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Green Transition Assessment, Spatial Correlation, and Obstacles Identification: Evidence from Urban Governance Data of 288 Cities in China

Author

Listed:
  • Ziao Yu

    (School of Economics, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Tianjiao Guo

    (School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    These authors contributed equally to this work.)

  • Xiaoqian Song

    (School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China
    China Institute of Urban Governance, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Lifan Zhang

    (School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Linmei Cai

    (School of Economics and Management, Yan’an University, Yan’an 716000, China)

  • Xi Zhang

    (School of Business, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Aiwen Zhao

    (College of Finance, Xuzhou University of Technology, Xuzhou 221018, China)

Abstract

The green transition of China’s cities is crucial for ecology civilization realization. Based on the driver–pressure–state–impact–response (DPSIR) framework, an integrated technique for order preference by similarity to ideal solution (TOPSIS) model with entropy weight, this study achieved the comprehensive assessment of the green transition of 288 province-level municipalities and prefecture-level cities in China over 18 years from 2002 to 2019, in addition to the spatial correlations and obstacles analysis. The results indicate that major cities in China have a more significant green transition value, and the eastern region is developing fast, while the northeast region is relatively slow. There was heterogeneous spatial distribution for green transition, because of the disequilibrium sustainable development of 288 cities. Green transition has a significantly positive spatial autocorrelation in the cities of China, the high–high significant clusters greatly increased, and the main locations changed from the northeast to southeast of China. Frequent obstacles were also found, including road infrastructure construction, water resources, and the green coverage of urban built-up areas. Based on these results, several policy implications were put forward, including the optimization of environmental laws and regulations, the development of green transportation infrastructure, resource conservation and the circular economy, the establishment of a green financial system, and increasing the linkage for the green transition of different cities.

Suggested Citation

  • Ziao Yu & Tianjiao Guo & Xiaoqian Song & Lifan Zhang & Linmei Cai & Xi Zhang & Aiwen Zhao, 2024. "Green Transition Assessment, Spatial Correlation, and Obstacles Identification: Evidence from Urban Governance Data of 288 Cities in China," Land, MDPI, vol. 13(3), pages 1-24, March.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:341-:d:1353013
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    References listed on IDEAS

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    3. Bivand, Roger & Müller, Werner G. & Reder, Markus, 2009. "Power calculations for global and local Moran's," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2859-2872, June.
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