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The technical efficiency of China's wind power list enterprises: An estimation based on DEA method and micro-data

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  • Xin-gang, Zhao
  • Zhen, Wei

Abstract

Promoting wind power is a long-term strategy of China to respond to both energy shortage and environmental pollution. Stimulated by various incentive policies, wind power generation in China has achieved tremendous growth. However, the China's wind power industry is still in its early development stage and various problems have emerged, seriously challenging the healthy functioning of the industry. Based on micro-data on wind power listed companies, the four-stage Data Envelopment Analysis (DEA) model is used to measure the technical efficiency of China's wind power enterprises and sketch the development of the wind power industry at the micro level. The results show that there exist non-efficiency problems arising from the diseconomies of scale in China's wind power industry during 2011–2015. The average value of the efficiency of wind power enterprises is 32.5%. According to the subsample analysis, large-scale enterprises, private-owned enterprises and enterprises taking wind farm conduction and operation as their main business, as well as enterprises located in the three northern area, have the highest efficiency in their respective categories. Drawn on this analysis, conclusions and policy implications are provided at the end of the paper.

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  • Xin-gang, Zhao & Zhen, Wei, 2019. "The technical efficiency of China's wind power list enterprises: An estimation based on DEA method and micro-data," Renewable Energy, Elsevier, vol. 133(C), pages 470-479.
  • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:470-479
    DOI: 10.1016/j.renene.2018.10.049
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    1. Fan, Xiao-chao & Wang, Wei-qing, 2016. "Spatial patterns and influencing factors of China׳s wind turbine manufacturing industry: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 482-496.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Fare, R. & Grosskopf, S. & Logan, J., 1985. "The relative performance of publicly-owned and privately-owned electric utilities," Journal of Public Economics, Elsevier, vol. 26(1), pages 89-106, February.
    4. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    5. Pei, Wei & Chen, Yanning & Sheng, Kun & Deng, Wei & Du, Yan & Qi, Zhiping & Kong, Li, 2015. "Temporal-spatial analysis and improvement measures of Chinese power system for wind power curtailment problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 148-168.
    6. Zhao, Zhen-Yu & Chang, Rui-Dong & Chen, Yu-Long, 2016. "What hinder the further development of wind power in China?—A socio-technical barrier study," Energy Policy, Elsevier, vol. 88(C), pages 465-476.
    7. Harold Fried & Shelton Schmidt & Suthathip Yaisawarng, 1999. "Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency," Journal of Productivity Analysis, Springer, vol. 12(3), pages 249-267, November.
    8. Vaninsky, Alexander, 2006. "Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis," Energy Economics, Elsevier, vol. 28(3), pages 326-338, May.
    9. Jianbo Yang & Qunyi Liu & Xin Li & Xiandan Cui, 2017. "Overview of Wind Power in China: Status and Future," Sustainability, MDPI, vol. 9(8), pages 1-12, August.
    10. Yu, Xiao & Qu, Hang, 2010. "Wind power in China--Opportunity goes with challenge," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(8), pages 2232-2237, October.
    11. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    13. Zhang, Sufang & Wang, Wei & Wang, Lu & Zhao, Xiaoli, 2015. "Review of China’s wind power firms internationalization: Status quo, determinants, prospects and policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1333-1342.
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