In the context of global climate change, coastal urban agglomerations encounter numerous challenges, especially under the condition of rapid urbanization, which consistently threatens human life, property, and well-being. This paper presents the Ecological-Risk Constrained Land Expansion Modeling Framework to explore the land expansion patterns of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) coastal urban agglomeration from a climate adaptation perspective. We utilized InVEST and GIS to quantify the ecosystem services and coastal vulnerability index (CVI) of urban agglomerations, delimiting the ecological resilience regions and risk resilience regions as constraints for land expansion simulation. Eventually, we simulated five scenarios of land expansion in the GBA in 2035 using the patch-generating land use simulation (PLUS) model. The results reveal that: (1) The low-resilience ecological spaces are radially distributed in a cross shape along the Pearl River, Hongliqi River and Xijiang River, with Guangzhou, Foshan and Dongguan overlapping more with the regions that undergo severe loss of ecosystem services and a high degree of landscape fragmentation. (2) The medium–high resilience risk space constitutes approximately 19.4%, and coastal areas such as Tolo Harbour in Pak Shek Kok, Hong Kong, and Ma On Shan New Town are exposed to potential marine disaster risks. (3) The hotspot areas of land expansion in the five scenarios indicate that the construction land of future urban agglomerations will expand towards the southeast. (4) From the perspective of balancing ecological benefits and economic benefits, the ecology and risk integrated coordination (ERIC) scenario provides a superior balance. These results emphasize the imperative of coordinating ecological protection and risk avoidance strategies in future urban expansion to achieve the safe, compact and sustainable development of coastal urban agglomerations.

The impact of urban morphology characteristics on regional thermal environments is a crucial topic in urban planning and climate adaptation research. However, existing studies are often limited to a single dimension and fail to fully reveal the spatiotemporal impact mechanisms of multi-dimensional urban morphology on thermal environments and their connection to regional planning policies. This study focuses on the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), combining quantitative data from landscape pattern indices, land use expansion patterns, and local climate zones (LCZs) derived from 2000 to 2020. By using geographically weighted regression and spatial autocorrelation analysis, we systematically explore the spatiotemporal effects and mechanisms of multi-dimensional urban morphology characteristics on regional thermal effects. We found the following points. (1) Built-up land patch density is significantly positively correlated with LST, with the urban heat island (UHI) effect spreading from core areas to the periphery; this corroborates the thermal environment differentiation features under the “multi-center, networked” spatial planning pattern of the GBA. (2) Outlying expansion mitigates local LST rise through an ecological isolation effect, and infill expansion significantly exacerbates the UHI effect due to high-intensity development, reflecting the differentiated impacts of various expansion patterns on the thermal environment. (3) LCZ spatial distribution aligns closely with regional planning, with the solar radiation shading effect of high-rise buildings significantly cooling daytime LSTs, whereas the thermal storage properties of traditional building materials and human heat sources cause nighttime LST increases; this reveals the deep influence of urban morphology mechanisms, building materials, and human activities on thermal environments. The findings provide scientific support for achieving a win–win goal of high-quality development and ecological security in the GBA while also offering a theoretical basis and practical insights for thermal environment regulation in high-density urban clusters worldwide.

With China’s urbanization advancing into an advanced phase, urban clusters experiencing swift expansion of urban land have emerged as “disaster areas” where ecological environment problems are highly concentrated. Therefore, paying attention to the habitat quality of urban agglomerations and their ecological cooling effect is crucial for improving the quality of the living environment and fostering sustainable development in the region. This research uses the Guangdong-Hong Kong-Macao Greater Bay Area (GHM-GBA) as a case study, applies the InVEST platform, Geographic Information System (GIS), and Binary Moran’s I index, and undertakes an investigation into the spatial interplay between the habitat quality pattern and the thermal environment within the urban clusters of the GHM-GBA between 2000 and 2020. Firstly, utilizing data on habitat threat density, the Habitat Quality Module on the InVEST platform is employed to measure the habitat quality and degradation extent of urban agglomerations. Secondly, based on MODIS satellite sensor data, the surface temperature is inverted and interpreted, and the spatial heterogeneity traits of the urban agglomeration’s thermal environment are examined. Then, the Binary Moran’s I index is used to clarify the spatial clustering and spatial dispersion relationship characteristics between habitat quality and thermal environment, including the network spatial distribution of regions with positive spatial correlation and negative spatial correlation. Ultimately, through the lens of urban design, this paper puts forward detailed planning recommendations to improve the quality of living spaces and thermal comfort within urban clusters. These suggestions serve as a reflection for the ecological sustainability, livability, and high-quality growth of the GHM-GBA.

Ecological security pattern (ESP) planning typically emphasizes ecological structure and connectivity, but may provide limited information on long-term functional degradation occurring within ecologically important areas. To address this gap, this study develops a baseline-referenced framework for diagnosing ecological resilience degradation (ERD) by integrating ecosystem service value (ESV) dynamics, minimum cumulative resistance (MCR) modeling, and explainable machine learning (XGBoost–SHAP). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, ERD was operationally defined as long-term functional degradation within ecologically important components of the 2000 baseline network, with net ESV decline from 2000 to 2020 used as the functional-degradation signal. Two complementary ERD patterns were distinguished: source erosion and corridor interruption. The results showed that ERD exhibited a pronounced core–periphery pattern, with higher degradation intensity concentrated along the Guangzhou–Foshan–Dongguan–Shenzhen urban development corridor. Integrated ERD covered 5452.69 km², of which source erosion accounted for 4238.32 km² (77.73%) and corridor interruption for 1214.37 km² (22.27%). Source erosion represented the dominant ERD pattern in terms of spatial extent. The XGBoost model showed moderate predictive performance under spatial block cross-validation (mean validation R² = 0.638 ± 0.043), and SHAP analysis indicated that vegetation condition, proximity to water bodies, nighttime light, and elevation were among the most influential factors associated with spatial variation in ERD intensity. NDVI contributions shifted from positive to negative around 0.65, while the distance-to-water response changed most rapidly within 200–300 m. These values are interpreted as empirical transition ranges in the model response. Overall, the proposed framework links long-term ecosystem functional degradation with baseline ecological network position and provides a spatially explicit basis for identifying functionally degraded ecological areas and supporting differentiated spatial prioritization and ecological management.