A Risk-Aware Dynamic Credit Allocation Mechanism in Green Supply Chains: An Agent-Based Model with ESG Metrics

Integrating Environmental, Social, and Governance (ESG) metrics into supply chain finance is critical for promoting sustainable development. However, the dynamic mechanisms through which real-time ESG performance influences credit allocation and, consequently, shapes credit risk and environmental risk exposures for financial institutions, remain poorly understood, especially when compared to traditional static and retrospective ESG evaluations. To address this, we developed an agent-based model that simulates interactions among green enterprises, a financial institution, and a regulator, featuring a dynamic credit algorithm that adjusts credit lines based on real-time ESG scores. Our simulations demonstrate that ESG-driven credit policies significantly boost green technology adoption among SMEs, raising adoption rates from 20% to over 85% under strong incentives, which in turn drives a substantial reduction of the supply chain’s carbon footprint by more than 50%. Notably, this environmental benefit is achieved without a commensurate surge in credit risk, as the non-performing loan ratio only experienced a moderate increase. Additionally, sensitivity analysis reveals a non-linear relationship between the ESG weighting in credit decisions and environmental outcomes, identifying a critical threshold for policy effectiveness. Our findings offer risk managers and policymakers evidence-backed strategies for designing dynamic incentives that effectively promote supply chain decarbonization while managing associated financial risks.