Chongqing Transport Planning and Research Institute, Chongqing, 401120, China.
Shanxi Environmental Protection Institute of Transport, Taiyuan, 030000, China.
Environ Sci Pollut Res Int. 2024 Nov;31(51):61292-61304. doi: 10.1007/s11356-024-34655-2. Epub 2024 Oct 17.
Carbon emissions from urban transportation significantly contribute to overall transportation emissions and are a major cause of the continuous rise in global temperatures. Understanding the spatial distribution and influencing factors of carbon emissions from operating vehicles can aid in formulating targeted policies and promoting emission reduction. To analyze the factors influencing urban traffic carbon emissions, we calculated emissions using trajectory data from operating vehicles in Shenzhen. We then used gradient boosting regression tree methods, specifically RF, XGBoost, and LightGBM models, to analyze the impact of the built environment on vehicle emissions. We used the XGBoost model for detailed factor analysis by comparing the models. The results indicate that bus stops, intersections, housing density, metro stops, and land use mix are the top five factors influencing emissions. When road density is 0-15 km/km, the distance from the city center is 0-6 km, and the population exceeds 2000/km, the built environment significantly reduces vehicle emissions.
城市交通产生的碳排放对交通运输整体碳排放的贡献巨大，是导致全球气温持续升高的主要原因。了解运营车辆的碳排放空间分布及其影响因素，可以帮助制定有针对性的政策，促进减排。为了分析影响城市交通碳排放的因素，我们使用了深圳运营车辆的轨迹数据来计算排放量。然后，我们使用梯度提升回归树方法（特别是 RF、XGBoost 和 LightGBM 模型）来分析建筑环境对车辆排放的影响。我们使用 XGBoost 模型进行了详细的因素分析，比较了这些模型。结果表明，公交车站、十字路口、住房密度、地铁站和土地利用混合是影响排放的前五个因素。当道路密度在 0-15km/km 之间，市中心距离在 0-6km 之间，人口超过 2000/km 时，建筑环境会显著降低车辆排放。
