Based on the background of rapid global ageing, research exploring urban green spaces and the subjective well-being of urban residents has become one of the key research interests. However, the evidence for assessing the real benefits of urban green spaces on the subjective well-being of older adults by comprehensively examining the features of urban green spaces is limited. We surveyed older urban green space users (n = 536) aged 60 years and older in Nanjing, China, and evaluated the impacts of spatial, green, and grey features of urban green spaces on older people's overall satisfaction with urban green spaces and subjective well-being. The results of the multiple linear regression model showed a strong association between the three types of urban green space features, overall satisfaction with urban green space, and subjective well-being of older adults. The study results indicated that the grey feature of urban green spaces emerged as the most influential factor (p = 0.004) among the three features of urban green spaces in relation to their effect on the subjective well-being of older adults. This implies that it is essential to focus on the importance of grey s of urban green spaces in the planning and design of urban green spaces for older adults' use of different urban green spaces and their sense of well-being. These results contribute to the development of healthy ageing policies and age-friendly urban green space management strategies in rapid ageing countries around the world.

This study establishes an integrative machine learning (ML) framework that bridges environmental psychology and data science to investigate the psychological well-being of older adults in urban green spaces (UGS). We applied decision tree (DT), random forest (RF), and artificial neural network (ANN) algorithms, complemented by SHAP interpretability, to multi-dimensional data from 536 seniors in Nanjing, China, aiming to identify key predictors of well-being. While DT achieved the highest accuracy (92.19 %), RF's ensemble approach (87.07 % accuracy) demonstrated superior robustness by effectively mitigating overfitting. Crucially, all models converged in identifying overall UGS satisfaction, a core subjective perceptual metric, as the paramount predictor, underscoring its primacy over traditional accessibility-centric paradigms. SHAP analysis further decoded this global satisfaction into actionable, psychologically salient elements, revealing nonlinear thresholds: wetland parks yielded significant well-being gains (ΔWOOP ≥3.5) only with frequent visits exceeding three weekly and high satisfactions of at least 4 out of 5, while safety facilities and vegetation diversity were identified as key design levers. Our methodology offers a replicable pipeline that balances predictive performance with psychological interpretability. These findings reposition UGS as scalable public health infrastructures for aging well, providing evidence-based, perception-centered strategies to enhance mental and emotional health in urban aging populations.

Urban Green Spaces (UGS) are integral components of the built environment, significantly contributing to its ecological, social, and performance dimensions, including microclimate regulation, occupant well-being, and energy efficiency. This decadal review (2015–2025) systematically analyzes 70 high-impact studies to propose a “Symbiotic Intelligence” framework. This framework integrates Generative AI, ethical algorithms, and innovations from the Global South to revolutionize the planning, design, and management of UGS within building landscapes and urban fabrics. Our analysis reveals that Generative AI can optimize participatory design processes and generate efficient planning schemes, increasing public satisfaction by 41% and achieving fivefold efficiency gains. Metaverse digital twins enable high-fidelity simulation of UGS performance with a mere 3.2% error rate, providing robust tools for building environment analysis. Ethical algorithms, employing fairness metrics and SHAP values, are pivotal for equitable resource distribution, having been shown to reduce UGS allocation disparities in low-income communities by 67%. Meanwhile, innovations from the Global South, such as lightweight federated learning and low-cost sensors, offer scalable solutions for building-environment monitoring under resource constraints, reducing model generalization error by 18% and decreasing data acquisition costs by 90%. However, persistent challenges-including data heterogeneity, algorithmic opacity (with only 23% of studies adopting interpretability tools), and significant data gaps in the Global South (coverage < 15%)-hinder equitable progress. Future research should prioritize developing UGS-climate-building coupling models, decentralized federated frameworks for building management systems, and blockchain-based participatory planning to establish a more robust foundation for sustainable built environments. This study provides an interdisciplinary roadmap for integrating intelligent UGS into building practices, contributing to the advancement of green buildings, occupant-centric design, and the overall sustainability and resilience of our built environment.