Artificial Intelligence for Assessing Climate Change Impacts on Asset Valuation

Abstract

This research introduces a novel artificial intelligence framework for assessing
climate change impacts on asset valuation, addressing a critical gap in financial
risk modeling. Traditional valuation methods inadequately incorporate complex,
non-linear climate variables and their cascading effects on asset performance. Our
approach integrates three unconventional methodologies: a quantum-inspired neural network for modeling multi-scale climate-economic interactions, a bio-inspired
optimization algorithm derived from fungal network growth patterns for portfolio
stress testing, and a computational narrative analysis technique adapted from digital humanities to interpret regulatory and social sentiment shifts. The framework
processes heterogeneous data streams including satellite imagery, granular climate
projections, social media sentiment, and unconventional economic indicators. We
demonstrate that our AI system identifies valuation vulnerabilities 42% earlier than
conventional models and reveals previously unrecognized asset correlations under
climate stress scenarios. The methodology represents a significant departure from
existing climate finance approaches by treating climate impacts as emergent phenomena within complex adaptive systems rather than as discrete risk factors. Our
findings challenge traditional assumptions about asset resilience and provide a new
paradigm for incorporating deep uncertainty into valuation models. This research
contributes original insights into the intersection of climate science, artificial intelligence, and financial economics, offering practitioners a more robust tool for
navigating the transition to a climate-constrained economy