Abstract
A comprehensive assessment of China’s environmental performance (EP) and an investigation into its driving factors are essential prerequisites for advancing environmental protection efforts. However, existing studies have often exhibited a one-sided EP evaluation approach and lacked a systematic perspective. Consequently, this study has adopted a holistic approach by integrating environmental protection and pollution within the same theoretical framework. We have employed the “P-S-R” model to comprehensively assess the EP of 272 cities from 2003 to 2019. Concurrently, we have applied the spatial Durbin model to analyze EP drivers utilizing three spatial matrices. The findings of this study reveal several vital insights. Firstly, the mean EP value for China is 0.1138, indicating a low level, but it demonstrates a consistent upward trend over the years. When comparing cities with high EP, they are predominantly situated in northern China, northeastern China, and certain areas along the southeastern coast. Secondly, from a spatial perspective, the directionality of EP exhibits a trend from “northeast to the southwest,” with the center of gravity located in and around Zhumadian, Henan Province, gradually shifting towards the northeast. The majority of cities fall within the H–H and L-L clusters, displaying significant positive spatial autocorrelation effects. Thirdly, EP drivers encompass a wide range of factors, including economic development, urbanization, resource dependence, industrial structure, infrastructure construction, environmental regulation, government regulatory capacity, scientific and technological innovation, and foreign direct investment. These drivers also exhibit significant spillover effects. Finally, the characteristics of EP development vary between resource-based cities (RBCs) and non-resource-based cities (non-RBCs), as well as among the eastern, central, and western regions. Moreover, there are disparities in the driving factors’ direct, indirect, and overall effects. Consequently, we must propose tailored strategies and recommendations to enhance EP, considering the heterogeneous effects of influencing factors across different city types, regions, and collaboration approaches.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Notes
See epi.yale.edu. https://epi.yale.edu/.
The changes in EPI evaluation methodology render historical EPI scores non-comparable, as differences in scores do not necessarily reflect alterations in environmental performance but are primarily attributable to changes in indicators selection and weight assignment methods, among other factors. Therefore, a ranking approach provides a more precise measurement. Higher rankings signify superior environmental performance.
To examine the presence of an “N”-shaped relationship, we initially introduce a cubic term. If the cubic term did not yield statistical significance, the introduction of a quadratic term was examined to ascertain the presence of a “U”-shaped relationship. If the quadratic term also lacked statistical significance, it indicated the absence of a nonlinear relationship, and a first-order term was employed for analysis.
Leakage effect: In the period preceding full implementation, not all polluting sectors or countries are subject to the regulations, potentially leading to unregulated areas offseting some of the pollution reduction achieved in regulated sectors and countries. Announcement effect: When regulatory measures are postponed, causing industries to modify their emissions behavior before policy implementation, which, in turn, may partially offset the emissions reductions achieved after the regulation takes full effect. This dynamic counterpart to the static leakage effect affects the distribution of emissions over time due to policy implementation not covering all time periods.
As the stress dimension is an inverse indicator, a negative coefficient indicates increased environmental stress.
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Funding
This work was supported by the National Social Science Foundation Major Project (No. 22&ZD113), Key Project of the National Social Science Foundation of China (No. 18AZD003), Key Project of the National Natural Science Foundation of China (No. 71933005), and National Natural Science Foundation Project (No. 71773092).
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Pengpeng Tian: conceptualization, formal analysis, methodology, investigation, writing—original draft. Zichun Pan: data curation and proofreading, methodology, writing—review, and editing. Yujie Shen: formal analysis, manuscript editing, investigation, data curation, and proofreading. Yuchun Zhu: funding acquisition, project administration, and supervision.
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Tian, P., Pan, Z., Shen, Y. et al. Spatio-temporal pattern assessment of China’s environmental performance and its spatial drivers: evidence from city-level data over 2003–2019. Environ Sci Pollut Res 31, 15223–15256 (2024). https://doi.org/10.1007/s11356-024-32069-8
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DOI: https://doi.org/10.1007/s11356-024-32069-8