Blue–green infrastructure (BGI) is an important nature-based solution for mitigating urban overheating, and machine learning (ML) techniques offer strong potential in capturing the complex, nonlinear relationships between influencing factors and BGI cooling effects. This study presents a systematic review of 54 journal articles published between 2015 and 2025 that applied ML to assess the cooling performance of BGI. We classified BGI into seven types across three spatial scales, evaluated the metrics and indicators used to characterize the BGI cooling effects, and examined the ML algorithms, model performance, and data sources adopted in reviewed studies. We found that 2D BGI morphology metrics and BGI physical and biological metrics are most frequently used as model inputs, whereas 3D morphology metrics are used less. Surface temperature-related indicators dominate model outputs, while air temperature and human heat stress indicators, which better reflect heat health hazards, remain underutilized. In terms of methodology, ML applications are primarily based on artificial neural networks for temporal regression and tree ensemble algorithms for spatial regression. Building on these findings, we identify key research gaps and advocate that future research should develop comprehensive multi-source and multi-scale BGI databases, improve model transparency and generalizability, and integrate energy-balance information into BGI-ML studies. Finally, we propose a systematic BGI-ML modeling framework to guide future research in this field.

The SI (SkeIeton-Infill) System originated from Japan and introduced to China in 2006. The build-up projects proved the technical feasibility of SI System in China. However, the localization transformation of SI System has not completed in China. Based on literature review, the main determinants of influence mechanism are revealed to be housing ownership model, residential scale, and residential form. The content analysis revealed the potential influences on the uptake of SI System in China. This research sought to reveal the influence mechanism of social environment on industrialized design system. The practical value of the research is providing guidance for policy makers and designers to help them improve design quality of SI System in China.