Other sections of this chapter highlight the importance of social science research in understanding the causes, consequences, and opportunities to respond to climate change. As with research on the physical and biological components of the climate system, this research depends on the availability of high-quality, long-term, and readily accessible observations of human systems, not only in the United States but also in areas of the world with relevant U.S. interests. Census data, economic productivity and consumption data, data on health and disease patterns, insurance coverage, crop yields, hazards exposure, and public perceptions and preferences are just some of the types of information that can be relevant for developing an improved understanding of human interactions with the climate system and for answering various decision-relevant questions related to the human dimensions of climate change. Socioeconomic data are also critical for linking environmental observations with assessments of climate-related risk, vulnerability, resilience, and adaptive capacity in human systems. As with other types of observations, long time series are needed to monitor changes in the drivers of climate change and trends in resilience and vulnerability. Such observational data are most useful when geocoded (linked to specific locations) and matched (aggregated or downscaled) to scales of interest to researchers and decision makers, and when human and environmental data are collected and archived in ways that facilitate linkages between these data.
Studies conducted in the 1970s and 1980s demonstrate the feasibility of data collection efforts that integrate across the engineering and social sciences to better understand and model energy consumption (Black et al., 1985; Cramer et al., 1984; Harris and Blumstein, 1984; Socolow, 1978). Linkage of data on land-cover change and its social and economic drivers has also been productive (NRC, 2005c, 2007i). Large-scale social science data collection efforts, ranging from the census to federally funded surveys such as the National Longitudinal Study of Adolescent Health, the Panel Study of Income Dynamics, the General Social Survey, and the National Election Studies show the feasibility and value of long-term efforts to collect high-quality social data. However, to date there has been no sustained support to collect comparable data at the individual or organizational level on environmentally significant behaviors, such as energy use and GHG emissions. As states and other entities adopt policies to limit GHG emissions, sustained and integrated efforts to collect data on environmentally significant consumption will be extremely helpful for monitoring progress and honing programs and policies.
Likewise, data on the impacts of climate change on human systems and on vulnerability and adaptation of human systems to global environmental changes are critically needed (NRC, 2009g,k). Examples include morbidity and mortality data associated with air and water quality, expanded data sets focusing on household risk-pooling strategies and adaptation options, and data on urban infrastructure vulnerabilities to extreme weather and climate events. Methods that allow aggregation of data from across a range of regions to develop national-scale understanding will sometimes be necessary, but local and regional vulnerability assessments will also be needed, and these depend on both local and appropriately downscaled information (Braden et al., 2009). The potential exists for greater use of remote sensing to develop indicators of vulnerability to various climate-related hazards and of the socioeconomic drivers of climate change. If validated against in situ measurements, such measures can allow for monitoring of human-climate interactions at much finer spatial and temporal scales than is currently feasible with surveys or other in situ measures of human variables.
There is also great potential in the use of mobile communications technology, such as cell and smart phones, as a vehicle for social science research that has fine temporal and spatial scales (Eagle et al., 2009; Raento et al., 2009; Zuwallack, 2009). Many data collection efforts previously undertaken for governmental administrative purposes, business purposes, or social science research not related to climate change could potentially support the research needed for understanding the human aspects of climate change and climate-related decision making, but only if they are geocoded and linked to other data sets. International, longitudinal databases such as the International Forestry and Institutions database (e.g., Chhatre and Agrawal, 2008) also have great potential to serve as a bridge between local, regional, national, and global processes, as well as for assessing the dynamics of change across time and space.
Finally, because most major social and economic databases have been developed for purposes unrelated to climate change, these data have significant gaps from the perspective of climate science. However, all climate-relevant socioeconomic and other human systems data need not necessarily be held in a single common observing system. They simply need to be inventoried, archived, and made broadly accessible to enable the kinds of integrative analyses that are necessary for the new climate change research. A major effort is needed both to develop appropriate local data collection efforts and to coordinate them into national and global systems. Initial progress can be made by coordination across specific domains and sectors (e.g., coastal vulnerabilities, health vulnerabilities) and across scales so that locally useful information also contributes to larger-scale indicators and vice versa. Data integration is also a critical need. Some of these issues are explored in the next subsection.
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