Journal of Ecology and Rural Environment ›› 2022, Vol. 38 ›› Issue (12): 1545-1556.doi: 10.19741/j.issn.1673-4831.2021.0469

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The Evolution of China's Rural Human Settlement Environment Level and the Spatio-temporal Heterogeneity of Its Driving Factors

LIU Bin, ZHAN Jing, CHEN Ming   

  1. School of Economics, Management and Law, University of South China, Hengyang 421001, China
  • Received:2021-08-02 Online:2022-12-25 Published:2023-01-10

Abstract: The continuous improvement of the rural human settlement environment level (RHSEL) is an important guarantee for the smooth promotion of rural revitalization. By constructing an evaluation index system and using a series of spatial data analysis methods, this paper investigates the spatio-temporal evolution characteristics of provincial RHSEL in China from 2006 to 2018 and analyzes the spatial-temporal heterogeneity of RHSEL's driving factors. The findings are as follows: (1) China's RHSEL has improved significantly, and the gap between soft and hard environments has been gradually converging. (2) The gradient gap of RHSEL between the eastern and central regions gradually narrowed. The RHSEL of the western region grew rapidly, while the growth momentum of the northeast region was slightly weak. The spatial structure of RHSEL in the eastern region is stable and shows the "club convergence". In contrast, the spatial structure in the western region shows a trend of internal differentiation. (3) The RHSEL in China is comprehensively influenced by industrial structure, urbanization rate, government support, road network density, openness, and other factors, and the influence of each factor on RHSEL is characterized by obvious spatial-temporal heterogeneity. Therefore, it is necessary to allocate resources according to local conditions in rural revitalization, which contributes to promoting the balanced, steady, and continuous improvement of RHSEL.

Key words: rural human settlement environment, driving factors, spatio-temporal heterogeneity, geographically and temporally weighted regression model

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