SHENGPENG WANG

Dr. SHENGPENG WANG

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 July 2023 in Land
Verified
Journal Article
Open access
PDF file
TLTianyu Li
Tianyu Li
SWSHENGPENG WANG
SHENGPENG WANG
PCPinyu Chen
Pinyu Chen
XLXiaoyi Liu
Xiaoyi Liu
XKXiang Kong
Xiang Kong
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.

14 October 2025 in Applied Spatial Analysis and Policy
Verified
Journal Article
Closed access
PDF file
SWSHENGPENG WANG
SHENGPENG WANG
TTTangwei Teng
Tangwei Teng
SHSenlin Hu
Senlin Hu
YPYajun Pan
Yajun Pan