Near-real-time CO2 traffic emission maps of 10 European cities based on high-resolution GPS-based data

On-road transportation is one of the major contributors to energy consumption and CO₂ emissions in global megacities, and high-resolution CO2 traffic emission maps are needed to analyze emission patterns. In this study, commercial GPS-based data provides hourly, road-specific information on vehicle speed and traffic volume, and machine learning models are employed to address data gaps and transform sample counts into real traffic flux. Combined with COPERT, we developed on-road transportation CO₂ emission maps for 10 selected cities in France, Germany, and the Netherlands. Our analysis offered insights into annual, per capita, and area-specific emissions for each city. Spatial emission patterns reflect urban structures and commuting behaviors, with cities such as Paris exhibiting concentrated hotspots along its ring road, whereas Berlin demonstrates a more uniform spatial distribution. Temporal variations reveal distinct weekly and seasonal trends, with more significant reductions during holidays and summer in French cities compared to German and Dutch cities. This approach enhances the spatial and temporal characterization of CO₂ emissions in on-road transportation compared to the previous method used in Carbon Monitor, indicating the potential of GPS-based data for supporting future efforts in emission monitoring and developing emission reduction policies.