events that will become more frequent and intense as a result of climate change (Schipper and Pelling, 2006). Efforts to reduce this kind of vulnerability work more effectively when they also meet every day developmental needs. For example, recent projects in the Simon Bolivar and La Cañita communities of Santo Domingo in the Dominican Republic have shown that the longest-lasting interventions have been purpose-built staircases (designed to facilitate evacuation during flood events) that are also used on a daily basis by residents of these communities. There are, however, some key differences. Climate change adaptation and disaster risk reduction operate in distinct time frames: Disaster risk and relief are relatively immediate and concentrated, whereas climate change evolves over longer periods. They also frequently occupy distinct spatial scales, with disaster risk reduction being focused on the local and national scales, and climate change policy often focused on the global scale (although many adaptation activities have taken place at a much more local level). Yet at the same time, existing disaster risks may well be exacerbated by climate change, and existing strategies for reducing disaster risk may contribute to broader adaptation goals (Renaud and Perez, 2010). There is also a distinct need for disaster risk reduction to engage more seriously with issues associated with global political economy; in addition, climate change adaptation needs to focus more deeply on local issues—as has been recognized through the theory and practice of community-based adaptation. There is also potential for greater integration around scales, knowledge and norms (Birkmann and von Teichmann, 2010).
Population data for adaptation analysis Integration of population data into adaptation analysis and intervention is at the heart of this book’s contribution. Better access to information on climate change and its consequences is at the centre of improving adaptation programming and people’s adaptive capacity. Agrawala and van Aalst (2008, p. 190) point to three paths to improved data access and usage: improving the usability of climate data; developing climate screening tools for programming; and identifying appropriate entry points for climate data. The World Bank’s Urban Risk Assessment (Dickson et al., 2012) suggests a methodology at the city level for bringing together a number of different data types and sources and integrating them into policy processes at the local level. These processes also need to be applied to population data for adaptation analysis, and the use of population data can, in turn, help in their achievement. One reason for hastening the integration of population data into vulnerability assessments and adaptation efforts is that, particularly over the past 10 years, technology for remote sensing of hazard impacts has improved in leaps and bounds. Shortly after the 2010 earthquake in Haiti, detailed images and maps of damage to the built environment were generated using pre- and post-data from satellite imagery, LIDAR (light detection and ranging), aerial photography and other technologies that could provide data quickly and efficiently, with limited on-the-ground presence in the midst of what was an extraordinarily difficult humanitarian crisis. Much of these data were aggregated onto Open Street Map, which provides data on streets, buildings and damage, as well as available 18
The De mogra ph y of Ada ptation to C l imate Ch ange
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