Assessing corporate sustainability with large language models: evidence from Europe

Abstract

Abstract Companies play a crucial role in achieving global sustainability goals, yet evidence on their progress across environmental, social, and governance (ESG) dimensions remains limited. We develop a machine learning framework to systematically extract ESG indicators from corporate reports. Applying this approach to annual and sustainability reports of 600 large European firms (2014–2023), we construct a dataset of 2.9 million ESG observations across environmental, social, and governance topics. We assess ESG transparency based on disclosures aligned with the European Sustainability Reporting Standards (ESRS) and evaluate ESG performance using extracted numerical indicators. Results reveal a pronounced transparency gap: firms in the top ESG rating decile disclose 22% more indicators than those in the bottom decile, although this gap narrows over time. Performance trends are uneven: while most social indicators remain largely stagnant, except for gains in gender equality, environmental indicators show some improvement. Reported scope 3 emissions increase sharply, largely reflecting improved disclosure. Our open-source framework enables systematic tracking of corporate ESG efforts.