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Modeling technological learning and its application for clean coal technologies in Japan

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  • Nakata, Toshihiko
  • Sato, Takemi
  • Wang, Hao
  • Kusunoki, Tomoya
  • Furubayashi, Takaaki

Abstract

Estimating technological progress of emerging technologies such as renewables and clean coal technologies becomes important for designing low carbon energy systems in future and drawing effective energy policies. Learning curve is an analytical approach for describing the decline rate of cost and production caused by technological progress as well as learning. In the study, a bottom-up energy-economic model including an endogenous technological learning function has been designed. The model deals with technological learning in energy conversion technologies and its spillover effect. It is applied as a feasibility study of clean coal technologies such as IGCC (Integrated Coal Gasification Combined Cycle) and IGFC (Integrated Coal Gasification Fuel Cell System) in Japan. As the results of analysis, it is found that technological progress by learning has a positive impact on the penetration of clean coal technologies in the electricity market, and the learning model has a potential for assessing upcoming technologies in future.

Suggested Citation

  • Nakata, Toshihiko & Sato, Takemi & Wang, Hao & Kusunoki, Tomoya & Furubayashi, Takaaki, 2011. "Modeling technological learning and its application for clean coal technologies in Japan," Applied Energy, Elsevier, vol. 88(1), pages 330-336, January.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:1:p:330-336
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    as
    1. Lee, Jong Jun & Kim, Young Sik & Cha, Kyu Sang & Kim, Tong Seop & Sohn, Jeong L. & Joo, Yong Jin, 2009. "Influence of system integration options on the performance of an integrated gasification combined cycle power plant," Applied Energy, Elsevier, vol. 86(9), pages 1788-1796, September.
    2. Zaporowski, Boleslaw, 2003. "Analysis of energy-conversion processes in gas-steam power-plants integrated with coal gasification," Applied Energy, Elsevier, vol. 74(3-4), pages 297-304, March.
    3. Nakata, Toshihiko & Kubo, Kazuo & Lamont, Alan, 2005. "Design for renewable energy systems with application to rural areas in Japan," Energy Policy, Elsevier, vol. 33(2), pages 209-219, January.
    4. Kannan, R., 2009. "Uncertainties in key low carbon power generation technologies - Implication for UK decarbonisation targets," Applied Energy, Elsevier, vol. 86(10), pages 1873-1886, October.
    5. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    6. Zaporowski, Boleslaw & Szczerbowski, Radoslaw, 2003. "Energy analysis of technological systems of natural gas fired combined heat-and-power plants," Applied Energy, Elsevier, vol. 75(1-2), pages 43-50, May.
    7. Williges, Keith & Lilliestam, Johan & Patt, Anthony, 2010. "Making concentrated solar power competitive with coal: The costs of a European feed-in tariff," Energy Policy, Elsevier, vol. 38(6), pages 3089-3097, June.
    8. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    9. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
    10. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    11. Neij, L, 1999. "Cost dynamics of wind power," Energy, Elsevier, vol. 24(5), pages 375-389.
    12. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    13. C. Harmon, 2000. "Experience Curves of Photovoltaic Technology," Working Papers ir00014, International Institute for Applied Systems Analysis.
    14. Tolis, Athanasios & Doukelis, Aggelos & Tatsiopoulos, Ilias, 2010. "Stochastic interest rates in the analysis of energy investments: Implications on economic performance and sustainability," Applied Energy, Elsevier, vol. 87(8), pages 2479-2490, August.
    15. Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
    16. Kim, Young Sik & Lee, Jong Jun & Kim, Tong Seop & Sohn, Jeong L. & Joo, Yong Jin, 2010. "Performance analysis of a syngas-fed gas turbine considering the operating limitations of its components," Applied Energy, Elsevier, vol. 87(5), pages 1602-1611, May.
    17. Zhou, Wenji & Zhu, Bing & Fuss, Sabine & Szolgayová, Jana & Obersteiner, Michael & Fei, Weiyang, 2010. "Uncertainty modeling of CCS investment strategy in China's power sector," Applied Energy, Elsevier, vol. 87(7), pages 2392-2400, July.
    18. Fuminori Sano, Keigo Akimoto, Takashi Homma and Toshimasa Tomoda, 2006. "Analysis of Technological Portfolios for CO2 Stabilizations and Effects of Technological Changes," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 141-162.
    Full references (including those not matched with items on IDEAS)

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