Nano-ESG: Extracting Corporate Sustainability Information from News Articles

TL;DR

This paper introduces Nano-ESG, a novel dataset and methodology that uses NLP and large language models to extract and analyze sustainability information from news articles about companies, providing an alternative to traditional ratings.

Contribution

It presents a new large-scale news dataset related to corporate sustainability and demonstrates the effectiveness of NLP and LLMs in extracting relevant sustainability insights.

Findings

LLMs can accurately identify sustainability-related content in news articles.

The dataset enables detailed analysis of company sustainability practices.

The approach offers an independent supplement to traditional sustainability ratings.

Abstract

Determining the sustainability impact of companies is a highly complex subject which has garnered more and more attention over the past few years. Today, investors largely rely on sustainability-ratings from established rating-providers in order to analyze how responsibly a company acts. However, those ratings have recently been criticized for being hard to understand and nearly impossible to reproduce. An independent way to find out about the sustainability practices of companies lies in the rich landscape of news article data. In this paper, we explore a different approach to identify key opportunities and challenges of companies in the sustainability domain. We present a novel dataset of more than 840,000 news articles which were gathered for major German companies between January 2023 and September 2024. By applying a mixture of Natural Language Processing techniques, we first identify relevant articles, before summarizing them and extracting their sustainability-related sentiment and aspect using Large Language Models (LLMs). Furthermore, we conduct an evaluation of the obtained data and determine that the LLM-produced answers are accurate. We release both datasets at https://github.com/Bailefan/Nano-ESG.