Empirical research on technical efficiency of wind power industry in China based on SFA method

Jiahui Zhang (), Yibing Wang () and Li Gao ()
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Jiahui Zhang: China University of Petroleum Beijing
Yibing Wang: China University of Petroleum Beijing
Li Gao: China University of Petroleum Beijing

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 4, No 26, 8817-8838

Abstract: Abstract In recent years, the vigorous development of the wind power industry has become an important measure in the transformation of energy structure in China. However, the overall low technical efficiency of wind farms has severely hindered wind power industrial development. It is of great practical significance to evaluate the technical efficiency (TE) of wind power in China and analyze its main factors. Most of the existing literature on the assessment of the TE of wind power in China focuses only on large listed companies and applies the traditional data envelopment analysis (DEA) while ignoring its potential shortcomings for TE estimation. Based on panel data, this paper used stochastic frontier analysis (SFA) to construct an analytical model for assessing the TE and influencing factors of Chinese wind farms and compared the results with those from DEA to verify the robustness. The empirical results showed that the TE of Chinese wind farms was generally low. The age of a wind farm and its power consumption have a negative impact on its technical efficiency, while the utilization of power generation equipment has a positive impact on its technical efficiency. Enhancing the technological innovation capabilities of wind power companies, speeding up the construction of supporting infrastructure and solving structural problems of wind power supply and demand are important measures for the wind power industry to improve the overall TE and promote industrial development in China.

Keywords: Wind farm; Technical efficiency; Stochastic frontier analysis; Data envelopment analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03072-9

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