| 摘要: |
| 作为数字经济时代的核心生产要素,数据要素在省域层面的发展水平及其时空演化特征,直接影响全国数字经济协调发展的整体进程。基于2011—2023年我国30个省份的面板数据,从基础配置、价值转化、行业渗透、标准管理4个核心维度切入,构建省域数据要素发展水平评价指标体系,并运用核密度估计、Dagum基尼系数分解及空间分析等方法,系统探究省域数据要素发展水平的差异来源与演进特征。研究发现:我国数据要素发展水平总体呈持续上升态势,整体差距呈波动下降的收敛特征；差异来源方面,区域间差异为总体差异的首要贡献者,其中“东部—西部”差距最为显著,区域内差异呈现西部地区差异最大、中部次之、东部最小的分布格局；在空间分布上,呈现“东强西弱、梯度递减”的格局。 |
| 关键词: 数据要素发展水平 区域差异 Dagum基尼系数 核密度估计 |
| DOI：10.13216/j.cnki.upcjess.2026.02.0011 |
| 分类号:F49 |
| 基金项目:国家社会科学基金一般项目(23BGL003) |
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| Chinas Data Element Development from a Provincial Perspective:Quantitative Measurement, Disparities, and Evolutionary Characteristics |
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JU Keyi, ZHANG Liangchen, LIU Hailing
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(School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, Jiangsu, China)
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| Abstract: |
| As a core production factor in the digital economy era, the development level of data elements at the provincial level and its spatiotemporal evolution characteristics directly influence the overall process of coordinated development of the national digital economy. Based on panel data from 30 provinces in China from 2011 to 2023, this study constructs an evaluation index system for the development level of provincial data elements from four core dimensions:infrastructure configuration, value transformation, industry penetration, standard management. Employing methods such as kernel density estimation, Dagum Gini coefficient decomposition, and spatial analysis, it systematically investigates the sources of disparities and evolutionary characteristics in the development level of provincial data elements. The findings reveal that the overall development level of data elements in China exhibits a sustained upward trend, with the overall disparity showing a fluctuating yet convergent decline. Regarding the sources of disparity, inter-regional differences are the primary contributor to the overall disparity, with the "eastern-western" gap being the most significant. Intra-regional disparities are characterized by the western region exhibiting the largest disparity, followed by the central region, while the eastern region shows the smallest. In terms of spatial distribution, a pattern of "strong in the east, weak in the west, with a gradient decline" is observed. |
| Key words: development level of data elements regional disparity Dagum Gini coefficient Kernel density estimation |
