Methodology: how we estimate emission savings

We estimate emission per trip based on (1) average emissions per mile for the average vehicle for a ride option (e.g. Uber Green), and (2) observed distance traveled. This approach lets us estimate reasonable counterfactual scenarios.

Average emissions per mile is estimated using the methodology for Uber’s Climate Assessment and Performance Report (CAsPR) as much as possible. For example, when a Vehicle Identification Number (VIN) is available to us, we use that information to get more detailed emissions data. When the CAsPR methodology is not sufficient to identify differences between regular products and low-emission products (mainly outside of US/Canada/Europe), we use vehicle trip records to estimate fuel and engine type of vehicles. For example, a vehicle that completed at least 10 Comfort Electric trips is assumed to be an electric vehicle, whereas a vehicle that completed at least 10 Uber Green trips is assumed to be a hybrid. When we identify engine type as a battery electric or fuel cell, we assume the associated emissions to be zero. For hybrid vehicles, we assume a 33% reduction in emissions compared to regular internal combustion vehicles. Note that when we are estimating emissions, we consider other factors such as fleet mix, which allows us to more accurately reflect that both Uber Green and also UberX include electric and hybrid vehicles.

Observed distance traveled is estimated based on GPS points. We use a map-match method to minimize the impact of GPS data error.

Emission “savings” represent the emissions a rider has avoided by directly requesting a lower-emission ride option. Emission savings are calculated as the difference in CO₂ emitted from low-emission ride options on the Uber app and those of their standard-emission counterparts. So, for instance, emissions from Uber Green trips are compared with those of UberX trips, and Comfort Electric emissions are compared with Uber Comfort emissions. When UberX or Comfort is not available in the market, we choose the most comparable product based on price range and popularity.