# Analyzing Sustainability Reports Using Natural Language Processing

arXiv: https://arxiv.org/abs/2011.08073 · HTML: https://mlsanity.com/paper/2011.08073/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 18 of 18)

- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [chatClimate: Grounding Conversational AI in Climate Science](https://mlsanity.com/paper/2304.05510/) (2023)
- [CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://mlsanity.com/paper/2307.15770/) (2023)
- [Towards Climate Awareness in NLP Research](https://mlsanity.com/paper/2205.05071/) (2022)
- [ESGReveal: An LLM-based approach for extracting structured data from ESG reports](https://mlsanity.com/paper/2312.17264/) (2023)
- [Towards Answering Climate Questionnaires from Unstructured Climate Reports](https://mlsanity.com/paper/2301.04253/) (2023)
- [SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation](https://mlsanity.com/paper/2412.10906/) (2024)
- [DEEPAG'E: Answering Questions in Portuguese about the Brazilian Environment](https://mlsanity.com/paper/2110.10015/) (2021)
- [NewsPanda: Media Monitoring for Timely Conservation Action](https://mlsanity.com/paper/2305.01503/) (2023)
- [Responsible Retrieval Augmented Generation for Climate Decision Making from Documents](https://mlsanity.com/paper/2410.23902/) (2024)
- [Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks](https://mlsanity.com/paper/2303.13373/) (2023)
- [Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals](https://mlsanity.com/paper/2404.13885/) (2024)
- [Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures](https://mlsanity.com/paper/2312.17337/) (2023)
- [AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements](https://mlsanity.com/paper/2502.07022/) (2025)
- [Tracking environmental policy changes in the Brazilian Federal Official Gazette](https://mlsanity.com/paper/2202.10221/) (2022)
- [Supply chain emission estimation using large language models](https://mlsanity.com/paper/2308.01741/) (2023)
- [Identifying Climate Targets in National Laws and Policies using Machine Learning](https://mlsanity.com/paper/2404.02822/) (2024)
- [ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation](https://mlsanity.com/paper/2603.13154/) (2026)

## Abstract (arXiv)

Climate change is a far-reaching, global phenomenon that will impact many aspects of our society, including the global stock market citedietz2016climate. In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG). However, given this abundance of data, sustainability analysts are obliged to comb through hundreds of pages of reports in order to find relevant information. We leveraged recent progress in Natural Language Processing (NLP) to create a custom model, ClimateQA, which allows the analysis of financial reports in order to identify climate-relevant sections based on a question answering approach. We present this tool and the methodology that we used to develop it in the present article.