High spatiotemporal resolution traffic CO₂ emission maps derived from Floating Car Data (FCD) for 20 European cities (2023)

Abstract. On-road transportation is a major contributor to CO₂ emissions in cities, and high-resolution CO₂ traffic emission maps are essential for analyzing emission patterns and characteristics. In this study, we developed new hourly CO₂ emission maps at 100 × 100 m resolution for 20 major cities in France, Germany, and the Netherlands in 2023.  We used commercial Floating Car Data (FCD) based on anonymized GPS signals periodically reported by individual vehicles, providing hourly information on mean speed and on the number of GPS sample counts per street. Machine learning models were developed to fill FCD data gaps and convert sample counts into actual traffic volumes, and the COPERT model was used to estimate speed- and vehicle type dependent emission factors. Hourly emissions, initially estimated at the street level, were aggregated to 100 × 100 m grid cells. Annual on-road CO₂ emissions across the 20 European cities in 2023 ranged from 0.4 to 7.6 Mt CO₂, with emissions strongly correlated with urban area (R² = 0.97) and, to a lesser extent, population size (R² = 0.72). Spatially, emissions are either highly concentrated along major highways in cities such as Paris and Amsterdam or more evenly distributed in cities such as Berlin and Bordeaux, highlighting the need for context-specific mitigation strategies. Temporally, this study shows the CO₂ emission fluctuations due to holiday periods, weekly activity cycles, and distinct usage profiles of different vehicle types. Due to the low latency of FCD, this approach could support near-real-time traffic emission mapping in the future. Our approach enhances the spatial and temporal characterization of CO₂ emissions in on-road transportation compared to the conventional method used in gridded inventories, indicating the potential of FCD data for near-real-time urban emission monitoring and timely policy making. The datasets generated by this study are available on Zenodo https://doi.org/10.5281/zenodo.16600210 (Shi et al., 2025).