A customer satisfaction–focused green supply chain network under carbon tax and carbon cap across heterogeneous regulatory regions

This study presents a novel integrated optimization model for designing a green supply chain network (GSCN). The model aims to minimize total cost and carbon emissions while maximizing service level. It operates across two geographically distinct regions, Region A and Region B, each subject to different carbon policies. The main contribution is the development of a comprehensive framework that combines economic efficiency, environmental sustainability, and customer satisfaction within a single mixed-integer nonlinear programming (MINLP) model. This framework incorporates a carbon cap mechanism in Region A and a carbon tax mechanism in Region B into the network structure. The proposed approach integrates cost, emission reduction, and service performance across heterogeneous regulatory zones. The model is implemented in GAMS and solved using an exact solution method. Numerical experiments with various problem sizes validate the model's applicability and robustness. Additionally, a real-world case study demonstrates the practical applicability and managerial relevance of the proposed model in a supply chain (SC) context. The results highlight important trade-offs among cost, carbon emissions, and service level. Quantity-based (cap) and price-based (tax) carbon controls influence network decisions differently. Overall, the model supports decision-makers in achieving sustainability goals without compromising profitability. This study provides practical managerial insights for designing environmentally responsible and customer-oriented SCs operating under dual‑carbon regulatory regimes.