Street design quality and socio-economic factors jointly influence housing prices, but their intertwined effects and spatial variations remain under-quantified. Housing prices not only reflect residents’ neighborhood experiences but also stem from the spillover value of public streets perceived and used by different users. This study takes Tianjin as a case and views the street environment as an immediate experience proxy for through-travelers, combining street view images and crowdsourced perception data to extract both subjective and objective indicators of the street environment, and integrating neighborhood and location characteristics. We use Geographical-XGBoost to evaluate the relative contributions of multiple factors to housing prices and their spatial variations. The results show that incorporating both subjective and objective street information into the Hedonic Pricing Model (HPM) improves its explanatory power, while local modeling with G-XGBoost further reveals significant heterogeneity in the strength and direction of effects across different locations. The results indicate that incorporating both subjective and objective street information into the HPM enhances explanatory power, while local modeling with G-XGBoost reveals significant heterogeneity in the strength and direction of effects across different locations. Street greening, educational resources, and transportation accessibility are consistently associated with higher housing prices, but their strength varies by location. Core urban areas exhibit a “counterproductive effect” in terms of complexity and recognizability, while peripheral areas show a “barely acceptable effect,” which may increase cognitive load and uncertainty for through-travelers. In summary, street environments and socio-economic conditions jointly influence housing prices via a “corridor-side–community-side” dual-pathway: the former (enclosure, safety, recognizability) corresponds to immediate improvements for through-travelers, while the latter (education and public services) corresponds to long-term improvements for residents. Therefore, core urban areas should control design complexity and optimize human-scale safety cues, while peripheral areas should focus on enhancing public services and transportation, and meeting basic quality thresholds with green spaces and open areas. Urban renewal within a 15 min walking radius of residential areas is expected to collaboratively improve daily travel experiences and neighborhood quality for both residents and through-travelers, supporting differentiated housing policy development and enhancing overall quality of life.

Street greening is widely recognized as influencing resident well-being and housing prices, and street-view imagery provides a fine-grained data source for quantifying urban microenvironments. However, existing research predominantly relies on single indicators such as the Green View Index (GVI) and overall green coverage/volume lacking a systematic analysis of how the hierarchical structure of trees, shrubs, and grass relates to housing prices. This study examines the high-density block context of Tianjin’s six urban districts. Using the Street Greening Space Structure (SGSS) dataset to construct greening structure configurations, we integrate housing-price data, neighborhood attributes, and 13,280 street-view images from the study area. We quantify how “visibility and hierarchical ratios” are capitalized on in the housing market and identify auditable threshold ranges and contextual gating. We propose an urban–forest structural system centered on visibility and hierarchical ratios that links street-level observability to ecosystem services. Employing an integrated framework combining Geographical-XGBoost (G-XGBoost) and SHapley Additive exPlanations (SHAP), we move beyond average effects to reveal structural detail and contextual heterogeneity in capitalization. Our findings indicate that tree visibility G_TVI is the most robust and readily capitalized price signal: when G_TVI increases from approximately 0.06 to 0.12–0.16, housing prices rise by about 8%–10%. Hierarchical structure is crucial: balanced tree–shrub ratios and moderate shrub–grass ratios translate “visible green” into functional green. Capitalization effects are environmentally conditioned—more pronounced along corridors with high centrality and accessibility—and are likewise common in dense East Asian metropolises (e.g., Beijing, Shanghai, Seoul, and Tokyo) and rapidly motorizing cities (e.g., Bangkok and Jakarta). These patterns suggest parametric prescriptions that prioritize canopy-corridor continuity and keep ratios within actionable threshold bands. We translate these findings into urban greening strategies that prioritize canopy continuity, under-canopy permeability, and maintainability, providing sustainability-oriented, parameterized guidance for converting urban greening structure into ecological capital for sustainable cities.

As extreme heat events become increasingly frequent worldwide, there is an urgent need for fine-scale assessment of urban heat risk and for identifying its key determinants. Conventional approaches often struggle to capture complex intra-urban spatial heterogeneity, limiting effective heat risk governance and resource allocation. This study applies the Hazard–Exposure–Vulnerability–Adaptation (HEVA) framework by integrating remote sensing, road network, and socio-demographic data. Using the CRITIC weighting method, we quantify and map a street-level heat risk index (HRI) in Tianjin, China. We further employ geographically weighted machine learning models to identify dominant drivers and to characterise nonlinear effects, interaction patterns, and spatially varying relationships. Model reliability is assessed by benchmarking geographically weighted models against global nonlinear baselines under three-fold cross-validation; GW-XGBoost achieves comparable explanatory power to the best global model (R² = 0.672) while yielding lower prediction errors (MAE = 0.142), supporting robust spatial inference. Results show that elevated heat risk is not confined to the urban core; instead, it is more pronounced in peripheral transitional zones around central districts. These areas often exhibit coincident heat stress and high population exposure, a higher concentration of vulnerable groups and ageing residential neighbourhoods, and comparatively limited access to medical and cooling resources. Mechanistically, greater development intensity is generally associated with higher heat risk, whereas higher vegetation cover tends to reduce risk; however, the strength and, in some locations, the direction of these effects vary substantially across streets. These findings suggest that heat risk management should prioritise peripheral transitional zones. Targeted interventions should balance development intensity, expand effective greening and shading, and improve the provision and accessibility of healthcare and cooling services to reduce street-level heat risk.

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration.

Against the backdrop of stock-space urban renewal, green-space governance in megacity cores increasingly requires a shift from expansion-oriented supply to the optimisation of existing spatial resources. This study examines the spatial equity of green-space provision in the central districts of seven Chinese megacities using high-resolution remote-sensing data from 2015, 2020, and 2025. We combined semantic segmentation, nested-buffer measurement, the Gini coefficient, Extreme Gradient Boosting (XGBoost), and Shapley Additive Explanations (SHAP) to evaluate the relationships between urban morphology and green-space provision. We first derived a pixel-level cumulative green-space provision index from nested-buffer measurements and calculated city-year Gini coefficients to quantify distributional inequality. XGBoost models were then used to predict the cumulative green-space provision index from urban morphology, and SHAP was applied to interpret the resulting nonlinear, model-derived associations. The results show that changes in the green-space provision index and its distributional inequality were not synchronous across cities. Beijing showed the largest increase in inequality, whereas Chengdu showed the strongest improvement; Tianjin recorded the fastest increase in the green-space provision index, while Beijing experienced a marked decline. Urban form showed nonlinear associations with the unit-level green-space provision index, helping to explain the spatial differentiation underlying the city-year Gini coefficients. Building density was the primary contributor in most city-year models, whereas compactness showed threshold-like associations, with positive effects at lower levels and inhibitory effects at higher levels. Statistical testing and clustering further indicated substantial heterogeneity among cities, which were categorised into three spatial patterns: centripetal agglomeration, peripheral expansion, and clustering/ring-shaped fluctuation. These findings provide morphology-based evidence for differentiated green-space governance in high-density megacity cores, though they should not be interpreted as a complete socioeconomic assessment of green-space equity.

With the increasing frequency of extreme-heat events, there is an urgent need to identify the key drivers of the urban thermal environment at fine spatial scales. Focusing on urban blocks within Beijing’s Fifth Ring Road, this study integrates Landsat 8 imagery acquired from March 2020 to February 2021 with multi-source data on land cover, buildings, population, pollution, and topography. LightGBM–SHAP, a theory-informed directed acyclic graph (DAG), CausalForestDML, and cross-fitted g-computation were employed to investigate predictive associations, Q25–Q75 average total treatment effects, and block-level responses to prespecified urban-morphology intervention scenarios for seasonal land surface temperature (LST). The within-season SHAP analyses consistently placed building height (BH) and building density (BD) among the relatively important predictors, whereas the predictive patterns of the normalized difference vegetation index (NDVI) and the proportion of impervious surfaces (ID) were more season-specific. The autumn and winter models also assigned relatively high within-season importance to the digital elevation model (DEM), PM_2.5, and CO₂ emission proxy. Because the four seasonal models differed in predictive performance and LST distributions, these cross-seasonal patterns were interpreted qualitatively rather than as direct comparisons of absolute SHAP values or rank positions. Causal-effect estimation further indicated that, under the primary DAG and identification assumptions, the Q25–Q75 point estimates were positive for BD and negative for BH in all four seasons. In summer, the Q25–Q75 effects of NDVI and ID were −0.772 and +1.548 °C, respectively. Intervention-scenario analysis further indicated that the estimated responses varied across blocks and seasons, emphasizing the importance of considering baseline urban conditions and common support when interpreting potential planning effects. Additional spatially blocked validation yielded lower predictive performance than random validation, while significant positive residual spatial autocorrelation remained in all four seasons. These findings may inform the local evaluation of season- and context-specific surface-temperature mitigation strategies within the observed-support range, but they should not be interpreted as universal planning prescriptions.