Automated Identification of Climate Risk Disclosures in Annual Corporate Reports
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Abstract (arXiv)
It is important for policymakers to understand which financial policies are effective in increasing climate risk disclosure in corporate reporting. We use machine learning to automatically identify disclosures of five different types of climate-related risks. For this purpose, we have created a dataset of over 120 manually-annotated annual reports by European firms. Applying our approach to reporting of 337 firms over the last 20 years, we find that risk disclosure is increasing. Disclosure of transition risks grows more dynamically than physical risks, and there are marked differences across industries. Country-specific dynamics indicate that regulatory environments potentially have an important role to play for increasing disclosure.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3.
5 papers.
- 2021ClimateBert: A Pretrained Language Model for Climate-Related Textcited by 89
- 2022Environmental Claim Detectioncited by 11
- 2023Evaluating TCFD Reporting: A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosurescited by 3
- 2023Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial riskscited by 3
- 2023Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoringcited by 0
- 2023Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring4 Aug 2023
- 2023Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks21 Mar 2023
- 2023Evaluating TCFD Reporting: A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures1 Feb 2023
- 2022Environmental Claim Detection1 Sep 2022
- 2021ClimateBert: A Pretrained Language Model for Climate-Related Text22 Oct 2021