Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures
December 28, 2023 ยท Declared Dead ยท ๐ Social Science Research Network
"No code URL or promise found in abstract"
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Authors
Tobias Schimanski, Chiara Colesanti Senni, Glen Gostlow, Jingwei Ni, Tingyu Yu, Markus Leippold
arXiv ID
2312.17337
Category
cs.CL: Computation & Language
Cross-listed
econ.GN
Citations
4
Venue
Social Science Research Network
Last Checked
5 months ago
Abstract
Nature is an amorphous concept. Yet, it is essential for the planet's well-being to understand how the economy interacts with it. To address the growing demand for information on corporate nature disclosure, we provide datasets and classifiers to detect nature communication by companies. We ground our approach in the guidelines of the Taskforce on Nature-related Financial Disclosures (TNFD). Particularly, we focus on the specific dimensions of water, forest, and biodiversity. For each dimension, we create an expert-annotated dataset with 2,200 text samples and train classifier models. Furthermore, we show that nature communication is more prevalent in hotspot areas and directly effected industries like agriculture and utilities. Our approach is the first to respond to calls to assess corporate nature communication on a large scale.
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