Spatioethnic Household Carbon Footprints in China and the Equity Implications of Climate Mitigation Policy: A Machine Learning Approach

Abstract

This article relies on the first and only representative survey data to estimate household carbon footprints (CFs) of China’s large yet vastly understudied ethnic minority population, documenting for the first time significant ethnic disparities in CFs driven by ethnic minorities’ relatively worse-off living standards: From 2010 to 2020, China’s ethnic minority population contributed less than 6 percent of residential emissions, about 50 percent less than expected based on population share alone. Next, results from a counterfactual policy analysis find that the distributive effects of a carbon tax are regressive in urban areas but not in rural areas, increasing within and between ethnic group inequality in urban China. A carbon tax with revenue-neutral schemes, by contrast, helps to mitigate existing inequalities in society, reducing income- and ethnic-based forms of inequality. Results are robust to machine learning techniques employed to simulate potential heterogeneous household abatement scenarios. The findings emphasize the potential benefits of a carbon tax, contributing to a more comprehensive understanding of climate justice and informing policy decisions that promote equitable outcomes for vulnerable segments of society.