Monitoring CO2 in diverse European cities: highlighting needs and challenges through characterisation

Abstract

For the development of a joint European capacity for monitoring CO2 emissions, we created the framework “CO2 Monitoring Challenges City Mapbooks v1.0” (CMC-CITYMAP). It includes a Jupyter notebook tool (Storm et al., 2025a, https://doi.org/10.18160/P8SV-B99F) which we use to characterise and cluster cities based on aspects relevant for different CO2 monitoring challenges. These include:

a. determining background levels of CO2 inflow into a city (“background challenge”).

b. separating the anthropogenic emissions from the influence of the biosphere (“biogenic challenge”).

c. representing spatially and temporally non-uniform emissions in models (“modelling challenge”).

d. implementing observation strategies not covered by the other challenges (“application-specific observational challenge”).

We provide and discuss the challenges on a city-by-city basis. Our primary focus, however, is on the relationships between cities: best practices and lessons learned from monitoring CO2 emissions in one city can be transferred to other cities with similar characteristics. Additionally, we identify cities with characteristics that strongly contrast with those of cities with existing urban monitoring systems.

While the notebook tool includes 308 cities, this paper focuses on the results for 96 cities with more than 200 000 inhabitants. We place a particular emphasis on Paris, Munich, and Zurich. These cities are pilot cities for the Horizon 2020-funded project Pilot Application in Urban Landscapes (“ICOS Cities”), where a range of urban CO2 monitoring methods are being implemented and assessed. According to our analyses, Zurich – and Munich especially – should be less challenging to monitor than Paris. Examining the challenges individually reveals that the most significant challenge relative to the other cities is the “modelling challenge” (c) for Zurich and Paris. Complex urban topography adds to the challenge for both cities, and in Zurich, the natural topography further amplifies the challenge. Munich has low scores across all challenges, but with the greatest challenge anticipated from the “application-specific observational challenge” (d). Overall, Bratislava (Slovakia) and Copenhagen (Denmark) are among the most distant from Paris, Munich, and Zurich in our dendrogram resulting from numerical cluster-analysis. This makes them strong candidates for inclusion in the ICOS Cities network, as they would potentially provide the most information on how to monitor emissions in cities that face different challenges.