# Automated Identification of Climate Risk Disclosures in Annual Corporate Reports

arXiv: https://arxiv.org/abs/2108.01415 · HTML: https://mlsanity.com/paper/2108.01415/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2021.

## Built on by (most cited first, 5 of 5)

- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [Environmental Claim Detection](https://mlsanity.com/paper/2209.00507/) (2022)
- [Evaluating TCFD Reporting: A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures](https://mlsanity.com/paper/2302.00326/) (2023)
- [Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks](https://mlsanity.com/paper/2303.13373/) (2023)
- [Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring](https://mlsanity.com/paper/2308.02622/) (2023)

## 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.