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

===== 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.

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