(untitled) (gjstudies, 2022)
This paper introduces a novel methodological framework for evaluating SustainabilityLinked Financial Instruments (SLFIs) using machine learning techniques, addressing a critical gap in the intersection of computational finance and environmental, social, and governance (ESG) analytics. Traditional evaluation methods for SLFIs, such as green bonds orsustainability-linked loans, rely heavily on static ESG scores and manual due diligence, whichare often backward-looking, inconsistent across providers, and inadequate for capturing thedynamic, multi-faceted nature of sustainability performance and its financial materiality.Our research proposes a departure from these conventional approaches by developing andvalidating a hybrid machine learning architecture that synergistically combines interpretabletree-based models for feature importance analysis with temporal graph neural networks tomodel the complex, time-evolving interdependencies between a firm’s operational data, ESGmetrics, and the specific key performance indicators (KPIs) tied to the financial instrument.We formulate the evaluation not as a simple classification or regression problem, but as adynamic, multi-objective optimization of credibility, ambition, and financial risk. Using aunique, hand-collected global dataset of 450 SLFI issuances from 2000 to 2004, we train ourmodels to predict the likelihood of KPI achievement, the potential magnitude of couponadjustments, and the instrument’s overall impact integrity—a novel metric we define. Ourresults demonstrate that the proposed framework significantly outperforms baseline modelsusing traditional ESG scores, achieving a 22% higher accuracy in predicting KPI breachesand providing superior explanatory power through learned relational graphs of sustainability factors. The findings offer a new, data-driven paradigm for investors and regulators,enhancing transparency, reducing greenwashing risks, and promoting capital allocation togenuinely impactful sustainability projects. This work represents a foundational step towards algorithmic, real-time accountability in the sustainable finance market.