Road network CO 2 concentration prediction and driving factors analysis based on vehicle-cruising observations and machine learning in Shenzhen, China

Enhanced urban CO2 monitoring and understanding its spatiotemporal patterns and driving factors are essential for emission management and climate change mitigation. This study developed a high-resolution framework for predicting and mapping CO2 concentrations within the road network of Shenzhen by integrating vehicle-cruising observations, street-view panoramas, and multisource remote sensing data. The proposed machine learning model demonstrated strong predictive performance (R² = 0.92). Furthermore, the effects of urban function, urban development intensity, traffic conditions, and environmental factors on CO2 concentrations were systematically examined using explainable machine learning techniques. The results indicate that in urban centres, human activities exert a substantial influence on CO2 levels. Effective non-motorway planning, accessible public transport systems, and diversified urban functions are associated with lower CO2 concentrations. Notably, areas with high vegetation cover were also found to exhibit elevated CO2 concentrations, and the influence of greenery on Shenzhen's CO2 levels was positive in November. By integrating multisource data from the perspectives of the urban landscape and street configuration, this study provides an interpretable approach for analysing the complex drivers of urban CO2 dynamics. Overall, the findings establish a cost-effective methodological framework for refined urban carbon monitoring and offer actionable insights to support low-carbon urban planning and evidence-based policy formulation.