Munich CO2 emission inventory for sectors public power and commercial and residential combustion for the years 2019–2024

Cities play a central role in climate mitigation, but often lack emission inventories with sufficient spatial and temporal resolution for detailed monitoring and policy evaluation. Existing inventories are frequently based on downscaled national statistics and generic temporal profiles, limiting their ability to capture city-specific activity patterns and local temporal variability. In this study, we present a high-resolution bottom-up emission inventory for the city of Munich, Germany, covering public power and commercial and residential combustion for the years 2019–2024. C O 2 emissions for fossil fuel and biofuel are quantified at 100 m spatial resolution and hourly temporal resolution. A building-level heat-demand proxy is calculated and used to spatially distribute reported fuel consumption. Hourly district heating heat-load data are used to train a machine-learning model to derive temporal profiles. For power plants, hourly fuel-input data enable temporal emission profiles, while total emissions are based on annual environmental reports. The inventory is evaluated by comparing it with national downscaled inventories and by assessing temporal profiles against energy production data and meteorological indicators. Spatial uncertainty is analyzed by comparing it with an alternative building-level model and a non-public model from the local operator. The inventory reveals interannual emission changes between 2019 and 2024 and shows the influence of external drivers such as the energy crisis and variations in mean annual temperature. We highlight the impact of using local data but also show that local inventories are constrained by the quality and availability of the input data. The inventory is released as open datasets.