IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i6p5172-d1097953.html
   My bibliography  Save this article

Spatial Correlation Network and Driving Factors of Urban Energy Eco-Efficiency from the Perspective of Human Well-Being: A Case Study of Shaanxi Province, China

Author

Listed:
  • Meixia Wang

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

  • Qingyun Zheng

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

  • Yunxia Wang

    (Business School, Shenzhen Technology University, Shenzhen 518118, China)

Abstract

It is very important to seek a sustainable improvement in human well-being under a limited resource supply and to promote the scientific and coordinated development of urban economic development, ecological environment protection, and human well-being. This paper constructs a human well-being index that includes economic well-being, culture and education well-being, and social development well-being as factors, and it incorporates the human well-being index into the evaluation system for urban well-being energy eco-efficiency (WEE). It uses the super-slack-based measure (SBM) model, which considers undesirable output, to measure the WEE of 10 prefecture-level cities in Shaanxi Province, China, from 2005 to 2019. The social network analysis (SNA) is used to describe the characteristics of the spatial correlation network of WEE and its spatiotemporal evolutionary trend, and the quadratic assignment procedure (QAP) analysis method is used to identify the driving factors that affect the spatial correlation network. The results show that, first, the WEE in Shaanxi is relatively low as a whole and varies greatly among regions, with the highest level in northern Shaanxi, followed by Guanzhong; the lowest level is in southern Shaanxi. Second, in Shaanxi, WEE has transcended geographical proximity into a complex, multi-threaded spatial correlation network, and Yulin is at the center of the network. Third, the network shows four sectors: the net overflow, main benefit, two-way overflow, and broker. Members in each sector have not fully exploited their advantages, and the whole network can be improved. Fourth, the differences in the economic development level, openness, industrial structure, and population are the main driving factors influencing the formation of the spatial correlation network.

Suggested Citation

  • Meixia Wang & Qingyun Zheng & Yunxia Wang, 2023. "Spatial Correlation Network and Driving Factors of Urban Energy Eco-Efficiency from the Perspective of Human Well-Being: A Case Study of Shaanxi Province, China," IJERPH, MDPI, vol. 20(6), pages 1-20, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:6:p:5172-:d:1097953
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/6/5172/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/6/5172/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peng, Benhong & Wang, Yuanyuan & Wei, Guo, 2020. "Energy eco-efficiency: Is there any spatial correlation between different regions?," Energy Policy, Elsevier, vol. 140(C).
    2. Huiping Wang & Qi Ge, 2022. "Analysis of the Spatial Association Network of PM 2.5 and Its Influencing Factors in China," IJERPH, MDPI, vol. 19(19), pages 1-15, October.
    3. 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.
    4. Zhang, Xin & Zhang, Xiaobo & Chen, Xi, 2017. "Happiness in the air: How does a dirty sky affect mental health and subjective well-being?," Journal of Environmental Economics and Management, Elsevier, vol. 85(C), pages 81-94.
    5. Ying Li & Yung-ho Chiu & Tai-Yu Lin, 2019. "Energy and Environmental Efficiency in Different Chinese Regions," Sustainability, MDPI, vol. 11(4), pages 1-26, February.
    6. Liu, Gengyuan & Yang, Zhifeng & Fath, Brian D. & Shi, Lei & Ulgiati, Sergio, 2017. "Time and space model of urban pollution migration: Economy-energy-environment nexus network," Applied Energy, Elsevier, vol. 186(P2), pages 96-114.
    7. Wu, Yi-Hua & Liu, Chia-Hao & Hung, Ming-Lung & Liu, Tzu-Yar & Masui, Toshihiko, 2019. "Sectoral energy efficiency improvements in Taiwan: Evaluations using a hybrid of top-down and bottom-up models," Energy Policy, Elsevier, vol. 132(C), pages 1241-1255.
    8. K Tone, 2002. "A strange case of the cost and allocative efficiencies in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1225-1231, November.
    9. Han, Yonghui & Zhang, Fan & Huang, Liangxiong & Peng, Keming & Wang, Xianbin, 2021. "Does industrial upgrading promote eco-efficiency? ─A panel space estimation based on Chinese evidence," Energy Policy, Elsevier, vol. 154(C).
    10. 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. Tone, Kaoru & Tsutsui, Miki, 2007. "Decomposition of cost efficiency and its application to Japanese-US electric utility comparisons," Socio-Economic Planning Sciences, Elsevier, vol. 41(2), pages 91-106, June.
    2. Yahong Liu & Hailian Sun & Lei Shi & Huimin Wang & Zhai Xiu & Xiao Qiu & Hong Chang & Yu Xie & Yang Wang & Chengjie Wang, 2021. "Spatial-Temporal Changes and Driving Factors of Land-Use Eco-Efficiency Incorporating Ecosystem Services in China," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    3. Gong, Wenchao, 2023. "A study on the effects of natural resource abundance and foreign direct investment on regional eco-efficiency in China under the target of COP26," Resources Policy, Elsevier, vol. 82(C).
    4. Suvvari Anandarao & S. Raja Sethu Durai & Phanindra Goyari, 2019. "Efficiency Decomposition in two-stage Data Envelopment Analysis: An application to Life Insurance companies in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 271-285, June.
    5. Peng, Benhong & Wang, Yuanyuan & Wei, Guo, 2020. "Energy eco-efficiency: Is there any spatial correlation between different regions?," Energy Policy, Elsevier, vol. 140(C).
    6. Wang, Xinbin & Wang, Zilong & Wang, Rong, 2023. "Does green economy contribute towards COP26 ambitions? Exploring the influence of natural resource endowment and technological innovation on the growth efficiency of China's regional green economy," Resources Policy, Elsevier, vol. 80(C).
    7. Tang, Chang & Xue, Yan & Wu, Haitao & Irfan, Muhammad & Hao, Yu, 2022. "How does telecommunications infrastructure affect eco-efficiency? Evidence from a quasi-natural experiment in China," Technology in Society, Elsevier, vol. 69(C).
    8. Liangen Zeng, 2021. "China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    9. Liangen Zeng & Haiyan Lu & Yenping Liu & Yang Zhou & Haoyu Hu, 2019. "Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015," Energies, MDPI, vol. 12(16), pages 1-21, August.
    10. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    11. 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.
    12. Ashrafi, Ali & Seow, Hsin-Vonn & Lee, Lai Soon & Lee, Chew Ging, 2013. "The efficiency of the hotel industry in Singapore," Tourism Management, Elsevier, vol. 37(C), pages 31-34.
    13. 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.
    14. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    15. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    16. Yu-Chuan Chen & Yung-Ho Chiu & Tzu-Han Chang & Tai-Yu Lin, 2023. "Sustainable Development, Government Efficiency, and People’s Happiness," Journal of Happiness Studies, Springer, vol. 24(4), pages 1549-1578, April.
    17. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    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. Junlong Li & Chuangneng Cai & Feng Zhang, 2020. "Assessment of Ecological Efficiency and Environmental Sustainability of the Minjiang-Source in China," Sustainability, MDPI, vol. 12(11), pages 1-15, June.

    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:20:y:2023:i:6:p:5172-:d:1097953. 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.