Reflections on cross-impact balances, a systematic method constructing global socio-technical scenarios for climate change research

Experiences with an algorithmic technique—cross-impact balances (CIB)—for exploring scenarios rather than relying solely upon expert intuitions are discussed. With CIB, two types of uncertainty for climate change research have been explored: (1) socio-technical uncertainties not represented explicitly in integrated assessment models (sometimes called “context scenarios”) and (2) sampling the space of possible futures to model. By applying CIB retrospectively and prospectively to two global socio-economic scenario exercises for climate change research (the Special Report on Emissions Scenarios and the Shared Socioeconomic Pathways), CIB proved instructive in two ways. First, CIB revealed system behaviors that were not obvious when social variables, such as quality of governance, were not captured explicitly by integrated assessment models. Second, CIB can algorithmically rank different plausible futures according to their self-consistency. These two capabilities have raised awareness about the limitations of accepting what may be “obvious” to model, as practices that focus solely on quantitative variables or rely upon intuitions for scenario analysis may result in detailed analyses of only a subset of important policy-relevant futures. From these experiences, systematic methods like CIB are recommended in conjunction with more detailed modeling to develop integrated socio-technical scenarios in energy-economy research.