CO2 prediction results

Enhanced urban CO₂ monitoring and understanding the spatiotemporal patterns and driving factors of urban CO₂ concentrations contribute to the effective management of urban CO₂ emissions and the development of strategies to mitigate climate change. This study focuses on accurately predicting and mapping CO₂ concentrations in Shenzhen’s road network by integrating vehicle-cruising CO₂ observations, street view panoramas, and multisource remote sensing data. By utilizing street view panoramas to capture the surrounding street configuration features and multisource remote sensing data to map nearby urban landscape features, we developed the CO₂ prediction model with R² of 0.92 and MAE of 3.297 ppm. Furthermore, we identified eight high-CO₂ concentration areas and examined impacts of urban function, urban development, traffic condition, environment condition on CO₂ concentrations by explainable machine learning techniques. In urban centers, human activities have a substantial impact on CO2 levels, noticeably increasing during peak commuting times. Effective non-motorway planning, convenient public transport, and diverse urban functions can help reduce CO₂ concentrations. Areas with high vegetation cover also show high CO₂ concentrations, and the impact of greenery on Shenzhen’s CO₂ concentrations is positive in November. This paper offers a new perspective on refined CO₂ emission observation and the analysis of complex driving factors, providing novel technical approaches that enhance the precision of CO₂ concentrations prediction, and offer interpretable methods for urban CO2 monitoring and management.