IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15381-d1269062.html
   My bibliography  Save this article

A Study on the Measurement and Influences of Energy Green Efficiency: Based on Panel Data from 30 Provinces in China

Author

Listed:
  • Yulin Lu

    (Directly Affiliated College, Shandong Open University, Jinan 250014, China
    These authors contributed equally to this work.)

  • Chengyu Li

    (College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China
    These authors contributed equally to this work.)

  • Min-Jae Lee

    (Department of Global Business, Mokwon University, Daejeon 35349, Republic of Korea)

Abstract

China’s rapid economic growth has inevitably led to serious resource depletion, environmental degradation, and a decline in social welfare. As such, establishing total-factor energy green efficiency (TFEGE) and exploring its factors are of paramount importance to bolster comprehensive energy efficiency and foster sustainable development. In this research, we deployed the spatial lag model (SLM) and data envelopment analysis (DEA), using energy, capital and labor as input indicators, GDP and social dimension metrics as desirable outputs, and “three wastes” as undesirable outputs, to assess the TFEGE across 30 provinces in China from 2001 to 2020. Employing the exploratory spatial data analysis (ESDA) method, we analyzed the spatial autocorrelation of TFEGE at national and provincial levels. Simultaneously, we examined the influencing factors of TFEGE using a spatial econometric model. Our study reveals that, throughout the examined period, the TFEGE in China has generally shown a steady decline. The TFEGE dropped from 0.630 to 0.553. The TFEGE of all regions in China also showed a downward trend, but the rate of decrease varied significantly across different regions. Among them, the TFEGE of the eastern region fluctuated between 0.820 and 0.778. The TFEGE of the northeast region decreased significantly from 0.791 to 0.307. The TFEGE of the western region decreased from 0.512 to 0.486. The TFEGE of the central region decreased from 0.451 to 0.424. Beijing, Guangdong, Hainan, Qinghai, and Ningxia showed an effective TFEGE, while for other provinces, it was ineffective. The TFEGE in all four major regions failed to achieve effectiveness. Its distribution pattern was east > west > northeast > central. The TFEGE across the 30 provinces showed positive spatial autocorrelation, indicating a strong spatial clustering trend. We found that while transportation infrastructure and technological progression exert a positive impact on TFEGE, elements such as industrial structure, energy composition, and foreign direct investment negatively influence TFEGE.

Suggested Citation

  • Yulin Lu & Chengyu Li & Min-Jae Lee, 2023. "A Study on the Measurement and Influences of Energy Green Efficiency: Based on Panel Data from 30 Provinces in China," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15381-:d:1269062
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15381/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/21/15381/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chang, Tzu-Pu & Hu, Jin-Li, 2010. "Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China," Applied Energy, Elsevier, vol. 87(10), pages 3262-3270, October.
    2. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, September.
    3. Wang, Zhao-Hua & Zeng, Hua-Lin & Wei, Yi-Ming & Zhang, Yi-Xiang, 2012. "Regional total factor energy efficiency: An empirical analysis of industrial sector in China," Applied Energy, Elsevier, vol. 97(C), pages 115-123.
    4. 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.
    5. Jin-Peng Liu & Qian-Ru Yang & Lin He, 2017. "Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques," Energies, MDPI, vol. 10(7), pages 1-14, July.
    6. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    7. Weibin Lin & Jin Yang & Bin Chen, 2011. "Temporal and Spatial Analysis of Integrated Energy and Environment Efficiency in China Based on a Green GDP Index," Energies, MDPI, vol. 4(9), pages 1-15, September.
    8. Pan, Xiongfeng & Ai, Bowei & Li, Changyu & Pan, Xianyou & Yan, Yaobo, 2019. "Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 428-435.
    9. Zhao, Linhai & Rasoulinezhad, Ehsan, 2023. "Role of natural resources utilization efficiency in achieving green economic recovery: Evidence from BRICS countries," Resources Policy, Elsevier, vol. 80(C).
    10. Shehzadi, Anam, 2023. "Energy efficiency and productivity in emerging and developing Asian countries: A firm level analysis," Journal of Asian Economics, Elsevier, vol. 88(C).
    11. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

    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. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    2. 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.
    3. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    4. Imanirad, Raha & Cook, Wade D. & Aviles-Sacoto, Sonia Valeria & Zhu, Joe, 2015. "Partial input to output impacts in DEA: The case of DMU-specific impacts," European Journal of Operational Research, Elsevier, vol. 244(3), pages 837-844.
    5. Ke Wang & Xueying Yu, 2017. "Industrial Energy and Environment Efficiency of Chinese Cities: An Analysis Based on Range-Adjusted Measure," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1023-1042, July.
    6. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    7. Abbas Mardani & Dalia Streimikiene & Tomas Balezentis & Muhamad Zameri Mat Saman & Khalil Md Nor & Seyed Meysam Khoshnava, 2018. "Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends," Energies, MDPI, vol. 11(8), pages 1-21, August.
    8. Rácz, Viktor J. & Vestergaard, Niels, 2016. "Productivity and efficiency measurement of the Danish centralized biogas power sector," Renewable Energy, Elsevier, vol. 92(C), pages 397-404.
    9. Chang, Ming-Chung, 2020. "An application of total-factor energy efficiency under the metafrontier framework," Energy Policy, Elsevier, vol. 142(C).
    10. Bi, Gong-Bing & Song, Wen & Zhou, P. & Liang, Liang, 2014. "Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model," Energy Policy, Elsevier, vol. 66(C), pages 537-546.
    11. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    12. Wang, Zhaohua & Feng, Chao, 2015. "Sources of production inefficiency and productivity growth in China: A global data envelopment analysis," Energy Economics, Elsevier, vol. 49(C), pages 380-389.
    13. Du, Huibin & Matisoff, Daniel C. & Wang, Yangyang & Liu, Xi, 2016. "Understanding drivers of energy efficiency changes in China," Applied Energy, Elsevier, vol. 184(C), pages 1196-1206.
    14. Aneta Karasek & Barbara Fura & Magdalena Zajączkowska, 2023. "Assessment of Energy Efficiency in the European Union Countries in 2013 and 2020," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    15. Lei, Ming & Yin, Zihan & Yu, Xiaowen & Deng, Shijie, 2017. "Carbon-weighted economic development performance and driving force analysis: Evidence from China," Energy Policy, Elsevier, vol. 111(C), pages 179-192.
    16. Tao Xu & Jianxin You & Hui Li & Luning Shao, 2020. "Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review," Energies, MDPI, vol. 13(14), pages 1-20, July.
    17. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    18. Huayong Niu & Zhishuo Zhang & Manting Luo, 2022. "Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning," IJERPH, MDPI, vol. 19(19), pages 1-28, October.
    19. Jin XU & Panagiotis ZERVOPOULOS & Zhenhua QIAN & Gang CHENG, 2012. "A Universal Solution For Units - Invariance In Data Envelopment Analysis," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 3(2), pages 121-128.
    20. Tao Ding & Ya Chen & Huaqing Wu & Yuqi Wei, 2018. "Centralized fixed cost and resource allocation considering technology heterogeneity: a DEA approach," Annals of Operations Research, Springer, vol. 268(1), pages 497-511, September.

    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:15:y:2023:i:21:p:15381-:d:1269062. 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.