This presentation of a paper, by BASE partners Xin Li, Jouni Paavola, Dabo Guan, Gil Penha-Lopes and Nigel Wright, was given at the DCE Science for the Environment conference.
Abstract
To date, research on adaptation strategies and measures has mostly used a top-down and sectoral approach to generate estimates of the costs and benefits of adaptation or more general lessons for adaptation planning and policy. While these studies are of value for national policy and decision makers, they are less useful for local governments, businesses and non- governmental organisations who will deliver adaptation on the ground and will need to plan for it. Costs, benefits, opportunities and barriers of adaptation are also highly contextual, which means that coordinated European efforts would need to acknowledge and accommodate variations in them between the member states.
BASE has developed a bottom-up strategy for assessing adaptation needs and their costs, benefits and effectiveness from the viewpoint of those who will plan for and deliver adaptation. Such research strategy faces several challenges. On one hand, in-depth case studies are resource intensive and, therefore, their number will remain limited. This constrains the traditional solution for improving data quality – increasing the sample size. On the other hand, even cases exploring adaptation in the same sector to same climate change impacts can be quite different because of different contexts. Furthermore, deploying teams with local knowledge to understand the contextuality of adaptation can lead to differences in the expertise of case study teams, which requires flexibility in how the research is conducted whilst guiding what it produces in terms of results. In this paper we outline how BASE will seek to coordinate case study research and to ensure its usefulness to top-down modelling efforts by using common starting points, methodological transparency and core questions as key instruments. We also highlight how research with limited number of case studies is best placed to map the range of possible outcomes rather than expected values.
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