IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v322y2022ics0306261922008352.html
   My bibliography  Save this article

Investment decision on carbon capture and utilization (CCU) technologies—A real option model based on technology learning effect

Author

Listed:
  • Liu, Jiangfeng
  • Zhang, Qi
  • Li, Hailong
  • Chen, Siyuan
  • Teng, Fei

Abstract

Carbon Capture and Utilization (CCU) technologies are crucial to achieving carbon neutrality targets. However, assessing the investment value and timing comprehensively is still challenging for CCU due to uncertainties in technologies and markets from a long-term perspective. In order to assist decision making, this work develops a new real option investment decision model based on the technology learning effect. In particular, the component-based two-factor technology learning curve approach is proposed to predict the future costs for each component. To verify this model, it is used to analyze two CCU processes, including the enhanced oil recovery using CO2 (CO2-EOR) and methanol synthesis from captured CO2 (CO2-MET). Results show that the proposed model can effectively predict the technology cost curve and find the optimal investment decision for the two applications considering various uncertainties. It is also found that the oil price at least needs to be over 80 $/barrel for CO2-EOR and the methanol price needs to be over 580 $/ton for CO2-MET respectively to trigger immediate investment. Comparatively, investing in CO2-MET projects is more economical than in CO2-EOR projects.

Suggested Citation

  • Liu, Jiangfeng & Zhang, Qi & Li, Hailong & Chen, Siyuan & Teng, Fei, 2022. "Investment decision on carbon capture and utilization (CCU) technologies—A real option model based on technology learning effect," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922008352
    DOI: 10.1016/j.apenergy.2022.119514
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922008352
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119514?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. O. Schmidt & A. Hawkes & A. Gambhir & I. Staffell, 2017. "The future cost of electrical energy storage based on experience rates," Nature Energy, Nature, vol. 2(8), pages 1-8, August.
    2. Chen, Ruoran & Deng, Tianhu & Huang, Simin & Qin, Ruwen, 2015. "Optimal crude oil procurement under fluctuating price in an oil refinery," European Journal of Operational Research, Elsevier, vol. 245(2), pages 438-445.
    3. Zhu, Lei & Fan, Ying, 2011. "A real options–based CCS investment evaluation model: Case study of China’s power generation sector," Applied Energy, Elsevier, vol. 88(12), pages 4320-4333.
    4. Jaber, M. Y. & Peltokorpi, J. & Glock, C. H. & Grosse, E. H. & Pusic, M., 2021. "Adjustment for cognitive interference enhances the predictability of the power learning curve," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 125295, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Chen, Siyuan & Zhang, Qi & Wang, Ge & Zhu, Lijing & Li, Yan, 2018. "Investment strategy for underground gas storage facilities based on real option model considering gas market reform in China," Energy Economics, Elsevier, vol. 70(C), pages 132-142.
    6. Edward S. Rubin & Sonia Yeh & David A. Hounshell & Margaret R. Taylor, 2004. "Experience curves for power plant emission control technologies," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 52-69.
    7. Ryoo, Seung Gul & Jung, Han Sol & Kim, MinJae & Kang, Yong Tae, 2021. "Bridge to zero-emission: Life cycle assessment of CO2–methanol conversion process and energy optimization," Energy, Elsevier, vol. 229(C).
    8. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    9. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.
    10. Myers, Stewart C & Turnbull, Stuart M, 1977. "Capital Budgeting and the Capital Asset Pricing Model: Good News and Bad News," Journal of Finance, American Finance Association, vol. 32(2), pages 321-333, May.
    11. Ritzenhofen, Ingmar & Spinler, Stefan, 2016. "Optimal design of feed-in-tariffs to stimulate renewable energy investments under regulatory uncertainty — A real options analysis," Energy Economics, Elsevier, vol. 53(C), pages 76-89.
    12. Upstill, Garrett & Hall, Peter, 2018. "Estimating the learning rate of a technology with multiple variants: The case of carbon storage," Energy Policy, Elsevier, vol. 121(C), pages 498-505.
    13. Robert S. Pindyck, 1999. "The Long-Run Evolutions of Energy Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-27.
    14. Fan, Jing-Li & Xu, Mao & Yang, Lin & Zhang, Xian & Li, Fengyu, 2019. "How can carbon capture utilization and storage be incentivized in China? A perspective based on the 45Q tax credit provisions," Energy Policy, Elsevier, vol. 132(C), pages 1229-1240.
    15. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    16. Zhang, Zhien & Pan, Shu-Yuan & Li, Hao & Cai, Jianchao & Olabi, Abdul Ghani & Anthony, Edward John & Manovic, Vasilije, 2020. "Recent advances in carbon dioxide utilization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 125(C).
    17. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, vol. 99(397), pages 569-596, September.
    18. Insley, Margaret, 2002. "A Real Options Approach to the Valuation of a Forestry Investment," Journal of Environmental Economics and Management, Elsevier, vol. 44(3), pages 471-492, November.
    19. Kraan, O. & Kramer, G.J. & Nikolic, I., 2018. "Investment in the future electricity system - An agent-based modelling approach," Energy, Elsevier, vol. 151(C), pages 569-580.
    20. Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, July.
    21. Liu, Hengwei & Gallagher, Kelly Sims, 2010. "Catalyzing strategic transformation to a low-carbon economy: A CCS roadmap for China," Energy Policy, Elsevier, vol. 38(1), pages 59-74, January.
    22. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    23. Guedes, José & Santos, Pedro, 2016. "Valuing an offshore oil exploration and production project through real options analysis," Energy Economics, Elsevier, vol. 60(C), pages 377-386.
    24. Chen, Weidong & Zeng, Yu & Xu, Chongqing, 2019. "Energy storage subsidy estimation for microgrid: A real option game-theoretic approach," Applied Energy, Elsevier, vol. 239(C), pages 373-382.
    25. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    26. Andrew William Ruttinger & Miyuru Kannangara & Jalil Shadbahr & Phil De Luna & Farid Bensebaa, 2021. "How CO 2 -to-Diesel Technology Could Help Reach Net-Zero Emissions Targets: A Canadian Case Study," Energies, MDPI, vol. 14(21), pages 1-21, October.
    27. Watanabe, Chihiro & Shin, Jae-Ho & Akaike, Shinichi & Griffy-Brown, Charla, 2009. "Learning and assimilation vs. M&A and innovation: Japan at the crossroads," Technology in Society, Elsevier, vol. 31(3), pages 218-231.
    28. Hanne Lamberts-Van Assche & Tine Compernolle, 2022. "Using Real Options Thinking to Value Investment Flexibility in Carbon Capture and Utilization Projects: A Review," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    29. Zhang, Feng & Wang, Xinhe & Hou, Xinting & Han, Cheng & Wu, Mingying & Liu, Zhongbing, 2022. "Variance-based global sensitivity analysis of a hybrid thermoelectric generator fuzzy system," Applied Energy, Elsevier, vol. 307(C).
    30. Agaton, Casper Boongaling & Guno, Charmaine Samala & Villanueva, Resy Ordona & Villanueva, Riza Ordona, 2020. "Economic analysis of waste-to-energy investment in the Philippines: A real options approach," Applied Energy, Elsevier, vol. 275(C).
    31. Chauvy, Remi & Meunier, Nicolas & Thomas, Diane & De Weireld, Guy, 2019. "Selecting emerging CO2 utilization products for short- to mid-term deployment," Applied Energy, Elsevier, vol. 236(C), pages 662-680.
    32. Yang, Lin & Xu, Mao & Yang, Yuantao & Fan, Jingli & Zhang, Xian, 2019. "Comparison of subsidy schemes for carbon capture utilization and storage (CCUS) investment based on real option approach: Evidence from China," Applied Energy, Elsevier, vol. 255(C).
    33. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    34. Jaber, M.Y. & Peltokorpi, J. & Glock, C.H. & Grosse, E.H. & Pusic, M., 2021. "Adjustment for cognitive interference enhances the predictability of the power learning curve," International Journal of Production Economics, Elsevier, vol. 234(C).
    35. Deeney, Peter & Cummins, Mark & Heintz, Katharina & Pryce, Mary T., 2021. "A real options based decision support tool for R&D investment: Application to CO2 recycling technology," European Journal of Operational Research, Elsevier, vol. 289(2), pages 696-711.
    36. Li, Sheng & Zhang, Xiaosong & Gao, Lin & Jin, Hongguang, 2012. "Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: Methodology and case studies," Applied Energy, Elsevier, vol. 93(C), pages 348-356.
    37. Rubin, Edward S. & Chen, Chao & Rao, Anand B., 2007. "Cost and performance of fossil fuel power plants with CO2 capture and storage," Energy Policy, Elsevier, vol. 35(9), pages 4444-4454, September.
    38. Chen, Siyuan & Zhang, Qi & Li, Hailong & Mclellan, Benjamin & Zhang, Tiantian & Tan, Zhizhou, 2019. "Investment decision on shallow geothermal heating & cooling based on compound options model: A case study of China," Applied Energy, Elsevier, vol. 254(C).
    39. Postali, Fernando A.S. & Picchetti, Paulo, 2006. "Geometric Brownian Motion and structural breaks in oil prices: A quantitative analysis," Energy Economics, Elsevier, vol. 28(4), pages 506-522, July.
    40. Chung-Li Tseng & Graydon Barz, 2002. "Short-Term Generation Asset Valuation: A Real Options Approach," Operations Research, INFORMS, vol. 50(2), pages 297-310, April.
    41. Moutis, Panayiotis & Skarvelis-Kazakos, Spyros & Brucoli, Maria, 2016. "Decision tree aided planning and energy balancing of planned community microgrids," Applied Energy, Elsevier, vol. 161(C), pages 197-205.
    42. Niall Mac Dowell & Paul S. Fennell & Nilay Shah & Geoffrey C. Maitland, 2017. "The role of CO2 capture and utilization in mitigating climate change," Nature Climate Change, Nature, vol. 7(4), pages 243-249, April.
    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. Zhang, Qi & Liu, Jiangfeng & Wang, Ge & Gao, Zhihui, 2024. "A new optimization model for carbon capture utilization and storage (CCUS) layout based on high-resolution geological variability," Applied Energy, Elsevier, vol. 363(C).
    2. Feng Liu & Tao Lv & Yuan Meng & Xiaoran Hou & Jie Xu & Xu Deng, 2022. "Low-Carbon Transition Paths of Coal Power in China’s Provinces under the Context of the Carbon Trading Scheme," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    3. Tom Savage & Antonio del Rio Chanona & Gbemi Oluleye, 2023. "Robust Market Potential Assessment: Designing optimal policies for low-carbon technology adoption in an increasingly uncertain world," Papers 2304.10203, arXiv.org.
    4. Sun, Bo & Fan, Boyang & Wu, Chun & Xie, Jingdong, 2024. "Exploring incentive mechanisms for the CCUS project in China's coal-fired power plants: An option-game approach," Energy, Elsevier, vol. 288(C).
    5. Fan, Jing-Li & Li, Zezheng & Ding, Zixia & Li, Kai & Zhang, Xian, 2023. "Investment decisions on carbon capture utilization and storage retrofit of Chinese coal-fired power plants based on real option and source-sink matching models," Energy Economics, Elsevier, vol. 126(C).
    6. Christiano B. Peres & Pedro M. R. Resende & Leonel J. R. Nunes & Leandro C. de Morais, 2022. "Advances in Carbon Capture and Use (CCU) Technologies: A Comprehensive Review and CO 2 Mitigation Potential Analysis," Clean Technol., MDPI, vol. 4(4), pages 1-15, November.
    7. Bo Sun & Jiajia Tao, 2024. "Investment Decisions of CCUS Projects in China Considering the Supply–Demand Relationship of CO 2 from the Industry Symbiosis Perspective," Sustainability, MDPI, vol. 16(12), pages 1-25, June.
    8. Sun, Bo & Fan, Boyang & Zhang, Yifan & Xie, Jingdong, 2023. "Investment decisions and strategies of China's energy storage technology under policy uncertainty: A real options approach," Energy, Elsevier, vol. 278(PA).
    9. Wang, Yihan & Wen, Zongguo & Xu, Mao & Kosajan, Vorada, 2024. "The carbon-energy-water nexus of the carbon capture, utilization, and storage technology deployment schemes: A case study in China's cement industry," Applied Energy, Elsevier, vol. 362(C).

    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. Chen, Siyuan & Liu, Jiangfeng & Zhang, Qi & Teng, Fei & McLellan, Benjamin C., 2022. "A critical review on deployment planning and risk analysis of carbon capture, utilization, and storage (CCUS) toward carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Rochedo, Pedro R.R. & Szklo, Alexandre, 2013. "Designing learning curves for carbon capture based on chemical absorption according to the minimum work of separation," Applied Energy, Elsevier, vol. 108(C), pages 383-391.
    3. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    5. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    6. Zhang, Mingming & Zhou, Dequn & Zhou, Peng, 2014. "A real option model for renewable energy policy evaluation with application to solar PV power generation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 944-955.
    7. Lei Zhu & Xing Yao & Xian Zhang, 2020. "Evaluation of cooperative mitigation: captured carbon dioxide for enhanced oil recovery," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1261-1285, October.
    8. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    9. 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).
    10. Lee, Shun-Chung & Shih, Li-Hsing, 2010. "Renewable energy policy evaluation using real option model -- The case of Taiwan," Energy Economics, Elsevier, vol. 32(Supplemen), pages 67-78, September.
    11. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.
    12. Yang, Lin & Lv, Haodong & Wei, Ning & Li, Yiming & Zhang, Xian, 2023. "Dynamic optimization of carbon capture technology deployment targeting carbon neutrality, cost efficiency and water stress: Evidence from China's electric power sector," Energy Economics, Elsevier, vol. 125(C).
    13. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    14. Zhang, Qi & Liu, Jiangfeng & Wang, Ge & Gao, Zhihui, 2024. "A new optimization model for carbon capture utilization and storage (CCUS) layout based on high-resolution geological variability," Applied Energy, Elsevier, vol. 363(C).
    15. Upstill, Garrett & Hall, Peter, 2018. "Estimating the learning rate of a technology with multiple variants: The case of carbon storage," Energy Policy, Elsevier, vol. 121(C), pages 498-505.
    16. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    17. Hanne Lamberts-Van Assche & Tine Compernolle, 2022. "Using Real Options Thinking to Value Investment Flexibility in Carbon Capture and Utilization Projects: A Review," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    18. Tan, Zhizhou & Zeng, Xianhai & Lin, Boqiang, 2023. "How do multiple policy incentives influence investors’ decisions on biomass co-firing combined with carbon capture and storage retrofit projects for coal-fired power plants?," Energy, Elsevier, vol. 278(PB).
    19. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    20. Liu, Haomin & Zhang, Zaixu & Zhang, Tao, 2022. "Shale gas investment decision-making: Green and efficient development under market, technology and environment uncertainties," Applied Energy, Elsevier, vol. 306(PA).

    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:322:y:2022:i:c:s0306261922008352. 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.