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Technology and demand forecasting for carbon capture and storage technology in South Korea


  • Shin, Jungwoo
  • Lee, Chul-Yong
  • Kim, Hongbum


Among the various alternatives available to reduce greenhouse gas (GHG) emissions, carbon capture and storage (CCS) is considered to be a prospective technology that could both improve economic growth and meet GHG emission reduction targets. Despite the importance of CCS, however, studies of technology and demand forecasting for CCS are scarce. This study bridges this gap in the body of knowledge on this topic by forecasting CCS technology and demand based on an integrated model. For technology forecasting, a logistic model and patent network analysis are used to compare the competitiveness of CCS technology for selected countries. For demand forecasting, a competition diffusion model is adopted to consider competition among renewable energies and forecast demand. The results show that the number of patent applications for CCS technology will increase to 16,156 worldwide and to 4,790 in Korea by 2025. We also find that the United States has the most competitive CCS technology followed by Korea and France. Moreover, about 5 million tCO2e of GHG will be reduced by 2040 if CCS technology is adopted in Korea after 2020.

Suggested Citation

  • Shin, Jungwoo & Lee, Chul-Yong & Kim, Hongbum, 2016. "Technology and demand forecasting for carbon capture and storage technology in South Korea," Energy Policy, Elsevier, vol. 98(C), pages 1-11.
  • Handle: RePEc:eee:enepol:v:98:y:2016:i:c:p:1-11
    DOI: 10.1016/j.enpol.2016.08.009

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    References listed on IDEAS

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    Cited by:

    1. Ma, Xuejiao & Wang, Yong & Wang, Chen, 2017. "Low-carbon development of China's thermal power industry based on an international comparison: Review, analysis and forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 942-970.
    2. Alexey Cherepovitsyn & Sergey Fedoseev & Pavel Tcvetkov & Ksenia Sidorova & Andrzej Kraslawski, 2018. "Potential of Russian Regions to Implement CO 2 -Enhanced Oil Recovery," Energies, MDPI, Open Access Journal, vol. 11(6), pages 1-22, June.
    3. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, Open Access Journal, vol. 12(12), pages 1-19, June.
    4. Y. Li & C.J.M. Kool & P.J. Engelen, 2016. "Hydrogen-Fuel Infrastructure Investment with Endogenous Demand : A Real Options Approach," Working Papers 16-12, Utrecht School of Economics.


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