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Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China

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  • Xiaoyan Sun

    (School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
    Research Center for Strategy of Global Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China)

  • Wenwei Lian

    (School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China)

  • Hongmei Duan

    (School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China)

  • Anjian Wang

    (Research Center for Strategy of Global Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
    Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China)

Abstract

As a significant energy consumer, China is under tremendous pressure from the international community to address climate change issues by reducing carbon emissions; thus, the use of clean energy is imperative. Wind power is an essential source of renewable energy, and improving the efficiency of wind power generation will contribute substantially to China’s ability to achieve its energy-saving and emission reduction goals. This paper measured the wind power efficiency of 30 provinces in China from 2012 to 2017 using the data envelopment analysis (DEA) method. Moran’s I index and the spatial Durbin model were applied to analyse the spatial distribution of the wind power efficiency and the spatial effects of influencing factors. The results show obvious differences in the spatial distribution of wind power efficiency in China; specifically, the wind power efficiency in the eastern and western regions is higher than that in the central areas. Moreover, wind power efficiency has a significant positive spatial correlation between regions: the eastern and western regions show certain high-high clustering characteristics, and the central area shows certain low-low clustering characteristics. Among the influencing factors, the fixed asset investment and carbon emission intensity of the wind power property have a negative impact on the efficiency of regional wind power production, while the urbanization process and carbon emission intensity have significant spatial spillover effects. The optimization of the economic structure, technological innovation and the construction of energy infrastructure are expected to improve the regional wind power efficiency. The results present a new approach for accurately identifying the spatial characteristics of wind power efficiency and the spatial effects of the influencing factors, thus providing a reference for policymakers.

Suggested Citation

  • Xiaoyan Sun & Wenwei Lian & Hongmei Duan & Anjian Wang, 2021. "Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12759-:d:682091
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    References listed on IDEAS

    as
    1. Zhang, Xing-Ping & Cheng, Xiao-Mei & Yuan, Jia-Hai & Gao, Xiao-Jun, 2011. "Total-factor energy efficiency in developing countries," Energy Policy, Elsevier, vol. 39(2), pages 644-650, February.
    2. Liu, Wen & Lund, Henrik & Mathiesen, Brian Vad, 2011. "Large-scale integration of wind power into the existing Chinese energy system," Energy, Elsevier, vol. 36(8), pages 4753-4760.
    3. Lam, J.C.K. & Woo, C.K. & Kahrl, F. & Yu, W.K., 2013. "What moves wind energy development in China? Show me the money!," Applied Energy, Elsevier, vol. 105(C), pages 423-429.
    4. Pieralli, Simone & Ritter, Matthias & Odening, Martin, 2015. "Efficiency of wind power production and its determinants," Energy, Elsevier, vol. 90(P1), pages 429-438.
    5. Ederer, Nikolaus, 2015. "Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1034-1046.
    6. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    7. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    8. Zeng, Jingjing & Liu, Ting & Feiock, Richard & Li, Fei, 2019. "The impacts of China's provincial energy policies on major air pollutants: A spatial econometric analysis," Energy Policy, Elsevier, vol. 132(C), pages 392-403.
    9. Papież, Monika & Śmiech, Sławomir & Frodyma, Katarzyna, 2019. "Factors affecting the efficiency of wind power in the European Union countries," Energy Policy, Elsevier, vol. 132(C), pages 965-977.
    10. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    11. Zhao, Xiaoli & Zhang, Sufang & Yang, Rui & Wang, Mei, 2012. "Constraints on the effective utilization of wind power in China: An illustration from the northeast China grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4508-4514.
    12. Zhang, Sufang & Andrews-Speed, Philip & Zhao, Xiaoli, 2013. "Political and institutional analysis of the successes and failures of China’s wind power policy," Energy Policy, Elsevier, vol. 56(C), pages 331-340.
    13. Zhang, Shijie & Wei, Jing & Chen, Xi & Zhao, Yuhao, 2020. "China in global wind power development: Role, status and impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    14. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    15. Iglesias, Guillermo & Castellanos, Pablo & Seijas, Amparo, 2010. "Measurement of productive efficiency with frontier methods: A case study for wind farms," Energy Economics, Elsevier, vol. 32(5), pages 1199-1208, September.
    16. 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.
    17. Chengyu Li & Qunwei Wang & Peng Zhou, 2020. "Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China," Complexity, Hindawi, vol. 2020, pages 1-13, November.
    18. Lantz, Eric & Mai, Trieu & Wiser, Ryan H. & Krishnan, Venkat, 2016. "Long-term implications of sustained wind power growth in the United States: Direct electric system impacts and costs," Applied Energy, Elsevier, vol. 179(C), pages 832-846.
    19. Zhou, P. & Jin, R.Y. & Fan, L.W., 2016. "Reliability and economic evaluation of power system with renewables: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 537-547.
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    Cited by:

    1. Wenwei Lian & Bingyan Wang & Tianming Gao & Xiaoyan Sun & Yan Zhang & Hongmei Duan, 2022. "Coordinated Development of Renewable Energy: Empirical Evidence from China," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    2. Ning Ren & Xiufan Zhang & Decheng Fan, 2022. "Influencing Factors and Realization Path of Power Decarbonization—Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019," IJERPH, MDPI, vol. 19(23), pages 1-24, November.
    3. George Ekonomou & Angeliki N. Menegaki, 2023. "China in the Renewable Energy Era: What Has Been Done and What Remains to Be Done," Energies, MDPI, vol. 16(18), pages 1-21, September.

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