A User Study of Perceived Carbon Footprint

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.


Présenté à:
Climate Change Workshop at NeurIPS, Vancouver, BC, Canada, December 8-14, 2019
Année
2019
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 Notice créée le 2020-02-21, modifiée le 2020-04-21

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