Carbon Labeling for Consumer Food Goods

We construct a model to predict how consumers will respond to better information about the carbon content of 42 foods and a nonfood composite as well as product categories through a label, and provide guidance as to what kinds of goods would provide the highest CO¬2eq emission reductions through a labeling scheme. Our model assumes that consumers value their individual carbon footprint, allowing us to utilize estimates of own- and cross-price elasticities of demand from the literature on demand analysis. We make three different assumptions about how consumers currently value their carbon footprint and find that when a label informs consumers, their baseline perception matters. We also find that carbon labels on alcohol and meat would achieve the largest decreases in carbon emissions.