长江流域资源与环境 >> 2024, Vol. 33 >> Issue (10): 2150-2164.doi: 10.11870/cjlyzyyhj202410007

• 自然资源 • 上一篇    下一篇

人工智能发展水平时空差异及影响因素研究——以长江经济带三大城市群为例

付华健1,蒋兵1,张力元2   

  1. (1.山东理工大学管理学院,山东 淄博 255000;2.长江大学经济与管理学院,湖北 荆州 434000)

  • 出版日期:2024-10-20 发布日期:2024-11-07

Temporal and Spatial Differences in the Development Level of Artificial Intelligence: A Case Study of the Three Major Urban Clusters in the Yangtze River Economic Belt

FU Hua-jian1, JIANG Bing1, ZHANG Li-yuan2   

  1. (1. School of Management, Shandong University of Technology, Zibo 255000, China;2. Yangtze University, School of Economics and Management, Jingzhou 434000, China)

  • Online:2024-10-20 Published:2024-11-07

摘要: 长江经济带作为全国高质量发展的重要增长极,探究其人工智能发展水平对构建中国人工智能发展新格局意义重大。通过构建人工智能发展水平综合评价指标体系,在利用纵横向拉开档次法测度2010~2020年长江经济带三大城市群71个城市人工智能发展水平的基础上,进一步采用Dagum基尼系数、核密度估计、空间马尔科夫链和空间杜宾模型对其差距贡献、动态演进趋势和影响因素进行了深入研究。研究表明:(1)长江经济带三大城市群的人工智能发展水平均呈稳定上升趋势,其中长三角城市群处于绝对领先地位,成渝城市群次之,长江中游城市群紧随其后,整体上人工智能发展水平的空间分布格局由“V”字型分布逐渐向上中下游依次递增的格局转变;(2)长三角城市群和长江中游城市群在研究期内整体基尼系数有所下降,而成渝城市群则呈上升态势,三大城市群间人工智能发展水平的整体差距有所扩大,城市群间差距是造成人工智能发展水平空间非均衡性的主要原因;(3)各城市群人工智能发展“极化”效应显著,长三角城市群和长江中游城市群均表现为“多极”分化,成渝城市群则由“多极”分化逐步发展为“两极”分化;(4)不同等级的人工智能发展具有明显的转移“惰性”,人工智能发展水平较高的城市对于周边城市的人工智能发展具有明显的空间作用,虹吸效应与溢出效应并存;(5)区域人口密度、产业升级、经济发展水平和市场化水平对长江经济带人工智能发展具有显著促进作用,但科技金融具有显著抑制作用,各影响因素的作用效果具有明显的区域异质性。


Abstract: The Yangtze River Economic Belt, as a crucial driving force for the high-quality development of the entire nation, holds significant implications for the establishment of a new paradigm in China's artificial intelligence (AI) development. Employing a comprehensive evaluation index system for AI development, this study utilized a stepwise longitudinal and latitudinal approach to appraise the AI development levels across 71 cities in the three principal city clusters of the Yangtze River Economic Belt from 2010 to 2020. Furthermore, sophisticated methodologies such as the Dagum Gini coefficient, kernel density estimation, spatial Markov chain, and spatial Durbin model were leveraged for an in-depth exploration of the disparities, dynamic evolution trends, and the factors affecting AI development. The findings revealed that: (1) AI development levels demonstrated a consistent upward trajectory. Notably, the Yangtze River Delta city cluster held a leading role, followed by the Chengdu-Chongqing city cluster, and the Yangtze River Midstream city cluster. Overall, the spatial distribution pattern of AI development shifted from a "V"-shaped distribution to an increasing pattern from upstream to downstream; (2) Gini coefficients for the Yangtze River Delta and Yangtze River Midstream city clusters decreased during the study period, while the Chengdu-Chongqing city cluster exhibited an increasing trend. The widening gaps of AI development levels among the three city clusters were the major contributor to spatial unevenness; (3) Notable "polarization" effects were observed in the AI development of each city cluster. Both the Yangtze River Delta and Yangtze River Midstream city clusters exhibited "multipolar" differentiation, while the Chengdu-Chongqing city cluster was in the transition from "multipolar" differentiation to "bipolar" differentiation; (4) Different levels of AI development exhibited a distinct transfer "inertia," with cities boasting higher AI development levels exerting a substantial spatial influence on the AI development of surrounding cities. Both siphon and spillover effects coexisted; (5) The regional population density, level of economic development, industrial upgrading, and marketization significantly promoted the development of artificial intelligence in the Yangtze River Economic Belt. However, the development of science and technology finance exerted a notable inhibitory effect.


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