A social learning approach to carbon capture and storage demonstration project management: An empirical analysis

Jia-Ning Kang, Yi-Ming Wei, Lancui Liu, Bi-Ying Yu and Hua Liao ()

Applied Energy, 2021, vol. 299, issue C, No S0306261921007443

Abstract: Carbon capture and storage (CCS) is an essential technology option for limiting global warming to well below 2 degrees Celsius. Demonstration is an important engineering measure towards large-scale CCS promotion. Implementing a demonstration project is a complicated process of system engineering management that involves a large number of strategic decisions. It is critical to investigate the potential knowledge-generating and decision-making processes embedded in the CCS demonstration, as well as to learn from past successes and failures. Learning-oriented approaches used to be primarily concerned with technical costs, which sparked a lengthy debate regarding the economic viability of CCS projects, but little attention was devoted to the social aspects of project implementation. Based on historical evidence on CCS demonstrations globally, this study developed a data-driven social learning model with high accuracy in estimating the chances of project success, combined with19 socio-technical characteristics. Our results imply that project performance (79.8%) is more relevant than policy incentives (12.8%) and media hype (7.4%) in explaining previous CCS triumphs. Among the seven policy-level characteristics, tax credits and regulatory legislation have grown in importance over the last 20 years, while the significance of grant subsidies has gradually waned. Furthermore, we found that modest hype assisted in the completion of planned projects, notably carbon dioxide storage projects. The sociocultural narrative of CCS does not yet have a set ending. This highlights the significance of telling compelling CCS stories in steering the future trajectory of technology and the low-carbon energy revolution.

Keywords: Carbon Capture and Storage Projects; Decision Simulation; Feasibility Evaluation; Random Forests Algorithm; Social learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2021.117336

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