Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909056849936384 |
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| author | Schimanski, Tobias Senni, Chiara Colesanti Gostlow, Glen Ni, Jingwei Yu, Tingyu Leippold, Markus |
| author_facet | Schimanski, Tobias Senni, Chiara Colesanti Gostlow, Glen Ni, Jingwei Yu, Tingyu Leippold, Markus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17337 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures Schimanski, Tobias Senni, Chiara Colesanti Gostlow, Glen Ni, Jingwei Yu, Tingyu Leippold, Markus Computation and Language General Economics Economics 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. |
| title | Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures |
| topic | Computation and Language General Economics Economics |
| url | https://arxiv.org/abs/2312.17337 |