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Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China

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Listed:
  • Xiang Liu
  • Xinyuan Zhang
  • Man Yuan
  • Jia Liu
  • Guolin Zhou

Abstract

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006–2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations.

Suggested Citation

  • Xiang Liu & Xinyuan Zhang & Man Yuan & Jia Liu & Guolin Zhou, 2024. "Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-22, March.
    • Handle: RePEc:plo:pone00:0300419
    DOI: 10.1371/journal.pone.0300419
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    References listed on IDEAS

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    1. Timo Kuosmanen, 2005. "Measurement and Analysis of Eco‐efficiency: An Economist's Perspective," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 15-18, October.
    2. Jyri Seppäläa & Matti Melanen & Ilmo Mäenpää & Sirkka Koskela & Jyrki Tenhunen & Marja‐Riitta Hiltunen, 2005. "How Can the Eco‐efficiency of a Region be Measured and Monitored?," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 117-130, October.
    3. Wang, H. & Zhou, P., 2018. "Assessing Global CO2 Emission Inequality From Consumption Perspective: An Index Decomposition Analysis," Ecological Economics, Elsevier, vol. 154(C), pages 257-271.
    4. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    5. 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.
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