From fragmentation to interoperability: How semantic models transform environmental, social, governance (ESG) reporting, knowledge, and sustainability governance

Environmental, social, and governance (ESG) reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global sustainability goals, and limited stakeholder usability. Numerous studies prove that ontology-based solutions can address several challenges that occur during ESG reporting activities. Although semantic technologies offer valuable benefits for ESG reporting, their utilization in this field remains constrained. Most ontology-based solutions remain in developmental stages, and they are not broadly utilized since organizations lack an understanding of how these tools would help address their reporting problems. This study performs a systematic literature review (SLR) that investigates 19 peer-reviewed studies obtained from Scopus and Web of Science under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards. The SLR identifies critical gaps: (1) existing ontology-driven solutions can address key problems in current ESG reporting; (2) quantitative evaluation methods are rarely integrated with semantic tools, limiting actionable insights; and (3) alignment with evolving standards like the Sustainable Development Goals (SDGs) remains superficial. Based on the SLR insights, this research develops a novel framework through SLR findings by combining ontology-driven methods with quantitative assessment techniques. The framework achieves standardization of various reporting standards through an ESG ontology system that maps essential concepts to build an extensive taxonomy. SDG targets become mutually compatible through established SDG ontologies to allow businesses to measure their activities against international sustainability goals. Fuzzy Multi-Criteria Decision-Making (MCDM) techniques used in combination with an ESG maturity model create quantitative measures to assess ESG performance. The method produces measurable performance indicators that are supported by clear semantic links that allow valid benchmark assessments combined with better data unification and improved decision-making capabilities. The research creates operational frameworks that enable ESG information interoperability, which advance sustainability governance innovation and guide ESG ontology transformations.