While established studies have explored interventions in the built environment (BE) and transportation sector to mitigate travel carbon emissions (TCE), planners still struggle to determine the most effective units of intervention, identify key variables, and determine their optimal values. This study addresses the gap by employing the extreme gradient boosting (XGBoost) model to create a multi-scale comparative framework. This study revealed that the relationship between the built environment and travel-related carbon emissions varies depending on the zoning and scale of the BE measurement unit. The explanatory power of TCE varies across different geographic units, with the 15-min walk distance buffer of residents being the most effective in explaining TCE. Most variables were nonlinearly associated with TCE, and the precise threshold of the association between BE attributes and TCE was quantified. Based on these findings, we provide precise and nuanced insights into BE interventions to reduce TCE.

中文翻译：

---

检查多尺度建筑环境干预措施以减少与旅行相关的碳排放


尽管已有研究探索了对建筑环境 (BE) 和交通部门的干预措施，以减少旅行碳排放 (TCE)，但规划者仍然难以确定最有效的干预单位、确定关键变量并确定其最佳值。本研究通过采用极限梯度提升 (XGBoost) 模型创建多尺度比较框架来解决这一差距。这项研究表明，建筑环境与旅行相关碳排放之间的关系因BE测量单位的分区和规模而异。不同地理单元的TCE解释力存在差异，其中居民15分钟步行距离缓冲对TCE的解释最为有效。大多数变量与TCE呈非线性相关，并且量化了BE属性与TCE之间关联的精确阈值。基于这些发现，我们为减少 TCE 的 BE 干预措施提供了精确而细致的见解。