Due to the limited availability of medical facilities and the urgency and irreplaceability of medical-seeking behaviors, the transportation processes used to access these resources inherently result in high carbon emissions. Unfortunately, pediatric medical facilities are among the least substitutable destinations, making it challenging to reduce travel-related CO₂ emissions by traditional means such as decreasing travel frequency or optimizing transportation means. This study proposes enhancing the spatial allocation of pediatric medical facilities to effectively reduce travel-related CO₂ emissions. This study selects 27 hospitals with pediatric departments in Tianjin as the research subject. It introduces a model for measuring travel-related CO₂ emissions for pediatric medical-seeking, STIRPAT, and ridge regression models as well as conducts simulations under various scenarios to test the hypotheses. Therefore, methods for enhancing the spatial allocation of pediatric medical facilities are proposed. The results show that (1) travel-related CO₂ emissions for pediatric medical-seeking are the highest in the city center, outpatient-related CO₂ emissions surpass inpatient ones, and children’s hospital-related CO₂ emissions are higher than those related to comprehensive hospitals, from which potential carbon reduction points can be explored; (2) children’s hospitals with multibranch and composite functional allocations can significantly reduce CO₂ emissions; (3) comprehensive hospitals can further alleviate CO₂ emissions from children’s hospitals by enhancing the medical level, transportation infrastructure, population distribution, and other spatial environmental factors; (4) from the perspective of low-carbon travel and equity, a spatial allocation strategy should be adopted for children’s hospitals that includes multiple branches and composite functions, while comprehensive hospitals should focus on service capacity, parity, supply–demand ratio, and the population density of children.

As global decarbonization strategies pivot towards the burgeoning sector of recreational mobility, the tension between the attractive force of high-quality amenities and the constraining capacity of transport infrastructure in urban parks has emerged as a critical planning dilemma for high-density metropolises. To disentangle this game mechanism, this study proposes a integrated Dual-Diagnostic Framework that synthesizes a modified gravity model, Grouped OLS regression, and an explainable XGBoost-SHAP algorithm to identify non-linear thresholds under spatial heterogeneity. Leveraging empirical data from Tianjin, a representative high-density metropolis, the analysis reveals a distinct bimodal distribution of carbon emissions from travel to comprehensive parks, confirming a fundamental structural divergence between urban and suburban mobility patterns. Crucially, the non-linear diagnosis uncovers a dominant Facility Configuration Induction mechanism within the suburban interface; here, park scale acts as the primary driver of excess travel, with its induction effect often overriding the mitigation potential of public transit until a specific critical mass is achieved. Consequently, the results identify a rigid threshold for bus station density alongside optimal intervals for park scale, providing quantitative benchmarks and differentiated governance strategies to resolve the paradox between park quality and carbon intensity.

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.