Distributed household effects of climate policy in the United States

Abstract

The net effects of various climate policies on households in the United States are assessed, with particular attention to the distribution of economic outcomes across geography, urbanity, and income groups. Climate policy has the potential to assess more costs to low-income households than high-income households (regressive) as well as more costs to rural households than metropolitan. The objective of this study was to improve the understanding of the potential for regressivity, geographic transfers, and rural-urban transfers among climate policy options and to test for ways to mitigate regressivity and unwanted transfers. Using different machine learning algorithms, I created a statistical model of the household carbon footprint (HCF) for an average household in each US Census tract. Policy outcomes were assessed by quantifying the net increase or decrease of annual household expenses (e.g.

electricity, utilities, and gasoline consumption) under 12 different policy scenarios, which included carbon pricing schemes, regulatory standards (Corporate Average Fuel Economy Standards, Clean Energy Standards, and the Clean Power Plan), and a scenario that combined carbon pricing and command-and-control regulation. I found that there is significant variation in carbon footprints with income and geography; income effects are mostly driven by higher footprints related to transportation and consumer products and services, while geographic effects are affected by the carbon intensity of the electricity grid. Carbon pricing, when accompanied with a dividend, is progressive for urban, rural, and suburban households. There are transfers from the Midwest and Plains to the Coasts when the dividend is evenly divided, but this can be mitigated though adjusting the dividend slightly (<8% increase or decrease).

Adjusting the dividend to increase the amount for low-income households and reduce the amount for high-income households benefits rural households more on average, but increases the overall heterogeneity of impacts within each income group. Adjusting the carbon dividend for both geography and urbanity increases the average benefit to low-income households and reduces the heterogeneity of impacts within income groups. The effects of the regulatory policy tends to be regressive and are, on average, a net cost to households who are low income - especially those in rural areas. Combining a carbon price and dividend with regulatory standards can remove the regressive trend of regulations, but regional and urban-rural transfers are harder to mitigate.