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Spatial association network structure of eco-efficiency and its influencing factors: Evidence from the Beijing-Tianjin-Hebei region in China

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  • Luo, Gongli
  • Wang, Xiaotong

Abstract

This paper uses the DEA-Malmquist model to measure the static and dynamic ecological efficiency of city and describes its spatial distribution characteristics. Then, we use social network analysis and the quadratic assignment procedure to analyze the spatial association network characteristics of ecological efficiency and its influencing factors. The results show that urban eco-efficiency is close being effective according to the DEA method but does not fully. The ecological total factor productivity of the city is increasing. The spatial distribution of technical progress is consistent with total factor productivity, which shows that the former is the main reason for the progress of the latter. The ecological efficiency spatial association network structure is obvious and shows the characteristics of multithreading and complexity. The changing trends of network density and associated relationships remain consistent. In general, there is an upward trend of volatility. Network connectedness, network hierarchy, and network efficiency show that the spatial association of ecological efficiency is inseparable. The overall network structure has better correlation and stability, but there are strict ecological efficiency spatial association network structural characteristics between cities. The analysis of individual network characteristics shows that the connection between cities is getting closer, but the spatial association needs strengthening. The correlation coefficients of green level, industrial structure, government financial support, and fixed asset investment are significantly positive, while the influence of economic level and opening up on the ecological efficiency spatial network are not significant.

Suggested Citation

  • Luo, Gongli & Wang, Xiaotong, 2023. "Spatial association network structure of eco-efficiency and its influencing factors: Evidence from the Beijing-Tianjin-Hebei region in China," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022003167
    DOI: 10.1016/j.ecolmodel.2022.110218
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    References listed on IDEAS

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    1. Monastyrenko, Evgenii, 2017. "Eco-efficiency outcomes of mergers and acquisitions in the European electricity industry," Energy Policy, Elsevier, vol. 107(C), pages 258-277.
    2. Giordano, P. & Caputo, P. & Vancheri, A., 2014. "Fuzzy evaluation of heterogeneous quantities: Measuring urban ecological efficiency," Ecological Modelling, Elsevier, vol. 288(C), pages 112-126.
    3. Xuesong Sun & Zaisheng Zhang, 2021. "Coupling and Coordination Level of the Population, Land, Economy, Ecology and Society in the Process of Urbanization: Measurement and Spatial Differentiation," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    4. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
    5. Lopez-Rodriguez, Jesus & Martinez-Lopez, Diego, 2017. "Looking beyond the R&D effects on innovation: The contribution of non-R&D activities to total factor productivity growth in the EU," Structural Change and Economic Dynamics, Elsevier, vol. 40(C), pages 37-45.
    6. 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.
    7. Qian, Xianhang & Wang, Ying & Zhang, Guangli, 2018. "The spatial correlation network of capital flows in China: Evidence from China's High-Value Payment System," China Economic Review, Elsevier, vol. 50(C), pages 175-186.
    8. Seiford, Lawrence M. & Zhu, Joe, 2005. "A response to comments on modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 161(2), pages 579-581, March.
    Full references (including those not matched with items on IDEAS)

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