Exploring the spatiotemporal structure and driving mechanism of digital village construction in China based on social network analysis and Geodetector.

Autor: Zhang L; School of Economics and Trade, Fujian Jiangxia University, Fuzhou, China.; Fujian Southeast Digital Economy Research Institute, Fuzhou, China., Zhou X; Fujian Southeast Digital Economy Research Institute, Fuzhou, China.; School of Economics and Management, Fuzhou University, Fuzhou, China.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Nov 15; Vol. 19 (11), pp. e0310846. Date of Electronic Publication: 2024 Nov 15 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0310846
Abstrakt: Clarifying the spatiotemporal structure and driving mechanism of China's digital village construction (DVC) is imperative for ameliorating regional disparities and fostering the holistic progression of DVC in China. This study assesses the index of DVC in 30 Chinese provinces from 2011 to 2021 using the Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) and dynamic GRA. It analyzes the spatiotemporal structure of DVC with kernel density, trend surface, and social network analysis techniques. Additionally, it employs Geodetector to elucidate the driving mechanism behind spatial differentiation in China's digital village development network. The results indicate that: (1) Although the index of DVC in China from 2011 to 2021 has shown progressive enhancement, the average DVC index for all regions throughout the years surveyed stands at 0.457, which means that the DVC in China is still at an early stage. (2) The overall network structure analysis suggests that the number of ties in China's DVC spatial correlation network grew slowly but still falls significantly short of the ideal number. Additionally, there is an increase in the network density of China's DVC over the years, providing strong evidence of spatial spillover effects within the network. (3) The block roles of the central and western regions are main inflow and bidirectional spillover while the block roles of the eastern region are agent and main outflow. (4) The main driving factors of DVC in China are investment in information infrastructure and fiscal expenditure on education. Bivariate enhancement effect and nonlinear enhancement were found to exist in all interactions of indicators. These findings offer theoretical insights and practical directives for improving DVC in China and its synergistic effects.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Zhang, Zhou. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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