Artificial Intelligence in Measuring Environmental Externalities for Accounting Purposes

Abstract

This paper introduces a novel methodological framework that integrates artificial intelligence with environmental accounting to measure and value ecological
externalities, a persistent challenge in sustainability reporting. Traditional accounting systems have consistently failed to adequately capture environmental costs due
to measurement complexities, temporal mismatches between cause and effect, and
the non-market nature of many ecosystem services. We propose a hybrid AI architecture combining symbolic reasoning systems, specifically tailored for regulatory
and accounting rule compliance, with deep learning models trained on multi-modal
environmental data streams. This architecture, termed the Environmental Externality Valuation Network (EEVN), autonomously identifies, quantifies, and monetizes externalities from corporate activities by processing satellite imagery, IoT
sensor data, supply chain records, and biogeochemical models. The core innovation
lies in its dual-pathway valuation engine: one pathway employs a reinforcement
learning agent to simulate long-term ecological impacts and their economic reverberations under different policy scenarios, while a concurrent pathway uses graph
neural networks to trace liability and cost allocation through complex corporate
ownership structures. We validate the EEVN framework through a case study
on watershed degradation from agricultural runoff, demonstrating its ability to
generate auditable, granular, and temporally dynamic externality accounts. Results indicate a significant improvement in measurement accuracy and a reduction
in valuation subjectivity compared to existing lifecycle assessment and contingent
valuation methods. This research contributes a fundamentally new computational
tool for green accounting, enabling the internalization of environmental costs with
unprecedented precision and scalability, thereby bridging a critical gap between
economic activity and planetary boundaries.