Measuring Mobility for Carbon Intensity

This is one part in a series of sustainability announcements, an overview of which can be found here. For our proposed methodology on measuring Travel Efficiency, see this blog post.

Carbon intensity metrics for the transportation sector

The lack of a standard approach for evaluating real-world emissions from transportation makes it difficult for anyone to understand climate progress, positive or negative, across all modes. For example, for conventional cars, fuel efficiency — distance possible per unit of fuel (or, inversely, fuel required per unit of driving distance) — is often used as a metric to measure climate change impact. Public transit operators, on the other hand, often report on total fuel consumption or total CO2 emissions. As the ultimate goal of transportation is moving people, we propose passenger Carbon Intensity — emissions per passenger-distance — as a standard metric that can be applied across all modes to understand climate-related performance.

California recently passed the Clean Miles Standard to apply this measure to reporting requirements for the State’s largest ride-sourcing (or ridehailing) companies. Environmental experts, such as the Union of Concerned Scientists, applauded the move as a step in the right direction to help governments plan for and manage the sector’s growing climate footprint.

We believe the measure has broad applicability to help many different actors — companies providing mobility solutions, cities and government agencies, or researchers — evaluate climate-related performance across all modes.

How much climate-related emissions are required to move a person one mile?

In consultation with the World Resources Institute (WRI), Uber has been working on an approach to measure passenger carbon intensity across modes, as well as engaging city leaders and NGOs to gather feedback and identify how it can complement city and government climate planning. Additionally, Uber consulted with WRI and Fehr & Peers to understand the limitations and opportunities of using data from its mobility platform to compute a real-world measure of carbon intensity with reasonable accuracy.

The approach builds a climate emissions layer on top of travel efficiency, another metric that Uber developed with Fehr & Peers to evaluate the productivity of moving people rather than cars. We aimed to develop a climate metric that could be used to evaluate any mode (e.g. bike, car, bus, etc.) taken by any means (e.g. personally owned and used, via a ride-sourcing app, transit service, etc.).

Source: World Resources Institute

Carbon intensity across modes and means

Carbon Intensity aims to capture all of the CO2 emissions resulting from energy consumed from activities directly related to moving people. To move a passenger, emissions could result from several activities:

  1. Efforts to refuel, maintain, service, park or relocate vehicles in preparation for passenger service
  2. Efforts to move vehicles to pick up passengers
  3. Efforts to move passengers

When applied to a personally owned and operated vehicle, the driver is generally a passenger too. In the case of someone driving alone in their own car, for instance — a mode of travel accounting for more than 60% of all vehicle-miles traveled, according to US government data — the vast majority of emissions come from the third activity of moving passengers. In some instances where the driver makes substantial unintentional or intentional deviations from their intended route, such as driving an additional 8 minutes to find a parking (as one study proposes as the average in major global cities), emissions from the first activity may warrant attention. When private car owners drive to pick people up or get groceries or other goods — activities that may add as much as 100 billion vehicle miles to US roads — the second activity may apply.

For a typical transit system, especially those with fixed-route service, all three activities can apply: (1) drivers or operators move vehicles around the yard or to service centers for routine upkeep, (2) vehicles move between the yard and their first or last passenger pick-up location, and (3) vehicles offer passenger trips between stops. Many agencies often record vehicle mileage accrued during the third activity as “revenue mileage” or “fare miles”. In all cases, the driver would likely not count as a passenger.

For ride-sourcing platforms and other on-demand transportation services, emissions can similarly result from all three activities. A driver may (1) reposition their vehicle to an area of higher trip demand, and (2) then accept a trip and travel en route to pick up an awaiting passenger. Finally, once the rider enters their vehicle, (3) the passenger portion of the journey begins. In some cases, drivers may also become passengers, such as when using destination-based features offered by some ride-source platforms to end rider trips near desired locations, or while participating in carpooling services.

Calculating Carbon Intensity

Simply put, this application of carbon intensity creates a ratio of two parameters: CO2 emissions from enabling passenger movement over passenger distance traveled.

This parameter aims to capture all CO2 emissions that can be reasonably measured and allocated to passenger movement. To the greatest extent feasible, emissions from all three activities listed above should be taken into account. All entities will face natural limitations for gathering data to calculate emissions, and must make necessary tradeoffs between accuracy and feasibility. On top of vehicle mileage data, the emissions parameter depends on fuel consumption, fuel efficiency, and fuel-type related emissions factors data.

This parameter captures the total distance traveled by each passenger inside a vehicle. Care must be taken to determine when to count drivers as passengers depending on the mode and means. For cases where the operator of the vehicle is traveling to the destination (i.e. personal driving or carpooling), the driver should be counted as a passenger. Uber’s methodology for travel efficiency covers the challenges of measuring passenger distance traveled at greater depth.

Carbon Intensity Application Example

Uber consulted with WRI experts to understand how data collected in the natural course of business on Uber’s ride-sourcing platform could be used to calculate a reasonably accurate estimate of carbon intensity based on real-world mobility.

A ride-sourcing trip could look like this:

  1. Driver drives for 3 miles with an empty vehicle to an area where the driver expects ride requests (emissions activity 1 from above, known as “open” status in the world of ride-sourcing)
  2. Driver accepts a ride request and moves to meet the rider 2 miles away (activity 2: “en-route”)
  3. Driver picks up 2 passengers and drives 5 miles to their destination (activity 3, “on-trip”)
Source: Fehr & Peers, Example for Illustrative Purposes

The Carbon Intensity of this trip could be computed as CO2 emissions associated with activities 1 through 3 divided by the passenger distance traveled. For illustrative purposes, we make the assumption that the trip occurs in a hybrid vehicle at average speeds and within an urban area. We assume this hybrid vehicle’s average fuel efficiency is 50 MPG.

Vehicle distance traveled through all three states (Online, En-Route and On-Trip) multiplied by the CO2 emission factor, gives total estimated emissions:

¹ US EPA’s emissions factor for gasoline fuel

To aggregate trips across multiple drivers and passengers, the above may be computed for all trips. Total estimated emissions divided by total passenger miles completed gives a miles-weighted average of carbon intensity. For the sake of simplicity in the example above, we assumed a static fuel efficiency and a perfect environment where all necessary data is available. Depending on the mode and means, some entities may have access to higher accuracy, real-world information on emissions (e.g. vehicle-speed-based fuel economy, start/stop emissions, or tailpipe mass spectroscopy samples). Many others may need to resort to higher level averages and reasonable assumptions of real-world activity. While carbon intensity practitioners should aim for high accuracy methods, we recognize the tradeoffs most will face between real-world accuracy and implementation feasibility.

Known limitations

The activities covered above do not account for upstream or downstream emissions associated with the drivers (e.g. commuting to work), vehicles themselves (e.g. vehicle manufacturing, recycling and disposal; maintenance inputs, etc.), fuel production, and those resulting from the corporate activities of organizations facilitating the mobility services. Some of these gaps could be covered by additional emissions factors for some modes, such as upstream fuel production or vehicle lifecycle emissions when data on majority of the vehicles’ useful life is covered by the analysis. In other cases, further work may be needed to understand which emissions could be accurately allocated to mobility services on a per-passenger-mile (or passenger-kilometer) basis.

Moving Forward

The transportation sector is one of the largest emitters in the world. Carbon Intensity metrics can apply to all passenger modes and means — both traditional and new — and evaluate emissions performance against key benchmarks. In simplest terms, the metric sets into ratio what we need less of, carbon emissions, against what we want more of to propel our cities and economies forward: the movement of people.

We welcome your thoughts and feedback on the above proposed approach. Please contact to provide input.



Insights and updates from the Uber Comms & Policy team

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