Research on the Coupling Mechanism Model of Circular Economy and Green Supply Chain Based on System Dynamics

¹ School of Management, Xi’an Jiaotong University, Xi’an, China
² School of Business, Zhuhai College of Science and Technology, Zhuhai, China
³ School of Business and Management, Jilin University, Changchun, China

^* Corresponding author: 1926036983@qq.com

Abstract

Amidst escalating global resource constraints and environmental pressures, the synergistic integration of Green Supply Chains (GSC) and Circular Economy (CE) has emerged as a pivotal strategy for enterprises to reconcile environmental sustainability with economic viability. This study employs system dynamics (SD) to develop a dynamic coupling model, aiming to unravel the intrinsic mechanisms underlying their synergistic interactions. First, through a systematic literature review, we synthesize core concepts of CE and GSC management, proposing a novel coupling framework that operationalizes the “6R” principles (Reduce, Reuse, Recycle, Recover, Redesign, Remanufacture) across the entire GSC lifecycle. Subsequently, leveraging causal loop diagrams and stock-flow modeling, we dissect dynamic feedback relationships among production, consumption, and recycling subsystems, quantifying key indicators such as coupling performance, supply chain profitability, and environmental impact. The model reveals how circular practices (e.g., reverse logistics optimization, remanufacturing capacity) and green supply chain operations (e.g., eco-design, low-carbon production) mutually reinforce each other, with government policies and consumer behavior acting as critical moderators. Finally, we acknowledge limitations in variable quantification and generalizability, suggesting future research to validate the model through industry-specific case studies (e.g., electronics, automotive) and refine feedback loops with real-world data. This research advances theoretical understanding of CE-GSC integration and provides actionable insights for enterprises to enhance sustainability performance via systemic optimization.