A Credit Rating Model for Green Credit Coupling Multi-Objective Particle Swarm Optimization with the Analytic Hierarchy Process

Abstract

China's strategic commitment to peak carbon emissions before 2030 and achieve carbon neutrality before 2060 has made green finance an increasingly important instrument for supporting low-carbon economic transformation. Conventional credit rating models remain dominated by financial indicators and therefore insufficiently incorporate environmental, social and governance (ESG) performance. To address this limitation, this paper develops an ESG-oriented credit rating framework for green credit by coupling multi-objective particle swarm optimization (MOPSO) with the analytic hierarchy process (AHP). The proposed model quantifies enterprises' carbon-neutrality performance, including carbon-emission monitoring and environmental compliance, and provides banks and other financial institutions with a decision-support basis consistent with the national dual-carbon agenda. The framework integrates financial soundness, environmental externalities, governance capacity and policy compliance into a unified rating mechanism. AHP is used to encode expert preference and construct interpretable initial weights, while MOPSO searches for Pareto-efficient solutions under multiple conflicting objectives, including default-risk minimization, ESG-performance maximization and green-credit profitability maximization. The resulting model can improve rating accuracy, enhance the transparency of green credit allocation, and support the development of a more standardized and dynamic green finance evaluation system.