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.

Research team

Joshua Skov

Joshua Skov an Instructor of Management at the University of Oregon Lundquist College of Business. He has been a consultant to business and government organizations on sustainability strategy for over 15 years. Skov has served over 50 clients in the U.S. and abroad, and served on advisory bodies for World Resources Institute, Oregon Department of Environmental Quality, ICLEI, and the National Academies. He is a co-founder and former principal of Good Company, a leading regional sustainability consultancy.

Aaron Toneys

Aaron Toneys is a Senior Associate at Good Company, an environmental consulting firm dedicated to making sustainability work. He is the principal developer of Good Company’s GHG inventory calculators and other environmental assessment tools. Mr. Toneys has worked on over 150 greenhouse gas inventories that include renewable energy facilities, alternative fuels technologies, construction projects, municipal government operations and entire communities.

Anne Brown

Anne Brown is an Assistant Professor in the School of Planning, Public Policy, and Management at the University of Oregon. Her research examines the intersection of equity, shared and innovative mobility, travel behavior, and transportation finance. Recent work analyzes how and when people change their travel behavior, the false equivalency between being car-less and car-free, and how parking reform can increase the supply of affordable housing.

Project description

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.

Research questions

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.

Broader impacts

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.