Under the background of global climate change and China’s “carbon peak and carbon neutrality” strategy, it is of great significance to assess the carbon sink benefits of urban park plant communities. This study took 20 plant community plots of 20 m × 20 m (400 m²) in Tientsin Water Park as the research object. Carbon sequestration capacity was characterized by carbon stock (CS) and annual carbon sequestration (ACS), and six community structure indicators were quantified: Vegetation Coverage (VC), Canopy Density (CD), Three-Dimensional Green Volume (3DGV), Tree-to-Shrub Ratio (TSR), Vertical Complexity (CV), and Number of Individuals (N). Spearman correlation analysis, principal component analysis, and regression analysis were adopted, and K-means clustering was introduced to identify vegetation structure–function groups, thereby exploring the statistical correlations between these structural characteristic indicators and carbon sink capacity indicators (CS and ACS). The results showed that (1) VC, CD, and 3DGV were significantly positively correlated with CS, suggesting that these factors may be more conducive to long-term carbon pool accumulation; (2) N was significantly positively correlated with ACS, and a nonlinear decreasing trend was observed in the current observation data; (3) the influence of TSR and CV on carbon storage and sequestration also showed a nonlinear correlation. Based on the above correlation findings, the community combinations with higher carbon sink performance in this case were screened out. And suggestions for low-carbon configuration of plant communities, centered on optimizing canopy structure, configuring high-carbon-sequestration tree species, and regulating reasonable density, were proposed, which can be used as a reference for forming hypotheses in subsequent confirmatory studies.

Extreme heat increasingly threatens public health and urban sustainability, creating a need for fine-scale assessments that connect spatial risk patterns with climate-resilient planning. This study assessed heat risk across 1639 blocks in Tianjin’s six central districts, China. A block-scale heat-risk index (HRI) was constructed within the Hazard–Exposure–Vulnerability–Adaptability (HEVA) framework by integrating remote-sensing, population, built-environment, socioeconomic, and public-service data using a modified CRITIC weighting method. Differences in HRI among Local Climate Zones (LCZs) were examined, and three machine-learning models were compared. Among the three evaluated models, Random Forest showed the best overall test-set performance, and supplementary five-fold spatial block validation retained the same model ranking and major feature-importance ordering despite lower absolute predictive performance. Feature importance, partial dependence plots, and SHapley Additive exPlanations were subsequently used to interpret feature contributions and nonlinear associations. HRI was lower in the resource-rich urban core and higher in peripheral transitional zones, accompanied by clear spatial differences in adaptive-resource provision. The urban core generally exhibited higher adaptive-resource provision, whereas peripheral areas showed more limited provision. Under the adopted HRI formulation, higher values of this component contributed mathematically to lower composite HRI, rather than demonstrating a realised risk-reduction effect. Built LCZs generally exhibited higher HRI than land-cover LCZs; LCZ 5 (open mid-rise) had the highest median HRI, whereas LCZ G (water) had the lowest. Higher sky view factor and vegetation cover were generally associated with lower HRI, while larger blocks, taller buildings, and greater distances from water bodies were associated with higher HRI. These findings support differentiated block-scale interventions, heat-response resource allocation, and climate-resilient urban regeneration.

Under global climate change and rapid urbanization, urban heat island and dry island effects threaten residents’ thermal comfort and public health, making microclimate regulation by urban green spaces a core issue. However, the interaction between vertical structure and horizontal spatial type of plant communities remains unclear. This study selected Tianjin Water Park as the study area. Based on field temperature and humidity observations during the high-temperature period in summer 2024, ENVI-met was locally validated and used for multi-scenario numerical simulations of typical plant communities. Results showed that all vegetation structure types had cooling and humidifying effects. Tree–shrub–herb communities showed the strongest cooling and humidifying performance, while shrub–herb communities showed the weakest performance (p < 0.05). A higher degree of horizontal enclosure was associated with more stable temperature and humidity conditions. For cooling, an interaction-dependent effect was found between vertical structure and horizontal spatial type: the cooling benefit of greater vertical structural complexity increased with the degree of enclosure. These findings clarify the independent and interactive mechanisms by which plant community structure regulates microclimate. They also provide a scientific basis and quantitative reference for optimizing plant arrangement in urban parks.

Rapid urbanization has intensified the demand for street designs that reconcile ecological quality with positive human experiences, particularly in high-density cities such as Tianjin, China. Streets function as key interfaces where ecological processes, social activities and human perception intersect. However, existing research tends to emphasize the amount of greenery while overlooking its structural characteristics, to treat perception as a psychological response decoupled from spatial context, and to make limited use of fine-grained functional data to examine how ecology and perception interact. This study develops an integrated analytical framework that combines the DeepLabV3+ model to extract the Urban Street Greenery Generalized Structure (USGGS) from Baidu Street View imagery with a vision transformer model trained on the Place Pulse 2.0 dataset to derive multidimensional perceptual metrics. Functional diversity is represented using point-of-interest (POI) data, and an enhanced Light Gradient Boosting Machine (LightGBM) model is employed to explore associations among greenery structure, perceived qualities and functional characteristics. Analyses of six urban districts in Tianjin indicate that ecological and perceived street qualities are closely related to the degree of coupling between vegetation structure and functional diversity. Streets characterized by multi-layered greenery and diverse, active functions tend to exhibit higher perceived aesthetics, safety and vitality, whereas streets with single-layer vegetation or functionally monotonous environments generally do not perform as well. Functional patterns appear to mediate relationships between greening and perception by shaping how ecological form is experienced through everyday social activities. Overall, the results suggest that closer coordination between ecological design and functional organization is important for fostering urban streets that combine environmental resilience with strong perceived appeal.