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Cost Evolution Mechanisms of Renewable Energy Technologies: Onshore Wind Power and Photovoltaics in China

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  • Shengyue Lu

    (School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Dan Wu

    (School of Environmental Science and Engineering, Hainan University, Haikou 570228, China
    Hainan University-UC Davis Joint Research Center on Energy and Transportation, Hainan University, Haikou 570228, China
    These authors contributed equally to this work.)

  • Xunzhou Ma

    (School of Economics, Southwest Minzu University, Chengdu 610041, China)

  • Guisheng Wu

    (Guangdong Communication Planning & Design Institute Group Co., Ltd., Guangzhou 510440, China)

  • Li Liu

    (School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
    Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou 510006, China
    The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, South China University of Technology, Ministry of Education, Guangzhou 510006, China)

  • Ziye Cheng

    (School of Environment and Energy, South China University of Technology, Guangzhou 510006, China)

  • Shiqiu Zhang

    (College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China)

Abstract

The unit costs of power generation of onshore wind and photovoltaics in China have dropped rapidly and significantly since 2010. Recent studies have indicated that the learning effect on cost reduction could have been overestimated due to the exclusion of the equipment-level installed capacity and the price of capital. To address this estimation bias, we constructed a research framework comprising a one-factor analysis model (OFAM), a two-factor analysis model (OFAM), and a multi-factor analysis model (MFAM) based on the Cobb–Douglas function and the cost minimization problem. This framework examines the determinants of unit costs in renewable energy generation in consideration of learning effects, scale effects, and price effects. This paper uses data from institutions such as IRENA and the World Bank to empirically analyze the contributions of these factors to reductions in the cost of onshore wind and photovoltaic power generation in China from 2010 to 2022. The results indicate that the learning-by-doing (LBD) effect has been overestimated, with scale effects accounting for a significant portion of the cost reduction. Moreover, the price of capital exerts a more pronounced influence on the levelized cost of electricity (LCOE) for photovoltaics. After factoring in equipment scale and capital costs, LBD continues to significantly reduce the LCOE of photovoltaics, with the LBD learning rate declining from 23.85% to 6.30%. Meanwhile, the impact of LBD on the LCOE of onshore wind technology ceases to be significant. Both technologies exhibit economies of scale, with scale effects accounting for 41.60% and 34.12% of the LCOE reductions for onshore wind and photovoltaics, respectively. Capital costs accounted for 32.50% of the LCOE reduction for photovoltaics. Therefore, future large-scale deployments of other costly renewable energy technologies may also benefit from the equipment-level scale and favorable bank interest rates in addition to learning-by-doing.

Suggested Citation

  • Shengyue Lu & Dan Wu & Xunzhou Ma & Guisheng Wu & Li Liu & Ziye Cheng & Shiqiu Zhang, 2026. "Cost Evolution Mechanisms of Renewable Energy Technologies: Onshore Wind Power and Photovoltaics in China," Energies, MDPI, vol. 19(7), pages 1-26, March.
  • Handle: RePEc:gam:jeners:v:19:y:2026:i:7:p:1679-:d:1908892
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