Global climate change and urbanization are causing temperatures to rise in cities, and the resulting heat island effect is becoming increasingly challenging. Urban trees play an essential part in a city’s ecosystems by offering a cooling impact through shading and transpiration; this results in reduced urban temperatures, increased thermal comfort for walkers, and an improved urban microclimate. This study integrates the cooling mechanisms, assessment methods, and socio-environmental effects of urban trees, proposing a comprehensive framework encompassing biophysical mechanisms, assessment models, and planning strategies to bridge the gap between research and practice. This review follows the PRISMA systematic review framework and synthesizes 128 eligible studies on the cooling effects of urban trees. Results show that trees reduce air temperature by approximately 0.5–5.8 °C and surface temperature by 2–12 °C, depending on canopy density, climate type, and measurement method. Morphological traits such as leaf area index (LAI > 3) and canopy density were the strongest predictors of cooling in humid and temperate climates, while physiological processes including transpiration enhanced cooling effectiveness under arid conditions. These findings provide quantitative thresholds and a conceptual framework that may support climate-adaptive urban planning and design.

A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between urban spatial morphology and the outdoor environment for the elderly, this study utilized parametric tools to establish a performance-driven workflow based on a “morphology generation–performance evaluation–morphology optimization” framework. Using survey data from 340 elderly neighborhoods in Beijing, a parametric urban morphology generation model was constructed. The following three optimization objectives were set: maximizing the winter pedestrian Universal Thermal Climate Index (UTCI), minimizing the summer pedestrian UTCI, and maximizing sunlight hours. Multi-objective optimization was conducted using a genetic algorithm, generating a “morphology–performance” dataset. Subsequently, the XGBoost (eXtreme Gradient Boosting) and SHAP (Shapley Additive Explanations) explainable machine learning algorithms were applied to uncover the nonlinear relationships among variables. The results indicate that optimizing spatial morphology significantly enhances environmental performance. For the summer elderly UTCI, the contributing morphological indicators include the Shape Coefficient (SC), Standard Deviation of Building Area (SA), and Deviation of Building Volume (SV), while the inhibitory indicators include the average building height (AH), Average Building Volume (AV), Mean Building Area (MA), and floor–area ratio (FAR). For the winter elderly UTCI, the contributing indicators include the AH, Volume–Area Ratio (VAR), and FAR, while the inhibitory indicators include the SC and porosity (PO). The morphological indicators contributing to sunlight hours are not clearly identified in the model, but the inhibitory indicators for sunlight hours include the AH, MA, and FAR. This study identifies the morphological indicators influencing environmental performance and provides early-stage design strategies for age-friendly neighborhood layouts, reducing the cost of later-stage environmental performance optimization.

This study explores the driving factors of household energy consumption in high-density residential areas of Beijing and proposes targeted energy-saving strategies. Data were collected through field surveys, questionnaires, and interviews, covering 16 influencing factors across household, building, environment, and transportation categories. A hyperparameter-optimized ensemble model (XGBoost, RF, GBDT) was employed, with XGBoost combined with genetic algorithm tuning performing best. SHAP analysis revealed that key factors varied by season but included floor level, daily travel distance, building age, greening rate, water bodies, and household age. The findings inform strategies such as optimizing workplace–residence layout, improving building insulation, increasing green spaces, and promoting community energy-saving programs. This study provides refined data support for energy management in high-density residential areas, enhances the application of energy-saving technologies, and encourages low-carbon lifestyles. By effectively reducing energy consumption and carbon emissions during the operational phase of residential areas, it contributes to urban green development and China’s “dual carbon” goals.

Landscape architects have a responsibility to society, the environment, the client, and other professional organizations. Professionals that undertake landscape-led planning projects need to adopt a comprehensive approach in all the associated aspects. This article summarizes a unique experience in landscape-led planning practice in the UK where most projects are either town planning or architect-led: (1) the housing scarcity problem in towns and villages, land within the Green Belt in the UK is in strict governance by planning policies. In this article, a case study of exceptional infilling villages in East Hertfordshire is used to drive a landscape-led, multi-disciplinary planning process and to analyze all expertise assignments involved; (2) materials were sought from a variety of sources including council official online guidance, public open access materials, project team communication, and consultation results which covered local residents engagement and anonymous comments during the publicity period of the planning decision. Desktop studies, site visits, and the expertise of the multi-disciplinary team were applied throughout the case; (3) with a year of planning teamwork, the proposed development of the study site met the local sustainable growth that planning permission for high-quality residential dwellings was granted by the Local Planning Authority; (4) since known landscape-led planning examples are limited in the UK, the case study formed a good example for scholars and researchers who are interested in planning in the Green Belt.