Editing IPCC:AR6/SROCC/Chapter-4 (section)

==== 4.4.4.3 Decision Analysis Methods ====

<div id="section-4-4-4-3decision-analysis-methods-block-1"></div>

<span id="introduction-4"></span>
===== 4.4.4.3.1 Introduction =====

Decision analysis methods are formal methods that help to identify alternatives that perform best or well with regard to given objectives. An alternative (also called response option or, as a sequence of options over time: adaptation pathway) is a specific combination of SLR responses (See Section 4.4.3). Each alternative is characterised for each possible future state-of-the world (e.g., levels of SLR or socioeconomic development) by one or several attributes, which may measure any relevant social, ecological, or economic effect associated with choosing and implementing the alternative (Kleindorfer et al., 1993 <sup>[[#fn:r2137|2137]]</sup> ). Attributes commonly used include cost of adaptation alternatives, monetary and non-monetary benefits of the SLR impacts avoided, or net present value (NPV), which is the difference between discounted monetised benefits over time and discounted costs over time. Formal decision analysis is one way to support social choices that is generally suggested for decision support if decisions are complex and involve large investments, as is frequently the case in coastal contexts in the face of SLR.

In order to be effective, decision analysis needs to be embedded in a governance process that accounts for societal needs and objectives (Sections 4.4.4.2 and 4.4.5). This is because decision analysis entails a number of normative choices about the objectives chosen, the criteria used, the specific methods and data applied, the set of alternatives considered, and the attributes used to characterise alternatives. These choices need to reflect the diversity of values, preference and goals of all stakeholders involved in and affected by a decision. Furthermore, decision analysis needs to consider all available knowledge, including all major uncertainties in both climate and non-climate factors, ambiguities in expert opinions, and differences in approaches, because a partial consideration of uncertainty and ambiguity could misguide the choice of adaptation alternatives ( ''high confidence'' ; Renn, 2008 <sup>[[#fn:r2138|2138]]</sup> ; Jones et al., 2014 <sup>[[#fn:r2139|2139]]</sup> ; Hinkel and Bisaro, 2016 <sup>[[#fn:r2140|2140]]</sup> ).

Since AR5, the literature on coastal decision analysis has advanced significantly, specifically addressing the large uncertainty about post-2050 SLR through i) using robust decision approaches instead of expected utility, ii) iterating or adapting decisions over time, and iii) increasing flexibility of responses. Each advance is elaborated below. Furthermore, the coastal decision analysis literature also stresses the consideration of multiple criteria or attributes, because adaptation often involves stakeholders with differing objectives and ways of valuing alternatives (Oddo et al., 2017 <sup>[[#fn:r2141|2141]]</sup> ). Many decision making methods combine each of the three advances highlighted here (Marchau et al., 2019 <sup>[[#fn:r2142|2142]]</sup> ). The suitability of each method depends strongly on the specific context, including available resources, technical capabilities, policy objectives, stakeholder preferences and available information.

<div id="section-4-4-4-3decision-analysis-methods-block-2"></div>

<span id="using-robustness-criteria-instead-of-expected-utility"></span>
===== 4.4.4.3.2 Using robustness criteria instead of expected utility =====

A growing literature on decision analysis of coastal adaptation advocates the use of RDM approaches instead of maximising expected utility approaches (Hallegatte et al., 2012 <sup>[[#fn:r2143|2143]]</sup> ; Haasnoot et al., 2013 <sup>[[#fn:r2144|2144]]</sup> ; Lempert et al., 2013 <sup>[[#fn:r2145|2145]]</sup> ; Wong et al., 2017 <sup>[[#fn:r2146|2146]]</sup> ). The core criterion to be considered for choosing between the two types of approaches is whether one is confronted with a situation of shallow or deep uncertainty ''(high confidence)'' (Lempert and Schlesinger, 2001 <sup>[[#fn:r2147|2147]]</sup> ; Kwakkel et al., 2010 <sup>[[#fn:r2148|2148]]</sup> ; Kwakkel et al., 2016b <sup>[[#fn:r2149|2149]]</sup> ; Hinkel et al., 2019 <sup>[[#fn:r2150|2150]]</sup> ). Uncertainty is shallow when a single unambiguous objective or subjective probability distribution can be attached to states-of-the-world. Uncertainty is deep, when this is not possible, either because there is no unambiguous method for deriving objective probabilities or the subjective probability judgements of parties involved differ (Cross-Chapter Box 4 in Chapter 1; Type 2).

Expected utility approaches can only be applied for identifying an optimal response in situations of shallow uncertainty. This is because these approaches require a probability distribution over states of the world in order to identify the optimal alternative which leads to the highest expected utility (i.e., the probability weighted sum of the utilities of all outcomes under a given alternative and all states-of-the-world; Simpson et al., 2016 <sup>[[#fn:r2151|2151]]</sup> ). A prominent example of this approach is cost-benefit analysis under risk, which assesses expected outcomes across states of the world in terms of NPV (the discounted stream of net benefits). Cost-benefit analysis has several well-known limitations, such as its sensitivity to discount rates and the difficulty to monetise ecological, cultural and other intangible benefits (Section 1.1.4) that have been widely discussed in the climate change literature (Chambwera et al., 2014 <sup>[[#fn:r2152|2152]]</sup> ; Kunreuther et al., 2014 <sup>[[#fn:r2153|2153]]</sup> ; Dennig, 2018 <sup>[[#fn:r2154|2154]]</sup> ).

In the context of coastal adaptation, uncertainty is only shallow if projected SLR does not significantly differ between low end (e.g., RCP2.6) and high end (e.g., RCP8.5) scenarios (Hinkel et al., 2019 <sup>[[#fn:r2155|2155]]</sup> ). The point in time when this is the case (i.e., time of scenario divergence) depends on what difference in expected utility matters to the particular stakeholders involved in a decision. The time of scenario divergence also differs across locations. In locations where the internal sea level variability is large as compared to relative SLR, it takes longer before the differences in sea levels under low end and high end scenarios become apparent. Figure 4.15 illustrates this effect for the ESL projections of this report (Sections 4.2.3.2 and 4.2.3.4), following the approach of Hinkel et al. (2019). Under the assumption that a 10% statistical distance between the distributions of RCP2.6 and RCP8.5 is decision relevant, scenario divergence occurs before 2050 for approximately two thirds of coastal sites with sufficient observational data, but for 7% of locations this occurs later than 2070.

In principle, a single unambiguous probability distribution on future sea levels could also be attained beyond the time of scenario divergence by attributing subjective probabilities to emission scenarios, but individuals may significantly disagree in their subjective probabilities, which again results in deep uncertainty (Lempert and Schlesinger, 2001 <sup>[[#fn:r2156|2156]]</sup> ; Stirling, 2010 <sup>[[#fn:r2157|2157]]</sup> ). For this reason, very few studies that assign subjective probabilities to emission scenarios are found in the literature (Woodward et al., 2014 <sup>[[#fn:r2158|2158]]</sup> ; Abadie, 2018 <sup>[[#fn:r2159|2159]]</sup> ). But even before the year of scenario divergence is reached, uncertainty about relative SLR can be deep, because of deep uncertainties about non-climatic contributors to relative sea level change, such as VLM during or after earthquakes and human-induced subsidence (Cross-Chapter 5 in Chapter 1; Section 4.2.2.4; Hinkel et al., 2019 <sup>[[#fn:r2160|2160]]</sup> ).

Under situations of deep uncertainty, RDM approaches aim to identify alternatives that perform reasonably well (i.e., ‘are robust’) under a wide range of states-of-the-world or scenarios and hence do not require probability assessments. These approaches include minimax or minimax regret (Savage, 1951), info gap theory (Ben-Haim, 2006 <sup>[[#fn:r2162|2162]]</sup> ), robust optimisation (Ben-Tal et al., 2009 <sup>[[#fn:r2163|2163]]</sup> ) and exploratory modelling methods that create a large ensemble of plausible future scenarios for each alternative, and then use search and visualisation techniques to extract robust alternatives (Lempert and Schlesinger, 2000 <sup>[[#fn:r2164|2164]]</sup> ). SLR examples of RDM include Brekelmans (2012) who minimise the average and maximum regret across a range of SLR scenarios for investments in dike rings in the Netherlands and Lempert et al. (2013) <sup>[[#fn:r2165|2165]]</sup> who apply RDM in Hoh-Chi-Minh City '''''.'''''

But even if SLR uncertainty is shallow, RDM are more suitable than expected utility approaches if parties involved or affected by a decision have a low uncertainty tolerance, because the goal of the uncertainty intolerant decision maker is to avoid major damages under most or all circumstances (Hinkel et al., 2019 <sup>[[#fn:r2166|2166]]</sup> ).

An adaptation strategy developed based on the maximisation of expected utility may not meet this goal, because worst case damages occurring can exceed expected damages by orders of magnitude.

The uncertainty tolerance of stakeholders is also determining how large of a SLR range needs to be considered in RDM. Stakeholders (i.e., those deciding and those affected by a decision) that have a high uncertainty tolerance (e.g., those planning for investments that can be very easily adapted) can use the combined ''likely'' range of RCP2.6 and RCP8.5 (0.29–1.10 m by 2100) for long-term adaptation decisions. For stakeholders with a low uncertainty tolerance (e.g. those planning for coastal safety in cities and long term investment in critical infrastructure) it is meaningful to also consider SLR above this range, because a 17% chance of GMSL exceeding this range under RCP8.5 is too high to be tolerated from this point of view (Ranger et al., 2013 <sup>[[#fn:r2167|2167]]</sup> ; Hinkel et al., 2015 <sup>[[#fn:r2168|2168]]</sup> ; Hinkel et al., 2019 <sup>[[#fn:r2169|2169]]</sup> ).

Independent of the debate about whether to apply expected utility or robust decision making approaches, there is an extensive literature that applies scenario-based cost-benefit analysis. For example, this approach has been applied for setting the safety standards of Dutch dike rings (Kind, 2014 <sup>[[#fn:r2170|2170]]</sup> ; Eijgenraam et al., 2016 <sup>[[#fn:r2171|2171]]</sup> ), exploring future protection alternatives for New York (Aerts et al., 2014 <sup>[[#fn:r2172|2172]]</sup> ), Ho Chi Minh City (Scussolini et al., 2017 <sup>[[#fn:r2173|2173]]</sup> ), and for many other locations. Scenario-based cost-benefit analysis differs from cost-benefit analysis under risk discussed above in that scenario-based cost-benefit analysis is not applied to rank alternatives across scenarios, but a ‘separate’ cost-benefit analysis is applied within each emission or SLR scenario considered. While this identifies the optimal alternative under each scenario, it does not formally address the problem faced by a coastal decision maker, namely to decide across scenarios (Lincke and Hinkel, 2018 <sup>[[#fn:r2174|2174]]</sup> ). Nevertheless, the results of scenario-based cost-benefit analysis (i.e., NPV of each alternative under each scenario) provide guidance for decision makers and can also be used as inputs (i.e., as attributes) to robust and flexible decision making approaches

<span id="figure-4.15"></span>
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'''Figure 4.15'''

<span id="figure-4.15-year-of-scenario-divergence-between-extreme-sea-level-projections-for-representative-concentration-pathway-rcp2.6-and-rcp8.5-for-all-tide-gauge-locations-with-sufficient-observational-data-relative-to-a-19862005-baseline-bottom-panel.-time-of-divergence-is-defined-using-a-10-threshold-in-the-statistical-distance-between-the-two-distributions-which-can-be-graphically"></span>
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'''Figure 4.15 | Year of scenario divergence between extreme sea level projections for Representative Concentration Pathway (RCP)2.6 and RCP8.5 for all tide-gauge locations with sufficient observational data relative to a 1986–2005 baseline (bottom panel). Time of divergence is defined using a 10% threshold in the statistical distance between the two distributions, which can be graphically […]'''
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[[File:94efe17ef80af59b16ef6c6a70f56b0c IPCC-SROCC-CH_4_15-3000x2996.jpg]]

Figure 4.15 | Year of scenario divergence between extreme sea level projections for Representative Concentration Pathway (RCP)2.6 and RCP8.5 for all tide-gauge locations with sufficient observational data relative to a 1986–2005 baseline (bottom panel). Time of divergence is defined using a 10% threshold in the statistical distance between the two distributions, which can be graphically interpreted as the first year in which at least 10% of the area under the probability distribution function (PDF) of RCP8.5 lies outside of the area under the upper half (i.e., above the 50th percentile) of the PDF of RCP2.6. Upper panels indicate the median and 5–95% range of future extreme sea level (ESL) relative to the 1986–2005 baseline for three tide gauge locations with low variability (Papeete), medium variability (New York) and high variability (Cuxhaven). Locations with low variability have a relatively early scenario divergence.

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<div id="section-4-4-4-3decision-analysis-methods-block-3"></div>

<span id="adapting-decisions-over-time"></span>
===== 4.4.4.3.3 Adapting decisions over time =====

Irrespective of whether expected utility or robustness criteria are applied, there is ''high confidence'' that an effective way of dealing with large uncertainties is adaptive decision making (also called iterative decision making, adaptive planning or adaptive management), which maintains that decision and decision analysis should be conducted within an iterative policy cycle. This approach includes monitoring of sea level variables and evaluation of alternatives in this light in order to learn from past decisions and collect information to inform future decisions (Haasnoot et al., 2013 <sup>[[#fn:r2175|2175]]</sup> ; Barnett et al., 2014 <sup>[[#fn:r2176|2176]]</sup> ; Burch et al., 2014 <sup>[[#fn:r2177|2177]]</sup> ; Jones et al., 2014 <sup>[[#fn:r2178|2178]]</sup> ; Wise et al., 2014 <sup>[[#fn:r2179|2179]]</sup> ; Kelly, 2015 <sup>[[#fn:r2180|2180]]</sup> ; Lawrence and Haasnoot, 2017 <sup>[[#fn:r2181|2181]]</sup> ). Such a staged approach is especially suitable for coastal adaptation due to the long lead and lifetimes of many coastal adaptation measures and the deep uncertainties in future sea levels (Hallegatte, 2009 <sup>[[#fn:r2182|2182]]</sup> ; Kelly, 2015 <sup>[[#fn:r2183|2183]]</sup> ). Prominent representatives of methods that entail this idea are Dynamic Adaptive Policy Pathways (Haasnoot et al., 2013 <sup>[[#fn:r2184|2184]]</sup> ) and Dynamic Adaptation Planning (Walker et al., 2001 <sup>[[#fn:r2185|2185]]</sup> ). An important prerequisite for any adaptive decision-making approach is a monitoring system that can detect sea level signals sufficiently early to enable the required responses (Hermans et al., 2017 <sup>[[#fn:r2186|2186]]</sup> ; Haasnoot et al., 2018 <sup>[[#fn:r2187|2187]]</sup> ; Stephens et al., 2018 <sup>[[#fn:r2188|2188]]</sup> ).

In recent years, many different frameworks for adaptive decision making have been put forward, including Adaptive Policy Making (Walker et al., 2001 <sup>[[#fn:r2189|2189]]</sup> ), Dynamic Adaptive Policy Pathways (Haasnoot et al., 2013 <sup>[[#fn:r2190|2190]]</sup> ), Dynamic Adaptive Planning (Walker et al., 2013 <sup>[[#fn:r2191|2191]]</sup> ), Iterative risk management (Jones et al., 2014 <sup>[[#fn:r2192|2192]]</sup> ) and Engineering Options Analysis (de Neufville and Smet, 2019). Each frameworks emphasises particular aspects of adaptive decision making and has merits in specific situations depending on the preferences, goals, uncertainties and information at stake (Marchau et al., 2019 <sup>[[#fn:r2193|2193]]</sup> ). Nevertheless, all of these frameworks share the following generic and iterative steps

# Set the stage: Identify current situation, objectives, options (alternatives) and uncertainties.
# Develop a dynamic plan, which consists of a basic plan plus contingency actions to be carried out based on observed triggers.
# Implement basic plan and monitor system for triggers.
# Monitor and act upon triggers.

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<span id="increasing-flexibility-of-responses"></span>
===== 4.4.4.3.4 Increasing flexibility of responses =====

An idea closely related to adaptive decision making is to keep future alternatives open by favouring flexible alternatives over non-flexible ones. An alternative is said to be ‘flexible’ if it allows switching to other alternatives once the implemented alternative is no longer effective. For example, a flexible protection approach would be to build small dikes on foundations designed for higher dikes, in order to be able to raise dikes in the future should SLR necessitate this.

A prominent and straightforward method that addresses the objective of flexibility is adaptation pathways analysis (Haasnoot et al., 2011 <sup>[[#fn:r2194|2194]]</sup> ; Haasnoot et al., 2012 <sup>[[#fn:r2195|2195]]</sup> ), which is one component of Dynamic Adaptive Policy Pathways. The method graphically represents alternative combinations of measures over time together with information on the conditions under which alternatives cease to be effective in meeting agreed objectives, as well as possible alternatives that will then be available. As time and SLR progress, monitoring may trigger a decision to switch to another alternative. Adaptation pathway analysis has been widely applied both in the scientific literature as well as in practical cases. Applications after AR5 include Indonesia (Butler et al., 2014 <sup>[[#fn:r2196|2196]]</sup> ), New York City (Rosenzweig and Solecki, 2014 <sup>[[#fn:r2197|2197]]</sup> ), Singapore (Buurman and Babovic, 2016 <sup>[[#fn:r2198|2198]]</sup> ) and Australia (Lin and Shullman, 2017 <sup>[[#fn:r2199|2199]]</sup> ). In New Zealand, the method has been included in national guidance for coastal hazard and climate change decision making (Lawrence et al., 2018 <sup>[[#fn:r2200|2200]]</sup> ). There is ''high confidence'' that the method is useful in interaction with decision makers and other stakeholders, helping to identify possible alternative sequences of measures over time, avoiding lock-in, and showing decision makers that there are several possible pathways leading to the same desired future (Haasnoot et al., 2012 <sup>[[#fn:r2201|2201]]</sup> ; Haasnoot et al., 2013 <sup>[[#fn:r2202|2202]]</sup> ; Brown et al., 2014 <sup>[[#fn:r2203|2203]]</sup> ; Werners et al., 2015 <sup>[[#fn:r2204|2204]]</sup> ).

Alternatives can also be characterised through multiple attributes such as costs, effectiveness, co-benefits, social acceptability, etc., which in turn can be used in multi-attribute decision making methods (Haasnoot et al., 2013 <sup>[[#fn:r2205|2205]]</sup> ). An important attribute is transfer cost, which is the cost of course correction (switching from one alternative to another), reflecting the potential for path dependency (Haasnoot et al., 2019 <sup>[[#fn:r2206|2206]]</sup> ). Delaying decisions and opting for flexible measures introduces extra costs, such as transfer costs. Also, flexible measures are often more expensive than inflexible ones, and damages may occur whilst delaying the decision. An important question therefore is whether it is cheaper to implement a flexible measure now or to wait and implement a less flexible (i.e., cheaper) measure later in time when more information is at hand.

Technically more demanding methods such as real-options analysis (Dixit et al., 1994 <sup>[[#fn:r2207|2207]]</sup> ), and decision tree analysis (Conrad, 1980 <sup>[[#fn:r2208|2208]]</sup> ), can also find pathways that are economically efficient in terms of flexibility and timing of adaptation. There is little application of these approaches in the SLR literature. For example, Woodward et al. (2014) applied real-options analysis to determine flood defences around the Thames Estuary, London, England; Buurman and Babovic (2016) for climate-proofing drainage networks in Singapore; Dawson et al. (2018) for coastal rail infrastructure in southern England; and Kim et al. (2018) for assessing flood defences in southern England. A requirement for applying real-options analysis and decision tree analysis is to quantify today how much will have been learned at a given point in time in the future. The few applications of these methods to SLR-related decisions in the literature have generally used ad-hoc assumptions. For example, Woodward et al. (2011) assumed either perfect learning (i.e., in 2040, which SLR trajectory is occurring will be known) or no learning (i.e., uncertainty ranges and confidence in these remains as today). Others have derived learning rates from comparing past progress in SLR projections and then applied these to the future. An example is given by Dawson et al. (2018) who derive learning rates from the 2002 and 2009 SLR projections of the UK Climate Impacts Programme and apply these in real-options analysis.

<div id="section-4-4-4-3decision-analysis-methods-block-5"></div>

<span id="research-needs"></span>
===== 4.4.4.3.5 Research needs =====

Four general gaps can be identified in the literature. First, the generation of SLR information is insufficiently coupled to the use of this information in decision analysis. This constitutes a limitation, as different coastal decision contexts require different decision analysis methods, which in turn require different SLR information. Specifically, applications of decision analysis methods generally convert existing sea level information to fit their method, often misinterpreting the information, making arbitrary assumptions or losing essential information in the process (Hinkel et al., 2015 <sup>[[#fn:r2210|2210]]</sup> ; Bakker et al., 2017 <sup>[[#fn:r2211|2211]]</sup> ; Van der Pol and Hinkel, 2018) . Second, with the exception of adaptation pathway analysis, methods of robust and flexible decision making are under-represented in the literature despite their suitability (Van der Pol and Hinkel, 2018) . Third, research is necessary to compare the various methods, to identity which methods are most suitable in which context and to develop consistent categorisations of methods (Hallegatte et al., 2012 <sup>[[#fn:r2212|2212]]</sup> ; Haasnoot et al., 2013 <sup>[[#fn:r2213|2213]]</sup> ; Hinkel et al., 2015 <sup>[[#fn:r2214|2214]]</sup> ; Watkiss et al., 2015 <sup>[[#fn:r2215|2215]]</sup> ; Dittrich et al., 2016 <sup>[[#fn:r2216|2216]]</sup> ; Suckall et al., 2018 <sup>[[#fn:r2217|2217]]</sup> ) . Fourth, future research needs to address how to embed decision analysis better in real world planning and decision making processes, recognising that adaptation to SLR is a multi-stakeholder process often characterised by conflicting interests and interdependence between stakeholders (Section 4.4.3). Addressing these gaps requires closer cooperation between SLR sciences, decision science, and planning and governance scholars. An underlying challenge is to design and integrate relevant formal decision making approaches into the heterogeneous reality of local planning and decision making cultures, institutions, processes and practices, often with community-specific needs and requirements (see Box 4.4).

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<span id="box-4.4-community-based-experiences-canadian-arctic-and-hawkes-bay-new-zealand"></span>