Digital Twin for Strategic Carbon Policies Management in Logistic Networks

We investigate the management of carbon policies in third-party logistics (3PL) networks by combining a Digital Twin (DT) representation of real transportation systems with gametheoretic decision models. The proposed framework integrates traffic, geospatial, and emission data to construct a weighted logistics graph, enabling the simulation and evaluation of carbon policies impacts under different decision-making schemes. We analyzed two game-theoretic formulations: a static non-cooperative game and a Stackelberg game with leader-follower dynamics. In both settings, equilibria are computed and compared against the centralized optimum using efficiency metrics such as the Price of Anarchy (PoA) and Price of Stability (PoS). Simulation results on a real case study in Central Anatolia, Türkiye, show that selfish routing induces significant inefficiencies in the static case, while the Stackelberg formulation substantially improves performance, reducing PoA to below than 1.01 at high incentive levels. However, the hierarchical structure also introduces utility asymmetries between players due to first-mover advantage. These results indicate that the integration of DT-based simulation with game theoretic models offers a robust analytical framework to quantify the trade-offs among efficiency, environmental objectives, and fairness in decentralized logistics networks under carbon policy constraints.