(untitled) (gjstudies, 2026)
This research introduces a novel computational framework that establishes predictive linkages between corporate environmental compliance metrics and the cost ofcapital, a relationship traditionally analyzed through qualitative or econometric lenses.We depart from conventional financial modeling by employing a hybrid methodologythat 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-evolvingsignal embedded within a firm’s operational, social, and regulatory network. We formulate the problem through the lens of information asymmetry and risk propagationin 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-TermMemory (LSTM) ensemble to process temporal compliance trajectories, and a MonteCarlo agent-based simulation to project capital market reactions. Trained and validated on a unique, hand-collected global dataset spanning 1995 to 2004, encompassingregulatory 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-linearthreshold effects and network-dependent spillovers previously undocumented in the literature, suggesting that the financial benefit of compliance is contingent on a firm’sposition within the industry network and the historical volatility of its compliancerecord. This work provides a foundational computational architecture for integratingsustainability metrics into real-time financial risk assessment, offering a novel, quantitative tool for investors, regulators, and corporate strategists navigating the intersectionof environmental governance and finance.