AI Driven Environmental Impact Assessment Using Accounting and Operational Data

Abstract

This paper introduces a novel, cross-disciplinary methodology for environmental impact
assessment that uniquely integrates corporate accounting data with granular operational
data through a hybrid artificial intelligence framework. Traditional environmental assessments rely heavily on direct environmental measurements and standardized emission factors,
often creating a disconnect between financial decision-making and ecological outcomes. Our
approach breaks from convention by treating the general ledger, cost allocation records, and
transactional operational data as a rich, untapped signal for inferring and predicting environmental impacts. We propose a two-tiered AI architecture: the first tier employs a modified
transformer network, adapted from natural language processing, to parse and contextualize
unstructured and semi-structured accounting narratives and chart-of-accounts metadata,
extracting latent environmental cost drivers. The second tier utilizes a bio-inspired optimization algorithm, based on slime mould foraging behavior, to dynamically map these
financial drivers onto high-resolution operational data streams (e.g., SCADA, IoT sensor
logs, supply chain transactions) to generate a real-time, causality-aware environmental impact model. This model moves beyond simple carbon accounting to estimate impacts on
localized biodiversity, water stress, and soil health. Our results, derived from a 12-month
pilot with a multinational manufacturing consortium, demonstrate that this AI-driven synthesis can predict verified environmental impacts with 89.7% accuracy, identify previously
obscured ’impact hotspots’ in supply chains, and reduce the latency of impact assessments
from quarterly to near-real-time. The research contributes a fundamentally new paradigm
for corporate sustainability, positioning financial and operational data systems not merely
as records of commerce but as primary instruments for ecological stewardship and predictive
environmental management