RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2024, Vol. 33 >> Issue (10): 2150-2164.doi: 10.11870/cjlyzyyhj202410007

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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

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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