IPCC Report.pdf - Adam Curry

Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and ResilienceChapter 11.4.4. ‘No Regrets,’ Robust Adaptation, and LearningThe mismatch between adaptation strategies and projected needs hasbeen characterized as the potential for regret, that is, opportunity costsassociated with decisions (and related path dependence, wherein earlierchoices constrain future circumstances and decisions) that are optimalfor one or a small number of possible climate futures but not necessarilyrobust over a wider range of scenarios (Lempert and Schlesinger, 2001).‘No regrets’ adaptation refers to decisions that have net benefits overthe entire range of anticipated future climate and associated impacts(Callaway and Hellmuth, 2007; Heltberg et al., 2009).To address the challenge of risk management in the dynamicallycomplex context of climate change and development, as well as underconditions where probabilistic estimates of future climatic conditionsremain imprecise, several authors have advanced the concept ofrobustness (Wilby and Dessai, 2010), of which ‘no regrets’ adaptation isa special case (Lempert and Groves, 2010). Robustness is a property ofa plan or strategy that performs well over a wide range of plausiblefuture scenarios even if it does not perform optimally in any particularscenario. Robust adaptation plans may perform relatively well even ifprobabilistic assessments of risk prove wrong because they aim toaddress both expected and surprising changes, and may allow diversestakeholders to agree on actions even if they disagree about values andexpectations (Brown and Lall, 2006; Dessai and Hulme; 2007; Lempertand Groves, 2010; Means et al., 2010; see also Section 1.3.2).As Section 1.4.3 highlights, currently, in many instances risks associatedwith extreme weather and other climate-sensitive hazards are often notwell managed. To be effective, adaptation would prioritize measuresthat increase current as well as future resilience to threats. Robustnessover time would increase if learning were a central pillar of adaptationefforts, including learning focused on addressing current vulnerabilitiesand enhancing current risk management (high confidence). Single-,double-, and triple-loop learning will all improve the efficacy ofmanagement strategies.The case studies in Chapter 9 highlight some important examples oflearning in disaster risk management relevant to a wide range of climatesensitivethreats and a variety of sectors. Section 9.2 provides examplesof how single- and double-loop learning processes – enhancing publichealth response capacity, augmenting early warning systems, andapplying known strategies for protecting health from the threat ofextreme heat in new settings – had demonstrable impacts on heatrelatedmortality, quickly shifting a region’s coping range with regard toextreme heat (Section 9.2.1). Other case studies, examining risk transfer(Section 9.2.13) and early warning systems (Section 9.2.11), provideinstances of how existing methods and tools can be modified anddeployed in new settings in response to changing risk profiles – examplesof both double- and triple-loop learning. Similarly, the case studies ongovernance (Section 9.2.12) and on the limits to adaptation in smallisland developing states (Section 9.2.9) provide examples of third-looplearning and transformative approaches to disaster risk management.ReferencesA digital library of non-journal-based literature cited in this chapter thatmay not be readily available to the public has been compiled as part ofthe IPCC review and drafting process, and can be accessed via either theIPCC Secretariat or IPCC Working Group II web sites.ACC, 2010: Informing an Effective Response to Climate Change. 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