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Spatial–temporal evolution and regional difference decomposition of urban environmental governance efficiency in China

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Abstract

The three-stage DEA method and super-SBM are combined to measure the urban environmental governance efficiency of 30 provinces in China from 2004 to 2016. Then, ArcGIS software is used to illustrate the spatial–temporal evolution of urban environmental governance efficiency, and DagumGiniratio is calculated to show the regional difference. The results indicate that the provinces characterized by high or low environmental governance efficiency are shown to cluster in space over time, and the spatial dependence of the environmental governance behavior of government is the greatest source of the regional difference of urban environmental governance efficiency. Our findings imply that the environmental governance behavior of government is spatial dependent, which highlights that the policies for improve environmental governance efficiency should be made globally.

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Notes

  1. DEA is an efficiency evaluation method that provides a comprehensive evaluation ofthe relative effectiveness of similar decision-making units (Charnes et al. 1978). It is a widely used linear programming technique for evaluate the performance of decision-making units on the basis of their inputs andoutputs.

  2. SBM (Tone 2001) is non-radial and deals with input/output slacks directly. The SBM returns an efficiency measure between 0 and 1, and gives unity if and only if the decision-making unit concerned is on the frontiers of the production possibility set with no input/output slacks.

  3. There are 34 provinces in China. For the reason of the lack of data, Tibet, Macau, Hong Kong and Taiwan are excluded from the sample. Therefore, the 30 provinces are Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, Hainan, Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.

  4. The eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi and Hainan, the central region includes Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan, and thewestern region includes Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.

  5. The averages of Gini ratio of urban EGE within region are 0.181, 0.153 and 0.171 for eastern region, central region and western region, respectively.

  6. The standard deviations of Gini ratio of urban EGE within region are 0.003, 0.001 and 0.001 for eastern region, central region and western region, respectively.

  7. The averages of Gini ratio of urban EGE between regions are 0.201, 0.212 and 0.180 for eastern and central region, eastern and western region, and central and western region, respectively.

  8. The standard deviations of Gini ratio of urban EGE between regions are 0.001, 0.003 and 0.001 for eastern and central region, eastern and western region, and central and western region, respectively.

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Funding

We would like to thank the funds from the National Natural Science Foundation of China (Grant No. 71863009), and the Natural Science Foundation of Hunan Province (Grant No. 2019JJ50482).

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Authors and Affiliations

  1. Business School, Jiangsu University of Technology, Changzhou, 213001, China

    Geng Peng & Fang Liu

  2. Business School, Tianjin University of Finance and Economics, Tianjin, 300222, China

    Xiaodan Zhang

  3. Shiliang Law School, Changzhou University, Changzhou, 213164, China

    Lijuan Ruan & Kaiyou Tian

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  1. Geng Peng
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  2. Xiaodan Zhang
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  3. Fang Liu
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  4. Lijuan Ruan
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Contributions

Peng and Liu conceived and designed the study. Liu, Zhang, Ruan and Tian collected the data and done the empirical analysis. Peng, Zhang and Liu wrote, reviewed, revised and edited the manuscript. All authors read and approved the manuscript.

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Correspondence to Fang Liu.

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Peng, G., Zhang, X., Liu, F. et al. Spatial–temporal evolution and regional difference decomposition of urban environmental governance efficiency in China. Environ Dev Sustain 23, 8974–8990 (2021). https://doi.org/10.1007/s10668-020-01007-2

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