Examining the relationship between the built environment and carbon emissions from operating vehicles: enlightenment from nonlinear models

• Published in: Environmental science and pollution research international
• Main Authors: Wang, Tao, Du, Fangfang, Ding, Keke, Qin, Wenwen, Sun, Lingbo
• Format: Journal Article
• Language: English
• Published: Germany 17.10.2024
• Subjects: Nonlinear; Built environment; Operating vehicles; Gradient boosting regression tree; Carbon emissions; Machine learning
• ISSN: 1614-7499; 1614-7499
• DOI: 10.1007/s11356-024-34655-2

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.