Collect | Homepage Advertisement | Contact us | Message

Arid Land Geography ›› 2026, Vol. 49 ›› Issue (10): 1993-2005.doi: 10.12118/j.issn.1000-6060.2025.474

• Digital Empowerment for Rural Development • Previous Articles     Next Articles

Spatiotemporal variations in the level of digital village construction in China and heterogeneity of its driving factors

LEI Ting1(), ZHANG Lina1(), LU Hao1,2   

  1. 1 School of Politics and Public Administration, Xinjiang University, Urumqi 830046, Xinjiang, China
    2 Institute of Rural Revitalization, Xinjiang University, Urumqi 830046, Xinjiang, China
  • Received:2025-08-06 Revised:2025-09-12 Online:2026-10-25 Published:2026-10-26
  • Contact: ZHANG Lina E-mail:ltvzbj@126.com;107552300173@stu.xju.edu.cn

Abstract:

The development of digital villages is a key initiative for achieving the deep integration of rural revitalization and the Digital China strategy, as well as the technological application and paradigm innovation of new productive forces in the agricultural and rural sectors. During the 15th Five-Year Plan period, advancing digital village construction must fully implement the value orientation and policy requirements of “developing new productive forces based on local conditions”. Based on data from 30 provincial-level regions in China spanning 2015—2023, this study constructs a comprehensive evaluation system. It employs the CRITIC-entropy weighting method to measure development levels and utilizes multiple spatial models to empirically examine the spatiotemporal patterns and driving mechanisms of digital village construction across China’s provincial-level regions. The findings are as follows: (1) The development levels indicate sustained growth but decelerating rates, exhibiting an east-high, west-low spatial gradient pattern. (2) Significant spatial agglomeration and “club convergence” effects exist among provincial-level regions, with widening absolute regional disparities and emerging polarization risks within the western regions. (3) Rural digital infrastructure construction, local fiscal investment in science and technology, and digital transaction levels exert stable positive driving effects, while having spatial spillover and competitive effects among neighboring regions. (4) Spatiotemporal geographically weighted regression models further reveal significant spatiotemporal heterogeneity in the intensity and direction of these factors, depicting differentiated patterns of dominant driving mechanisms across regions. These findings provide a reference for implementing tailored strategies and precise resource allocation in digital village construction.

Key words: new quality productivity, spatial pattern, driving factors, adaptation to local conditions, heterogeneity analysis