AI Driven Tools for Environmental Accounting Data Quality Assurance

Abstract

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, reliant
on manual audits and rule-based systems, are ill-equipped to handle the volume, velocity, and
heterogeneity of modern environmental data streams. This paper introduces a novel, hybrid
AI-driven framework for environmental accounting data quality assurance that uniquely integrates three unconventional methodologies: (1) a neuro-symbolic reasoning engine that merges
logical constraints from environmental reporting standards with deep learning pattern recognition, (2) a cross-modal data fusion module inspired by biomimetic sensory integration, designed
to 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 on
statistical deviation but on ecological and operational implausibility. Our methodology moves
beyond error detection to perform context-aware data repair and uncertainty quantification,
proposing probable corrections with confidence intervals based on learned data relationships
and domain knowledge graphs. We implement and evaluate this framework on a novel, multisource dataset comprising five years of corporate environmental disclosures, IoT sensor feeds
from industrial facilities, and corresponding regulatory filings. Results demonstrate a 47% improvement in recall for detecting subtle, context-dependent data inconsistencies compared to
best-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 identified
missing data points, as validated by domain experts. This research contributes a fundamentally
new 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.