney, 2012]] ; [[#Slotte--2014|Slotte and Hämäläinen, 2014]] ; [[#Abbas--2015|Abbas and Howard, 2015]] ; [[#Marttunen--2017|Marttunen et al., 2017]] ; [[#Korhonen--2020|Korhonen and Wallenius, 2020]] ; [[#French--2021|French, 2021]] ).

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[[File:34622b1f05ef57da670dfdbd919a03b1 IPCC_AR6_WGII_Figure_17_007.png]]

'''Figure 17.7 |''' '''Relationships between different processes of decision-making to manage climate-related risks in the real world, noting that, when appropriate, some aspects may only require experience to be re-used.''' (1) Formulation of risks of concern and accompanying policies and objectives for managing those risks, forming prescriptive models for the decision maker. (2) Knowledge, understanding and observations of the real world are used to assess past and current impacts and future risks using descriptive models, based on the perspectives and prescriptive models arising from (1). If not well formulated from other experience, processes in (1) and (2) interact to make sense of the world and what needs to be done. In iterative management, (1) and (2) also form the basis for monitoring, reviewing and evaluating effectiveness of adaptations. (3) Use of decision-support and decision-analytic tools to appraise costs and benefits of different options for ameliorating future risks. The double-headed arrow indicates where two-way interactions occur between different activities (likely to be iterative, feedback and nonlinear processes); modelling and assessments are repeated and revised in tandem with the planning and evaluation of options, based on interactions with the policymakers and stakeholders. (4) The decision maker, which may be a group of people, interacts with the evaluation of options (two-way interaction) and interprets the efficacy of the options and the implications for the real world, ultimately choosing one or more actions to satisfy the policy objectives to manage the risks. ( '''5''' ) Implementation of the actions in the real world, which may be once-only actions or instigation of a feedback management system that enables ongoing adjustments to meet objectives.

The cognitive phase of ‘ ''analysing and exploring'' ’ uses models and existing data and/or knowledge services as available to explore the relevance/efficacy of adaptations to ameliorate risk or to meet other adaptation objectives, as well as possible flow-on effects of those actions ( [[#17.3.1|Section 17.3.1.4]] ). Sensitivity and robustness analyses can be useful if conditions are favourable to supplement the decision analysis, setting bounds on some of the residual uncertainty ( ''high confidence'' ) ( [[#Borgonovo--2016|Borgonovo and Plischke, 2016]] ; [[#Ferretti--2016|Ferretti et al., 2016]] ). Validation of models and verification of data ( [[#Tittensor--2018|Tittensor et al., 2018]] ) are becoming highlighted as important steps in this phase or in the sense-making phase, particularly in their capacity to understand and test decision makers and stakeholders’ perceptions ( ''medium confidence'' ). Randomisation methods, Bayesian methods, interval methods, multi-criteria decision analysis (MCDA), decision-making under deep uncertainty (DMDU) and economic and financial approaches (e.g., Real Options Analysis) are tools of choice in this phase ( ''high confidence'' ) (Table 17.4) ( [[#Abbas--2015|Abbas and Howard, 2015]] ; [[#Bendoly--2016|Bendoly and Clark, 2016]] ; [[#Borgonovo--2016|Borgonovo and Plischke, 2016]] ; [[#Iooss--2017|Iooss and Saltelli, 2017]] ; [[#Korhonen--2020|Korhonen and Wallenius, 2020]] ; [[#Saltelli--2020|Saltelli et al., 2020]] ). Decision-support tools in the provision of data and/or modelling methods are regularly used in this and the sense-making phase ( ''high confidence'' ) ( [[#17.3.1.2|Section 17.3.1.2]] ).

The phase of interpreting the analyses to make decisions on climate adaptation followed by implementation is the least described in the literature (Figure 17.8). Decision process management tools and methods for communicating choices, outcomes and implementation are expected to be used to provide support in this phase, particularly for understanding whether the advice is fit for purpose, and the efficacy of choices are clear ( ''low confidence'' ) ( [[#Spetzler--2016|Spetzler et al., 2016]] ).

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[[File:0264e4c2abe136a1320fb72de84b23e4 IPCC_AR6_WGII_Figure_17_008.png]]

'''Figure 17.8 |''' '''Decision-analytic tools used across different geo-political scales and how they contributed to decision outcomes.''' Points comprise the type of decision-making body (C = Community; G = Government; B = Business/Industry; F = Finance; N = NGO; A = All categories) coupled with the reference number in square brackets, which correspond to numbered references in the case studies of Table 17.4. Colours of the points correspond to the class of decision-analytic tool: Bayesian (red), DMDU (decision-making under deep uncertainty) (brown), decision process management (dark blue), economic and financial methods (purple), interval methods (light blue), MCDA—full ranking (light green) or partial ranking (dark green), soft elicitation (Black).

'''Table 17.4 |''' Characteristics of the main approaches to decision analysis with respect to their ''Cynefin'' context, the manner in which they can be used to address different uncertainties, where they may be used in different cognitive phases of the decision-making process, the resources required and some case studies for further exploring how they might be used. Numbers in square brackets after references in case studies refer to the references plotted in Figure 17.8.

{| class="wikitable"
|-
| colspan="10"| '''A: Bayesian methods''' ( [[#Keeney--1993|Keeney and Raiffa, 1993]] ; [[#Smith--2010|Smith, 2010]] ; [[#Gelman--2013|Gelman et al., 2013]] ; [[#Reilly--2013|Reilly and Clemen, 2013]] ; [[#Abbas--2015|Abbas and Howard, 2015]] ; [[#Sperotto--2017|Sperotto et al., 2017]] ; [[#Marchau--2019|Marchau et al., 2019]] )

A structured approach to assembling information around the consequences of choices, either by modelling, by analysis of multiple scenarios or by structuring deliberation; underpinned by a theoretical base, coherent assumptions and powerful computational methods; can use both observational data and expert knowledge, weighting them appropriately; same approaches as in artificial intelligence algorithms. Biases (information, stakeholders, decision makers) can be made explicit. Traditionally, Bayesian methods computationally identify an ‘optimal’ decision, based on maximising the expected utility across a number of specified requirements, represented as functions.

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| colspan="10"| '''Examples''' include the general application of decision network models ( [[#Richards--2013|Richards et al., 2013]] ; [[#Sperotto--2017|Sperotto et al., 2017]] ); the use of decision network analyses based on elicitation to choose adaptations to coastal management in a lagoonal area in Italy ( [[#Catenacci--2013|Catenacci and Giupponi, 2013]] ) and coastal community in UK ( [[#Jäger--2018|Jäger et al., 2018]] ); combination of economic models and decision models to assess research and development priorities ( [[#Baker--2011|Baker and Solak, 2011]] ); combining outputs from models, observations and opinions in a decision framework for assessing climate impacts on water nutrient loads in Italy ( [[#Sperotto--2019|Sperotto et al., 2019]] ) and a general review for water resource management ( [[#Phan--2019|Phan et al., 2019]] ); combining results from different dynamic models to assess human mortality from ozone in the USA ( [[#Alexeeff--2016|Alexeeff et al., 2016]] ); assessing adaptive capacity of surf lifesaving in Australia ( [[#Richards--2016|Richards et al., 2016]] ); and assessing urban flood risks in Denmark (Åström et al., 2014).

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources required'''''

| rowspan="2" colspan="2"| '''''Case studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| Construction of hierarchical models, belief nets ( [[#Sperotto--2017|Sperotto et al., 2017]] ; [[#Phan--2019|Phan et al., 2019]] ), decision trees ( [[#Keeney--1993|Keeney and Raiffa, 1993]] ) and influence diagrams ( [[#Keeney--1993|Keeney and Raiffa, 1993]] ; [[#Reilly--2013|Reilly and Clemen, 2013]] ) supplemented by many soft elicitation techniques helps build models for quantitative analysis ( [[#Gelman--2003|Gelman, 2003]] ; [[#Bendoly--2016|Bendoly and Clark, 2016]] ).

| colspan="2"| Bayesian updating and expected utility analysis supplemented by robustness and sensitivity analyses ( [[#Rios%20Insua--1999|Rios Insua, 1999]] ; [[#Rios%20Insua--2000|Rios Insua and Ruggeri, 2000]] ; [[#French--2009|French et al., 2009]] ; [[#Smith--2010|Smith, 2010]] ; [[#Reilly--2013|Reilly and Clemen, 2013]] ; [[#Abbas--2015|Abbas and Howard, 2015]] ).

| colspan="2"| Use of graphical models (decision trees, belief nets and influence diagrams) and sensitivity plots can help make transparent and explain reasoning for strategy to stakeholders and implementers ( [[#Bendoly--2016|Bendoly and Clark, 2016]] ) and provide for auditable building of consensus.

| colspan="2"| Bayesian decision-analytic models can be applied with increasing complexity and sophistication to any given problem. Coherence between different levels of sophistication can be maintained. Thus, the resources can be tailored to the time and support available for the analysis. The most sophisticated analyses are computationally demanding.

| colspan="2"| Alexeeff et al. 2016) [1], Åström et al. (2014) [2], [[#Baker--2011|Baker and Solak (2011)]] [3], [[#Catenacci--2013|Catenacci and Giupponi (2013)]] [4], [[#Jäger--2018|Jäger et al. (2018)]] [5], [[#Phan--2019|Phan et al. (2019)]] [6], [[#Richards--2013|Richards et al. (2013)]] [7], Richards et al. (2016) [8], [[#Sperotto--2017|Sperotto et al. (2017)]] [9], [[#Sperotto--2019|Sperotto et al. (2019)]] [10]

|-
| colspan="3"| '''''Uncertainties'''''

| colspan="7"| '''''Cynefin context'''''

|-
| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| colspan="2"| ''Known''

| colspan="2"| ''Knowable''

| colspan="2"| ''Complex''

| ''Chaotic''

|-
| All can be modelled probabilistically, perhaps supplemented by sensitivity analysis ( [[#Rios%20Insua--1999|Rios Insua, 1999]] ; [[#Rios%20Insua--2000|Rios Insua and Ruggeri, 2000]] ; [[#Iooss--2017|Iooss and Saltelli, 2017]] ). Deep uncertainties can be investigated via scenarios ( [[#French--2020|French, 2020]] ).

| colspan="2"| Uncertainties resolved or reduced by discussion, then values modelled by multi-attribute values and utilities ( [[#Keeney--1992|Keeney, 1992]] ; [[#Keeney--1993|Keeney and Raiffa, 1993]] ; [[#Gregory--2012|Gregory et al., 2012]] ). Residual uncertainties explored via sensitivity analysis.

| colspan="2"| Any stochastic uncertainties modelled probabilistically; otherwise, deterministic modelling with sensitivity analysis. Value functions tend to be used more than utility functions ( [[#Keeney--1993|Keeney and Raiffa, 1993]] ; [[#Goodwin--2014|Goodwin and Wright, 2014]] ).

| colspan="2"| Epistemic uncertainties updated via Bayesian statistics/machine learning, then remaining stochastic uncertainties modelled probabilistically. Full Bayesian decision modelling possible ( [[#French--2009|French et al., 2009]] ; [[#Smith--2010|Smith, 2010]] ; [[#Abbas--2015|Abbas and Howard, 2015]] ).

| colspan="2"| More exploratory analysis ( [[#Gelman--2003|Gelman, 2003]] ) to understand behaviours with less complex Bayesian modelling support by sensitivity and robustness studies ( [[#Rios%20Insua--1999|Rios Insua, 1999]] ; [[#French--2003|French, 2003]] ). Scenario-focused decision analysis to cope with deep uncertainties ( [[#French--2020|French, 2020]] ). Careful deliberations to construct values and utilities. ( [[#Keeney--1993|Keeney and Raiffa, 1993]] ; [[#Gregory--2012|Gregory et al., 2012]] ).

| Formal modelling impossible. Much exploratory work to identify potential causes and effects. Little if any complex analysis.

|-
|
|-
| colspan="10"| '''B: Decision-making under deep uncertainty (DMDU)''' ( [[#Hallegatte--2012|Hallegatte et al., 2012]] ; [[#Weaver--2013|Weaver et al., 2013]] ; [[#Marchau--2019|Marchau et al., 2019]] ; [[#Workman--2021|Workman et al., 2021]] )

Deep uncertainty relates to circumstances in which data are too sparse, experts are in too much disagreement or time is too short to model the uncertainty. As such, DMDU methods are focused on working in the ''Cynefin'' Complex Space context. Approaches emphasise robustness (‘no regrets’ options) and the use of scenarios, and often link well with scenario-focused robust Bayesian studies (Cross-Chapter Box DEEP in this Chapter). DMDU studies draw in many other approaches to decision analysis, using them to identify robust rather than optimal strategies, as in robust decision-making (RDM). DMDU analyses can help decision makers to think contingently and build a more wide-ranging recognition of the risks. They often integrate with other classes of tools.

|-
| colspan="10"| '''Examples''' include RDM for hydro-power design using down-scaled climate data in Sub-Saharan Africa ( [[#Taner--2017|Taner et al., 2017]] ), RDM for water management in California, USA ( [[#Lempert--2010|Lempert and Groves, 2010]] ), the Colorado River, USA, and for international climate investment strategies ( [[#Groves--2019|Groves et al., 2019]] ), use of decision scaling ( [[#Brown--2019|Brown et al., 2019]] ), comparison of RDM and Info-gap methods ( [[#Hall--2012|Hall et al., 2012]] ) and review of using climate modelling in RDM ( [[#Weaver--2013|Weaver et al., 2013]] ).

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources required'''''

| rowspan="2" colspan="2"| '''''Case studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| Some of the simpler DMDU tools complement soft elicitation tools and can help to identify relevant scenarios and help formulate problems.

| colspan="2"| Many Bayesian or MCDA tools can be used here but with DMDU’s additional emphasis on robustness and the exploration of several/many scenarios.

| colspan="2"| DMDU with its emphasis on robustness encourages contingency planning in implementation with careful monitoring to identify emerging risks.

| colspan="2"| Some of the simpler models do not require substantial resources, but the application of parallel sophisticated analyses in several scenarios can be computationally demanding. Also, the emphasis on discussion of robustness can be demanding on the time of problem-owners, experts and stakeholders.

| colspan="2"| [[#Brown--2019|Brown et al. (2019)]] [11], [[#Groves--2019|Groves et al. (2019)]] [12], [[#Hall--2012|Hall et al. (2012)]] [13], [[#Lempert--2010|Lempert and Groves (2010)]] , [14], Taner et al. (2017) [15], [[#Weaver--2013|Weaver et al. (2013)]] [16]

|-
| colspan="3"| '''''Uncertainties'''''

| colspan="7"| '''''Cynefin context'''''

|-
| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| colspan="2"| ''Known''

| colspan="2"| ''Knowable''

| colspan="2"| ''Complex''

| ''Chaotic''

|-
| Methods are designed for deep epistemic uncertainties. Some can deal with stochastic uncertainties. Analytical uncertainties seldom accounted for.

| colspan="2"| Some DMDU methods draw on MCDA methods and thus consider ambiguity and value uncertainties. In any case, DMDU methods support wide deliberation with stakeholders.

| colspan="2"| Deep uncertainty is absent, but the principles and processes of decision-making may be used.

| colspan="2"| Deep uncertainty is absent, but the principles of decision-making may be used.

| colspan="2"| The complex and chaotic spaces are home to deep uncertainties. DMDU tools and more particularly processes are relevant here. The emphasis on robustness is very relevant. The tools themselves are relatively simply structured but are effective at stimulating discussion.

| Deep uncertainties are rife in the chaotic contexts. DMDU emphases on robustness and possible scenarios can stimulate creative discussions of ill-understood issues.

|-
|
|-
| colspan="10"| '''C: Decision process management''' ( [[#Raz--2001|Raz and Micheal, 2001]] ; [[#Dalkir--2005|Dalkir, 2005]] ; Burstein and W. Holsapple, 2008; [[#Jashapara--2011|Jashapara, 2011]] ; [[#Bonczek--2014|Bonczek et al., 2014]] ; [[#Sauter--2014|Sauter, 2014]] ; [[#Holsapple--2019|Holsapple et al., 2019]] )

A range of tools and techniques to help manage the decision-making process and support risk management and the implementation of the chosen strategy. Some tools organise data and analyses, often being built on a geographic information system, known as decision support tools. Others manage processes, organising workflows. Some have inevitably expanded in function to support decision-making itself, even though their primary focus might be on, say, implementation and monitoring risks. Such tools are closely related to knowledge management systems; knowledge management processes and decision process management differ more in terminology than in substance.

|-
| colspan="10"| '''Examples''' include tools for agriculture ( [[#Biehl--2017|Biehl et al., 2017]] ), evaluating and comparing CMIP climate models ( [[#Parding--2020|Parding et al., 2020]] ), development of action cycles ( [[#Park--2012|Park et al., 2012]] ) and decision support systems across a range of sectors and decision-group applications ( [[#Papathanasiou--2016|Papathanasiou et al., 2016]] ).

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources required'''''

| rowspan="2" colspan="2"| '''''Case studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| Process, project, knowledge elicitation and risk management tools help identify how to structure decision-making processes. Decision process tools can capture details for implementation and document process for audit trail.

| colspan="2"| Tools help structure decision-making processes and ensure timely involvement of problem owners, stakeholders, and experts. Knowledge management tools can capture details for implementation and document process for audit trail.

| colspan="2"| Project management tools plan implementation and risk management tools identify what to monitor during implementation. Knowledge management tools maintain audit trail and track reasoning for choices made during implementation.

| colspan="2"| Decision process management tools can reduce resources needed in the decision-making process. However, this assumes that the tools are already installed on local information systems and that the analysis team is experienced in using them. Otherwise, resource is needed to understand and train in the use of the tools.

| colspan="2"| [[#Biehl--2017|Biehl et al. (2017)]] [17], Papathanasiou et al. (2016) [18], [[#Parding--2020|Parding et al. (2020)]] [19], [[#Park--2012|Park et al. (2012)]] [20]

|-
| colspan="3"| '''''Uncertainties'''''

| colspan="7"| '''''Cynefin context'''''

|-
| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| colspan="2"| ''Known''

| colspan="2"| ''Knowable''

| colspan="2"| ''Complex''

| ''Chaotic''

|-
| Not designed to address uncertainties involved in the decision itself, but may handle project risks in the decision process, especially implementation.

| colspan="2"| Not usually addressed, since ambiguities and value uncertainties will be addressed in the decision-making itself, but may use those values in risk management of implementation.

| colspan="2"| Simple project management tools may be sufficient here.

| colspan="2"| Project management and risk management tools apply easily here.

| colspan="2"| Project management and risk management tools may be used, but attention needs to be paid to risks that are complex in nature with little knowledge of precise relationships between cause and effects.

| Project management and risk management tools may be used, but attention needs to be paid to risks that are complex in nature with little knowledge of precise relationships between cause and effects.

|-
|
|-
| colspan="10"| '''D: Economic and financial methods''' ( [[#Howell--2001|Howell et al., 2001]] ; [[#Pearce--2006|Pearce et al., 2006]] ; [[#Boardman--2017|Boardman et al., 2017]] ; [[#Atkinson--2018a|Atkinson et al., 2018a]] ; [[#Hurlbert--2019|Hurlbert et al., 2019]] )

Stem from economic theory and accounting practices: for example, cost–benefit analysis, which seeks to price out all aspects of the consequence of a strategy, portfolio analysis, or real options theory, which seeks to value financial investments allowing for their risks and the contingent buying and selling. Such methods are perceived as objective when dealing with tangibles, but are more controversial in their valuing of intangibles. Since these methods model uncertainties with probabilities and then work with expectations, they share much in common with Bayesian methods. However, many applications of cost–benefit analysis omit any detailed treatment of uncertainty.

|-
| colspan="10"| '''Examples''' examine the economic costs and benefits of adaptation pathways for storm water infrastructure in Singapore ( [[#Manocha--2017|Manocha and Babovic, 2017]] ), and a coastal mega city, Los Angeles in the USA ( [[#de%20Ruig--2019|de Ruig et al., 2019]] )

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources required'''''

| rowspan="2" colspan="2"| '''''Case studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| In themselves, these methods do not support sense-making and modelling, though discussions of how to value impacts, both tangible and intangible can be catalytic in understanding the issues.

| colspan="2"| These tools focus mainly on analysis and evaluating the costs and benefits of various options. They are not designed to be used interactively so are more often deployed and communicated via reports than interactive workshops.

| colspan="2"| Since community-based adaptation (CBA) methods do not emphasise the analysis of uncertainties and risks, they are less suited for use in developing and communicating an implementation plan. Real options with their emphasis on contingency are much more suited ( [[#Fischhoff--2015|Fischhoff, 2015]] ).

| colspan="2"| Cost–benefit analysis for complex projects is a major undertaking, with much data collection needed to value outcomes. Real options also require data on risks and uncertainties. Both may have high computational needs.

| colspan="2"| [[#de%20Ruig--2019|de Ruig et al. (2019)]] [21], [[#Manocha--2017|Manocha and Babovic (2017)]] [22]

|-
| colspan="3"| '''''Uncertainties'''''

| colspan="7"| '''''Cynefin context'''''

|-
| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| colspan="2"| ''Known''

| colspan="2"| ''Knowable''

| colspan="2"| ''Complex''

| ''Chaotic''

|-
| Cost–benefit methods usually deal with uncertainty via expectations with little attention to probability distributions; real options methods tend to treat uncertainty in much more sophisticated ways. Both methods, when applied fully have many points of contact with Bayesian methods ( [[#Neely--2001|Neely and de Neufville, 2001]] ; [[#Bedford--2005|Bedford et al., 2005]] )

| colspan="2"| These methods reduce all value and preference information to financial equivalents. The key issue is to find a market in which all outcomes may be valued financially. Modern CBA methods use much more subtle techniques for this than those applied in the last century ( [[#Bedford--2005|Bedford et al., 2005]] ; [[#Saarikoski--2016|Saarikoski et al., 2016]] ).

| colspan="2"| Although CBA and many financial methods work in theory, the complexity makes them seldom worth the effort.

| colspan="2"| The methods may be applied to evaluate complex projects, but CBA tends to ‘average out’ rather than analyse uncertainty.

| colspan="2"| The recognition of the need to treat deep uncertainties using real options has been investigated ( [[#Hallegatte--2012|Hallegatte et al., 2012]] ; [[#Buurman--2016|Buurman and Babovic, 2016]] ).

| Formal modelling impossible. Much exploratory work to identify potential causes and effects. Little if any complex analysis.

|-
| colspan="10"| '''E: Interval methods''' ( [[#Shafer--1976|Shafer, 1976]] ; [[#Pedrycz--2011|Pedrycz et al., 2011]] )

Because of concerns that the statistical accuracy of some data is unknown, and that decision makers and experts cannot make numerical judgements accurately, analyses have been suggested which work with ranges of values in categories (intervals) as their inputs. While avoiding accuracy issues, weakening the arithmetic may result in other foundational assumptions not being met, including some basic principles of rationality. Different types of uncertainty can often be confused, and the analyses can contradict basic probability theory. Interval models of semantics and imprecision can be useful in exploring ambiguity and value uncertainty, though modelling rather than resolving such uncertainties does not necessary help in decision-making. Some interval methods can be thought of more as sensitivity techniques applied to other decision-analytic approaches. Typical approaches here relate to the fuzzy or possibility theory, and evidential reasoning.

|-
| colspan="10"| '''Examples''' include using fuzzy methods to assess climate adaptations in ports in China ( [[#Yang--2018|Yang et al., 2018]] ), water supply vulnerability in South Korea ( [[#Kim--2013|Kim and Chung, 2013]] ) and resilience of the Nile River Delta ( [[#Batisha--2015|Batisha, 2015]] ); and evidential reasoning in an environmental impact assessment for flood mitigation in Manila Philippines ( [[#Gilbuena--2013|Gilbuena et al., 2013]] ).

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources required'''''

| rowspan="2" colspan="2"| '''''Case studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| The emphasis on modelling ambiguity may help structure a model initially, but the lack of structures to model and explore complex interdependencies may inhibit the ability to build a valid representation of the issues.

| colspan="2"| If there are substantial data available, then even the simplest of these methods can produce useful results. But with small quantities of data, their data analysis may be too inefficient. Evidential reasoning MCDA can be insightful on the preference side.

| colspan="2"| The emphasis on linguistic uncertainty may in some cases mask some of the issues ( [[#French--1995|French, 1995]] ).

| colspan="2"| Many methods are rather simple in application and require only moderate resources, but they may face issues in scaling up to major complex problems.

| colspan="2"| [[#Batisha--2015|Batisha (2015)]] [23], [[#Gilbuena--2013|Gilbuena et al. (2013)]] [24], [[#Kim--2013|Kim and Chung (2013)]] [25], [[#Yang--2018|Yang et al. (2018)]] [26]

|-
| colspan="3"| '''''Uncertainties'''''

| colspan="7"| '''''Cynefin context'''''

|-
| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| colspan="2"| ''Known''

| colspan="2"| ''Knowable''

| colspan="2"| ''Complex''

| ''Chaotic''

|-
| There are issues of operational definition of quantities in some methodologies. Some simpler interval methods have no concept of conditionality so cannot model learning effectively, but there are some very sophisticated theories of evidence that can. Interval methods can also provide sensitivity analyses for Bayesian and MCDA methods ( [[#Shafer--1976|Shafer, 1976]] ; [[#Rios%20Insua--1990|Rios Insua, 1990]] ).

| colspan="2"| Some methods can be simplistic, with quantities not being operationally defined. The evidential reasoning approach to MCDA allows exploration of the relative weights on different criteria or between levels in criteria ( [[#Xu--2012|Xu, 2012]] ; [[#Zhang--2017|Zhang et al., 2017]] ).

| colspan="2"| Methods can be applied here without major issue, possibly because the simple, repetitive nature of the problem allows access to much data and the possibility of tuning the methods to the application.

| colspan="2"| Since the methods often capture rather than explore and resolve ambiguity and value uncertainties, they can hide issues. Also, the lack, in some cases, of operational definitions may mean that some quantification is dubious. Evidential reasoning methods can help analyse conflicting objectives ( [[#French--1995|French, 1995]] ; [[#Xu--2012|Xu, 2012]] ).

| colspan="2"| The recognition of the need to treat deep uncertainties using real options has been investigated ( [[#Hallegatte--2012|Hallegatte et al., 2012]] ; [[#Buurman--2016|Buurman and Babovic, 2016]] ).

| The ability to deal with ambiguity may be helpful in poorly understood situations, but the emphasis on capturing ambiguity may ultimately slow the building of understanding.

|-
|
| colspan="2"|
|
|-
| colspan="10"| '''F: Multi-criteria decision analysis (MCDA): Full ranking and optimal seeking''' ( [[#Bell--2001|Bell et al., 2001]] ; [[#Belton--2002|Belton and Stewart, 2002]] ; [[#Bouyssou--2006|Bouyssou et al., 2006]] ; [[#Zopounidis--2010|Zopounidis and Pardalos, 2010]] ; [[#Tzeng--2011|Tzeng and Huang, 2011]] ; [[#Velasquez--2013|Velasquez and Hester, 2013]] ; [[#Kumar--2017|Kumar et al., 2017]] )

Covers many approaches: indeed, Bayesian, DMDU and interval methods are sometimes considered MCDA. Some MCDAs seek an optimal or best strategy; others form partial rankings, eliminating weak strategies but not discriminating fully between the better ones. Many MCDA methods eschew dealing with uncertainties and focus on modelling and exploring conflicting objectives and balancing these. MCDA techniques are especially useful in working with senior decision makers in setting policy and broad objectives, and in processes of stakeholder engagement.

|-
| colspan="10"| '''Examples''' include ranking adaptation and mitigation priorities at a national level in the Netherlands ( [[#de%20Bruin--2009|de Bruin et al., 2009]] ), Lithuania ( [[#Streimikiene--2013|Streimikiene and Balezentis, 2013]] ) and Bangladesh ( [[#Haque--2016|Haque, 2016]] ), in the forestry sector in Nicaragua ( [[#Guillén%20Bolaños--2018|Guillén Bolaños et al., 2018]] ) and in emissions trading in the European Union ( [[#Konidari--2007|Konidari and Mavrakis, 2007]] ).

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources required'''''

| rowspan="2" colspan="2"| '''''Case studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| There is growing experience in combining soft elicitation with tools to formulate problems ( [[#Marttunen--2017|Marttunen et al., 2017]] ). Many MCDA tools naturally encourage discussion and deliberation on developing appropriate value structures. However, exploration and formulation of stochastic and epistemological uncertainties is less developed ( [[#Durbach--2020a|Durbach and Stewart, 2020a]] ).

| colspan="2"| Emphasis is usually on analysing and exploring, resolving conflicting objectives. MCDA methods come into their own at this stage of the process. Sensitivity tools and intuitive graphical displays exist for many of the methods ( [[#Gunawan--2005|Gunawan and Azarm, 2005]] ; [[#Boardman--2017|Boardman et al., 2017]] ).

| colspan="2"| Use of graphical models and sensitivity plots can help explain reasoning for strategy to stakeholders and implementers ( [[#Bendoly--2016|Bendoly and Clark, 2016]] ).

| colspan="2"| The more exploratory methods can be quite light in terms of computational resource, but require interactions with decision makers and stakeholders in workshops. Methods with use complex stochastic mathematical programming can be computationally demanding and require substantial data.

| colspan="2"| ( [[#de%20Bruin--2009|de Bruin et al., 2009]] ) [27], ( [[#Guillén%20Bolaños--2018|Guillén Bolaños et al., 2018]] ) [28], ( [[#Haque--2016|Haque, 2016]] ) [29], ( [[#Konidari--2007|Konidari and Mavrakis, 2007]] ) [30], ( [[#Streimikiene--2013|Streimikiene and Balezentis, 2013]] ) [31]

|-
| colspan="3"| '''''Uncertainties'''''

| colspan="7"| '''''Cynefin context'''''

|-
| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| colspan="2"| ''Known''

| colspan="2"| Knowable

| colspan="2"| Complex

| Chaotic

|-
| These methods tend to focus on balancing and resolving conflicting objectives and include little or no analysis of stochastic and epistemic uncertainties. Interactive methods that use complex objective functions do need to consider convergence criteria for analytic uncertainties.

| colspan="2"| Many methods here use multi-attribute value functions and focus on using weights to explore different emphases on conflicting objectives. One very popular method is analytic hierarchy processing (AHP) ( [[#Saaty--1980|Saaty, 1980]] ) though this has issues in scaling up to evaluate more than a handful of policies.

| colspan="2"| Usually in the known context, the objective function is well understood; but in cases where it is not, interactive multi-objective programming can offer a way forward (Klamroth et al., 2018).

| colspan="2"| If the objective function is not well understood, then these methods can be useful and can be extended to stochastic programming, but epistemic uncertainties are not really addressed ( [[#Gutjahr--2016|Gutjahr and Pichler, 2016]] ).

| colspan="2"| Methods can explore conflicting objectives, but seldom are able to address deep epistemic uncertainties, unless combined with scenarios ( [[#Stewart--2013|Stewart et al., 2013]] ; [[#Marchau--2019|Marchau et al., 2019]] ; [[#Durbach--2020a|Durbach and Stewart, 2020a]] ).

| Formal modelling impossible. Much exploratory work to identify potential causes and effects. Little if any complex analysis.

|-
|
|-
| colspan="10"| '''G: Multi-criteria decision analysis (MCDA): Partial ranking''' ( [[#Roy--1996|Roy, 1996]] ; [[#Bell--2001|Bell et al., 2001]] ; [[#Belton--2002|Belton and Stewart, 2002]] ; [[#Bouyssou--2006|Bouyssou et al., 2006]] ; [[#Behzadian--2010|Behzadian et al., 2010]] ; [[#Zopounidis--2010|Zopounidis and Pardalos, 2010]] ; [[#Tzeng--2011|Tzeng and Huang, 2011]] ; Bouyssou and others, 2012; De Smet and Lidouh, 2012; [[#Velasquez--2013|Velasquez and Hester, 2013]] ; [[#Figueira--2016|Figueira et al., 2016]] ; [[#Govindan--2016|Govindan and Jepsen, 2016]] )

|-
| colspan="10"| '''Examples''' include developing criteria for assessing climate protection strategies and applying these to retrofitting a school to manage climate risks in Germany ( [[#Markl-Hummel--2014|Markl-Hummel and Geldermann, 2014]] ); evaluating outranking approaches for managing heat stress in a large city in Australia ( [[#El-Zein--2015|El-Zein and Tonmoy, 2015]] ); using MCDA to manage the interactions of climate change with tourism in Greece (Michailidou et al., 2016); and identifying priorities to manage droughts and floods in agriculture in Bangladesh ( [[#Xenarios--2015|Xenarios and Polatidis, 2015]] ).

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources Required'''''

| rowspan="2" colspan="2"| '''''Case Studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| Graphical representations of partial orders are useful in model formulation, and the emphasis on exploring what can be said objectively about dominance relations can build a kernel of consensus between decision makers and stakeholders.

| colspan="2"| ELECTRE and PROMETHEE implementations of outranking approaches have many tools for exploring partial relations and analysing agreements and the reasoning behind these.

| colspan="2"| The analysis of dominance can provide a sound footing for building risk registers to aid implementation. Understanding the kernel of consensus can also aid communication.

| colspan="2"| If an outranking algorithm is essentially combinatorial in its approach, then for complex problems there may be computational problems. Some of the methods may require less interaction with decision-makers and stakeholders if they can deduce many partial relations from objective data.

| colspan="2"| ( [[#El-Zein--2015|El-Zein and Tonmoy, 2015]] ) (Markl- [32], Hummel and Geldermann, 2014) [33], (Michailidou et al., 2016) [34], ( [[#Xenarios--2015|Xenarios and Polatidis, 2015]] ) [35]

|-
| colspan="4"| '''''Uncertainties'''''

| colspan="6"| '''''Cynefin context'''''

|-
| colspan="2"| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| ''Known''

| colspan="2"| Knowable

| colspan="2"| Complex

| Chaotic

|-
| colspan="2"| Modelling of all forms of uncertainty including epistemic uncertainty is not the primary objective of these methods. Stochastic uncertainty may be included as probability distributions, but there is no formalism for learning to address epistemic uncertainties ( [[#Hyde--2003|Hyde et al., 2003]] ; [[#Behzadian--2010|Behzadian et al., 2010]] ; [[#Gervásio--2012|Gervásio and Simões da Silva, 2012]] ).

| colspan="2"| Partial ranking or outranking methods seek, first of all, to identify dominance between options and preference relations that can be agreed somewhat objectively. Thus, first they eliminate suboptimal alternatives before seeking a fuller ranking. Ambiguity and value uncertainty may also be quantified ( [[#Behzadian--2010|Behzadian et al., 2010]] ; [[#Figueira--2016|Figueira et al., 2016]] ; [[#Govindan--2016|Govindan and Jepsen, 2016]] ).

| Usually in the known context, the objective function is well understood; but when it is not, outranking methods can identify a partial ranking without needing too many interactions with problem-owners.

| colspan="2"| Since epistemic uncertainties are not fully addressed, these methods can only help in relation to conflicting objectives, but robustness to uncertainties will need addressing ( [[#Hyde--2003|Hyde et al., 2003]] ).

| colspan="2"| Outranking methods may be combined with scenarios to explore and analyse decisions under deep uncertainty ( [[#Hyde--2003|Hyde et al., 2003]] ; [[#Durbach--2014|Durbach, 2014]] ).

| Formal modelling impossible. Much exploratory work to identify potential causes and effects. Little if any complex analysis.

|-
|
|-
| colspan="10"| '''H: Soft elicitation''' ( [[#Rosenhead--2001|Rosenhead and Mingers, 2001]] ; [[#Shaw--2006|Shaw et al., 2006]] ; [[#Shaw--2007|Shaw et al., 2007]] ; [[#Ackermann--2012|Ackermann, 2012]] ; [[#Bendoly--2016|Bendoly and Clark, 2016]] )

Also known as problem structuring, it is the process of asking problem owners, experts and stakeholders for the knowledge, perceptions, beliefs, uncertainties and values that a model needs to embody before being populated with numbers. Methods here help in problem formulation, structuring understanding: for example, cognitive maps, soft operational research diagrams, soft systems, prompts such as PESTLE and other qualitative tools ( [[#Prober--2017|Prober et al., 2017]] ; [[#Symstad--2017|Symstad et al., 2017]] ). The output of soft elicitation can lead to the building of sophisticated quantitative models ( [[#Symstad--2017|Symstad et al., 2017]] ) and can also structure communications and deliberations with stakeholders. Exploratory data analysis and visual analytics are also relevant. Soft elicitation has enormous advantages in setting the frame for communication between all parties ( [[#Prober--2017|Prober et al., 2017]] ); there are many cases in which the clarity brought by framing the issues well has obviated the need for formal quantitative analysis.

|-
| colspan="10"| '''Examples''' include Adaptation Pathway planning and elicitation on managing a national park in the USA ( [[#Symstad--2017|Symstad et al., 2017]] ), poverty alleviation in a province in Indonesia ( [[#Butler--2016|Butler et al., 2016]] ), woodland landscapes in Australia ( [[#Prober--2017|Prober et al., 2017]] ) and general considerations for contested adaptations ( [[#Bosomworth--2017|Bosomworth et al., 2017]] ).

|-
| colspan="6"| '''''Cognitive phase'''''

| rowspan="2" colspan="2"| '''''Resources required'''''

| rowspan="2" colspan="2"| '''''Case Studies'''''

|-
| colspan="2"| ''Sense-making and modelling''

| colspan="2"| ''Analysing and exploring''

| colspan="2"| ''Interpreting and implementing''

|-
| colspan="2"| Soft elicitation tools provide much support to sense-making, formulating problems and identifying relevant issues to be addressed ( [[#Shaw--2006|Shaw et al., 2006]] ; [[#Shaw--2007|Shaw et al., 2007]] ; [[#Ackermann--2012|Ackermann, 2012]] ).

| colspan="2"| Soft elicitation is not relevant to quantitative analysis and evaluation per se, but can support the exploration of residuals to understand the quality of the models and detect further factors to be addressed.

| colspan="2"| The results of soft elicitation provide the dimensions for communication by identifying the issues that are important to stakeholders and building understanding in those implementing the policies.

| colspan="2"| Physical resources requirements are relatively slight: sometimes post-its and a white board can be sufficient, though modern visual analytics can require substantial computing resource. However, the demands on the time of problem owners, stakeholders and experts can be significant.

| colspan="2"| ( [[#Bosomworth--2017|Bosomworth et al., 2017]] ) [36], ( [[#Butler--2016|Butler et al., 2016]] ) [37], ( [[#Prober--2017|Prober et al., 2017]] ) [38], ( [[#Symstad--2017|Symstad et al., 2017]] ) [39]

|-
| colspan="4"| '''''Uncertainties'''''

| colspan="6"| '''''Cynefin context'''''

|-
| colspan="2"| ''Stochastic, epistemic, analytical''

''(descriptive modelling)''

| colspan="2"| ''Ambiguity''

''value''

''(prescriptive modelling)''

| ''Known''

| colspan="2"| Knowable

| colspan="2"| Complex

| Chaotic

|-
| colspan="2"| Soft elicitation tools are available to elicit problem-owners’ and experts’ perceptions of these uncertainties and, more particularly, dependences and independences between them. Exploratory data analysis is also relevant ( [[#Steed--2013|Steed et al., 2013]] ; [[#Bendoly--2016|Bendoly and Clark, 2016]] ).

| colspan="2"| There are tools to catalyse deliberations and help problem-owners and stakeholders clarify their meanings and contextualise their values to the specific issues being considered ( [[#Keeney--1992|Keeney, 1992]] ).

| Usually, problems falling into known contexts are well understood and there is little need to elicit or structure models to perform analyses.

| colspan="2"| Problems falling into knowable space are usually well structured and problem owners’ values are also well understood. However, there may be a need to explore error structures in preparation to estimate parameters in the models ( [[#Gelman--2003|Gelman, 2003]] ; [[#Steed--2013|Steed et al., 2013]] ; [[#Fekete--2016|Fekete and Primet, 2016]] ).

| colspan="2"| Many soft elicitation tools were developed for complex contexts: 'wicked' problems with deep uncertainties: e.g., soft systems, cognitive maps and similar tools to elicit perceptions of relationships between entities and problem owners' and stakeholder's values ( [[#Keeney--1992|Keeney, 1992]] ; [[#Rosenhead--2001|Rosenhead and Mingers, 2001]] ).

| Soft elicitation tools and processes can be used to catalyse creative thinking about poorly understood contexts.

|}

<div id="17.3.1.1.2" class="h4-container"></div>

<span id="types-and-capacity-of-models-to-support-decision-making"></span>
===== 17.3.1.1.2 Types and capacity of models to support decision-making =====

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‘Descriptive models’ of socio-biophysical systems and their responses to different drivers ( [[#Argyris--2017|Argyris and French, 2017]] ; [[#French--2018|French and Argyris, 2018]] ; [[#Saltelli--2020|Saltelli et al., 2020]] ) and ‘prescriptive models’, which capture the beliefs, values and objectives of decision makers and stakeholders ( [[#Parnell--2013|Parnell et al., 2013]] ; [[#Keisler--2014|Keisler et al., 2014]] ; [[#French--2018|French and Argyris, 2018]] ), provide the foundations of sense making ( ''high confidence'' ) and thereby influencing the options and choices available in the phase of analysis and exploration ( ''medium confidence'' ) ( [[#Gorddard--2016|Gorddard et al., 2016]] ).

Socio-biophysical models may be qualitative network models, statistical models or dynamic mathematical models ( [[#Melbourne-Thomas--2017|Melbourne-Thomas et al., 2017]] ). Qualitative network modelling can help assess the nature and consequences of the interactions, as well as facilitate understanding of possible structures to be used in dynamic models for assessing long-term adaptation options ( [[#Reckien--2013|Reckien et al., 2013]] ; [[#Reckien--2014|Reckien, 2014]] ; [[#Reckien--2014|Reckien and Luedeke, 2014]] ; [[#Symstad--2017|Symstad et al., 2017]] ). These approaches help articulate the direct and indirect effects of fixed, long-term engineering or structural adaptations. Dynamic stochastic modelling ( [[#Fulton--2014|Fulton and Link, 2014]] ; [[#Ianelli--2016|Ianelli et al., 2016]] ) has been used to assess short- to medium-term interactions of more dynamic and variable sectors, such as those with annual adjustments and management of water, agriculture, land and marine uses ( [[#Holsman--2019|Holsman et al., 2019]] ; [[#Hollowed--2020|Hollowed et al., 2020]] ; [[#Bahri--2021|Bahri et al., 2021]] ). On a longer time frame, scenarios are used to test long-term interactions but often with less variability and chance ( [[#Giupponi--2013|Giupponi et al., 2013]] ; [[#Adam--2014|Adam et al., 2014]] ; [[#Rosenzweig--2017|Rosenzweig et al., 2017]] ).

Many sensitivity analyses based on scenarios, including procedures to randomise across model uncertainty, relate to descriptive dynamic mathematical models with the user of the models characterised as an objective observer ( [[#Borgonovo--2016|Borgonovo and Plischke, 2016]] ; [[#Ferretti--2016|Ferretti et al., 2016]] ; [[#Symstad--2017|Symstad et al., 2017]] ; [[#French--2020|French, 2020]] ). Bayesian approaches enable these descriptive analyses to take account of the subjective choices in model construction and implementation ( [[#Abbas--2015|Abbas and Howard, 2015]] ; [[#Sperotto--2017|Sperotto et al., 2017]] ; [[#Jäger--2018|Jäger et al., 2018]] ; [[#Sperotto--2019|Sperotto et al., 2019]] ; [[#French--2020|French, 2020]] ). Organising descriptive analyses and deciding on a suitable option across a diversity of opinions among stakeholders use prescriptive processes, which can be supported with prescriptive modelling tools ( [[#Williamson--2012|Williamson and Goldstein, 2012]] ; [[#Gelman--2013|Gelman et al., 2013]] ; [[#Abbas--2015|Abbas and Howard, 2015]] ; [[#Dias--2018|Dias et al., 2018]] ; [[#Phan--2019|Phan et al., 2019]] ; [[#Hanea--2021|Hanea et al., 2021]] ). These approaches are subjective, in that they are constrained or directed by the particular views and emphases of the decision-making group ( [[#Gorddard--2016|Gorddard et al., 2016]] ). Not all tools are appropriate for all these activities.

Decision makers will be better able to choose decision-analytic methods when they have an understanding of the types, scale and breadth of uncertainties around the climate risk ( ''high confidence'' ) ( [[#Symstad--2017|Symstad et al., 2017]] ). The ''Cynefin'' framework ( [[#Snowden--2002|Snowden, 2002]] ; [[#French--2013|French, 2013]] ) is a policy-driven framework that broadly categorises the decision context of uncertainty within which decision makers and policy analysts may find themselves ( ''medium confidence'' ) ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ; [[#Helmrich--2020|Helmrich and Chester, 2020]] ). As ''Cynefin'' has helped frame previous IPCC presentations on contexts of uncertainty ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ) and has a community of practice to consult on its use ( [[#French--2020|French, 2020]] ), it is used here, also because it considers the uncertainty in knowledge around cause and effect in general terms, rather than specifically focusing on uncertainty in formal models. [[#Helmrich--2020|Helmrich and Chester (2020)]] show how ''Cynefin'' can be used to frame climate adaptation decision-making in the infrastructure sector.

The ''Cynefin'' contexts relate to how well the system is understood for knowing precisely the outcomes of actions that may be taken, ranging from known, knowable and complex to chaotic. If a context is known or knowable, then it will be possible to build sophisticated models and make sound predictions. If the context is complex and chaotic the outcomes of actions will be less predictable, no matter how complex the models may be, although more complex dynamic models may be useful to test ‘what if’ scenarios in these cases ( [[#Marchau--2019|Marchau et al., 2019]] ). Under complex and chaotic circumstances an ensemble of models and approaches may be needed to help categorise a satisfactory ‘solution space’ across the broad knowledge of relationships and dependencies, but will need to have iterative processes to update and refine adaptations as knowledge improves ( [[#Marchau--2019|Marchau et al., 2019]] ).

<div id="17.3.1.1.3" class="h4-container"></div>

<span id="uncertainty-and-attitudes-to-risk"></span>
===== 17.3.1.1.3 Uncertainty and attitudes to risk =====

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Uncertainty does not just relate to what might happen given climate drivers or adaptations, but also to how much one values potential consequences ( [[#Butler--2016|Butler et al., 2016]] ; [[#Beven--2018a|Beven et al., 2018a]] ; Cross-Chapter Boc DEEP; [[#Beven--2018b|Beven et al., 2018b]] ; [[#French--2020|French, 2020]] ) ( ''high confidence'' ); the balance between how particular decision analyses address uncertainties relating to the external world (descriptive models) and those relating to the values driving the decision-making (prescriptive models) is important ( [[#Butler--2016|Butler et al., 2016]] ). Some analyses partially ignore uncertainties relating to the former in order to focus on conflicts in the values held by different stakeholders and help structure debate ( [[#Korhonen--2020|Korhonen and Wallenius, 2020]] ; [[#French--2020|French, 2020]] ), while others build very sophisticated models of the external world to predict potential consequences, but in doing so lose transparency and risk becoming untrustworthy black boxes to many stakeholders ( ''low confidence'' ) ( [[#Peterson--2020|Peterson and Thompson, 2020]] ).

Much of the readily available literature on how uncertainties affect decision-making relates to the uncertainty in the biophysical models, with a recognition that the choice of tools will be influenced by the types of uncertainty to be addressed ( [[#Le%20Cozannet--2017|Le Cozannet et al., 2017]] ; [[#Symstad--2017|Symstad et al., 2017]] ; [[#Beven--2018a|Beven et al., 2018a]] ; [[#Beven--2018b|Beven et al., 2018b]] ; [[#Durbach--2020b|Durbach and Stewart, 2020b]] ; [[#French--2020|French, 2020]] ). While terminology varies among disciplines, three types of uncertainty are important in understanding assessments of the future from descriptive models: epistemic (uncertainty in model construction relating to the lack of knowledge about the system being represented), analytic (the degree to which a model fits observations, and its accuracy) and stochastic (the natural variability or randomness in the system). The probability of an event arising in the future is determined from all three uncertainties, noting that stochastic uncertainty is a property of the system rather than a limitation of research ( [[#Le%20Cozannet--2017|Le Cozannet et al., 2017]] ; [[#Beven--2018a|Beven et al., 2018a]] ; [[#Beven--2018b|Beven et al., 2018b]] ).

Uncertainty in what constitutes a risk of concern is increasingly identified as important to consider when managing risk (Chapter 16; [[#Butler--2016|Butler et al., 2016]] ; [[#Prober--2017|Prober et al., 2017]] ; [[#French--2020|French et al., 2020]] ; [[#Reis--2020|Reis and Shortridge, 2020]] ). The uncertainty here arises from what is an acceptable risk. Acceptability relates to the value or importance of the consequence, which may include moral and ethical uncertainties ( [[#Prober--2017|Prober et al., 2017]] ), as well as how ambiguous the understanding of the consequence may be between different groups ( [[#Beven--2018a|Beven et al., 2018a]] ; [[#Beven--2018b|Beven et al., 2018b]] ). The development of strategies to ameliorate risk will benefit from considering these two uncertainties in specifying the risk to be managed ( [[#Prober--2017|Prober et al., 2017]] ; [[#French--2020|French et al., 2020]] ) because they can help set boundaries on a required likelihood of success, rather than simply casting stakeholders or decision makers as risk averse or risk tolerant, and can help identify and accept pathways of success ( [[#Gregory--2012|Gregory et al., 2012]] ). This can be important when decisions need to be made well in advance of the actions needing to take effect, such as for many climate risks (Chapter 1; Chapter 16; [[#17.2|Section 17.2.3]] ; Cross-Chapter Box DEEP in this Chapter).

Elicitation methods help reduce these uncertainties ( ''high confidence'' ) ( [[#Butler--2016|Butler et al., 2016]] ; [[#Prober--2017|Prober et al., 2017]] ; [[#Symstad--2017|Symstad et al., 2017]] ; [[#Beven--2018b|Beven et al., 2018b]] ). In addition, informal decision processes can assist in developing consensus in approaches and outcomes ( [[#Orlove--2020|Orlove et al., 2020]] ).

<div id="17.3.1.2" class="h3-container"></div>

<span id="decision-analytic-methods-used-in-decision-making-and-climate-risk-management"></span>
==== 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]] ).

<div id="17.3.1.3" class="h3-container"></div>

<span id="approaches-to-support-decision-making"></span>
==== 17.3.1.3 Approaches to Support Decision-Making ====

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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 =====

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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 =====

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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]] ).

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

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

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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 =====

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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]] ).

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===== 17.3.1.3.5 Adaptive feedback management =====

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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.

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=== 17.3.2 Integration across Portfolios of Adaptation Responses ===

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In recent years, methods for simultaneously considering multiple societal and sectoral objectives, climate risks and adaptation options have been emerging, often termed ‘integrated’ approaches ( [[#Hadka--2015|Hadka et al., 2015]] ; [[#Garner--2016|Garner et al., 2016]] ; [[#Rosenzweig--2017|Rosenzweig et al., 2017]] ; [[#Giupponi--2017a|Giupponi and Gain, 2017a]] ; [[#Stelzenmuller--2018|Stelzenmuller et al., 2018]] ; [[#Marchau--2019|Marchau et al., 2019]] ). Different decision-making approaches can be complementary ( [[#Kwakkel--2016|Kwakkel et al., 2016]] ), and multiple approaches will be needed to manage risks across sectors, in space and over short to long time scales ( [[#17.6|Section 17.6]] ).

Higher-level integration was first presented in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) ( [[#Burton--2012|Burton et al., 2012]] ; [[#Lal--2012|Lal et al., 2012]] ; [[#O’Brien--2012|O’Brien et al., 2012]] ) and includes concepts of planning, coordination and mainstreaming ( [[#Lal--2012|Lal et al., 2012]] ), consideration of cross-scale dynamics and nested vulnerabilities ( [[#Klein--2014|Klein et al., 2014]] ), and decision-making across governments and sectors ( [[#Denton--2014|Denton et al., 2014]] ; [[#Mimura--2014|Mimura et al., 2014]] ).

Since AR5, recognition of the importance of using integrated adaptation to improve climate risk management across the nexus between many sectors and across regions has increased ( ''high confidence'' ) ( [[#Harrison--2016|Harrison et al., 2016]] ; [[#Challinor--2018|Challinor et al., 2018]] ). This was highlighted in the Special Report on Climate Change and Land ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ); advanced planning and integration of adaptation responses are needed over many levels ( ''medium confidence'' ) ( [[#Göpfert--2019|Göpfert et al., 2019]] ; [[#17.6|Section 17.6]] ; [[#Woodruff--2019|Woodruff and Regan, 2019]] ). The complexity of managing this nexus may be compounded by the potential for antagonistic or synergistic effects among and between climate impacts, and changes arising from local sectoral activities and independent adaptation responses to those risks ( ''high confidence'' ) ( [[#Crain--2008|Crain et al., 2008]] ; [[#Piggott--2015|Piggott et al., 2015]] ; [[#Adger--2018|Adger et al., 2018]] ; [[#Brown--2018|Brown et al., 2018]] ; [[#Stelzenmuller--2018|Stelzenmuller et al., 2018]] ; [[#Simpson--2021|Simpson et al., 2021]] ), such as the cross-sectoral demands for freshwater ( [[#Xue--2015|Xue et al., 2015]] ; [[#Azhoni--2018|Azhoni et al., 2018]] ). Integrated adaptation will also help facilitate management of new and emerging risks, help identify when response plans may need to be changed in light of the dynamics of risk over time, and help identify solutions that are less likely to constrain future options for adapting to future needs ( [[#Wise--2016|Wise et al., 2016]] ).

Implicit to managing cross-sectoral interactions, including the nexus concept, is that the interlinkages between multiple sectors are systemic, and therefore solutions to challenges arising from any one sector can only be satisfactorily addressed by considering the connections to other sectors at the same time ( [[#Wichelns--2017|Wichelns, 2017]] ). Challenges for integrated adaptation include: (1) to sufficiently capture the complexities between the nexus dimensions ( [[#Weitz--2017|Weitz et al., 2017]] ); (2) to adequately consider the time, costs and challenges of coordination and cooperation ( [[#Wichelns--2017|Wichelns, 2017]] ); (3) to consider the political economy in which progress towards more integrated solutions could take place, not only accounting for technological requirements ( [[#Leck--2015|Leck and Roberts, 2015]] ); (4) to obtain sufficient temporal or spatial data to capture the interactions between natural and social processes ( [[#Shannak--2018|Shannak et al., 2018]] ); (5) to connect these considerations to decision-making and policy processes in order to gain insights into the conditions for collaboration and coordination across sectors, including external dynamics and political and cognitive factors determining change ( [[#Weitz--2017|Weitz et al., 2017]] ); and (6) to develop a coherent framework against which to assess results and observations ( [[#Crain--2008|Crain et al., 2008]] ; [[#Wichelns--2017|Wichelns, 2017]] ).

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'''Cross Chapter Box DEEP | Effective adaptation and decision-making under deep uncertainties'''
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Authors: Carolina Adler (Switzerland/Chile/Australia), Robert Lempert (USA), Andrew Constable (Australia), Marjolijn Haasnoot (the Netherlands), Judy Lawrence (New Zealand), Katharine J. Mach (USA), Simon French (UK), Robert Kopp (USA), Camille Parmesan (USA), Mauricio Dominguez Aguilar (Mexico), Elisabeth A. Gilmore (USA), Rachel Bezner Kerr (Canada), Adugna Gemeda (Ethiopia), Cristina Tirado-von der Pahlen (USA/Spain), Debora Ley (Mexico), Rupa Mukerji (India).

'''Decision-relevant uncertainties for managing climate risk'''
Adaptation decision-making can benefit from assessments that support planning for both ‘what is most likely ’ as well as for stress-testing adaptation options over a range of scenarios (Sections 11.7 and 17.3; Cross-Chapter Box.5 in SROCC Chapter 1). This Cross-Chapter Box summarises how deep uncertainties ( [[IPCC:Wg2:Chapter:Chapter-1#1.2|Section 1.2]] ; [[#IPCC--2019a|IPCC, 2019a]] ) can be assessed in decision-making and addressed practically for adaptation.

The concept of deep uncertainty has evolved in IPCC assessments, expanding beyond a focus on reducing uncertainty, to also considering a range of tools and approaches that guide robust and timely decisions to address climate risks. Deep uncertainty is defined as circumstances where experts or stakeholders do not know or cannot agree on one or more of the following: (1) appropriate conceptual models that describe relationships among drivers in a system; (2) the probability distributions used to represent uncertainty about variables and parameters; and/or (3) how to weigh and value desirable alternative outcomes (Cross-Chapter Box 5 in Chapter 1; [[#Lempert--2003|Lempert et al., 2003]] ; [[#IPCC--2019a|IPCC, 2019a]] ; [[#IPCC--2019c|IPCC, 2019c]] ).

Decisions by individuals, households, the private sector, governments and public–private partnerships are generally made with partial or uncertain information. This is also the case for adaptation and development decisions where there is often deep uncertainty about the impacts and the societal conditions, preferences and priorities, and responses over time. Under such conditions, decision makers employ decision processes and scientific information differently from situations where most decision-relevant information is available, uncontested and confidently characterised with single joint probability distribution. Assuming scientific information is certain, when it is not, is a barrier to effective communication of risks and to successful decisions under uncertainty, increasing the potential for failure and regret of investments, lost opportunities and transfers of costs to future generations ( [[#Sarewitz--2000|Sarewitz and Byerly, 2000]] ; [[#Marchau--2019|Marchau et al., 2019]] ; Sections 11.7 and 17.6).

Addressing deep uncertainty is contextual as it depends on the decision options available, outcomes at stake and the available scientific information (Box 1.1. in [[#Marchau--2019|Marchau et al., 2019]] ). The IPCC uncertainty guidance note ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ) addresses only the latter (see also [[#Mastrandrea--2011|Mastrandrea and Mach, 2011]] ; [[IPCC:Wg2:Chapter:Chapter-1#1.3.4|Section 1.3.4]] ). Deep uncertainty is generally more salient when policy-relevant statements have ''low confidence'' or lack relevant data or information, or in cases where significant uncertainty contributes to disagreements and disputes ( [[#Sriver--2018|Sriver et al., 2018]] ). Recent work has also included moral uncertainty ( [[#MacAskill--2020|MacAskill et al., 2020]] ) by evaluating the outcomes of alternative strategies with analyses organised around different perspectives on the appropriate principles of justice ( [[#Ciullo--2020|Ciullo et al., 2020]] ; [[#17.3|Section 17.3]] ; [[#Jafino--2021|Jafino et al., 2021]] ; [[#Lempert--2021|Lempert and Turner, 2021]] ).

To better communicate deep uncertainty, WGI AR6 complements projections of likely global mean sea level change, driven by processes in which there is at least ''medium confidence'' , with projections that incorporate ice-sheet processes in which there is ''low confidence'' ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.3|Section 9.6.3]] in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). The latter are accompanied by storylines to highlight the physical processes that would generate extreme outcomes (Box 9.4 in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). These ''low confidence'' projections and storylines are useful because the likelihood of high-end (&gt;1.5 m) global mean sea level (GMSL) rise in the 21st century is difficult to determine but important to consider in coastal settings (e.g., Cross-Chapter Paper 2; Cross-Chapter Box SLR in Chapter 3). High-end GMSL rise by 2100 could be caused by earlier-than-projected disintegration of marine ice shelves, the abrupt, widespread onset of marine ice sheet instability and marine ice cliff instability around Antarctica, or faster-than-projected changes in the surface mass balance and dynamical ice loss from Greenland (Box TS.4 in [[#Arias--2021|Arias et al., 2021]] ; Box 9.4 in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). In a low-likelihood, high-impact storyline and a high CO 2 emissions scenario, such processes could in combination contribute more than one additional metre of sea level rise by 2100 (Box TS.4 in [[#Arias--2021|Arias et al., 2021]] ; [[IPCC:Wg2:Chapter:Chapter-9#9.6.3|Section 9.6.3]] and Box 9.4 in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). Other hazards assessed in WGI AR6 that address similar aspects relevant for decision-making under deep uncertainty include drought ( [[IPCC:Wg2:Chapter:Chapter-8#8.4.1|Section 8.4.1.6]] in [[#Douville--2021|Douville et al., 2021]] ; [[IPCC:Wg2:Chapter:Chapter-11#11.6|Section 11.6.5]] in [[#Seneviratne--2021|Seneviratne et al., 2021]] ), flood ( [[IPCC:Wg2:Chapter:Chapter-8#8.4.1|Section 8.4.1.5]] in [[#Douville--2021|Douville et al., 2021]] ; [[IPCC:Wg2:Chapter:Chapter-11#11.5|Section 11.5.5]] in [[#Seneviratne--2021|Seneviratne et al., 2021]] ) and wildfire weather (days) (Section 11.8.3 and Box 11.2 in [[#Seneviratne--2021|Seneviratne et al., 2021]] ), among others.

'''Approaches and information requirements for managing deep uncertainty'''
Many approaches are available for evaluating robust decisions under conditions of deep uncertainty (Sections 17.3 and 11.7; Box 11.5 in Chapter 11). The majority use multiple scenarios to stress-test adaptation options and explore how alternative adaptation pathways might evolve under a range of different conditions (Swanson and Bhadwal, 2009). Approaches differ in terms of their focus, types of strategies best addressed, and data and other resources required ( [[#Marchau--2019|Marchau et al., 2019]] ).

‘Low regret’ options are one relatively simple and common approach to deep uncertainty (Sections 17.3 and 17.6) expected to perform well over a wide range of scenarios and represent one example of robust strategies. However, such options will generally be insufficient for adaptive responses to adapt over long time frames and to avoid lock-in of investments (Section 11.7; Box 11.5 in Chapter 11).

‘Adaptation pathways’ provide another approach for addressing deep uncertainty and staging decisions over time ( [[#Haasnoot--2013|Haasnoot et al., 2013]] ), by linking the choice of near-term adaptation actions with pre-determined future thresholds. Observation of such thresholds trigger subsequent actions in the planning or implementation stages of adaptation strategies. Adaptation pathways can begin with low-regret, near-term actions that aim to create and preserve future options to adjust if and when necessary. Alternative pathways can be explored and evaluated to design an adaptive plan with short-term actions and long-term options.

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Cross Chapter Box DEEP

Climate resilient development (CRD), and the pathways (CRDPs) to it, can also involve decision-making under deep uncertainty. Literature assessed in sectoral and regional chapters of this report present several examples of potential risks to achieving development goals under climate change, at global as well as national and local levels ( ''high confidence'' ) (Chapter 18). Achieving CRD depends on negotiation, contestation and reconciliation of trade-offs among diverse actors, who in turn value preferred outcomes differently with respect to associated climate risks and uncertainties, hence the prospect for deep uncertainty to manifest ( [[IPCC:Wg2:Chapter:Chapter-18#18.5|Section 18.5]] ). Deep uncertainty also characterises the development process itself, given that fundamental changes and disruptions are part of the transformational changes required to shift towards CRDPs.

The ‘keeping options open’ approach, i.e. plans that use a series of sequential decisions and actions in the near term to avoid closing off potentially promising future options ( [[#Rosenhead--2001|Rosenhead, 2001]] ; Section 2.6) or, by using real options, takes near-term actions that create currently unavailable options in the future ( [[#Kwakkel--2020|Kwakkel, 2020]] ). Deep uncertainty approaches use a wide range of storylines as scenarios to test low-regret options and to provide information relevant for potential thresholds for use in adaptation pathways ( [[#Haasnoot--2013|Haasnoot et al., 2013]] ; Boxes 11.4, 11.6; Sections 11.7, 17.3).

Deep uncertainty approaches enhance the value of monitoring to detect signals of change in a timely manner ( ''medium confidence'' ). Actionable warning can come from climate signals, and socioeconomic indicators/signposts, including drivers of change, vulnerability and impacts, best suited for timely, reliable and convincing signals for decision-making that anticipate future changes and the need for adaptation or the potential to seize opportunities ( [[#Hermans--2017|Hermans et al., 2017]] ; [[#Haasnoot--2018|Haasnoot et al., 2018]] ; [[#Stephens--2018|Stephens et al., 2018]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). For early warning signals to be decision relevant, they need to have institutional connectivity to enable action ( [[#Haasnoot--2018|Haasnoot et al., 2018]] ; Sections 1.4, 11.4, 11.7; Table 11.18) ( ''medium confidence'' ).

'''Examples and case studies from across the WGII report'''
There are diverse examples of the practical application of deep uncertainty methods across different climate change hazards in many regions of the world. For instance, low-regret options have been used to address the impacts and risks of landslides and debris flows in mountains (Section [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ). Their frequency and magnitude are already widely experienced (Section [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ) and projected to increase (Section [https://www.ipcc.ch/chapter/17#CCP5.3.2.1 CCP5.3.2.1] ). However, managing these associated risks also requires joint consideration of projected vulnerabilities and exposure of people and infrastructure, including the multiple and dynamic non-climate-related factors that are relevant for how the impacts manifest in context, such as population growth and land use planning ( [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ). Here, context-specific deliberative processes are used that include scenarios to guide and specify preventive measures with higher effectiveness than protective (infrastructure) measures could achieve alone. Low-regret adaptation involves raising awareness and accounting for long planning horizons to address the uncertainties associated with such risks, for instance in mountain regions, including education (Sections [https://www.ipcc.ch/chapter/17#CCP5.4.1 CCP5.4.1] ; [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ), with co-benefits such as addressing changes in water availability for supply and demand ( [https://www.ipcc.ch/chapter/17#CCP5.4.1 CCP5.4.1] ).

Adaptation pathways have been used to address SLR and changes in extreme rainfall through flood risk and management (Cross-Chapter Box SLR in Chapter 3; CCP2; Sections 13.2, 11.3 and 11.7): for example, adaptive plans in the Netherlands ( [[#Van%20Alphen--2016|Van Alphen, 2016]] ; [[#Bloemen--2019|Bloemen et al., 2019]] ), climate resilient development in Bangladesh ( [[#Hossain--2018|Hossain et al., 2018]] ; [[#Zevenbergen--2018|Zevenbergen et al., 2018]] ), adaptive spatial pathways for infrastructure retreat and for flood risk management in New Zealand ( [[#Lawrence--2019|Lawrence et al., 2019]] a; [[#Kool--2020|Kool et al., 2020]] ) and adaptive strategies such as in the cities of London ( [[#Ranger--2013|Ranger et al., 2013]] ; [[#Hall--2019|Hall et al., 2019]] ), New York ( [[#Rosenzweig--2014|Rosenzweig and Solecki, 2014]] ) and Los Angeles ( [[#Aerts--2018a|Aerts et al., 2018a]] ). This approach is mainstreamed into guidance documents such as the Climate Risk Informed Decision Analysis (CRIDA) ( [[#Mendoza--2018|Mendoza et al., 2018]] ), national guidance and policy briefs to address coastal hazards and sea level rise planning in New Zealand ( [[#Lawrence--2018|Lawrence et al., 2018]] ; [[#Lawrence--2019b|]] [[#Lawrence--2019|Lawrence et al., 2019]] b ), planning for sea level rise in California (OCP, 2018) and synthesis documents by the government of Canada on marine coasts ( [[#Lemmen--2016|Lemmen et al., 2016]] ). Furthermore, examples from the UK, New Zealand and the Netherlands point to the development of monitoring plans to detect signals for climate adaptation ( [[#Stephens--2017|Stephens et al., 2017]] ; [[#Haasnoot--2018|Haasnoot et al., 2018]] ; [[#Bloemen--2019|Bloemen et al., 2019]] ).

Climate-smart planning, with a focus on keeping options open, can play a role in reducing species extinction rates (Sections 2.5, 2.6). When and where and for whom particular irreversible impacts will occur is deeply uncertain, for example the extinction of a species. Even at the lowest emissions scenarios, some local species will become extinct, but estimates of extinction risk are highly uncertain, typically varying by factors of two to three even for one species ( [[IPCC:Wg2:Chapter:Chapter-2#2.5|Section 2.5]] ) ( ''medium confidence'' ). Risks of species’ extinctions are lowered by reducing emissions, but keeping options open for as long as possible and avoiding irreversible actions are key to developing a climate-resilient adaptive pathway so that real-time climate-driven changes can inform actions. Nature-based solutions (NBS) are emerging as key players for mitigation. With smart planning, NBS offer approaches that not only provide substantial mitigation, but also considerable adaptation benefit to biodiversity, and human health and well-being. Done poorly, such projects can result in large negative impacts on humans and nature. An NBS climate-sensitive decision framework leading to ‘win-win’ solutions for mitigation and adaptation is shown in Figure 1 Cross-Chapter Box NATURAL in [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] (see also Sections 2.4.2.5, 2.5, 2.6, 5.4.4.4 and 5.14.1; Cross-Chapter Box ILLNES in Chapter 2; Cross-Chapter Box COVID in Chapter 7).

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Cross Chapter Box DEEP

In view of these multiple and diverse examples, it is evident that the application of deep uncertainty methods is enabling decisions to be made in a timely manner that avoid foreseeable and undesirable outcomes and take opportunities as they arise ( ''high confidence'' ).

'''Prospects for adaptation decision-making'''
Deep uncertainty is increasingly salient for decision-making as recognition of climate-related risks and related uncertainties has increased ( ''high confidence'' ). These risks can compound and cascade to become new risks, increasing the breadth, frequency and severity of climate change impacts and the consequently increasing scale and scope of adaptation ( ''high confidence'' ) (Cross-Chapter Box Extremes in Chapter 2; Sections 1.3.1.2, 2.3, 2.5, 2.6, 11.5, 11.7, and [https://www.ipcc.ch/chapter/17#CCP5.3.1 CCP5.3.1] ). Waiting until uncertainties are resolved (if they ever can) may leave little or no time to adapt. The lead time for planning and implementation of adaptation can take decades ( [[#Haasnoot--2020b|Haasnoot et al., 2020b]] ; Cross-Chapter Box SLR in Chapter 3), and socioeconomic developments can lock in undesirable pathways where underlying vulnerabilities and exposure, such as poverty, conflict and their associated displacement of people, remain unaddressed (Sections 5.13.4, 16.5.2.3.8; Cross-Chapter Box Migrate in Chapter 7).

Overall, there is growing evidence that effective implementation of strategies developed for deeply uncertain problems require adequate mandates and funding frameworks, preparedness and disaster response plans, and monitoring and evaluation of the strategy outcomes, against how the future unfolds ( ''medium confidence'' ). Collaborative and adaptive governance arrangements, and education and awareness raising, promote learning environments for community engagement, and are essential for the effective implementation of robust adaptation plans ( ''medium confidence'' ) (Sections 5.14.1, 17.3 and 11.7).

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