Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression

Understanding the associations between the built environment and road traffic CO2 emissions is crucial for developing strategies to mitigate carbon emissions. However, previous research struggled to capture complex spatial relationships accurately due to classical geospatial models' limitations and the challenges of estimating CO2 emissions from operational vehicle data or limited sample sizes. Therefore, we introduce a novel model that leverages extensive vehicle trajectory data for estimating road traffic CO2 emissions. Furthermore, we develop a geographically convolutional neural network weighted regression (GCNNWR) model to analyze the correlation between the built environment and these emissions. This model employs convolutional neural networks to effectively capture non-linear spatial relationships. An empirical analysis was conducted in Beijing, China, demonstrating the superiority of the GCNNWR model in accommodating spatial heterogeneity compared to conventional geospatial models. Our findings provide critical insights into optimizing the built environment to minimize CO2 emissions.