Detected trends in impacts can be attributed to climate change or other drivers either using process-based models or building statistical models of the relationship between observed impacts and a number of explanatory variables. Most scholarly work in CCVIA focuses on the explanation of impacts through biophysical variables and are labeled here impact attribution. Some work also tries to explain impacts through socio-economic variables. These approaches are sometimes also called vulnerability or adaptive capacity indicators in the literature, however they differ from the vulnerability indicators described above in that the latter ones do not consider data on observed impacts (Yohe and Tol 2002; Hinkel 2011a). In order to avoid confusion, we only use the term vulnerability indicator for those approaches that do not use data of observed impacts, as described above.

The attribution of impacts to social system variables, face two major challenges. First, in contrast to the case of natural systems, general simulation models of social systems are not available and the development of statistical models is only promising when systems can be described by few variables and a lot of data is available. The systems considered in the context of climate change are, however, generally complex social-ecological systems, which means that in principle, they cannot be described using few variables and simple (or linear) statistical regression models (Barnett et al. 2008; Hinkel 2011a). Second, the time-series data records available for impacts caused by climate change are often not long enough for building reliable statistical models (Bouwer 2011).

Table 2.4: Impact-analytical methods (2) HERE