The Carbon Footprint of Ride-Hailing: GHG Inventory Methodology
Our goal is to provide a path for creating locally specific analysis to inform policy and planning. Specifically, we aim to produce tools for communities to generate initial answers for themselves.
Ride-hailing services such as Lyft and Uber have emerged quickly as a transportation option in many cities in the U.S. and globally, now providing about 6.5 million trips daily worldwide.
Simultaneously, many cities and metropolitan regions have transportation policy aimed at reducing greenhouse gas emissions. Our goal is to provide a path for creating locally specific analysis to inform policy and planning. Specifically, we aim to produce tools for communities to generate initial answers for themselves. In particular, we hope to inform local policy discussions in which climate goals are important for transportation planning.
For more details and project results, see the full paper and accompanying presentation.
What is the carbon footprint of Lyft and Uber?
How can communities quantify the impact of ride-hailing as part of local climate action?
More broadly, what is the connection between climate action and the sharing economy?
We aim to produce a simple quantitative template for communities to use in turning transportation and ride-hailing data into greenhouse gas emissions estimates, in the context of their greenhouse gas inventories. These calculations will, ideally, estimate both direct effects (i.e., emissions from transportation by ride-hailing use) as well as secondary or indirect effects (i.e., increases or decreases in total trips, as well as changes in modal split as a result of the presence of ride-hailing as an option). The research will consist of developing and refining the approach, and populating it with observed or estimated coefficients.
Sharing-economy business models are increasingly permeating consumption in a variety of spheres, from transportation (Lyft, Uber) and lodging (airbnb) to outdoor equipment (Spinlister), private parking spaces (Parkatmyhouse), and just about anything (Streetbank). Unfortunately, despite the substantial reshaping of consumption in many cases, the attention around these models rarely pays more than qualitative lip service to the net environmental impact associated with them. We hope to build a specific and easily-propagated methodology that can serve as an example of quantitative rigor for one important situation, while simultaneously emphasizing the areas of data uncertainty and policy opportunity in the sharing economy broadly.