Artificial Intelligence for Assessing Climate Change Impacts on Asset Valuation (gjstudies, 2022)
This research introduces a novel artificial intelligence framework for assessingclimate change impacts on asset valuation, addressing a critical gap in financialrisk modeling. Traditional valuation methods inadequately incorporate complex,non-linear climate variables and their cascading effects on asset performance. Ourapproach integrates three unconventional methodologies: a quantum-inspired neural network for modeling multi-scale climate-economic interactions, a bio-inspiredoptimization algorithm derived from fungal network growth patterns for portfoliostress testing, and a computational narrative analysis technique adapted from digital humanities to interpret regulatory and social sentiment shifts. The frameworkprocesses heterogeneous data streams including satellite imagery, granular climateprojections, social media sentiment, and unconventional economic indicators. Wedemonstrate that our AI system identifies valuation vulnerabilities 42% earlier thanconventional models and reveals previously unrecognized asset correlations underclimate stress scenarios. The methodology represents a significant departure fromexisting climate finance approaches by treating climate impacts as emergent phenomena within complex adaptive systems rather than as discrete risk factors. Ourfindings challenge traditional assumptions about asset resilience and provide a newparadigm for incorporating deep uncertainty into valuation models. This researchcontributes original insights into the intersection of climate science, artificial intelligence, and financial economics, offering practitioners a more robust tool fornavigating the transition to a climate-constrained economy