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We analyze the distributional effects of climate policy by examining heterogeneity in households’ carbon intensity of consumption. We construct a novel dataset that includes information on the carbon intensity of 1.7 million individual households from 88 countries. First, we show that horizontal differences are generally larger than vertical differences. Then, we use supervised machine learning to analyze the non-linear contribution of household characteristics to the prediction of carbon intensity of consumption. Household income, proxied by total household expenditures, is usually an insufficient predictor for the additional costs of climate policy. Including household-level information beyond household income increases the accuracy of prediction. Our results highlight that, depending on the context, some compensation policies may be more effective in reducing overall heterogeneity than others.