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The Keyword

New ways we’re helping reduce transportation and energy emissions

Image shows a map with directions for a two-wheeled vehicle.
Image shows EV range associated with driving directions.

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1

Reductions in stops estimates are based on early data points from Google’s analysis of traffic patterns before and after recommended adjustments to traffic signals that were implemented during tests conducted in 2022 and 2023. Emissions reductions estimates are modeled using a Department of Energy emissions model. A single fuel-based vehicle type is used as an approximation for all traffic, and it is not yet adjusted for local vehicle mix. These data points are averaged from coordinated intersections, and are subject to variation based on existing scenarios. We expect these estimates to evolve over time and look forward to sharing continued results as we perform additional analysis.

2

Google uses an AI prediction model to estimate the expected fuel or energy consumption for each route option when users request driving directions. We identify the route that we predict will consume the least amount of fuel or energy. If this route is not already the fastest one and it offers meaningful energy and fuel savings with only a small increase in driving time, we recommend it to the user. To calculate enabled emissions reductions, we tally the fuel usage from the chosen fuel-efficient routes and subtract it from the predicted fuel consumption that would have occurred on the fastest route without fuel-efficient routing and apply adjustments for factors such as: CO2e factors, fleet factors, well-to-wheels factors, and powertrain mismatch factors. We then input the estimated prevented emissions into the EPA’s Greenhouse Gas Equivalencies Calculator to calculate equivalent cars off the road for a year. The cumulative figure covers estimated emissions prevented after fuel-efficient routing was launched, from October 2021 through September 2023. Enabled emissions reductions estimates include inherent uncertainty due to factors that include the lack of primary data and precise information about real-world actions and their effects. These factors contribute to a range of possible outcomes, within which we report a central value.


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