DRIVE v1.0: a data-driven framework to estimate road transport emissions and temporal profiles

Kühbacher, Daniel; Chen, Jia; Aigner, Patrick; Ilic, Mario; Super, Ingrid; Denier van der Gon, Hugo

doi:10.5194/gmd-18-9967-2025

Articles | Volume 18, issue 23

https://doi.org/10.5194/gmd-18-9967-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-9967-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 23

Model description paper

|

11 Dec 2025

Model description paper |

| 11 Dec 2025

DRIVE v1.0: a data-driven framework to estimate road transport emissions and temporal profiles

Daniel Kühbacher, Jia Chen, Patrick Aigner, Mario Ilic, Ingrid Super, and Hugo Denier van der Gon

Supplement

https://doi.org/10.5194/gmd-18-9967-2025-supplement

Model code and software

DRIVE v1.0 - A data-driven framework to estimate road transport emissions and temporal profiles Daniel Kühbacher et al. https://doi.org/10.5281/zenodo.14644298

Short summary

We present DRIVE v1.0, a data-driven framework to estimate road transport emissions, their temporal profiles, and the associated uncertainties. The method was applied to the city of Munich, where we present bottom-up emission estimates for the years 2019 to 2022. The estimates are compared against official municipal reports as well as national and European downscaled inventories.