With the acceleration of climate change and urbanisation, Chinese cities are facing increasingly severe flood risks. To address this challenge, China began implementing its sponge city policy in 2013, leveraging low-impact development, green infrastructure construction, and integrated water resource management to enhance urban resilience to floods and improve water security. This study utilises the Web of Science database as a reference, retrieving 201 relevant literature sources. From these, 61 studies closely related to China’s sponge city policy and urban flood management were selected. CiteSpace was employed to conduct keyword co-occurrence and temporal evolution analyses, comprehensively outlining the research hotspots and developmental trajectory of this field. The results indicate that research content has gradually shifted from early engineering-based flood control models to multi-objective, interdisciplinary comprehensive management, encompassing flood risk assessment, policy implementation mechanisms, integration of green infrastructure, and economic feasibility analysis. Based on this, this paper constructs an analytical framework incorporating technical, environmental, institutional, and social dimensions to integrate existing research findings, while identifying gaps in cross-scale coordination, smart management, and public participation. The research conclusions can provide valuable references for future policy optimisation and urban sustainable development.

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance.

Streets are major public spaces in high-density cities, and their visual environments are closely related to shaping emotional experience and wellbeing. However, existing studies often examine macro-scale urban form and pedestrian-level streetscape perception separately, while paying limited attention to nonlinear relationships and spatial heterogeneity. This limits the evidence available for fine-grained urban renewal in high-density contexts. Focusing on the area within Shanghai’s Outer Ring, this study develops a large-scale street-view dataset of 512,764 Baidu Street View images. Six perceptual dimensions—safety, lively, beautiful, wealthy, boring, and depressing—are estimated using a perception model trained on Place Pulse 2.0 and integrated into a composite Psychological and Emotional Index (PEI). XGBoost–SHAP is used to examine nonlinear relationships and threshold effects between perceptions and environmental indicators, while MGWR is employed to capture spatial nonstationarity and scale-dependent effects. The results show significant spatial heterogeneity and positive spatial autocorrelation across the six perceptual dimensions and the PEI. Compared with traditional morphological indicators, visual features showed stronger explanatory power and clearer threshold effects. Population density acts as a globally stable negative factor, whereas visual entropy and mixture show strong local sensitivity. These findings provide a data-driven basis for identifying context-specific priorities in urban renewal and spatial governance in high-density cities.

The heat island effect and air stagnation issues caused by high-density built-up areas are becoming increasingly severe. Optimising urban ventilation structures and establishing ventilation corridors have become key approaches to improving the urban thermal environment and enhancing liveability. However, traditional methods for constructing ventilation corridors often rely on empirical weighting or linear models, which struggle to accurately reveal the resistance coefficients of resistance indicators and fail to reflect the threshold at which indicators transition between positive and negative impacts. Consequently, this study employs Shanghai, China, as a case study, integrating machine learning models with the minimum cost path (MCR) model. Key variables were screened through multiple linear regression and variance inflation factor (VIF) analysis. Subsequently, machine learning models were compared to select the optimal model, with parameter optimisation conducted using Optuna, followed by computational implementation. The results indicate that built environment factors (such as building height, shape complexity, and road density) exert a significantly greater influence on ventilation potential than natural green space factors. By introducing the SHAP method, the positive and negative effects of each indicator on the ventilation environment and their threshold relationships were revealed. Negative indicators were converted into ventilation resistance factors to construct a resistance surface. Building upon this, cold and heat sources were identified using LST, NPP, and population density data. The MCR model was then employed to calculate the minimum resistance paths from cold to heat sources, forming an urban ventilation corridor network. The results indicate that primary corridors align with prevailing wind directions, following urban rivers and low-density green spaces. This study reveals the nonlinear effects of building and green space elements on ventilation systems, proposing machine learning-based optimisation strategies for ventilation corridors. It provides quantitative decision support for mitigating the urban heat island effect and enhancing city livability.