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Resolving or managing uncertainties for carbon capture and storage: Lessons from historical analogues

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  • Watson, Jim
  • Kern, Florian
  • Markusson, Nils

Abstract

Carbon capture and storage (CCS) technologies are often highlighted as a crucial component of future low carbon energy systems in the UK and internationally. Whilst these technologies are now in the demonstration phase world-wide, they are still characterised by a range of technical, economic, policy, social and legal uncertainties. This paper applies a framework for the analysis of these uncertainties that was previously developed by the authors to a historical evidence base. This evidence base comprises nine case studies, each of which focuses on a technology that is partly analogous to CCS. The paper's analysis of these case studies examines the conditions under which the uncertainties concerned have been at least partly resolved, and what lessons can be drawn for CCS. The paper then uses the case study evidence to discuss linkages between the uncertainties in the analysis framework, and how these linkages differ from those that were originally expected. Finally, the paper draws conclusions for the methodological approach that has been used and for strategies to develop and deploy CCS technologies.

Suggested Citation

  • Watson, Jim & Kern, Florian & Markusson, Nils, 2014. "Resolving or managing uncertainties for carbon capture and storage: Lessons from historical analogues," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 192-204.
    • Handle: RePEc:eee:tefoso:v:81:y:2014:i:c:p:192-204
    DOI: 10.1016/j.techfore.2013.04.016
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    References listed on IDEAS

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    1. Colpier, Ulrika Claeson & Cornland, Deborah, 2002. "The economics of the combined cycle gas turbine--an experience curve analysis," Energy Policy, Elsevier, vol. 30(4), pages 309-316, March.
    2. Rubin, Edward S. & Yeh, Sonia & Antes, Matt & Berkenpas, Michael & Davison, John, 2007. "Use of experience curves to estimate the future cost of power plants with CO2 capture," Institute of Transportation Studies, Working Paper Series qt46x6h0n0, Institute of Transportation Studies, UC Davis.
    3. Reiner, D.M & Herzog, H.J, 2004. "Developing a set of regulatory analogs for carbon sequestration," Energy, Elsevier, vol. 29(9), pages 1561-1570.
    4. Grubler, Arnulf, 2010. "The costs of the French nuclear scale-up: A case of negative learning by doing," Energy Policy, Elsevier, vol. 38(9), pages 5174-5188, September.
    5. Cowan, Robin, 1990. "Nuclear Power Reactors: A Study in Technological Lock-in," The Journal of Economic History, Cambridge University Press, vol. 50(3), pages 541-567, September.
    6. Hadjilambrinos, Constantine, 2000. "Understanding technology choice in electricity industries: a comparative study of France and Denmark," Energy Policy, Elsevier, vol. 28(15), pages 1111-1126, December.
    7. Rubin, Edward S & Taylor, Margaret R & Yeh, Sonia & Hounshell, David A, 2004. "Learning curves for environmental technology and their importance for climate policy analysis," Energy, Elsevier, vol. 29(9), pages 1551-1559.
    8. Markusson, Nils & Kern, Florian & Watson, Jim & Arapostathis, Stathis & Chalmers, Hannah & Ghaleigh, Navraj & Heptonstall, Philip & Pearson, Peter & Rossati, David & Russell, Stewart, 2012. "A socio-technical framework for assessing the viability of carbon capture and storage technology," Technological Forecasting and Social Change, Elsevier, vol. 79(5), pages 903-918.
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    2. Kern, Florian & Gaede, James & Meadowcroft, James & Watson, Jim, 2016. "The political economy of carbon capture and storage: An analysis of two demonstration projects," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 250-260.
    3. Jingjing Xie & Yujiao Xian & Guowei Jia, 2023. "An investigation into the public acceptance in China of carbon capture and storage (CCS) technology," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 28(5), pages 1-22, June.
    4. Watson, Jim & Gross, Rob & Ketsopoulou, Ioanna & Winskel, Mark, 2015. "The impact of uncertainties on the UK's medium-term climate change targets," Energy Policy, Elsevier, vol. 87(C), pages 685-695.
    5. Torabi, Nooshin & Bekessy, Sarah A., 2015. "Bundling and stacking in bio-sequestration schemes: Opportunities and risks identified by Australian stakeholders," Ecosystem Services, Elsevier, vol. 15(C), pages 84-92.
    6. Themann, Dörte & Brunnengräber, Achim, 2021. "Using socio-technical analogues as an additional experience horizon for nuclear waste management A comparison of wind farms, fracking, carbon capture and storage (CCS) with a deep-geological nuclear w," Utilities Policy, Elsevier, vol. 70(C).

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