  It is clear by now that climate change mitigation relies on our capacity to
guide urban systems towards a low-carbon phase and that the urban
transportation sector plays a major role in this transition. It is estimated
that around 30% of total CO2 emissions worldwide come from the urban
transportation sector. Regardless of its importance, detailed estimations of
transport-related emissions in cities are still rare to find, hindering our
capacity to understand and reduce them. This work aims to develop a replicable
and fast method for GHG estimation from GPS (Global Positioning System) data
and to introduce a simple sinuosity-based algorithm for such. We applied the
method for 1 year of GPS data in the city of Rio de Janeiro. Our results were
compared to top-down estimations from fuel consumption and proved to be valid
after a simple data filling process. Our GPS-based approach allowed for much
finer spatial and temporal descriptions of emissions and we further showed
possible policy insights that can be extracted from the estimated emissions
based on the proposed method.
