A Dynamic Fuzzy Climate Hazard Timing Model for Regulating Green Finance
Abstract
Transitioning to a sustainable economy is essential to achieving the UN's SDGs by 2030. Green financing, a cornerstone of this shift, is influenced by interest rates. This study employs dynamic programming to develop a climate hazard timing model, linking environmental performance to interest rates. Findings suggest monetary authorities should widen the gap between green bond and conventional interest rates during environmental shocks to boost green financing's feasibility. A novel environmental elasticity metric quantifies disparities in interest rates relative to global climate risks. Using fuzzy logic, the study assesses global climate risk (GCR) for 175 countries (2000-2019), identifying Qatar as most and Puerto Rico as least vulnerable. Results show 38.3% of countries face low, 28% moderate, and 33.7% high vulnerability. Recommendations include interest rate adjustments based on climate risk levels, crucial for economic unions like BRICS and the EU. © 2025, IGI Global Scientific Publishing.