A Framework for Developing Adaptation Plans
2. Uncertainty, formally speaking, is different from risk (where there is a known probability distribution); however, it was impossible to write this book without using the word “risk,” so we have not adhered to a formal use of the two terms. 3. These works stand in contrast to a large academic literature on adaptation that focuses on frameworks and definitions. While helpful in framing the discussion of what adaptation is, that literature does not offer much in the way of practical guidance for preparing and implementing an adaptation program. 4. Many strategies have been written up, and in some cases, attempts have been made to provide a critical analysis of what appears to work. For a comparative analysis of these strategies, see Heinz Center (2007). 5. A whole cottage industry has sprung up on how to define and measure vulnerability. This is partly because different disciplines or fields of research (for example, catastrophic risk management, ecology, social protection, and climate change) use similar terms for different purposes, or use many different terms to describe the same fundamental concept. For a recent overview of the literature on the topic and a discussion of how this framework fits with other approaches, see Füssel (2007). 6. This is the framework that was presented in the 2001 Intergovernmental Panel on Climate Change (McCarthy et al. 2001) and further developed since by a number of authors. 7. The index uses principal component analysis to calculate the sensitivity and adaptive capacity subindices, as well as to combine all three indices into the overall vulnerability index. Principal component analysis is a statistical technique that picks the weight given to each component of an index formula in order to best explain the variance in the data. The exposure subindex is from Baettig, Wild, and Imboden (2007) and uses a simple linear formula to combine the underlying variables. 8. The institutional measures are from the Worldwide Governance Indicators Project (Kaufman, Kraay, and Mastruzzi 2008) and include measures of voice and accountability; political stability and absence of violence; and an aggregate governance measure of government effectiveness, regulatory quality, rule of law, and control of corruption. 9. For disaster impact data, see EM-DAT (Emergency Events Database), Centre for Research on the Epidemiology of Disasters (CRED), Université Catholique de Louvain. http://www.emdat.be/Database/terms. html. 10. Thus, while a survey of 928 peer-reviewed articles published in academic journals found no disagreement with the consensus view, public opinion polls show high but far from universal belief in climate change (Oreskes 2004). 11. This section is based on the review of six urban regions’ adaptation plans and processes by the Clean Air Partnership (Ligeti, Penney, and Wieditz 2007). 12. There is evidence that even where averages are moving slowly enough and with enough predictability to theoretically provide the necessary time and signals to identify optimal adaptation strategies, the “noise” introduced by extreme weather events and normal climatic variability reduces the value of these signals (Burton and Lim 2005). In other
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