  We propose a statistical model to understand people's perception of their
carbon footprint. Driven by the observation that few people think of CO2 impact
in absolute terms, we design a system to probe people's perception from simple
pairwise comparisons of the relative carbon footprint of their actions. The
formulation of the model enables us to take an active-learning approach to
selecting the pairs of actions that are maximally informative about the model
parameters. We define a set of 18 actions and collect a dataset of 2183
comparisons from 176 users on a university campus. The early results reveal
promising directions to improve climate communication and enhance climate
mitigation.
