Numerous studies have explored the impact of urban morphology and geometry on outdoor thermal comfort, intending to provide practical guidelines for urban designers. However, research findings have been inconsistent, in part due to differences in the climatic settings and the investigated heat-stress indicators. This study proposes a parametric-simulation framework to observe the behavior of thermal comfort according to the possible combinations of building density (BD), street aspect ratio (AR), and orientation. Conducted specifically under a hot-and-humid tropical-savanna summer condition, the study found that building density and aspect ratio were negatively correlated to the Universal Thermal Climate Index (UTCI), with R² coefficients of 0.99 and 0.91, respectively. The UTCI was improved by a 1.0 °C per 10% increase in BD and by a 1.02 °C per unit of AR increase. The performance of street orientation was significantly influenced by wind direction, and strong inter-influences were found between the three morphology factors. These findings are useful guidelines not only for designing urban morphology but also for intuitively identifying the need for complementary vegetation and cooling materials when morphology indicators cannot reach their efficiency targets (e.g., when AR < 3.0 or building density is limited by local regulations and project specifications).

This study presents an innovative approach to mitigating the urban heat island (UHI) effect by constructing a cold island spatial pattern (CSP) from the perspective of landscape connectivity, integrating three-dimensional (3D) urban morphology and meteorological factors for the first time. Unlike traditional studies that focus on isolated patches or single-city scales, we propose a hierarchical framework for urban agglomerations, combining morphological spatial pattern analysis (MSPA), landscape connectivity assessment, and circuit theory to a construct CSP at the scale of urban agglomeration. By incorporating wind environment data and 3D building features (e.g., height, density) into the resistance surface, we enhance the accuracy of cooling network identification, revealing 39 cold island sources, 89 cooling corridors, and optimal corridor widths (600 m) in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA). Ultimately, a three-tiered heat island mitigation framework for urban agglomerations was established based on the CSP. This study offers an innovative perspective on urban climate adaptability planning within the context of contemporary urbanization. Our methodology and findings provide critical insights for future studies to integrate multiscale, multidimensional, and climate-adaptive approaches in urban thermal environment governance, fostering sustainable urbanization under escalating climate challenges.

Green space improves outdoor thermal comfort and promotes the residents’ physical and mental health. Currently, many cities are using green volume indicators to evaluate the quality of residential green space and to help form the latest evaluation standards. However, from the perspective of plant green volume, the impact of green space structures on thermal mitigation has not been emphasized. Furthermore, the cost of green space will change with the green space structures. Therefore, we evaluate the impact of changes in green space structures on thermal mitigation and costs from the perspective of plant green volume and aim to find suitable allocations of plant green volume, which balance thermal mitigation with cost savings. This study analyzed the regional climate and residential green space structures. Then, we discussed the relationship between thermal mitigation and the costs of different structures. The analytical results indicated that thermal comfort improves as the green volume of trees increases, with costs also increasing. Where the green volume exceeds 70%, no “extreme heat stress” moments occur for the whole day. When the green volume of trees increases from 20% to 60%, the thermal mitigation is 3.62 °C. However, the mitigation is only 1.74 °C when the green volume increases from 60% to 100%, which is less than half of the former, numerically. This study suggests that the green volume of trees in residential green spaces should be controlled at 60%–70% and the corresponding green volume of shrubs should be 30%–40%. This study rationally balances the thermal mitigation and costs of residential green space, which will directly guide residential green space planning in real situations.

Urban vegetation coverage is a core index in urban planning, which has been confirmed to be an effective indicator for the urban thermal environment. Through the urban thermal environment, this study aims to further quantify the impact of vegetation coverage on urban energy consumption. Chenzhou, a typical hilly city, was selected as the study object for its diversified vegetation coverages. Remote sensing technology and correlation and regression models were employed in this study. Firstly, the data of land surface temperature and vegetation coverage were calculated with remote sensing technology, followed by data analysis with the correlation and regression models. Then, employing the “λ-T” model, a statistical model corresponding to urban temperature and energy, this study clarified the impact of temperature on urban energy consumption. Finally, through urban temperature, this study analyzed the impact of urban greening coverage on urban energy consumption. This study shows that when the temperature ranges from 22 °C to 28.9 °C, every 10% of additional vegetation coverage will reduce the air conditioning energy demands by 5.5%, and when the temperature is between 28.9 °C and 37 °C, every 10% of additional vegetation coverage will reduce the mean air conditioning energy demands by 2.4%.

Land use and land cover change represent major anthropogenic carbon emission sources, yet most existing studies on landscape patterns and carbon emissions predominantly focus on plain urban agglomerations, with limited empirical evidence from terrain-restricted hilly regions. Taking Xuancheng, a typical hilly city in the Yangtze River Delta, as the study area, this paper utilized seven time-series of Landsat remote-sensing datasets spanning 1990–2020. Integrating the land use dynamic degree, transfer matrix, landscape pattern indexes, calibrated carbon coefficients, Spearman correlation analysis and grey relational analysis, this study explored the associations between land use patterns and carbon emissions. Results show that built-up areas expanded 4.08-fold in the past three decades and emerged as the dominant carbon source, while forests maintained persistent carbon sequestration. The largest patch index of built-up area exhibited the strongest correlation with carbon emissions; cultivated land and forest displayed temporally synchronous fluctuations with emissions rather than exerting independent causal effects. Terrain constraints drive axial urban sprawl along transport corridors, elevating correlations of edge-related indexes and generating carbon response patterns distinct from those observed in plain cities. The two-step correlation analysis framework proved suitable for small-sample long-term datasets. These findings suggest that curbing contiguous built-up area expansion, optimizing urban morphology, and strengthening ecological connectivity should be prioritized in low-carbon spatial planning for hilly cities.

Surface water is an important component of urban land-cover systems, but its relationship with land surface temperature (LST) is difficult to interpret in lake-rich urban agglomerations where water bodies, shorelines, vegetation, built-up land, and terrain are closely interwoven. This study examined the association between surface-water fraction and daytime LST in the Wuhan Urban Agglomeration, China, across six benchmark years from 2000 to 2025. MODIS daytime LST, Landsat-derived water maps, GHSL built-up data, vegetation indices, and terrain variables were integrated on a 250 m grid. Surface-water fraction was measured within each grid cell and surrounding windows from 250 to 2000 m. Summer and winter models were compared, and between-location differences were separated from within-location water-fraction changes. Spatial validation, city-specific models, and robustness checks were used to assess consistency. Higher surface-water fraction was associated with lower daytime LST in both seasons. The strongest standardized association occurred at 500 m in summer, whereas the winter association strengthened toward 2000 m. Between-location differences were much larger than within-location changes, and the negative association was retained across all cities. These results suggest that surface-water fraction should be interpreted as a scale- and season-dependent land-cover indicator rather than as a fixed cooling measure.