As a key branch of online-to-offline (O2O) retail, the online-to-home (O2H) model enables goods acquisition through instant delivery, fundamentally reorienting urban retail spatial configuration from “accessibility” to “efficiency”. Using Jincheng, the main urban area of Tianjin as a case study, this research formulated a Goods Acquisition Efficiency (GAE) index to quantify the time-based efficiency gain of O2H over the conventional OIS (offline in-store) mode. An integrated XGBoost-SHAP approach was utilized to examine the spatial variations in efficiency gains and their associated factors. The results reveal that: (1) Efficiency gains follow a concentric pattern, increasing from the core to the periphery (Inner: 0.18; Middle: 0.20; Outer: 0.26), suggesting that O2H provides more pronounced benefits in peripheral areas where retail provision remains limited; (2) The dominant factors vary across zones: environmental attributes in the Inner Urban Zone, transportation and economic factors in the Outer Urban Zone; (3) O2H and OIS exhibit a complementary rather than substitutive relationship—physical stores in inner-city areas can maintain their current configuration, while peripheral zones may benefit from enhanced O2H fulfillment or conversion to micro-fulfillment centers. The GAE index and zonal comparison framework offer methodological references for differentiated optimization of urban retail networks.

Demand-side management is increasingly important for low-carbon transport governance. However, many studies assume relatively stable travel preferences and pay limited attention to behavioural changes under sudden external shocks. This study proposes an Event–Behaviour–Resilience framework and applies Natural Language Processing to Sina Weibo data to examine travel responses to extreme heat and refined oil price adjustments. The results show asymmetric response patterns. Oil price increases were associated with cost-based low-carbon substitution, with new-energy vehicle intentions accounting for 64.4% of the share. In contrast, extreme heat was associated with both trip reduction and motorised travel. Travel reduction reached 52.4%, while ride-hailing or taxi responses accounted for 24.6%. A quadratic fitting analysis identified 38.0–39.0 °C as an observed transition interval, within which high-carbon motorised willingness began to exceed low-carbon slow mobility willingness. Group-level analysis showed unequal behavioural flexibility. While 80.0% of the general population reduced travel under extreme heat, the forced mobility group showed limited travel reduction and maintained a high level of low-carbon willingness at 86.87%. XGBoost-SHAP results indicated that temperature, emotional valence, and behavioural constraints contributed to low-carbon mobility intention. These findings suggest that behavioural responses can help identify spatial interventions for low-carbon transport, especially in relation to heat exposure, mobility flexibility, and access to adaptive travel options.