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

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

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)330-336
Number of pages7
JournalApplied Energy
Issue number1
Publication statusPublished - 2011 Jan


  • Carbon tax
  • Clean coal technologies
  • Energy model
  • Learning curve
  • Spillover effect
  • Technological learning


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