Top-down and bottom-up decision-making for climate change adaptation. An application to flooding
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There is strong scientific consensus on the evidence of anthropogenic climate change which will increasingly present social, economic and institutional challenges. The Fifth Assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) established that ‘human influence on the climate system is clear’ and that ‘changes in many extreme weather and climate events have been observed since about 1950’ (IPCC 2014a). Associated impacts include sea level rise and increased likelihood of extreme weather worldwide such extreme rainfall, heat waves, hurricanes and tornados (IPCC 2014a; Klijn et al. 2015). Climate change adaptation is the adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects in order to minimise the impacts and to take advantage of new opportunities (IPCC 2007). Many vulnerable countries, regions and cities have accepted that some form of adaptation is inevitable (Swart et al. 2014). This thesis contributes to the research on decision-making for climate change adaptation in order to reduce vulnerability. Both bottom-up and top-down analyses are applied to complement one another with an application to flooding. Flood risk is expected to increase in the UK under climate change (Alfieri et al. 2016; Scottish Government 2016) associated significant economic damage (CEA 2007). From a top-down perspective, the thesis explores how to enhance economic decision-making under climate change uncertainty. In a situation of uncertainty the costs may be clear and immediate whereas the benefits are uncertain and often only realised in the distant future. This impedes the use of standard decision-making tools such as cost-benefit analysis that rely on the quantification of (expected) costs and benefits. The thesis begins on the macro scale with a taxonomy of economic decision-making tools for climate change adaptation, discusses the sector level and subsequently proceeds to the case study micro-scale with applications of adaptation decision-making. First, the potential of alternative decision-making tools, so-called robust decision-making approaches, is examined. The strengths and weaknesses of these tools relative to traditional decision-making processes such as CBA are explored and their future potential in the adaptation process evaluated. It is found that robust decision-making tools under uncertainty provide performance across a range of climate change scenarios, but they may yield lower overall performance if compared with the alternative strategy under the actual climate outturn. Furthermore, they are resource intense and decision makers need to balance the resources required for employing the methods with the added value they can offer. A flow-chart is developed to provide guidance on which decision-making tool should be applied depending on the scale and type of adaptation project. On the sector level, the economic appraisal of adaptation options for agriculture is explored. Agriculture is particularly vulnerable to climate change due to the direct impacts of weather and climate on agricultural output and the sector plays an indispensable role in providing (and improving) food security as well as creating employment. Many of the adaptation options in agriculture involve short-term managerial changes and can be appraised with standard economic decision-making and the options can be carried out after the climate signal has been observed. For those adaptations that do require a longer time to take effect or are long-lived and are (partly) irreversible in nature, robust approaches have a valuable role to play in decision-making. Suggestions are made regarding how robust decisionmaking tools under uncertainty can be practically applied to adaptations in agriculture, outlining the data needs and the steps of the data analysis for three different applications. On the micro level, for a case study in the Eddleston Water catchment in the Scottish borders, UK, two different economic appraisal tools are applied. These include a cost-benefit analysis of afforestation as a flood management measure under different climate change scenarios which can provide important insights for adaptation decisions when robust decision-making tools under uncertainty are not feasible due to resource constraints. It is found that the flood risk under climate change increases substantially in the case study area which needs to be taken into consideration for economic appraisal. The results of the CBA reveal that all modelled scenarios of afforestation have positive NPVs which are driven by further eco-system services (including climate regulation, water quality and recreation) rather than flood regulation benefits. It is concluded that eco-system services beyond flood regulation should be considered for the appraisal of NFM to enable policy-makers to make informed decisions. Second, the Expected values can be used in situations of quantifiable uncertainty, i.e risk. But for climate change we do not have a strong methodology to assess these subjective probabilities. They cannot be fully based on the past, because climate change is a new process for which we have no historical equivalent. Models share common flaws in their assumptions and their dispersion in results cannot be used to assess the real uncertainty (Hallegatte, 2012). The term deep uncertainty (Lempert et al., 2003) or severe uncertainty is used (Ben-Haim, 2006) in these contexts. Such uncertainty is characterised as a condition where decision makers do not know or cannot agree upon a model that adequately describes cause and effect or its key parameters (Walker et al., 2012). This leads to a situation where it is not possible to say with confidence whether one future state of the world is more plausible than another. The robust decision-making tool under uncertainty real option analysis is applied to the same case study to allow for adjusting adaptation options over time by integrating lessons learned about climate change in the appraisal process. A simplified ROA is presented to minimise the life cycle cost of a system that aims to prevent flooding of a return period of 1/20 using tools which should be available to most public authorities. This includes the use of UKCP09 climate data, analysis of changes of peak flow under the measure implemented, cost structures for the measure and damage cost under different outcomes. The analysis can be carried out in an excel spread sheet with the aforementioned types of input. The results of the analysis demonstrate that the obtained strategy is significantly cheaper than planting for the worst case scenario and presents the potential for learning under climate change uncertainty as a way to allocate resources in a more efficient way. The complementing bottom up approach investigates behavioural barriers to decisionmaking for adaptation. Standard economic theory tells us that self-interest will motivate most actors to engage in efficient private adaptation as long as the costs do not exceed the benefits. Thus, we would expect households at flood risk to invest in flood adaptation measures. However, it has been observed that households do not necessarily take action to protect themselves and their assets from flooding. In a study carried out in co-operation with 36 communities around Scotland, protection motivation theory is used to explain the uptake of household flood protection and whether community led flood action groups can increase uptake. It is found that flood action groups directly and indirectly influence the uptake of some flood protection measures positively in particular if tailored information is provided. Overall, it is concluded that both top-down and bottom-up approaches play an important role to move towards an economically efficient adaptation in the context of flooding. From a top-down perspective, uncertainty should be explicitly acknowledged and included in economic decision-making for adaptation (to flooding) to make an informed decision. The type of analysis will depend on the adaptation project and resources at hand. Developing and fostering bottom-up tools such as flood action groups to increase the uptake of the type of household flood protection with a benefit-cost ratio above 1 may also contribute towards the more efficient allocation of resources.