IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v299y2021ics0306261921007443.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921007443
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117336?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    3. 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.
    4. V. Krishnan & Karl T. Ulrich, 2001. "Product Development Decisions: A Review of the Literature," Management Science, INFORMS, vol. 47(1), pages 1-21, January.
    5. 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.
    6. Gert Jan Kramer & Martin Haigh, 2009. "No quick switch to low-carbon energy," Nature, Nature, vol. 462(7273), pages 568-569, December.
    7. 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.
    8. 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.
    9. 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.
    10. Dunia López-Pintado & Duncan J. Watts, 2008. "Social Influence, Binary Decisions and Collective Dynamics," Rationality and Society, , vol. 20(4), pages 399-443, November.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    3. Boerner, Lars & Severgnini, Battista, 2015. "Time for growth," LSE Research Online Documents on Economics 64495, London School of Economics and Political Science, LSE Library.
    4. Nemet, Gregory F. & Zipperer, Vera & Kraus, Martina, 2018. "The valley of death, the technology pork barrel, and public support for large demonstration projects," Energy Policy, Elsevier, vol. 119(C), pages 154-167.
    5. Christoph Engel & Alon Klement & Karen Weinshall Margel, 2017. "Diffusion of Legal Innovations: The Case of Israeli Class Actions," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2017_11, Max Planck Institute for Research on Collective Goods, revised Jan 2018.
    6. Golberg, Alexander, 2015. "Environmental exergonomics for sustainable design and analysis of energy systems," Energy, Elsevier, vol. 88(C), pages 314-321.
    7. Raphael Suire & Jérome Vicente, 2009. "Why do some places succeed when others decline? A social interaction model of cluster viability," Journal of Economic Geography, Oxford University Press, vol. 9(3), pages 381-404, May.
    8. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.
    9. Francesco Bogliacino & Giorgio Rampa, 2012. "Quality risk aversion, conjectures, and new product diffusion," Journal of Evolutionary Economics, Springer, vol. 22(5), pages 1081-1115, November.
    10. Guo, Xiaoli & Ryvkin, Dmitry, 2022. "When is intergroup herding beneficial?," Mathematical Social Sciences, Elsevier, vol. 120(C), pages 66-77.
    11. Garth Heutel & Erich Muehlegger, 2015. "Consumer Learning and Hybrid Vehicle Adoption," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(1), pages 125-161, September.
    12. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    13. Flachsbarth, Insa & Grassnick, Nina & Masood, Amjad & Bruemmer, Bernhard, 2018. "The Uneven Spread of Private Food Quality Standards over Time and Space," 2018 Annual Meeting, August 5-7, Washington, D.C. 274197, Agricultural and Applied Economics Association.
    14. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    15. H Peyton Young & Lucas Merrill Brown, 2016. "The Diffusion of a Social Innovation: Executive Stock Options from 1936," Economics Series Working Papers 777, University of Oxford, Department of Economics.
    16. Koster, Maurice & Lindner, Ines & Molina, Elisenda, 2010. "Networks and collective action," DES - Working Papers. Statistics and Econometrics. WS ws104830, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    18. Mira Frick & Yuhta Ishii, 2015. "Innovation Adoption by Forward-Looking Social Learners," Cowles Foundation Discussion Papers 1877, Cowles Foundation for Research in Economics, Yale University.
    19. Korkmaz, Özge & Erer, Elif & Erer, Deniz, 2022. "Internet access and its role on educational inequality during the COVID-19 pandemic," Telecommunications Policy, Elsevier, vol. 46(5).
    20. Sebastián Moreno & Danilo Bórquez-Paredes & Valentina Martínez, 2023. "Analysis of the Characteristics and Speed of Spread of the “FUNA” on Twitter," Mathematics, MDPI, vol. 11(7), pages 1-18, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921007443. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.