Distributed Effects of Climate Policy: A Machine Learning Approach

Christopher Knittel
Massachusetts Institute of Technology
February 22, 2021 – 04:00 – 05:15 PM (CET)
Live virtual event: please register for this talk via Zoom


Seminar Abstract
We employ machine learning techniques to estimate household carbon footprints (HCFs) for the average household in each Census tract—geographic areas that represent roughly 4,000 people. We find that there is significant variation in carbon footprints across income and geography; income effects are driven by higher footprints related to trans-portation and consumer products and services, while geographic effects are primarily a result of the variable carbon intensity of the electricity grid. Using these footprints,we assess the net effects of various climate policies on households in the United States paying particular attention to the distribution across geography, urbanity, and income groups. Our objective is to improve the understanding of the potential for regressivity, geographic transfers, and rural-urban transfers among climate policy options and test for ways to control for transfers—preserving transfers from high-income households to low-income households, but mitigating transfers from rural areas to urban areas and from the Midwest and South to the Coasts. Our focus is on the net increase or decrease of annual household expenses under 12 different policy scenarios, which included both carbon pricing schemes and regulatory standards. We find regulatory standards tend to be regressive and, on average, are a net cost to low-income households—especially those in rural areas. Carbon pricing, when accompanied with a dividend, is progressive for urban, rural, and suburban households, with the average low-income household receiving a larger dividend check than they spend in carbon taxes. However, there are transfers from the Midwest and Plains to the Coasts when the dividend is evenly divided. We show that this can be mitigated through adjusting the dividend slightly  (<8% increase or decrease). Increasing the progressive structure of a policy benefits rural households more on average, but increases the overall heterogeneity of impacts within each income group. Reducing the transfers between geographic regions and urban-rural households increases the average benefit to low-income households and reduces the heterogeneity of impacts within income groups. We encourage policy makers to assess and control for unwanted transfers between households.


Speaker Bio
Christopher Knittel is the George P. Shultz Professor of Applied Economics in the Sloan School of Management at the Massachusetts Institute of Technology. He is also the Director of MIT’s Center for Energy and Environmental Policy Research which has served as the hub for social science research on energy and the environmental since the late 1970s. Professor Knittel also co-directs of The E2e Project, a research initiative between MIT and UC Berkeley to undertake rigorous evaluation of energy efficiency investments. He joined the faculty at MIT in 2011, having taught previously at UC Davis and Boston University. Professor Knittel received his B.A. in economics and political science from the California State University, Stanislaus in 1994 (summa cum laude), an M.A. in economics from UC Davis in 1996, and a Ph.D. in economics from UC Berkeley in 1999. His research focuses on environmental economics, studying how firms and consumers respond to policies. He is a Research Associate at the National Bureau of Economic Research in the Productivity, Industrial Organization, and Energy and Environmental Economics groups. He is the co-editor of the Journal of Public Economics, and an associate editor of the Journal of Transportation Economics and Policy, and Journal of Energy Markets, having previously served as an associate editor of The American Economic Journal -- Economic Policy and The Journal of Industrial Economics. His research has appeared in The American Economic Review, The American Economic Journal, The Review of Economics and Statistics, The Journal of Industrial Economics, The Energy Journal and other academic journals.


Admission information
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