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

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  • Kang, Jia-Ning
  • Wei, Yi-Ming
  • Liu, Lan-cui
  • Yu, Bi-Ying
  • Liao, Hua

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.

Suggested Citation

  • Kang, Jia-Ning & Wei, Yi-Ming & Liu, Lan-cui & Yu, Bi-Ying & Liao, Hua, 2021. "A social learning approach to carbon capture and storage demonstration project management: An empirical analysis," Applied Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921007443
    DOI: 10.1016/j.apenergy.2021.117336
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    1. Say, Nuriye Peker & Yucel, Muzaffer, 2006. "Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth," Energy Policy, Elsevier, vol. 34(18), pages 3870-3876, December.
    2. Azadeh, A. & Tarverdian, S., 2007. "Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption," Energy Policy, Elsevier, vol. 35(10), pages 5229-5241, October.
    3. Gert Jan Kramer & Martin Haigh, 2009. "No quick switch to low-carbon energy," Nature, Nature, vol. 462(7273), pages 568-569, December.
    4. David M. Reiner, 2016. "Learning through a portfolio of carbon capture and storage demonstration projects," Nature Energy, Nature, vol. 1(1), pages 1-7, January.
    5. Dunia López-Pintado & Duncan J. Watts, 2008. "Social Influence, Binary Decisions and Collective Dynamics," Rationality and Society, , vol. 20(4), pages 399-443, November.
    6. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    7. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    8. V. Krishnan & Karl T. Ulrich, 2001. "Product Development Decisions: A Review of the Literature," Management Science, INFORMS, vol. 47(1), pages 1-21, January.
    9. Stewart Russell & Nils Markusson & Vivian Scott, 2012. "What will CCS demonstrations demonstrate?," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 17(6), pages 651-668, August.
    10. Bass, Frank M, 1980. "The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations," The Journal of Business, University of Chicago Press, vol. 53(3), pages 51-67, July.
    11. Jiaquan Li & Zhifu Mi & Yi-Ming Wei & Jingli Fan & Yang Yang & Yunbing Hou, 2019. "Flexible options to provide energy for capturing carbon dioxide in coal-fired power plants under the Clean Development Mechanism," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(8), pages 1483-1505, December.
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    1. Sara Yasemi & Yasin Khalili & Ali Sanati & Mohammadreza Bagheri, 2023. "Carbon Capture and Storage: Application in the Oil and Gas Industry," Sustainability, MDPI, vol. 15(19), pages 1-32, October.
    2. Kévin Nadarajah & Laurent Brun & Stéphanie Bordel & Emeline Ah-Tchine & Anissa Dumesnil & Antoine Marques Mourato & Jacques Py & Laurent Jammes & Xavier Arnauld De Sartre & Alain Somat, 2024. "A Three-Stage Psychosocial Engineering-Based Method to Support Controversy and Promote Mutual Understanding between Stakeholders: The Case of CO 2 Geological Storage," Energies, MDPI, vol. 17(5), pages 1-15, February.
    3. Yanbin Li & Yanting Sun & Yulin Kang & Feng Zhang & Junjie Zhang, 2023. "An Optimal Site Selection Framework for Near-Zero Carbon Emission Power Plants Based on Multiple Stakeholders," Energies, MDPI, vol. 16(2), pages 1-26, January.
    4. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).

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