Abstract
Based on 2004–2017 Chinese interprovincial panel data, we construct the regional environmental pollution indicators, analyze the influencing factors and spillover effects of environmental pollution under the conditions of time lag, spatial lag, and spatiotemporal lag by using the STIRPAT model and dynamic spatial Durbin model, and discuss the spatiotemporal characteristics of regional environmental pollution in China. The results show that the overall regional environmental pollution in China is on the decline. Environmental pollution has strengthened the characteristics of strong in the East and weak in the West, and the characteristics of strong in the South and weak in the North began to appear. Population scale, economic growth, and industrial scale will increase environmental pollution in the region, and the environmental regulation intensity and pollution control investment will reduce environmental pollution in the region. The spillover effects of the influencing factors of regional environmental pollution in China are different. In the short term, the influencing factors have a greater impact on the neighboring areas, while in the long term, they have a greater impact on the region. Hence, the critical approach to achieving sustainable development is to give full play to the factors which can reduce environmental pollution and to effectively control the factors which will promote environmental pollution.
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The authors are grateful to the reviewers and editors for their insightful and profound opinions and suggestions, which have helped to improve the quality of the paper. The authors would like to thank Minghao Zhang and Minglang Zhang for their participation in the literature review, data collation, and topic discussion of the project. The authors would also like to thank our dear friend Chen Chen for her contribution to the grammar revision and wording of this paper.
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Hou, ., Zhu, Y. Analysis of spillover effects of regional environmental pollution: an interprovincial study in China based on spatiotemporal lag model. Environ Sci Pollut Res 29, 836–853 (2022). https://doi.org/10.1007/s11356-021-15739-9
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DOI: https://doi.org/10.1007/s11356-021-15739-9