metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license: cc-by-nc-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids: []
pretty_name: EnvironmentalClaims
dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': 'no'
            '1': 'yes'
  splits:
    - name: train
      num_bytes: 346686
      num_examples: 2117
    - name: validation
      num_bytes: 43018
      num_examples: 265
    - name: test
      num_bytes: 42810
      num_examples: 265
  download_size: 272422
  dataset_size: 432514

Dataset Card for environmental_claims

Dataset Description

Homepage: climatebert.ai
Repository:
Paper: arxiv.org/abs/2209.00507
Leaderboard:
Point of Contact: Dominik Stammbach

Dataset Summary

We introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies.

Supported Tasks and Leaderboards

The dataset supports a binary classification task of whether a given sentence is an environmental claim or not.

Languages

The text in the dataset is in English.

Dataset Structure

Data Instances

{
    "text": "It will enable E.ON to acquire and leverage a comprehensive understanding of the transfor- mation of the energy system and the interplay between the individual submarkets in regional and local energy supply sys- tems.",
    "label": 0
}

Data Fields

text: a sentence extracted from corporate annual reports, sustainability reports and earning calls transcripts
label: the label (0 -> no environmental claim, 1 -> environmental claim)

Data Splits

The dataset is split into:

train: 2,400
validation: 300
test: 300

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

Our dataset contains environmental claims by firms, often in the financial domain. We collect text from corporate annual reports, sustainability reports, and earning calls transcripts.

For more information regarding our sample selection, please refer to Appendix B of our paper, which is provided for citation.

Who are the source language producers?

Mainly large listed companies.

Annotations

Annotation process

For more information on our annotation process and annotation guidelines, please refer to Appendix C of our paper, which is provided for citation.

Who are the annotators?

The authors and students at University of Zurich with majors in finance and sustainable finance.

Personal and Sensitive Information

Since our text sources contain public information, no personal and sensitive information should be included.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Dominik Stammbach
Nicolas Webersinke
Julia Anna Bingler
Mathias Kraus
Markus Leippold

Licensing Information

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit creativecommons.org/licenses/by-nc-sa/4.0.

If you are interested in commercial use of the dataset, please contact markus.leippold@bf.uzh.ch.

Citation Information

@misc{stammbach2022environmentalclaims,
  title = {A Dataset for Detecting Real-World Environmental Claims},
  author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
  year = {2022},
  doi = {10.48550/ARXIV.2209.00507},
  url = {https://arxiv.org/abs/2209.00507},
  publisher = {arXiv},
}

Contributions

Thanks to @webersni for adding this dataset.