A Behavioral Risk-Adjusted Framework for Green Financing Adoption

Thesis Abstract

This study investigates how behavioral risk factors interact with traditional risk measures to influence the adoption of green financing in banking institutions, addressing the gap that risk perceptions and decision heuristics moderate pursuit of environmentally sustainable funding. The aim is to develop and validate a Behavioral Risk-Adjusted Framework (BRAF) that integrates psychological, social, and cognitive determinants with financial risk assessment to explain and predict banks’ propensity to extend green credit and allocate capital to green projects. Specific objectives are (1) to identify behavioral risk constructs—loss aversion, ambiguity aversion, overconfidence, and social norms—that systematically affect green financing decisions; (2) to integrate these constructs with traditional risk indicators (credit risk, market risk, liquidity risk) into a cohesive framework; (3) to operationalize a composite Green Financing Adoption Index (GFAI) and examine its determinants; (4) to test the framework across banks of varying sizes (commercial, retail, and investment banks) within a robust cross-sectional design; and (5) to provide policy and managerial recommendations for enhancing green financing uptake without compromising financial soundness. The methodology employs a mixed-methods approach. A quantitative phase surveys 600 banking decision-makers (risk managers, lending officers, and portfolio managers) across 60 banks in a developed market economy, using a structured questionnaire that measures behavioral risk constructs (validated scales for Prospect Theory-linked biases and social norm perception) and traditional risk metrics, supplemented by archival data on green lending volumes and portfolio quality from banks’ annual reports and regulatory disclosures. Confirmatory factor analysis validates the measurement model, followed by structural equation modeling to test hypothesized relationships and the overall fit of the Behavioral Risk-Adjusted Framework. The study then conducts a qualitative phase comprising 40 in-depth interviews with senior executives to triangulate findings and illuminate contextual drivers, using thematic analysis guided by the theoretical lens of Behavioral Finance and Theory of Planned Behavior. Robustness checks include multilevel modeling to account for bank-level heterogeneity and propensity score matching to mitigate selection bias when linking behavioral risk to green financing outcomes. Expected findings indicate that behavioral risk factors significantly moderate the relationship between traditional risk measures and green financing adoption, with loss aversion and ambiguity aversion dampening green lending despite favorable macroeconomic signals, while social norms and perceived organizational climate positively reinforce green credit growth. The GFAI is anticipated to exhibit strong predictive validity (out-of-sample R-squared improvements of 12–18%) and to identify threshold effects where behavioral factors either accelerate or hinder green financing uptake under varying regulatory and market conditions. The study contributes to knowledge by operationalizing a novel, integrative framework that unites Behavioral Finance concepts with risk-based capital allocation in the green finance domain, providing a measurable instrument (GFAI) for banks and regulators to diagnose and cultivate adoption channels aligned with sustainability targets. The findings offer practical implications for risk governance, incentive design, and reporting frameworks, suggesting, for instance, that embedding behavioral risk dashboards and nudges within credit committees can reduce bias and enhance green loan origination without compromising credit quality. The research concludes that a calibrated balance between risk discipline and behavioral considerations is essential for scalable green financing, and it recommends policy instruments such as standardized green taxonomies, enhanced disclosure of behavioral risk exposures, and training programs to mitigate cognitive biases among lending professionals.