As China’s urbanization has shifted from high-speed to high-quality development, Urban Land Use Efficiency (ULUE) has become an important scale for evaluating urban connotative development. However, existing research has paid less attention to errors caused by different urban environmental factors and random disturbances in ULUE. Therefore, the purpose of this study is to eliminate the impact of environmental factors and random disturbances on ULUE measurement results by placing different cities under the same environmental conditions. First, a three-stage DEA envelopment analysis (T-DEA) model is introduced to calculate the ULUE of 16 prefecture-level cities in Anhui Province from 2001 to 2020. On this basis, the kernel density estimation model, gravity center model, and geographic detector models are used to study the spatial and temporal evolution and driving factors. The results show that (1) the ULUE increases nonlinearly with time, with an increase of 12.74%; (2) the overall peak of ULUE is on the rise, and changes from a single peak to a multi-peak, indicating that ULUE is constantly improving and that there is multi-level differentiation between different cities; (3) during the study period, the center of gravity of the ULUE value moved 22.66 km to the northwest; the overall moving distance was small, and the moving rate was slow; and (4) the influence of the interaction of double driving factors on ULUE is significantly greater than that of a single driving factor, and the factors of urban built-up area and degree of openness, as the key driving factors affecting ULUE, also have a degree of duality. In addition, to achieve efficient urban land use and to coordinate the environmental differences faced by different cities, the government must formulate systematic policies and development strategies considering the spatial characteristics of urban land use efficiency and the complexity of the driving factors.

The rational utilization of cultivated land resources is central to ensuring both ecological and food security in the Yangtze River Economic Belt (YREB), holding strategic significance for regional sustainable development. Using panel data from 2010 to 2023 for 130 cities in the YREB, this study examines a spatial correlation network (SCN) for non-grain land use (NGLU) and its driving forces via a modified gravity model, social network analysis (SNA), and quadratic assignment procedure regression. The results show the following: (1) The risk of NGLU continues to increase, with the spatial pattern evolving from a “single-peak right deviation” pattern to a “multi-peak coexistence” pattern featuring three-level polarization and gradient transmission, primarily driven by economic potential disparities. (2) The SCN has increased in density, but its pathways are relatively singular. Node functions exhibit significant differentiation, with high-degree nodes forming “control poles”, high-intermediate nodes dominating cross-regional risk transmission, and low-proximity nodes experiencing “protective marginalization”. Node centrality distribution is highly connected with the regional development gradient. (3) The formation of the spatial network is jointly driven by multiple factors. Geographical proximity, economic potential differences, comparative benefit differences, non-agricultural employment differences, and factor mobility all positively contribute to the spillover effect. Conversely, implementing cultivated land protection policies and the regional imbalance in local industrial development path dependence significantly inhibit the non-grain trend. This study further reveals that a synergistic governance system characterized by “axial management, node classification, and edge support” should be recommended to prevent the gradient risk transmission induced by economic disparities, providing a scientific basis for achieving sustainable use of regional cultivated land resources and coordinated governance of food security.

Human activity intensity has become an increasingly significant driver of changes in ecosystem services, and understanding their spatiotemporal relationships is essential for watershed ecological governance. Taking the Taihu Lake Basin as the study area, this research utilizes multi-source geospatial data from 2003 to 2023. The InVEST model is employed to assess four key ecosystem services: water yield, soil conservation, carbon storage, and habitat quality. A human activity intensity (HAI) index is constructed using the entropy-weight method, while bivariate spatial autocorrelation and geographically weighted regression are applied to disentangle the spatiotemporal evolution, spatial associations, and local variations in the associations between HAI and ecosystem services. The results indicate that (1) HAI has continuously intensified, with high-intensity areas undergoing an approximately 9.5-fold expansion and exhibiting a distinct “high in the east, low in the west” pattern; (2) carbon storage and habitat quality show persistent declines, whereas water yield and soil conservation exhibit fluctuating upward trends, with an overall “high in the southwest, low in the northeast” distribution; (3) HAI is significantly positively correlated with water yield and negatively correlated with carbon storage, soil conservation, and habitat quality, with 2018 identified as a critical turning point; and (4) the associations exhibit notable spatial non-stationarity, with the proportion of significant grid cells following a hierarchical order of habitat quality > water yield > carbon storage > soil conservation. Zones with strong regression coefficients occur only as localized patches, while the majority of the basin is characterized by low-to-moderate driving intensity. This study further reveals three core mechanisms—land-use path dependence, policy-induced threshold responses, and spatial spillover associations—providing a scientific basis for differentiated ecological management and sustainable development in the Taihu Lake Basin.