AI Driven Tools for Environmental Accounting Data Quality Assurance (gjstudies, 2026)
Environmental accounting data, critical for regulatory compliance, corporate sustainability reporting, and investment decisions, suffers from systemic quality issues including inconsistency,incompleteness, and contextual ambiguity. Traditional quality assurance (QA) methods, relianton manual audits and rule-based systems, are ill-equipped to handle the volume, velocity, andheterogeneity of modern environmental data streams. This paper introduces a novel, hybridAI-driven framework for environmental accounting data quality assurance that uniquely integrates three unconventional methodologies: (1) a neuro-symbolic reasoning engine that mergeslogical constraints from environmental reporting standards with deep learning pattern recognition, (2) a cross-modal data fusion module inspired by biomimetic sensory integration, designedto harmonize quantitative sensor data with qualitative, unstructured textual reports, and (3)a generative adversarial network (GAN)-based anomaly detection system that learns the highdimensional manifold of ’plausible’ environmental data states, flagging outliers not just onstatistical deviation but on ecological and operational implausibility. Our methodology movesbeyond error detection to perform context-aware data repair and uncertainty quantification,proposing probable corrections with confidence intervals based on learned data relationshipsand domain knowledge graphs. We implement and evaluate this framework on a novel, multisource dataset comprising five years of corporate environmental disclosures, IoT sensor feedsfrom industrial facilities, and corresponding regulatory filings. Results demonstrate a 47% improvement in recall for detecting subtle, context-dependent data inconsistencies compared tobest-in-class rule-based systems, and a 62% reduction in false-positive anomaly alerts. Furthermore, the system successfully generated contextually plausible corrections for 89% of identifiedmissing data points, as validated by domain experts. This research contributes a fundamentallynew paradigm for data QA, shifting from deterministic validation to probabilistic, contextsensitive assurance, with significant implications for the reliability of sustainability metrics,carbon markets, and environmental policy enforcement.