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

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  • Markusson, Nils
  • Chalmers, Hannah

Abstract

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.

Suggested Citation

  • Markusson, Nils & Chalmers, Hannah, 2013. "Characterising CCS learning: The role of quantitative methods and alternative approaches," Technological Forecasting and Social Change, Elsevier, vol. 80(7), pages 1409-1417.
    • Handle: RePEc:eee:tefoso:v:80:y:2013:i:7:p:1409-1417
    DOI: 10.1016/j.techfore.2011.12.010
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    Cited by:

    1. Binz, Christian & Truffer, Bernhard, 2017. "Global Innovation Systems—A conceptual framework for innovation dynamics in transnational contexts," Research Policy, Elsevier, vol. 46(7), pages 1284-1298.
    2. John Michael Humphries Choptiany & Ron Pelot & Kate Sherren, 2014. "An Interdisciplinary Perspective on Carbon Capture and Storage Assessment Methods," Journal of Industrial Ecology, Yale University, vol. 18(3), pages 445-458, May.
    3. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

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