Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration.

The building sector is a major source of global carbon emissions, with embodied carbon playing an increasingly critical role. This study quantitatively compares the life-cycle embodied carbon of three residential building design patterns (cast-in situ, CIS; design for disassembly, DfD; and skeleton–infill, SI) under a unified scenario of a 90-year service life with functional renovations every 30 years. A total of 36 cases, derived from twelve prototypical residential designs implemented in each pattern, were evaluated via life-cycle assessment following the EN 15978 standard. The results show that the SI pattern reduces embodied carbon by 45–55% compared to CIS, while the DfD pattern achieves a 35–45% reduction (with SI pattern consistently performing best). Structural system selection also influences outcomes, with shear wall structures reducing emissions by 7–14% compared to frame systems. Plan layout effects were marginal. The analysis indicates that the design pattern is the dominant factor influencing embodied carbon outcomes. SI pattern yields the largest carbon reduction by pairing a long-lived structural frame with flexible infill that extends service life and adaptability, while DfD lowers material demand via component reuse. These findings highlight the substantial benefits of circular design strategies and support a shift toward more adaptable, long-lifespan, and low-carbon residential design.

The spatial heterogeneity of social vulnerability in China’s floodplains remains underexplored, particularly regarding how rapid urbanization and unique demographic shifts-specifically the “floating population” and an aging society-reshape human-water relationships. This study quantifies flood exposure across 361 Chinese cities by integrating the 2020 Seventh National Population Census with high-resolution (250 m) floodplain maps via a pixel-based spatial aggregation approach. This methodology captures both absolute population scales and relative structural dependencies within hazard zones, effectively eliminating estimation bias from administrative-level averages. We propose a coupled Principal Component Analysis and Multiscale Geographically Weighted Regression (PCA-MGWR) framework to mitigate multicollinearity among socioeconomic variables while capturing spatially varying scale effects. The analysis identifies four distinct vulnerability dimensions: floating population (PC-FP), livelihood instability (PC-LI), low education (PC-EL), and lack of action capabilities (PC-AC). The PCA-MGWR model (R² = 0.713) significantly outperforms traditional OLS and GWR models in explanatory power. Crucially, results reveal a “Paradox of Wealth Exposure” along the developed Yangtze River Economic Belt, where affluent regions exhibit higher flood exposure, challenging the conventional poverty-driven vulnerability narrative. Furthermore, the driving mechanisms exhibit significant spatial non-stationarity: (1) In eastern megacities, the floating population shows a statistical “risk aversion” effect driven by housing market sorting, yet remains vulnerable due to “institutional segregation” in emergency management; (2) In old industrial bases and eastern plains, an “in-situ aging” phenomenon has created a “spatial lock-in,” trapping elderly populations in high-risk zones. These findings suggest that “mobility” and “aging” have emerged as critical drivers of vulnerability in China alongside economic factors. Consequently, we advocate for spatially differentiated governance strategies, shifting from “one-size-fits-all” defenses to adaptive policies that address specific regional socio-hydrological traps.

Accurately quantifying the driving mechanisms of land surface temperature (LST) is fundamental to developing climate-resilient urban strategies. However, traditional linear models often fail to capture the complex nonlinear interactions and spatial non-stationarity inherent in urban thermal environments, especially when hindered by multicollinearity among morphological indicators. This study proposes a multi-scale spatial explainability attribution framework by integrating an XGBoost machine learning model with SHAP (SHapley Additive Explanations) to decipher the thermal dynamics of Changchun, a representative cold-region city in China. Utilizing a 500 m grid-based dataset, we incorporated 3D urban morphology (BVD), land cover (NDVI, NDWI), and socioeconomic factors. The results indicate that the XGBoost model achieves superior predictive performance (R² = 0.694) compared to traditional OLS models. SHAP global attribution identified Building Volume Density (BVD) as the primary warming driver, as its three-dimensional volume creates “thermal traps” through radiation trapping and reduced ventilation. Notably, NDVI exhibits a significant nonlinear “cooling threshold effect” at 0.3, beyond which its mitigation efficiency stagnates or even reverses due to vegetation fragmentation and heat-induced physiological stress. Furthermore, spatial mapping reveals a distinct “sign reversal” in NDWI’s impact, reflecting the dualistic thermal regulation of water bodies across different urban–rural gradients. These findings suggest that urban thermal management strategies should shift from merely restricting 2D surface occupancy (e.g., Building Density) to a more sophisticated approach focused on precisely controlling 3D volume intensity (BVD). This study provides a “point-to-area” diagnostic tool supporting a transition to spatially targeted urban planning interventions.

Rapid urbanization and climate change have severely exacerbated the urban heat island (UHI) effect in high-density subtropical megacities. Traditional linear models often fail to capture the complex, non-linear thermal responses driven by three-dimensional (3D) urban morphology and socio-ecological interactions. This study proposes a data-driven analytical framework explicitly tailored for macro/mesoscale climate-resilient urban planning to deconstruct the non-linear associations of Land Surface Temperature (LST) in Shenzhen, China. Integrating multi-source spatial data into a 500 m grid, we utilized the eXtreme Gradient Boosting (XGBoost) algorithm for high-precision LST modeling (R² = 0.7851, MAE = 1.1381 °C) and applied the SHapley Additive exPlanations (SHAP) approach for spatial interpretability. The results reveal critical non-linear thresholds: vegetation (NDVI) cooling efficiency saturates at 0.8, while impervious surfaces (ISA) transition into dominant heating drivers beyond 0.7. Notably, a synergistic effect indicates that high building volume density (BVD) significantly amplifies the marginal cooling benefits of vegetation. Furthermore, local SHAP attribution combined with K-Means clustering facilitated the delineation of four distinct thermal management zones. This framework shifts UHI mitigation from broad, uniform policies to precise, data-driven spatial diagnostics, offering actionable “one zone, one policy” strategies for sustainable architectural and climate-resilient urban planning.

Linear tourism corridors can stimulate regional economic revitalization, but they may also intensify land conversion, fragment habitats, and challenge the long-term sustainability of ecologically sensitive landscapes. Resolving this tension requires a transition from qualitative zoning to data-driven, threshold-informed spatial governance. This study develops a continuous analytical pipeline to support land-use governance along China National Highway 331 (G331). We integrated principal component analysis (PCA) with a Bayesian-optimized eXtreme Gradient Boosting (XGBoost) model, validated through Spatial Block Cross-Validation to reduce spatial data leakage and provide a more conservative assessment of geographic transferability. Shapley Additive Explanations (SHAP) was used to interpret localized non-linear associations, threshold patterns, and spatially heterogeneous model responses. Empirical results indicate that anthropogenic socioeconomic intensity is the dominant predictive driver associated with spatial variation in ecological quality. The SHAP analysis identified model-derived threshold patterns, including an approximate population-density threshold around 4000 people per square kilometer and a corridor-distance response around 50 km from the G331 highway. To translate these model-derived explanatory insights into spatial governance scenarios, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to approximate the Pareto trade-off frontier between ecological integrity and socioeconomic expansion. This multi-objective optimization delineated three spatial governance scenarios and identified a Pareto-elbow configuration that supports compatible-use management. This closed-loop framework provides a transferable analytical approach for sustainability-oriented corridor governance by identifying where development may be concentrated, where ecological buffers should be strengthened, and where strict conservation should be prioritized.