Skip to main content

The meteoric rise of shale oil and gas drilling in the United States poses significant challenges for reducing greenhouse gas emissions. The methane emitted from these operations has 34 times greater short-term global warming potential than CO2, thus contributing aggressively to climate change. Existing methods of measuring methane emissions are imprecise and expensive, severely limiting regulators’ ability to monitor and enforce regulations. Without reliable estimates of methane emissions, regulators are unable to efficiently target leaks, incentivize their prevention, and realize climate benefits.

This project will develop and test a data-driven approach to methane emissions monitoring and regulation, in close partnership with Colorado regulators. It will leverage large-scale administrative data such as permitting records and historical inspections to build a supervised machine learning model that predicts methane leaks at facilities. These predictions can then be used to target the collection of high-resolution emissions measurements at the facility level using state-of-the-art satellites. This breakthrough combination of machine learning and new measurement technology would provide regulators with a potentially highly cost-effective and scalable inspection targeting framework. Finally, a randomized controlled trial will rigorously estimate the impact of applying the novel technology for regulatory enforcement, and estimate the benefits of improved emissions monitoring.

Researchers

Thomas R. Covert

Assistant Professor, Booth School of Business

Michael Greenstone

Milton Friedman Distinguished Service Professor in Economics; Director, Becker Friedman Institute & Energy Policy Institute at Chicago (EPIC)