Sustainable network analysis and coordinated development simulation of urban agglomerations from multiple perspectives

Abstract

Sustainable and coordinated development plays a crucial role in urban planning. However, previous studies have not fully explored the complex interplay between different factors that affect coordinated development within urban agglomerations. To address this gap, our study proposes a novel network-based approach that combines qualitative analysis and quantitative verification to assess the coordinated development of urban agglomerations. In this study, we focused on the Yangtze River Delta Urban Agglomeration (YRDUA) as our study area. Firstly, we constructed a network for sustainable development in urban agglomerations based on Sustainable Development Goals (SDG) data. Next, we analyzed the spatial patterns of intercity resource exchange flows to gain a more comprehensive and diversified perspective on regional coordinated development. By integrating qualitative and quantitative analysis, we then used the Exponential Random Graph Model (ERGM) to identify the driving factors of coordinated development. Finally, we optimized the development score of urban subgroups by using a greedy algorithm and determined the direction for coordinated development among different cities in the YRDUA. The findings of our study reveal that the development potential and optimal agglomeration pattern of urban subgroups in the YRDUA are closely linked to intercity cooperation, science and technology industries, foreign investment, and multiple interaction flows. Furthermore, our results suggest that local governments should focus on building a multi-core network flow pattern and fully utilizing the distinctive advantages of urban subgroups according to local conditions and time. These insights can guide policymakers and urban planners in designing more coordinated and efficient urban agglomerations.