Projects: Data-driven Environmental Enforcement
The Energy and Environment Lab invites a postdoc to collaborate on a suite of projects that leverage advances in monitoring technology and machine learning approaches to inform environmental policy, under the mentorship of Michael Greenstone, the Milton Friedman Distinguished Service Professor in Economics, the College, and the Harris School; Director of the Energy and Environment Lab, the Becker Friedman Institute, and the Energy Policy Institute at Chicago.
Congestion and Traffic Safety
Many cities across the United States have adopted Vision Zero, the policy goal of eliminating all traffic-related deaths and serious injuries. But what are the costs of achieving Vision Zero and what are the most efficient policy instruments to get there? Monitoring technologies offer the potential to revolutionize urban policy by providing governments with big data to inform policymaking. As part of NYC’s Vision Zero, the Department of Transportation is more than quintupling the number of speed cameras in the city. Leveraging our access to unique data of taxi, for-hire vehicle, and city fleet trips to model the impacts of new traffic cameras on vehicle crashes, slowdowns, and congestion spillovers. The post-doc would utilize large administrative datasets on camera enforcement, vehicle crashes, segment-level traffic speeds, and high-resolution driver behavior, to help measure the costs and benefits of enforcement strategies for fatality/injury reduction to inform optimal policy for urban traffic safety.
Leveraging Satellite Data to Reduce Oil & Gas Methane Emissions
The meteoritic rise of shale oil and gas (O&G) drilling in the United States poses significant challenges for reducing greenhouse gas emissions. The methane emitted has around 30 times greater short-term global warming potential than CO2, contributing aggressively to climate change. Reliable estimates of emitted methane are essential to fully understand and mitigate the environmental threat presented by shale drilling. While some estimates suggest that approximately 2.3% of gross natural gas production is leaked per year, accurate monitoring of emissions remains extremely challenging. Currently, regulators visit individual facilities to measure emissions; but due to budgetary constraints and a fast-growing industry inspector can visit only a fraction of the facilities each year. This project will leverage a wealth of administrative data and novel remote sensing data from recently-launched satellites to estimate facility-level methane emissions. Leveraging these unique data and state-of-the-art machine learning techniques, the project will help regulators re-design their monitoring and enforcement strategy to realize improvements in regulatory efficiency and reductions in greenhouse gases.
Beyond Inspection Targeting: Deterrence through Machine Learning
Building on a three-year partnership with the Environmental Protection Agency (EPA), this project aims to scale a machine learning-driven framework across inspection targeting programs at EPA. The Clean Water Act (CWA) is one program where data-driven inspection targeting can directly influence environmental policy. Using state-of-the-art machine learning models, we can generate risk scores for the likelihood individual firms will violate CWA standards, and use these model-generated risk scores to study facility compliance behavior and identify the most effective approaches to deterrence in a randomized field trial.
Mentor: Michael Greenstone, Milton Friedman Distinguished Service Professor in Economics, the College, and the Harris School, University of Chicago; Director, Becker Friedman Institute for Research in Economics; Director, Energy Policy Institute at the University of Chicago (EPIC); Director, Tata Center for Development at the University of Chicago
Michael Greenstone is the Milton Friedman Distinguished Service Professor in Economics, the College, and the Harris School, as well as the Director of the Becker Friedman Institute and the interdisciplinary Energy Policy Institute at the University of Chicago. He previously served as the Chief Economist for President Obama’s Council of Economic Advisers, where he co-led the development of the United States Government’s social cost of carbon. Greenstone also directed The Hamilton Project, which studies policies to promote economic growth, and has since joined its Advisory Council. He is an elected member of the American Academy of Arts and Sciences, a fellow of the Econometric Society, and a former editor of the Journal of Political Economy. Before coming to the University of Chicago, Greenstone was the 3M Professor of Environmental Economics at MIT.
Greenstone’s research, which has influenced policy globally, is largely focused on uncovering the benefits and costs of environmental quality and society’s energy choices. His current work is particularly focused on testing innovative ways to increase energy access and improve the efficiency of environmental regulations around the world. Additionally, he is producing empirically grounded estimates of the local and global impacts of climate change as a co-director of the Climate Impact Lab. He also created the Air Quality Life Index™ that provides a measure of the gain in life expectancy communities would experience if their particulates air pollution concentrations are brought into compliance with global or national standards.
Greenstone received a Ph.D. in economics from Princeton University and a BA in economics with High Honors from Swarthmore College.