(untitled) (gjstudies, 2024)
This paper introduces a novel, cross-disciplinary methodology that applies artificial intelligence to the complex domain of environmental accounting, specifically the analysis ofprovisions and contingent liabilities. Traditional approaches to this critical financial reporting area are largely manual, reliant on expert judgment, and often inconsistent, leadingto significant valuation uncertainties and reporting discrepancies across organizations andjurisdictions. We propose and evaluate a hybrid AI framework that synergistically combines a rule-based expert system, trained on international financial reporting standards(IFRS) and environmental regulations, with a deep learning component based on a modified Transformer architecture. This deep learning model is specifically designed to processand analyze unstructured textual data from corporate environmental reports, legal documents, scientific assessments, and regulatory filings. The system’s primary innovation liesin its ability to semantically parse qualitative disclosures, quantify narrative risk descriptions, and probabilistically model the financial implications of environmental events—suchas soil remediation obligations, end-of-life asset decommissioning, or litigation from pollution—which are inherently uncertain in timing and amount. Our methodology divergesfrom conventional financial AI applications by explicitly modeling epistemic (knowledgebased) and aleatory (random) uncertainty within its architecture, providing not just a pointestimate but a confidence distribution for potential liability valuations. We validate theframework using a newly constructed dataset of 850 corporate annual reports and sustainability disclosures from extractive, manufacturing, and utility sectors globally from 1995 to2004. Results demonstrate that the AI system achieves a 92.7% concordance rate with apanel of audit experts in identifying material environmental provisions, and its probabilisticvaluation outputs correlate with subsequent actual cash outflows with an R² of 0.81, significantly outperforming traditional linear regression models (R² of 0.52) and human analystconsensus forecasts (R² of 0.65). Furthermore, the system uncovered systematic patternsof under-provisioning in industries facing emerging, long-tail environmental risks, a findingwith substantial implications for financial stability and environmental governance. The research contributes original insights by demonstrating how AI can bring rigor, scalability,and transparency to a subjective area of accounting, effectively acting as a computationalauditor for environmental risk. It establishes a new paradigm for ’explainable AI in sustainability finance,’ where model decisions are traceable to specific regulatory clauses anddisclosed evidence.