Transforming Sustainability Reporting through Artificial Intelligence: A Systematic Literature Review (2014–2026)

Abstract

This study examines the transformation of sustainability reporting and the integration of Artificial Intelligence (AI) in ESG reporting, sustainability disclosure, and greenwashing detection. The review is motivated by the increasing complexity of sustainability reporting standards, the growing volume of ESG-related data, and persistent concerns regarding the credibility of symbolic or inconsistent sustainability disclosures. Using a Systematic Literature Review (SLR), this study analyzes 30 selected articles published between 2014 and 2026. The literature selection process was reconstructed using the PRISMA 2020 flow and supported by inclusion and exclusion criteria, quality appraisal, and thematic narrative synthesis. The findings indicate that sustainability reporting has shifted from voluntary non-financial disclosure toward a more strategic, data-driven, and digitally integrated reporting practice. AI, machine learning, Natural Language Processing (NLP), Large Language Models (LLMs), and data analytics are increasingly used to support ESG data processing, narrative extraction, emissions reporting, financial risk analysis, and greenwashing detection. Nevertheless, AI integration also introduces challenges related to data quality, model transparency, algorithmic bias, assurance, and regulatory readiness. This study contributes to the literature by mapping five synthesis themes and outlining future research directions for responsible AI-based sustainability reporting governance.

Keywords:  Sustainability Reporting, ESG, Artificial Intelligence, Machine Learning, Greenwashing