Learning Rates in Wind Energy: Cross-country Analysis and Policy Applications for Russia

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This article performs a meta-analysis of data on learning rates in wind energy, obtained from building single- and dual-factor learning curve models detailed by countries and technology development periods. It reveals a significant difference in learning rates mainly due to design and efficiency of government support programs. Multiple case studies were performed in order to interpret these results. This study proves that the maximal learning rate in wind energy can be achieved by financial support of R&D on the early stage of technological development and by attracting large manufacturers of wind turbines and other electric generation equipment on later stages. Given the fact that wind equipment manufacturing technologies are currently well developed and the global market of wind turbines is highly competitive, the tactic of obtaining technologies in exchange for access to the domestic market may prove successful even with a small domestic market capacity.Keywords: wind energy, learning curves, power engineering, economic analysisJEL Classifications: O33, Q42, Q47, Q48


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Author Biographies

Svetlana Ratner, Institute of Control Science

Leading Reseacher 

Еvgenii Khrustalev, Central Economics and Mathematics Institute, Russian Academy of Sciences

Head of Depertment, Department of simulations of interractions of economical objects




How to Cite

Ratner, S., & Khrustalev, Еvgenii. (2018). Learning Rates in Wind Energy: Cross-country Analysis and Policy Applications for Russia. International Journal of Energy Economics and Policy, 8(3), 258–266. Retrieved from https://www.econjournals.com/index.php/ijeep/article/view/6234