Geographical Patterns and Influencing Mechanisms of Digital Rural Development Level at the County Scale in China

• Published in: Land (Basel) Vol. 12; no. 8; p. 1504
• Main Authors: Li, Tianyu, Wang, Shengpeng, Chen, Pinyu, Liu, Xiaoyi, Kong, Xiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Analysis; Clustering; Construction; Correlation; Differentiation; Digital divide; Digital economy; digital rural; Digital technology; Digitization; Economic analysis; Economic development; Economic growth; Economics; Geodetector; Geography; Liu, Timothy; Modernization; Political parties; Regions; Rural areas; Rural development; Rural environments; rural revitalization; Society; spatial differentiation; Spatial distribution; Spatial variations; Technology and civilization; Urbanization; China
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land12081504

Digital rural development has become an emerging dynamic force for high-quality rural development in China. This paper constructs the “environmental-economic-social” analysis framework for digital rural development, analyzes the spatial variation of the digital rural development level (DRDL) in Chinese counties in 2020, and conducts the factor detection and interaction detection of its influencing factors. It is found that: (1) digital rural development has its own unique spatial differentiation mechanism, which can be analyzed from three dimensions: environmental system, economic system, and social system, which play a fundamental role, decisive role, and a magnifying effect on digital rural development, respectively. (2) The DRDL in China’s counties has significant spatial distribution, spatial correlation, and spatial clustering characteristics. The DRDL in general shows a decreasing distribution trend from coastal to inland regions, and the overall differences in DRDL mainly come from intra-regional differences rather than inter-regional differences. The rural infrastructure digitalization dimension has stronger spatial correlation while the spatial correlation of the rural governance digitalization dimension is weaker. There are obvious hotspot and coldspot areas in the DRDL, with large differences between the coldspot and hotspot areas of different sub-dimensions. (3) The spatial divergence of the DRDL is closely related to geographical elements and is the result of the combined effect of several geographical factors. The factor detection results show that the dominant factors within the four regions are significant different. The interaction detection results show that the driving force of the two-factor interaction is stronger than that of the single-factor interaction and that the interaction among the factors further deepens the spatial differentiation of the DRDL.