IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i24p13046-d699645.html
   My bibliography  Save this article

Concept Evolution and Multi-Dimensional Measurement Comparison of Urban Energy Performance from the Perspective of System Correlation: Empirical Analysis of 142 Prefecture Level Cities in China

Author

Listed:
  • Lei Wang

    (School of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China)

  • Wei Li

    (School of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China)

  • Guomin Li

    (School of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China)

  • Guozhen Zhang

    (School of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

In order to clarify the evolution characteristics and direction of urban energy performance concepts, reveal the research dimensions, determine the performance results and differences, and clarify the reference benchmark, this study depicts the main systems involved in the process of urban energy utilization, demonstrates their relevance guided by the system view, and proposes the measurement indicators in the economic, environmental, and well-being dimensions. The measurement model of each dimension is constructed using the corresponding models of Data Envelopment Analysis. Taking 142 prefecture level cities in China as examples, the energy performance in different dimensions is measured and compared. The energy performance levels are close in the economic and environmental dimensions. However, the results of the well-being dimension are different from these first two dimensions, and the performance levels among cities differ more. In the economic, environmental, and well-being dimension, 22, 28 and 16 cities have reached the effective frontier, respectively, and the performance benchmark cities of 15, 15 and 5 provinces are non-provincial capital cities, respectively. Based on the above analysis, the “chain” framework evolution direction of concept and measurement is proposed, and this study provides benchmarks and policy suggestions to improve energy performance.

Suggested Citation

  • Lei Wang & Wei Li & Guomin Li & Guozhen Zhang, 2021. "Concept Evolution and Multi-Dimensional Measurement Comparison of Urban Energy Performance from the Perspective of System Correlation: Empirical Analysis of 142 Prefecture Level Cities in China," IJERPH, MDPI, vol. 18(24), pages 1-21, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13046-:d:699645
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/24/13046/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/24/13046/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    2. Bing Xue & Yong Geng & Katrin Müller & Chengpeng Lu & Wanxia Ren, 2014. "Understanding the Causality between Carbon Dioxide Emission, Fossil Energy Consumption and Economic Growth in Developed Countries: An Empirical Study," Sustainability, MDPI, vol. 6(2), pages 1-9, February.
    3. Gozgor, Giray & Lau, Chi Keung Marco & Lu, Zhou, 2018. "Energy consumption and economic growth: New evidence from the OECD countries," Energy, Elsevier, vol. 153(C), pages 27-34.
    4. Balsalobre-Lorente, Daniel & Shahbaz, Muhammad & Roubaud, David & Farhani, Sahbi, 2018. "How economic growth, renewable electricity and natural resources contribute to CO2 emissions?," Energy Policy, Elsevier, vol. 113(C), pages 356-367.
    5. Chen, Chih Cheng, 2017. "Measuring departmental and overall regional performance: applying the multi-activity DEA model to Taiwan׳s cities/counties," Omega, Elsevier, vol. 67(C), pages 60-80.
    6. Hang, Ye & Sun, Jiasen & Wang, Qunwei & Zhao, Zengyao & Wang, Yizhong, 2015. "Measuring energy inefficiency with undesirable outputs and technology heterogeneity in Chinese cities," Economic Modelling, Elsevier, vol. 49(C), pages 46-52.
    7. Jeni Klugman & Francisco Rodríguez & Hyung-Jin Choi, 2011. "The HDI 2010: new controversies, old critiques," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(2), pages 249-288, June.
    8. Oh, Dong-hyun, 2010. "A metafrontier approach for measuring an environmentally sensitive productivity growth index," Energy Economics, Elsevier, vol. 32(1), pages 146-157, January.
    9. Wang, Zhaohua & He, Weijun & Wang, Bo, 2017. "Performance and reduction potential of energy and CO2 emissions among the APEC's members with considering the return to scale," Energy, Elsevier, vol. 138(C), pages 552-562.
    10. 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.
    11. Chen, Ping-Yu & Chen, Sheng-Tung & Hsu, Chia-Sheng & Chen, Chi-Chung, 2016. "Modeling the global relationships among economic growth, energy consumption and CO2 emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 420-431.
    12. Patterson, Murray G, 1996. "What is energy efficiency? : Concepts, indicators and methodological issues," Energy Policy, Elsevier, vol. 24(5), pages 377-390, May.
    13. Li, Zhi & Ouyang, Xiaoling & Du, Kerui & Zhao, Yang, 2017. "Does government transparency contribute to improved eco-efficiency performance? An empirical study of 262 cities in China," Energy Policy, Elsevier, vol. 110(C), pages 79-89.
    14. Shafiei, Sahar & Salim, Ruhul A., 2014. "Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis," Energy Policy, Elsevier, vol. 66(C), pages 547-556.
    15. Śmiech, Sławomir & Papież, Monika, 2014. "Energy consumption and economic growth in the light of meeting the targets of energy policy in the EU: The bootstrap panel Granger causality approach," Energy Policy, Elsevier, vol. 71(C), pages 118-129.
    16. Zhang, Ning & Zhou, P. & Choi, Yongrok, 2013. "Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis," Energy Policy, Elsevier, vol. 56(C), pages 653-662.
    17. Kan, Siyi & Chen, Bin & Chen, Guoqian, 2019. "Worldwide energy use across global supply chains: Decoupled from economic growth?," Applied Energy, Elsevier, vol. 250(C), pages 1235-1245.
    18. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    19. De Luca, G. & Fabozzi, S. & Massarotti, N. & Vanoli, L., 2018. "A renewable energy system for a nearly zero greenhouse city: Case study of a small city in southern Italy," Energy, Elsevier, vol. 143(C), pages 347-362.
    20. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    21. Keirstead, James, 2013. "Benchmarking urban energy efficiency in the UK," Energy Policy, Elsevier, vol. 63(C), pages 575-587.
    22. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "DEA radial measurement for environmental assessment and planning: Desirable procedures to evaluate fossil fuel power plants," Energy Policy, Elsevier, vol. 41(C), pages 422-432.
    23. 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.
    24. George Halkos & Kleoniki Natalia Petrou, 2019. "Analysing the Energy Efficiency of EU Member States: The Potential of Energy Recovery from Waste in the Circular Economy," Energies, MDPI, vol. 12(19), pages 1-32, September.
    25. Färe, Rolf & Grosskopf, Shawna & Pasurka, Carl A., 2007. "Environmental production functions and environmental directional distance functions," Energy, Elsevier, vol. 32(7), pages 1055-1066.
    26. 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.
    27. Wenjuan Gao & Xiaohao Ding & Ran Chen & Weifang Min, 2019. "An Empirical Study of the Role of Higher Education in Building a Green Economy," Sustainability, MDPI, vol. 11(23), pages 1-14, December.
    28. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    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. Zhou, D.Q. & Wang, Qunwei & Su, B. & Zhou, P. & Yao, L.X., 2016. "Industrial energy conservation and emission reduction performance in China: A city-level nonparametric analysis," Applied Energy, Elsevier, vol. 166(C), pages 201-209.
    2. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    3. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    4. Cheng, Zhonghua & Liu, Jun & Li, Lianshui & Gu, Xinbei, 2020. "Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces," Energy Economics, Elsevier, vol. 86(C).
    5. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    6. 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.
    7. Ying Li & Yung-Ho Chiu & Liang Chun Lu, 2018. "Regional Energy, CO 2 , and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach," Energies, MDPI, vol. 11(8), pages 1-20, August.
    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. Xiangyu Teng & Danting Lu & Yung-ho Chiu, 2019. "Emission Reduction and Energy Performance Improvement with Different Regional Treatment Intensity in China," Energies, MDPI, vol. 12(2), pages 1-18, January.
    10. Zebin Zheng & Wenjun Xiao & Ziye Cheng, 2023. "China’s Green Total Factor Energy Efficiency Assessment Based on Coordinated Reduction in Pollution and Carbon Emission: From the 11th to the 13th Five-Year Plan," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    11. Ding, Li-Li & Lei, Liang & Zhao, Xin & Calin, Adrian Cantemir, 2020. "Modelling energy and carbon emission performance: A constrained performance index measure," Energy, Elsevier, vol. 197(C).
    12. Zhang, Yue-Jun & Jiang, Lin & Shi, Wei, 2020. "Exploring the growth-adjusted energy-emission efficiency of transportation industry in China," Energy Economics, Elsevier, vol. 90(C).
    13. Feng, Chao & Wang, Miao, 2018. "Analysis of energy efficiency in China's transportation sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 565-575.
    14. 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.
    15. Wang, H. & Zhou, P. & Zhou, D.Q., 2013. "Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis," Energy Economics, Elsevier, vol. 40(C), pages 795-803.
    16. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2017. "Non-radial metafrontier approach to identify carbon emission performance and intensity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 664-672.
    17. Wu, Rongxin & Lin, Boqiang, 2021. "Does industrial agglomeration improve effective energy service: An empirical study of China’s iron and steel industry," Applied Energy, Elsevier, vol. 295(C).
    18. Jie Wu & Beibei Xiong & Qingxian An & Jiasen Sun & Huaqing Wu, 2017. "Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs," Annals of Operations Research, Springer, vol. 255(1), pages 257-276, August.
    19. Du, Kerui & Lu, Huang & Yu, Kun, 2014. "Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach," Applied Energy, Elsevier, vol. 115(C), pages 491-501.
    20. Qunwei Wang & Peng Zhou & Zengyao Zhao & Neng Shen, 2014. "Energy Efficiency and Energy Saving Potential in China: A Directional Meta-Frontier DEA Approach," Sustainability, MDPI, vol. 6(8), pages 1-17, August.

    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:jijerp:v:18:y:2021:i:24:p:13046-:d:699645. 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.