Conference Agenda

Carbon pricing reduces emissions, but revenue recycling increases them. In 240,000 households in 22 EU countries, a €50/tCO2 increase in the carbon price significantly reduces emissions (-17\%), but disproportionately affects low-income households with higher carbon intensity of consumption, increasing inequality. Revenue redistribution reduces inequality but increases aggregate emissions, revealing an equity-pollution dilemma. While national equal per capita (EPC) transfers reduce the Gini index by 2.7\% and increase emissions by 2.2\%, EU EPC transfers reduce inequality by 4.2\% but increase emissions by 2.4\%. However, the additional impact of the dilemma on emissions is small compared to inequality-preserving transfers and, more importantly, to the reduction in baseline emissions.

Even if efficient on aggregate, Pigouvian policies may constitute an expected welfare loss for a large number of voters, which can hinder their political acceptability. Under full information, the aggregate gains could be redistributed to perfectly compensate the losers, but when the policy maker doesn't observe individual outcomes, they can at best target transfers based on observables associated with those outcomes, and the effectiveness of this “tagging” in increasing acceptability depends on the strength of the associations. I study this problem in the context of carbon pricing in the transportation sector by constructing a unique data set that links a hypothetical referendum on a real-world abatement target to a rich set of administrative data at the individual level, and estimating a model of voting that allows me to evaluate the expected support for the target under counterfactual fuel prices and transfer allocations. Using experimental variation in the prices and transfers presented to the respondents, I'm able to identify the distributions of their expected costs and willingnesses to pay for the policy target, while the administrative data makes it possible to condition these on essentially all observable characteristics that a policy maker might feasibly use as tags. I compare the expected support achievable under different assumptions on the policy maker's information set and other constraints, finding that the idiosyncracity of willingness to pay in particular limits the potential of tagging, especially if the transfers can only be positive like in revenue recycling. There is also a trade-off between political acceptability and welfare, following from the fundamental logic that a welfare-maximizing policy maker would, ceteris paribus, allocate transfers to the individuals that gain the highest utility from them, but when the utility gain of a voter gets high enough, their probability of supporting the policy reaches a point of diminishing returns.

Digital production and consumption represent 3,2% of the French greenhouse gas emissions. This paper seeks to analyze the distributional effect and efficiency of a tax on mobile internet to limit the environmental impact of digital. To this aim, we theoretically study the reaction of a monopolist who sells mobile data subscriptions to two types of consumers. We demonstrate that in this market with price discrimination, the monopolist decreases the price and the data allowance of these subscriptions following the tax implementation. Then, we empirically study the French households’ reaction to the tax implementation according to the market structure. We find that, unlike a tax on goods sold at unit price, a tax on mobile phone subscriptions is not regressive.

We analyze the distributional impacts of climate policy instruments by examining heterogeneity in households' carbon intensity of consumption. We construct a novel dataset that covers information about the carbon intensity of 1.5 million individual households from 88 countries, representative for more than 5 billion people. Using supervised machine learning, we analyze the non-linear contribution of household characteristics to predicting carbon intensity of consumption on a country-level.

Our results show that horizontal differences, i.e., differences within income groups, are generally larger than vertical differences, i.e., differences between income groups. Including household-level information beyond total household expenditures, such as information about vehicle ownership, space and energy use, increases accuracy of predicting households' carbon intensity. The importance of such features is country-specific and model accuracy varies across the sample. We identify six clusters of countries that differ with respect to the distribution of climate policy costs and their determinants. Our results highlight that some compensation measures may be more effective in reducing horizontal heterogeneity in some contexts than in others.