Promoting older adults’ active travel (AT) is important for healthy ageing, yet the optimal spatial units and scales for built environment (BE) interventions remain unclear. Existing studies often ignore the Modifiable Areal Unit Problem and fail to distinguish macro-scale land-use patterns from micro-scale street design under potentially nonlinear behavior–environment relationships. This study aims to clarify how multi-scale BE influences older adults’ AT and to identify the most effective intervention scale. Using survey data from 2494 older adults in Wuhan, China, we construct six behaviorally meaningful sliding units (5, 10, and 15 min walking network buffers and distance-equivalent Euclidean buffers), derive macro- and micro-scale indicators from GIS, census data, and street view images, and build separate Extreme Gradient Boosting (XGBoost) models with Accumulated Local Effects plots for interpretation. A model comparison reveals pronounced scale effects: network-based buffers systematically outperform circular buffers, and the 15 min walking network buffer emerges as the optimal intervention unit. Across all scales, BE variables contribute more to model performance than socio-demographic factors, and macro-scale attributes (e.g., land-use mix, facility density, and transit access) consistently outweigh micro-scale street features. Nonlinear effects and thresholds are identified for key density, accessibility, and streetscape indicators. These findings underscore the necessity of multi-scale analysis and support planning “15 min life circles” for older adults that prioritize macro-scale land-use and facility optimization, complemented by targeted, context-specific street-level improvements to create safe, age-friendly walking environments.

Active school travel (AST) is an important source of daily physical activity for children, yet its environmental correlates remain mixed and may be more complex than conventional linear models suggest. This study examines the associations of school-travel distance, built environment, socio-demographic attributes, and natural environment with children’s AST in Wuhan, China. Using 2020 travel survey data for children aged 6–15 and an XGBoost–SHAP framework, the study evaluates relative importance, nonlinear association patterns, and pairwise interactions among key variables. The results show that school-travel distance is the dominant correlate of AST, accounting for 44.07% of total model importance. Built-environment variables collectively contribute 36.24%, substantially exceeding socio-demographic attributes, while terrain also shows a non-negligible role. Several built-environment variables, including transit station density, road density, intersection density, land-use entropy, and POI density, display clear nonlinear patterns and directional shifts. Distance and age further condition the associations of other variables with AST, indicating substantial heterogeneity across distance bands and developmental stages. Overall, the findings suggest that AST should not be understood through average main effects alone, and that more age-sensitive, distance-sensitive, and context-specific planning strategies are needed to support child-friendly school travel.