A scalable approach to high-resolution, bottom-up GHG emission inventories using open data

Targeting and tracking climate change mitigation efforts requires accurate bottom-up inventories of GHG emissions, verified by independent atmospheric measurements. So far, most policy decisions have been based on annual emission inventories at national and city scales. Inventories with higher resolution in both space (sub-city) and time (daily to hourly), while generally more uncertain, have major advantages. First, they are a key input to inverse modelling of emission sources from atmospheric measurements, which offers a semi-independent approach to verify bottom-up estimates. Second, they can serve as simulation tools to assess the impact of specific interventions (from policy to industrial standards and household behavior) on GHG emissions and measured atmospheric concentrations. Third, by offering more localized emission estimates almost in real time, they may act as more powerful motivators of behavioral and policy change when used to communicate and track climate action. 

Here we present a simple approach to develop bottom-up inventories of carbon dioxide emissions from road traffic (at street level) and residential space heating (in a 100-m grid) using crowd-sourced data from OpenStreetMap and other publicly available data sources. Our approach can be easily scaled to all of Germany and, with some modifications, can be tailored to a wide range of contexts and applications. We demonstrate the approach for the cities of Mannheim and Heidelberg, in the Rhine-Neckar Metropolitan Area in Germany. 

Emissions from road traffic are derived from multiplying estimates of average daily traffic volume – based on road type information, number of lanes, and population density – by speed- and fuel-dependent emission factors and data about the national vehicle fleet composition. Space heating emissions rely primarily on gridded data from the 2022 German census on population density, living space per capita, heating energy carriers, and building age.

We validate our traffic volume estimates with independent traffic count data and compare our emission estimates to available inventories. Road traffic emissions in the Rhine-Neckar region were 1.6% higher than TNO estimates for the region (Super et al. 2021), a widely used inventory of disaggregated emission in Europe. Our residential space heating emissions estimates were slightly lower than estimates from emissions inventories for the cities of Mannheim and Heidelberg (12% and 8%, respectively), largely attributable to the type of emission factors used in the calculations.