A User Study of Perceived Carbon Footprint
This work addresses the challenge of climate communication for the general public, but it is incremental as it builds on existing methods for perception modeling.
The researchers tackled the problem of understanding people's perception of their carbon footprint by developing a statistical model based on pairwise comparisons of actions, collecting data from 176 users and 2183 comparisons, with early results showing potential to improve climate communication and mitigation.
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.