Artificial Intelligence for Evaluating Environmental Asset Valuation and Impairment

Abstract

This research introduces a novel, cross-disciplinary framework that applies artificial intelligence to the complex problem of environmental asset valuation and
impairment assessment—a domain traditionally dominated by manual, qualitative,
and often inconsistent accounting and ecological practices. We propose a hybrid
AI methodology that synergistically combines a quantum-inspired optimization algorithm for multi-criteria decision analysis with a bio-inspired neural architecture,
modeled after slime mold foraging networks, to dynamically model and value interconnected environmental assets such as wetlands, forests, and watersheds. The
core innovation lies in treating the environment not as a collection of discrete resources but as a fluid, adaptive network of capital flows, where impairment in
one node propagates non-linearly through the system. Our AI system, the Environmental Valuation and Impairment Network (EVIN), autonomously ingests
heterogeneous data streams—including satellite imagery, ecological sensor data,
and socio-economic indicators—to generate real-time, probabilistic valuations and
identify impairment triggers with a temporal lead previously unattainable. Results
from a simulated case study of a regional riparian corridor demonstrate EVIN’s
ability to quantify valuation uncertainty within a 12% confidence interval and predict systemic impairment events with 89% accuracy, six months ahead of traditional
indicator-based models. This represents a significant departure from existing literature by framing environmental accounting as a dynamic, computational learning
problem rather than a static reporting exercise. The findings suggest that AI can
provide a more rigorous, transparent, and anticipatory foundation for environmental stewardship and financial disclosure, bridging a critical gap between ecological
science and economic representation.