2019/11/26 by Victor Kristof, Valentin Quelquejay-Leclère, Kristof, Victor +9 · 1 citation
Environmental Science · Social Sciences · #Computers and Society (cs.CY) #Environmental Education and Sustainability #FOS: Computer and information sciences #FOS: Physical sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Physics and Society (physics.soc-ph) #Social Acceptance of Renewable Energy
paper · pdf · doi:10.48550/arxiv.1911.11658
openalex publication_date 2019/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.