Publicação

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

Ver documento

Detalhes bibliográficos
Resumo:This 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.
Autores principais:Rodel, Kevin
Assunto:Environmental Social Governance (ESG) Sustainability Reporting Disclosure Benchmarking Corporate sustainability Reporting directive (CSRD) European Sustainability Standard reporting (ESRS) Machine learning (ML) BERT ESG BERT model Natural language processing (NLP)
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

Registos relacionados

Registos relacionados