Connected and automated vehicles (CAVs) are expected to yield significant

We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges towards household energy conservation. The average response is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -30 to +10 kWh. Selective targeting of treatment using the forest raises social net benefits by 12-120 percent, depending on the year and welfare function. Pre-treatment consumption and home value are the strongest predictors of treatment effect. We find suggestive evidence of a "boomerang effect": households with lower consumption than similar neighbors are the ones with positive TE estimates.

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We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges toward household energy conservation. The average response to treatment is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -40 to +10 kWh. Households learn to reduce more over time, conditional on having responded in year one. Pre-treatment consumption and home value are the most commonly used predictors in the forest. The results suggest the ability to use machine learning techniques for improved targeting and tailoring of treatment.

Research spanning several disciplines has repeatedly documented disproportionate pollution exposure in low-income communities and communities of color. Among the various proposed causes of this pattern, those that have received the most attention are income inequality, discrimination, and firm costs (of inputs and regulatory compliance). We argue that an additional channel -information -is likely to play an important role in generating disparities in pollution exposure. We present multiple reasons for a tendency to underestimate pollution burdens. Using a model of housing choice, we then derive conditions under which "hidden" pollution leads to an inequality -even when all households face the same lack of information. This inequality arises when households sort according to known pollution and other disamenities, which we show are positively correlated with hidden pollution. To help bridge the gap between environmental justice and economics, we discuss the relationship between hidden information and three different distributional measures: exposure to pollution; exposure to hidden pollution; and welfare loss due to hidden pollution.