Characterising CCS learning:the role of quantitative methods and alternative approaches

A number of energy scenario studies have suggested that carbon capture and storage (CCS) could make a significant contribution to reducing global carbon dioxide (CO2) emissions. This would require efforts to ensure rapid development and deployment. Since there is limited experience of CCS systems, it is hard to define ‘business as usual’ development. This leads to significant uncertainty for policy makers and other stakeholders with regard to characterising potential CCS pathways and assessing the scope for and risks of acceleration. Quantitative analytical approaches to projecting costs and other parameters typically depend on best current estimates of critical input data, as well as implicit or explicit assumptions about technology development pathways and contextual factors such as evolving regulatory requirements. There are significant limitations in current quantitative (and qualitative) data on CCS that lead to significant difficulties in identifying robust assumptions. One way to handle this is to develop multiple scenarios to illustrate the uncertainty. Another strategy is to make more use of qualitative methods for analysing CCS innovation processes. This latter approach could help to avoid some of the issues associated with CCS cost uncertainty and instead re-focus attention on understanding critical aspects of innovation processes