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Carbon capture and storage—Investment strategies for the future?

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  • Rammerstorfer, Margarethe
  • Eisl, Roland

Abstract

The following article deals with real options modeling for investing into carbon capture and storage technologies. Herein, we derive two separate models. The first model incorporates a constant convenience yield and dividend for the investment project. In the second model, the convenience yield is allowed to follow a mean reverting process which seems to be more realistic, but also increases the model’s complexity. Both frameworks are to be solved numerically. Therefore, we calibrate our model with respect to empirical data and provide insights into the models’ sensitivity toward the chosen parameter values. We found that given the recently observable prices for carbon dioxide, an investment into C O2-storage facilities is not profitable.

Suggested Citation

  • Rammerstorfer, Margarethe & Eisl, Roland, 2011. "Carbon capture and storage—Investment strategies for the future?," Energy Policy, Elsevier, vol. 39(11), pages 7103-7111.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:11:p:7103-7111
    DOI: 10.1016/j.enpol.2011.08.022
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    2. Andreas Schröder & Friedrich Kunz & Jan Meiss & Roman Mendelevitch & Christian von Hirschhausen, 2013. "Current and Prospective Costs of Electricity Generation until 2050," Data Documentation 68, DIW Berlin, German Institute for Economic Research.
    3. Zheng, Yawen & Gao, Lin & Li, Sheng & Wang, Dan, 2022. "A comprehensive evaluation model for full-chain CCUS performance based on the analytic hierarchy process method," Energy, Elsevier, vol. 239(PD).
    4. Xiping Wang & Shaoyuan Qie, 2018. "Study on the investment timing of carbon capture and storage under different business modes," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 8(4), pages 639-649, August.
    5. Cristóbal, Jorge & Guillén-Gosálbez, Gonzalo & Kraslawski, Andrzej & Irabien, Angel, 2013. "Stochastic MILP model for optimal timing of investments in CO2 capture technologies under uncertainty in prices," Energy, Elsevier, vol. 54(C), pages 343-351.
    6. Herui Cui & Tian Zhao & Ruirui Wu, 2018. "An Investment Feasibility Analysis of CCS Retrofit Based on a Two-Stage Compound Real Options Model," Energies, MDPI, vol. 11(7), pages 1-19, July.
    7. Rahman, Arief & Richards, Russell & Dargusch, Paul & Wadley, David, 2023. "Pathways to reduce Indonesia’s dependence on oil and achieve longer-term decarbonization," Renewable Energy, Elsevier, vol. 202(C), pages 1305-1323.
    8. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    9. Dai, C. & Cai, Y.P. & Li, Y.P. & Sun, W. & Wang, X.W. & Guo, H.C., 2014. "Optimal strategies for carbon capture, utilization and storage based on an inexact mλ-measure fuzzy chance-constrained programming," Energy, Elsevier, vol. 78(C), pages 465-478.
    10. Zhang, Xian & Wang, Xingwei & Chen, Jiajun & Xie, Xi & Wang, Ke & Wei, Yiming, 2014. "A novel modeling based real option approach for CCS investment evaluation under multiple uncertainties," Applied Energy, Elsevier, vol. 113(C), pages 1059-1067.

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