using machine learning techniques to analyze ESG reports: what machine learning can uncover in ESG reports to auton the European sustainability reporting standards

Silva, Nuno André daWagenknech, TobiasRodel, Kevin2026-03-312026-03-312025-01-202025-01-20http://hdl.handle.net/10362/201937This work explores the application of natural language processing (NLP) to analyze Environmental, Social, and Governance (ESG) reports, addressing challenges in analyzing and benchmarking ESG efforts. The developed ESGAnalyzer extracts ESG topics and trends on a sectoral, industrial, and country level and the ESRSAnalyzer provides insights into the reporting of European Sustainability Reporting Standards (ESRS). Despite data constraints, the models achieved accuracies of 88% and 82%. The findings highlight variability in ESG practices, offering actionable benchmarks and insights, accelerating report analysis. By advancing these analytics, this study contributes to the understanding of ESG trends and theadaptation of ESRS regulation.engEnvironmentalSocialGovernance (ESG)SustainabilityReportingDisclosureBenchmarkingCorporate sustainability Reporting directive (CSRD)European Sustainability Standard reporting (ESRS)Machine learning (ML)BERTESG BERT modelNatural language processing (NLP)using machine learning techniques to analyze ESG reports: what machine learning can uncover in ESG reports to auton the European sustainability reporting standardsmaster thesis204133661