Environmental Claim Detection

TL;DR

This paper introduces the task of environmental claim detection, providing an annotated dataset and models to identify such claims at scale, aiding the transition to a green economy.

Contribution

It presents the first dataset and models for environmental claim detection, enabling automated analysis of corporate environmental claims.

Findings

Environmental claims in earnings calls have increased since 2015.

The dataset and models facilitate reliable detection of environmental claims.

This work supports transparency and comparability in environmental communications.

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.