IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i22p12759-d682091.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/22/12759/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/22/12759/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Pieralli, Simone & Ritter, Matthias & Odening, Martin, 2015. "Efficiency of wind power production and its determinants," Energy, Elsevier, vol. 90(P1), pages 429-438.
    6. 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.
    7. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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. 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.
    15. 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.
    16. 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.
    17. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Chen, Hao & Chen, Jiachuan & Han, Guoyi & Cui, Qi, 2022. "Winding down the wind power curtailment in China: What made the difference?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Dong, Changgui & Qi, Ye & Dong, Wenjuan & Lu, Xi & Liu, Tianle & Qian, Shuai, 2018. "Decomposing driving factors for wind curtailment under economic new normal in China," Applied Energy, Elsevier, vol. 217(C), pages 178-188.
    4. Qingwei Shi & Hong Ren & Weiguang Cai & Jingxin Gao, 2020. "How to Set the Proper CO 2 Reduction Targets for the Provincial Building Sector of China?," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    5. Patrick Gasser & Marco Cinelli & Anna Labijak & Matteo Spada & Peter Burgherr & Miłosz Kadziński & Božidar Stojadinović, 2020. "Quantifying Electricity Supply Resilience of Countries with Robust Efficiency Analysis," Energies, MDPI, vol. 13(7), pages 1-35, March.
    6. 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.
    7. 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.
    8. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    9. Zifa Liu & Wenhua Zhang & Changhong Zhao & Jiahai Yuan, 2015. "The Economics of Wind Power in China and Policy Implications," Energies, MDPI, vol. 8(2), pages 1-18, February.
    10. Demiral, Elif E. & Sağlam, Ümit, 2021. "Eco-efficiency and Eco-productivity assessments of the states in the United States: A two-stage Non-parametric analysis," Applied Energy, Elsevier, vol. 303(C).
    11. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    12. Ibrahim Yilmaz, 2023. "A Hybrid DEA–Fuzzy COPRAS Approach to the Evaluation of Renewable Energy: A Case of Wind Farms in Turkey," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    13. Shuangjie Li & Li Li & Liming Wang, 2020. "2030 Target for Energy Efficiency and Emission Reduction in the EU Paper Industry," Energies, MDPI, vol. 14(1), pages 1-17, December.
    14. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    15. Fernández, David & Pozo, Carlos & Folgado, Rubén & Jiménez, Laureano & Guillén-Gosálbez, Gonzalo, 2018. "Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index," Applied Energy, Elsevier, vol. 212(C), pages 1563-1577.
    16. Bian, Yiwen & He, Ping & Xu, Hao, 2013. "Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach," Energy Policy, Elsevier, vol. 63(C), pages 962-971.
    17. Fang-Rong Ren & Ze Tian & Yu-Ting Shen & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Energy, CO 2 , and AQI Efficiency and Improvement of the Yangtze River Economic Belt," Energies, MDPI, vol. 12(4), pages 1-17, February.
    18. Ma, Ding & Fei, Rilong & Yu, Yongsheng, 2019. "How government regulation impacts on energy and CO2 emissions performance in China's mining industry," Resources Policy, Elsevier, vol. 62(C), pages 651-663.
    19. 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.
    20. Sun, Bing & Yu, Yixin & Qin, Chao, 2017. "Should China focus on the distributed development of wind and solar photovoltaic power generation? A comparative study," Applied Energy, Elsevier, vol. 185(P1), pages 421-439.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12759-:d:682091. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.