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Distributed Effects of Climate Policy: A Machine Learning Approach

Tomas W. Green and Christopher R. Knittel

September 2020

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 transportation 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 bene t 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.

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