IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v98y2016icp1-11.html
   My bibliography  Save this article

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

Author

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

Abstract

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421516304244
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2016.08.009?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. Tzimas, Evangelos & Mercier, Arnaud & Cormos, Calin-Cristian & Peteves, Stathis D., 2007. "Trade-off in emissions of acid gas pollutants and of carbon dioxide in fossil fuel power plants with carbon capture," Energy Policy, Elsevier, vol. 35(8), pages 3991-3998, August.
    2. Marshall, Jonathan Paul, 2016. "Disordering fantasies of coal and technology: Carbon capture and storage in Australia," Energy Policy, Elsevier, vol. 99(C), pages 288-298.
    3. Stern,Nicholas, 2007. "The Economics of Climate Change," Cambridge Books, Cambridge University Press, number 9780521700801, September.
    4. Cantner, Uwe & Graf, Holger, 2006. "The network of innovators in Jena: An application of social network analysis," Research Policy, Elsevier, vol. 35(4), pages 463-480, May.
    5. Setiawan, Andri D. & Cuppen, Eefje, 2013. "Stakeholder perspectives on carbon capture and storage in Indonesia," Energy Policy, Elsevier, vol. 61(C), pages 1188-1199.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. 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.
    8. Martínez Arranz, Alfonso, 2015. "Carbon capture and storage: Frames and blind spots," Energy Policy, Elsevier, vol. 82(C), pages 249-259.
    9. Frank M. Bass & Kent Gordon & Teresa L. Ferguson & Mary Lou Githens, 2001. "DIRECTV: Forecasting Diffusion of a New Technology Prior to Product Launch," Interfaces, INFORMS, vol. 31(3_supplem), pages 82-93, June.
    10. Vijay Mahajan & Eitan Muller & Frank M. Bass, 1995. "Diffusion of New Products: Empirical Generalizations and Managerial Uses," Marketing Science, INFORMS, vol. 14(3_supplem), pages 79-88.
    11. Barry L. Bayus, 1993. "High-Definition Television: Assessing Demand Forecasts for a Next Generation Consumer Durable," Management Science, INFORMS, vol. 39(11), pages 1319-1333, November.
    12. Gibbins, Jon & Chalmers, Hannah, 2008. "Carbon capture and storage," Energy Policy, Elsevier, vol. 36(12), pages 4317-4322, December.
    13. Jae Young Choi & Seongkyoon Jeong & Kyunam Kim, 2015. "A Study on Diffusion Pattern of Technology Convergence: Patent Analysis for Korea," Sustainability, MDPI, vol. 7(9), pages 1-24, August.
    14. Odeh, Naser A. & Cockerill, Timothy T., 2008. "Life cycle GHG assessment of fossil fuel power plants with carbon capture and storage," Energy Policy, Elsevier, vol. 36(1), pages 367-380, January.
    15. Teck-Hua Ho & Sergei Savin & Christian Terwiesch, 2002. "Managing Demand and Sales Dynamics in New Product Diffusion Under Supply Constraint," Management Science, INFORMS, vol. 48(2), pages 187-206, February.
    16. Seongkyoon Jeong & Jong-Chan Kim & Jae Young Choi, 2015. "Technology convergence: What developmental stage are we in?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 841-871, September.
    17. 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.
    18. Akbilgic, Oguz & Doluweera, Ganesh & Mahmoudkhani, Maryam & Bergerson, Joule, 2015. "A meta-analysis of carbon capture and storage technology assessments: Understanding the driving factors of variability in cost estimates," Applied Energy, Elsevier, vol. 159(C), pages 11-18.
    19. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    20. Xu, Yuan & Liu, Guangjian, 2015. "Carbon capture and storage for Hong Kong's fuel mix," Utilities Policy, Elsevier, vol. 36(C), pages 43-45.
    21. Mansouri, Noura Y. & Crookes, Roy J. & Korakianitis, Theodosios, 2013. "A projection of energy consumption and carbon dioxide emissions in the electricity sector for Saudi Arabia: The case for carbon capture and storage and solar photovoltaics," Energy Policy, Elsevier, vol. 63(C), pages 681-695.
    22. Minhi Hahn & Sehoon Park & Lakshman Krishnamurthi & Andris A. Zoltners, 1994. "Analysis of New Product Diffusion Using a Four-Segment Trial-Repeat Model," Marketing Science, INFORMS, vol. 13(3), pages 224-247.
    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. 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. Ye Li & Clemens Kool & Peter-Jan Engelen, 2020. "Analyzing the Business Case for Hydrogen-Fuel Infrastructure Investments with Endogenous Demand in The Netherlands: A Real Options Approach," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    3. Kang, Jia-Ning & Wei, Yi-Ming & Liu, Lan-cui & Wang, Jin-Wei, 2021. "Observing technology reserves of carbon capture and storage via patent data: Paving the way for carbon neutral," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    4. Ko, Yu-Chia & Zigan, Krystin & Liu, Yu-Lun, 2021. "Carbon capture and storage in South Africa: A technological innovation system with a political economy focus," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    5. Xiping Wang & Hongdou Zhang, 2018. "Optimal design of carbon tax to stimulate CCS investment in China's coal‐fired power plants: A real options analysis," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 8(5), pages 863-875, October.
    6. Alexey Cherepovitsyn & Sergey Fedoseev & Pavel Tcvetkov & Ksenia Sidorova & Andrzej Kraslawski, 2018. "Potential of Russian Regions to Implement CO 2 -Enhanced Oil Recovery," Energies, MDPI, vol. 11(6), pages 1-22, June.
    7. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, vol. 12(12), pages 1-19, June.
    8. He, Xi-jun & Meng, Xue & Dong, Yan-bo & Wu, Yu-ying, 2019. "Demand identification model of potential technology based on SAO structure semantic analysis: The case of new energy and energy saving fields," Technology in Society, Elsevier, vol. 58(C).
    9. Sinigaglia, Tiago & Eduardo Santos Martins, Mario & Cezar Mairesse Siluk, Julio, 2022. "Technological evolution of internal combustion engine vehicle: A patent data analysis," Applied Energy, Elsevier, vol. 306(PA).
    10. 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.
    11. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(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. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    2. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    3. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    4. Chul-Yong Lee & Min-Kyu Lee, 2017. "Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    5. Kim, Namwoon & Srivastava, Rajendra K. & Han, Jin K., 2001. "Consumer decision-making in a multi-generational choice set context," Journal of Business Research, Elsevier, vol. 53(3), pages 123-136, September.
    6. Olivier Toubia & Jacob Goldenberg & Rosanna Garcia, 2014. "Improving Penetration Forecasts Using Social Interactions Data," Management Science, INFORMS, vol. 60(12), pages 3049-3066, December.
    7. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    8. Peter Viebahn & Emile J. L. Chappin, 2018. "Scrutinising the Gap between the Expected and Actual Deployment of Carbon Capture and Storage—A Bibliometric Analysis," Energies, MDPI, vol. 11(9), pages 1-45, September.
    9. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting new and renewable energy supply through a bottom-up approach: The case of South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 207-217.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    12. Wenjing Shen & Izak Duenyas & Roman Kapuscinski, 2014. "Optimal Pricing, Production, and Inventory for New Product Diffusion Under Supply Constraints," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 28-45, February.
    13. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    14. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    15. Ramírez-Hassan, Andrés & Montoya-Blandón, Santiago, 2020. "Forecasting from others’ experience: Bayesian estimation of the generalized Bass model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 442-465.
    16. Roettereng, Jo-Kristian Straete, 2016. "How the global and national levels interrelate in climate policymaking: Foreign Policy Analysis and the case of Carbon Capture Storage in Norway's foreign policy," Energy Policy, Elsevier, vol. 97(C), pages 475-484.
    17. Qin, Ruwen & Nembhard, David A., 2012. "Demand modeling of stochastic product diffusion over the life cycle," International Journal of Production Economics, Elsevier, vol. 137(2), pages 201-210.
    18. Kivi, Antero & Smura, Timo & Töyli, Juuso, 2012. "Technology product evolution and the diffusion of new product features," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 107-126.
    19. Vakratsas, Demetrios & Kolsarici, Ceren, 2008. "A dual-market diffusion model for a new prescription pharmaceutical," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 282-293.
    20. Jonathan Lee & Peter Boatwright & Wagner A. Kamakura, 2003. "A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music," Management Science, INFORMS, vol. 49(2), pages 179-196, February.

    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:enepol:v:98:y:2016:i:c:p:1-11. 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/locate/enpol .

    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.