Wenxi Zhang

The coupling coordination of urbanization and urban resilience is a key issue for achieving sustainable urban development. Taking 49 prefecture-level and above cities in the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD)—China’s three major coastal urban agglomerations—as research objects, this paper constructs an evaluation index system for urbanization and urban resilience, and employs the CRITIC–entropy weight composite method, the coupling coordination degree model and kernel density estimation to measure the evolutionary characteristics of coupling coordination between urbanization and urban resilience in the three agglomerations during 2013–2023, and further identifies the key influencing factors using a panel fixed-effects model. The results show that (1) the coupling coordination degree of the three urban agglomerations increased continuously, with the PRD taking the lead in 2023 (0.6650), the YRD ranking second (0.6315) and the BTH ranking third (0.5627); however, their coupling coordination paths diverged markedly, specifically manifesting as a catch-up convergence pattern in the BTH, a balanced advancement pattern in the YRD and a polarization-divergence pattern in the PRD. (2) Opening-up and digital economy development are robust positive drivers of the coupling coordination level, and their positive effects are largely universal across regions; urban population agglomeration and government intervention constitute significant negative constraints, among which the inhibitory effect of government intervention exhibits genuine regional heterogeneity, being most prominent in the YRD, followed by the PRD, while failing the significance test in the BTH; technological innovation fails to pass the significance test; the full-sample effect of spatial structure is insignificant, but its influence exhibits significant regional heterogeneity, with a significantly positive association in the PRD. This study systematically reveals the regional differentiation rules governing the synergistic evolution of urbanization and resilience in the three urban agglomerations, providing empirical support for differentiated policies to promote the deep integrated development of urbanization and urban resilience.

Industrial land allocation spatial morphology (ILASM) is crucial for economic development and the advancement of new-type industrialization. However, existing studies lack a comprehensive understanding of the evolutionary characteristics of the spatial morphology of industrial land allocation, let alone its driving factors and the spatial heterogeneity of their effects. Therefore, this paper classifies industrial land allocation spatial morphology into traditional industrial land allocation spatial morphology (TILASM) and high-tech industrial land allocation spatial morphology (HILASM), then evaluates and identifies their characteristics based on the precise geographic coordinates of each industrial land parcel between 2007 and 2024 in the Yangtze River Delta (YRD). Subsequently, the Random Forest Regression model and Multi-Scale Geographically Weighted Regression model are integrated to systematically investigate the driving factors and spatiotemporal patterns of ILASM, including both TILASM and HILASM. The results show that from 2007 to 2024, different types of ILASM exhibited distinct spatiotemporal evolutionary characteristics. Specifically, first, in terms of the evolution of spatial distribution direction, overall industrial land allocation exhibited a pronounced agglomeration pattern, extending from the western (slightly northern) part of the region to the eastern (slightly southern) part. Moreover, the evolutionary direction of traditional industrial land allocation was consistent with that of overall industrial land allocation. However, high-tech industrial land allocation exhibited an agglomeration trend extending from west (slightly south) to east (slightly north). Second, in terms of evolution of agglomeration pattern, the spatial distribution of TILASM evolved from three-core dispersed configuration to a multi-core linkage, before reverting to a multi-core dispersed state; by contrast, both ILASM and HILASM exhibited a spatial pattern that progressed from dispersion to contiguous agglomeration. Third, the spatial distribution characteristics of different types of ILASM were shaped by the combined influence of natural conditions, economic development, social environment, innovation environment and infrastructure. However, the dominant driving factors differed among them. Specifically, the number of foreign-invested enterprises exhibited a negative influence on ILASM, while having positive effects on both TILASM and HILASM. The effects of patent applications, population density and internet penetration rate on ILASM; foreign-invested level and slope proportion on TILASM; as well as road density, labor quality and foreign invested level on HILASM all exhibited U-shaped relationships. Moreover, the influences of opening-up level and per capital road area on ILASM and HILASM displayed relatively complex N-shaped relationships. Finally, the effects of these crucial drivers displayed significant spatial non-stationarity and certain gradient effects, manifesting in southern–northern, western–eastern and core–periphery spatial differentiation patterns. Overall, this study provides scientific evidence and practical references for optimizing the spatial allocation of industrial land.