科研管理

科研管理 ›› 2025, Vol. 46 ›› Issue (7): 24-35.DOI: 10.19571/j.cnki.1000-2995.2025.07.003

• 论文 • 上一篇    下一篇

中国城市数实融合水平的时空演化及影响因素研究

郭东,李琳,庞国光   

  1. 湖南大学经济与贸易学院，湖南 长沙410079
  • 收稿日期:2024-06-27 修回日期:2024-11-28 出版日期:2025-07-20 发布日期:2025-07-14
  • 通讯作者: 李琳
  • 基金资助:
    国家社会科学基金后期资助项目:“长江中游城市群创新共同体演化机制与治理体系研究”（22FJLB007，2022—2024）；湖南省研究生科研创新基金项目：“数实融合赋能城市经济韧性的机理、效应及政策研究”（LXBZZ2024055,2024—2025）。

Research on the spatial-temporal evolution of digital-real integration  levels in Chinese cities and its influencing factors

Guo Dong, Li Lin, Pang Guoguang   

  1. School of Economics and Trade, Hunan University, Changsha 410079, Hunan, China
  • Received:2024-06-27 Revised:2024-11-28 Online:2025-07-20 Published:2025-07-14

摘要：     测度中国城市数实融合发展水平并分析其时空演化与影响因素，能为加快推动数字中国战略提供量化支撑。以中国283个城市为研究对象，文章通过采用熵值法、耦合协调度、核密度、Dagum基尼系数和空间计量模型等方法，探究2011—2021年中国城市数实融合的时空演变特征及影响因素。结果表明：（1）样本期内，数实融合水平呈逐年上升的趋势，但整体水平相对较低，区域间差距扩大趋势明显；东部领先，中部、西部和东北较为滞后，但在空间上呈多点开花、由点到线再到面的空间演化特征。（2）四大区域在波峰移动、分布趋势、极化情况上均有各自演化形态，整体演化趋势良好，但出现了轻微的两极分化现象。（3）数实融合的相对差异呈现先缩小后扩大的特征，且区域间差异是造成总体差异变化的主要来源。（4）数实融合水平呈现明显的“集聚俱乐部”趋势，空间集聚类型以“高高”和“低低”集聚为主，且集聚趋势相对稳定。（5）影响因素分析显示，经济基础、融资约束、产业支撑、政府支持和创新能力均能显著提高数实融合水平，但人才保障的促进作用不显著。

关键词: 数实融合, 水平测度, 时空演化, 区域差异, 影响因素

Abstract:     Measuring the development level of digital-real integration in Chinese cities and analyzing its spatial-temporal evolution and influencing factors can provide quantitative support for accelerating the Digital China strategy. Taking 283 cities in China as the research objects, this paper explored the spatial-temporal evolution characteristics and influencing factors of digital-real integration in Chinese cities from 2011 to 2021 by adopting methods such as entropy method, coupling coordination degree, kernel density, Dagum Gini coefficient, and spatial econometric model. The results showed that: (1) during the sample period, the level of digital-real integration shows an increasing trend year by year, but the overall level is relatively low, and the trend of widening inter-regional gaps is evident; the eastern region leads, while the central, western, and northeastern regions lag behind, but spatially exhibit the characteristics of blossoming in multiple points, evolving from points to lines and then to planes; (2) the four major regions have their respective evolution patterns in terms of peak shifts, distribution trends, and polarization, with an overall good evolution trend but a slight polarization phenomenon emerging; (3) the relative differences in digital-real integration show a characteristic of first narrowing and then expanding, and the inter-regional differences are the main source of the overall differences; (4) the level of digital-real integration exhibits a pronounced "agglomeration club" trend, with "high-high" and "low-low" agglomerations dominating the spatial agglomeration types, and the agglomeration trend is relatively stable; and (5) the analysis of influencing factors reveals that economic fundamentals, financing constraints, industrial support, government support, and innovation capabilities can significantly improve the level of digital-real integration, but the role of talent security in promoting it is not significant.

Key words: digital-real integration, level measurement, spatial-temporal evolution, regional difference, influencing factor