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Learning of Power Technologies in China: Staged Dynamic Two-Factor Modeling and Empirical Evidence

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  • Yan Xu

    (School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China)

  • Jiahai Yuan

    (.School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jianxiu Wang

    (School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China)

Abstract

Cost evolution has an important influence on the commercialization and large-scale application of power technology. Many researchers have analyzed the quantitative relationship between the cost of power technology and its influencing factors while establishing various forms of technical learning curve models. In this paper, we focus on the positive effects of the policy on research and development (R&D) learning by summarizing and comparing four energy technology cost models based on learning curves. We explore the influencing factors and dynamic change paths of power technology costs. The paper establishes a multi-stage dynamic two-factor learning curve model based on cumulative R&D investment and the installed capacity. This work presents the structural changes of the influencing factors at various stages. Causality analysis and econometric estimation of learning curves are performed on wind power and other power technologies. The conclusion demonstrates that a “learn by researching” approach had led to cost reduction of wind power to date, but, in the long term, the effect of “learn by doing” is greater than that of “learn by researching” when R&D learning is saturated. Finally, the paper forecasts the learning rates and the cost trends of the main power technologies in China. The work presented in this study has implications on power technology development and energy policy in China.

Suggested Citation

  • Yan Xu & Jiahai Yuan & Jianxiu Wang, 2017. "Learning of Power Technologies in China: Staged Dynamic Two-Factor Modeling and Empirical Evidence," Sustainability, MDPI, vol. 9(5), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:861-:d:99167
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    References listed on IDEAS

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    Cited by:

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    2. He Huang & DaPeng Liang & Liang Liang & Zhen Tong, 2019. "Research on China’s Power Sustainable Transition Under Progressively Levelized Power Generation Cost Based on a Dynamic Integrated Generation–Transmission Planning Model," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
    3. 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).
    4. Yi Zhou & Alun Gu, 2019. "Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    5. Gurkan Calmasur & Meryem Emre Aysin, 2020. "Regional Technological Learning in Turkish Cement Industry," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 204-216.

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