Predictive Models Linking Environmental Compliance and Cost of Capital

Abstract

This research introduces a novel computational framework that establishes predictive linkages between corporate environmental compliance metrics and the cost of
capital, a relationship traditionally analyzed through qualitative or econometric lenses.
We depart from conventional financial modeling by employing a hybrid methodology
that integrates machine learning algorithms with network theory and agent-based simulations, creating a dynamic, multi-scale predictive system. Our approach treats environmental compliance not as a static binary variable but as a complex, time-evolving
signal embedded within a firm’s operational, social, and regulatory network. We formulate the problem through the lens of information asymmetry and risk propagation
in financial ecosystems, where compliance data acts as a mitigator of perceived operational and regulatory risk. The methodology combines a Graph Convolutional Network
(GCN) to model the inter-firm contagion of compliance reputations, a Long Short-Term
Memory (LSTM) ensemble to process temporal compliance trajectories, and a Monte
Carlo agent-based simulation to project capital market reactions. Trained and validated on a unique, hand-collected global dataset spanning 1995 to 2004, encompassing
regulatory filings, bond yields, and equity risk premia for firms in high-impact sectors,
our model demonstrates a predictive accuracy for cost-of-capital shifts that exceeds traditional panel regression models by a significant margin. The results reveal non-linear
threshold effects and network-dependent spillovers previously undocumented in the literature, suggesting that the financial benefit of compliance is contingent on a firm’s
position within the industry network and the historical volatility of its compliance
record. This work provides a foundational computational architecture for integrating
sustainability metrics into real-time financial risk assessment, offering a novel, quantitative tool for investors, regulators, and corporate strategists navigating the intersection
of environmental governance and finance.