Vegetation coverage is a critical indicator for assessing urban ecosystems and is essential for sustainable development. However, the evolution patterns and driving mechanisms of vegetation change at the urban agglomeration scale remain underexplored. This study used the Google Earth Engine (GEE) to compute the kernel Normalized Difference Vegetation Index (kNDVI) for the Beibu Gulf Urban Agglomeration (BGUA), an important emerging coastal urban cluster in southern China, from 2000 to 2022. Trend analysis was employed to examine spatiotemporal changes in kNDVI, and an interpretable machine learning framework was applied to quantify the nonlinear, spatially heterogeneous effects of environmental and anthropogenic drivers. The results show that (1) kNDVI showed a general increasing trend, with medium-to-high kNDVI predominating. Approximately 91.91% of the region maintained an improving trend, whereas vegetation degradation concentrated in the core urban areas. (2) The Categorical Boosting model demonstrated superior performance in predicting kNDVI compared to other machine learning models. (3) The SHAP analysis identified land cover, elevation, and nighttime lights as the primary determinants of kNDVI change. These factors exhibited significant spatial heterogeneity in their nonlinear effects. These findings provide theoretical insights and practical guidance for ecological planning and environmental management in urban agglomerations.

Balancing urban resilience (UR) and urban efficiency (UE) is essential for achieving sustainable development within China’s high-density urban agglomerations. We investigate their synergistic relationship in the Yangtze River Delta urban agglomeration, one of the world’s most densely populated regions. UR and UE between 2010 and 2022 were quantified via an integrated AHP-EWM-TOPSIS approach and the Super-EBM model. The improved Haken model was utilized to describe their synergistic relationship. Furthermore, the dynamic evolution and transition mechanisms of UR-UE synergy were examined from the perspective of spatiotemporal interactions. The key findings are as follows: (1) UR acts as the dominant order parameter driving sustainable development. (2) Synergy levels remain suboptimal throughout the study period, with a spatial distribution pattern of “core metropolitan-driven, provincial capital-synergized.” (3) Four distinct transition patterns were identified, demonstrating significant path dependency and spatial lock-in effects in synergistic state evolution. This study establishes UR as the dominant factor for sustainability in mature, high-density agglomerations. The findings advance understanding of complex system dynamics in such regions and provide a theoretical foundation for diagnosing developmental bottlenecks, designing targeted policies to optimize UR-UE synergy, and fostering sustainable development.