湖北农业科学 ›› 2025, Vol. 64 ›› Issue (7): 230-238.doi: 10.14088/j.cnki.issn0439-8114.2025.07.039

• 经济·管理 • 上一篇    下一篇

广东省数字乡村发展水平时空演变及驱动因素

唐威, 包乌兰托亚   

  1. 青岛农业大学经济管理学院(合作社学院),山东 青岛 266109
  • 收稿日期:2025-01-20 出版日期:2025-07-25 发布日期:2025-08-22
  • 通讯作者: 包乌兰托亚(1984-),女(蒙古族),内蒙古兴安盟人,副教授,博士,主要从事农业经济与乡村发展研究,(电子信箱)201301007@qau.edu.cn。
  • 作者简介:唐 威(2000-),男,广东肇庆人,在读硕士研究生,研究方向为农业经济,(电子信箱)1946360377@qq.com。
  • 基金资助:
    教育部人文社会科学项目(24YJC790005); 山东省社会科学规划研究项目(24CRWJ11)

Spatiotemporal evolution and driving factors of digital village development in Guangdong Province

TANG Wei, BAO Wulantuoya   

  1. College of Economics and Management (College of Cooperatives), Qingdao Agricultural University, Qingdao 266109, Shandong, China
  • Received:2025-01-20 Published:2025-07-25 Online:2025-08-22

摘要: 以2013—2022年广东省除深圳外的20个地级市为研究对象,构建涵盖5大维度的数字乡村发展水平评价指标体系,采用熵权TOPSIS法对发展水平进行测度,并运用Kernel密度估计法、Moran’s I指数与地理探测器模型系统分析其时空演化特征与驱动因素。结果显示,广东省数字乡村发展水平整体持续上升,但区域差异明显,珠三角地区发展领先,粤东西北地区存在“数字鸿沟”;数字乡村发展水平呈现稳定的空间集聚特征,“高-高”聚集区主要集中于珠三角地区,存在结构性“跃迁”城市;数字乡村发展的驱动机制呈现从政府支持等外生驱动向创新投入、农业水平等内生驱动转变趋势。最后,针对广东省区域差异、政策联动与创新驱动等方面提出有针对性的对策建议。

关键词: 数字乡村, 发展水平, 时空演变, 驱动因素, 广东省

Abstract: The 20 prefecture-level cities in Guangdong Province (excluding Shenzhen City) from 2013 to 2022 were taken as the research objects. An evaluation index system for digital village development covering five key dimensions was constructed. The entropy-weighted TOPSIS method was adopted to measure the development levels. The Kernel density estimation method, Moran’s I index, and the GeoDetector model were utilized to systematically analyze the spatiotemporal evolution characteristics and driving factors. It was shown that the overall development level of digital villages in Guangdong Province had been continuously rising. However, significant regional disparities existed, with the Pearl River Delta region taking the lead and the eastern, western, and northern regions of Guangdong facing a “digital divide”. The development level of digital villages demonstrated stable spatial agglomeration characteristics. “High - high” clusters were mainly concentrated in the Pearl River Delta region, and there were cities with structural “leapfrogging”. The driving mechanism of digital village development showed a trend of transformation from exogenous drivers such as government support to endogenous drivers such as innovation input and agricultural level. Finally, targeted countermeasures and suggestions were put forward regarding regional differences, policy linkage, and innovation-driven development in Guangdong Province.

Key words: digital village, development level, spatiotemporal evolution, driving factors, Guangdong Province

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