Analysis of the Temporal and Spatial Pattern and Convergence Characteristics of High-Quality Sustainable Economic Development of Urban Agglomeration

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Abstract
Based on the sample data of 149 cities in ten major urban agglomerations from 2004 to 2019, the entropy method, Dagum Gini coefficient, and three-dimensional kernel density estimation method are used to calculate and describe the spatial pattern of the high-quality, sustainable economic development of these ten major urban agglomerations. We then use the spatial econometric model to estimate the β convergence trend within the urban agglomerations and among the urban agglomerations at different levels. Our main findings include the following: First, the urban agglomeration tends to develop a high-quality economy, but the gap between the urban agglomerations can be large. Second, although the gap within the overall group of urban agglomerations is expanding, the gap between high-quality individuals and the average is constantly shrinking; the gap between groups is still the leading cause of the spatial gap, with a contribution rate of 70.51%. Third, all urban agglomerations have an absolute and conditional β convergence trend, and the convergence speed presents the characteristics of “high level slow, low level fast”. Government intervention, financial development, urbanization, and human capital contribute to the high-quality, sustainable economic development of each urban agglomeration. There is a heterogeneous influence; there is also absolute and conditional β convergence among urban agglomerations at all levels, and the convergence rate presents a gradient characteristic of “third level > second level > first level”, and by balancing the financial relationships between city groups within each level, development differences can promote the dynamic coordination of high-quality, sustainable economic development rates.
Funding Information
  • general program of Shaanxi innovation support project, Shaanxi Provincial Science and Technology Department (2023-CX-RKX-135, 2023-CX-RKX-077, 2022D057, 23JX190)
  • Social Science Foundation of Shaanxi Province of China, the People’s Government of Shaanxi Province (2023-CX-RKX-135, 2023-CX-RKX-077, 2022D057, 23JX190)
  • Xi’an Municipal Social Science Foundation, the People’s Government of Xi’an municipal (2023-CX-RKX-135, 2023-CX-RKX-077, 2022D057, 23JX190)

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