A User Study of Perceived Carbon Footprint

TL;DR

This study investigates how people perceive their carbon footprint using a statistical model and pairwise comparisons, revealing insights that could improve climate communication and mitigation strategies.

Contribution

It introduces a novel active-learning approach to model perception of carbon footprint through pairwise comparisons of actions.

Findings

Collected 2183 comparisons from 176 users

Identified promising strategies for climate communication

Developed a new statistical model for perception analysis

Abstract

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.