A scalable method for high-resolution bottom-up GHG emission inventories using open data

Accurately targeting and tracking climate change mitigation efforts requires detailed bottom-up greenhouse gas (GHG) emission inventories verified by independent atmospheric measurements. While policy decisions often rely on annual national or city-scale inventories, higher-resolution data, both spatially (sub-city) and temporally (daily to hourly), though more uncertain, provide key advantages. First, they are input to inverse modelling of emission sources from atmospheric measurements, which offers a semi-independent approach to verify bottom-up estimates. Second, they can enable simulations to evaluate the impact of interventions, such as changes of policies, industrial standards or household behaviour, on emissions and atmospheric concentrations. Third, by providing localized, near real-time emissions data that can enhance the communication and tracking of climate actions, they can motivate both behavioural and policy shifts.

This study presents a straightforward method to create high-resolution carbon dioxide emission inventories for road traffic (street level) and heating (building level) using publicly available data such as OpenStreetMap. Scalable across Germany and adaptable to diverse contexts and applications, the method is demonstrated in Mannheim and Heidelberg, part of the Rhine-Neckar Metropolitan Area.

For road traffic, emissions are derived by estimating daily traffic volumes using road type, lane count, and population density, combined with speed- and fuel-specific emission factors and national vehicle fleet data. Heating emission estimates combine building data with gridded census data on heating energy sources and building age.

We validate our traffic volume against independent traffic count data and compare our emissions with existing inventories. Road traffic emissions in the Rhine-Neckar region exceeded the regional estimates of TNO (Super et al., 2021), a widely used European inventory, by 1.6%. Building heating emissions were 12% and 8% lower than inventory estimates for Mannheim and Heidelberg, respectively, primarily due to differences in emission factor assumptions.