Generating linked technology-socioeconomic scenarios for emerging energy transitions

• Published in: Applied energy 2019-04, Vol.239, p.1402-1423
• Main Authors: Small, Mitchell J., Wong-Parodi, Gabrielle, Kefford, Benjamin M., Stringer, Martin, Schmeda-Lopez, Diego R., Greig, Chris, Ballinger, Benjamin, Wilson, Stephen, Smart, Simon
• Format: Article
• Language: English
• Subjects: Bayesian Belief Network (BBN); Carbon capture and storage (CCS); Decarbonisation; Energy system transitions; Expert judgment; Scenario generation
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.01.215

•A Bayesian network model is used to generate scenarios for a new energy technology.•Scenarios consider technical and sociopolitical factors and their interactions.•Application to CCS predicts that improvements in both sets of factors are necessary. The formulation and use of scenarios is now a fundamental part of national and global efforts to assess and plan for climate change. While scenario development initially focused on the technical dimensions of energy, emissions and climate response, in recent years parallel sets of shared socio-economic pathways have been developed to portray the values, motivations, and sociopolitical and institutional dimensions of these systems. However, integrating the technical and social aspects of evolving energy systems is difficult, with transitions dependent on highly uncertain technological advances, social preferences, political governance, climate urgency, and the interaction of these elements to maintain or overcome systemic inertia. A broad range of interdisciplinary knowledge is needed to structure and evaluate these processes, many of which involve a mix of qualitative and quantitative factors. To structure and facilitate the necessary linkages this paper presents an approach for generating a plausible range of scenarios for an emerging energy technology. The method considers influences among technical and social factors that can encourage or impede necessary improvements in the performance and cost of the technology, as well the processes affecting public acceptance and the establishment of governance structures necessary to support effective planning and implementation. A Bayesian network is used to capture relationships among the technological and socioeconomic factors likely to affect the probability that the technology will achieve significant penetration and adoption. The method is demonstrated for carbon capture and storage (CCS): a potential technology on the pathway to deep decarbonization. A preliminary set of expert elicitations is conducted to illustrate how relationships between these factors can be estimated. This establishes a prior or baseline network that can be subsequently analyzed by choosing either optimistic or pessimistic assumptions for respective groups of technical and social variables, identifying sets of key factors that limit or encourage successful deployment.