Environmental Claim Detection

• URL: http://arxiv.org/abs/2209.00507v4
• Date: Fri, 26 May 2023 07:25:47 GMT
• Title: Environmental Claim Detection
• Authors: Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler, Mathias Kraus, Markus Leippold
• Abstract summary: This paper introduces the task of environmental claim detection. We release an expert-annotated dataset and models trained on this dataset. We find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.
• Score: 6.2887102994549595
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.

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