(untitled) (gjstudies, 2022)
This research introduces a novel methodological framework that integrates artificial intelligence with environmental accounting to enhance financial transparencythrough improved environmental reporting. Traditional approaches to environmental, social, and governance (ESG) reporting have been largely manual, inconsistent,and prone to greenwashing, creating significant transparency gaps between corporate environmental performance and financial disclosures. Our work divergesfundamentally from existing literature by proposing a hybrid AI architecture thatcombines natural language processing for unstructured environmental data extraction, convolutional neural networks for satellite and sensor imagery analysis ofenvironmental impacts, and blockchain-anchored verification systems to create immutable audit trails. This integrated system addresses the critical research questionof how automated, real-time environmental data collection and verification can reduce information asymmetry and improve the reliability of sustainability-linkedfinancial instruments. We developed and tested our framework using a proprietarydataset comprising environmental reports from 200 multinational corporations between 1998 and 2004, alongside corresponding financial disclosures and satelliteimagery of operational sites. Our results demonstrate that AI-driven environmental reporting systems can identify material discrepancies between reported andactual environmental performance with 94.3% accuracy, compared to 67.8% for traditional audit methods. Furthermore, implementation of such systems correlateswith a 31.7% reduction in cost of capital for firms with previously poor environmental transparency records, indicating improved investor confidence. The originality of this work lies in its cross-disciplinary synthesis of environmental sciencedata streams with financial verification mechanisms through AI mediation, creatingwhat we term ’environmental-financial informatics’ as a new subfield. This researchprovides both a novel technical framework and empirical evidence that automatedenvironmental transparency systems can materially improve financial market efficiency and corporate accountability, offering a pathway toward more sustainablecapitalism.