Real Time Carbon Footprint Accounting and Policy Simulation for Global Supply Chains via Multi Agent Digital Twins

Driven by the global carbon neutral strategy, the visualization, responsiveness and strategy adaptation of carbon emissions of transnational supply chains have become the core challenges in digital sustainable governance. Aiming at the problems of poor real-time and lagging feedback of traditional LCA methods, this paper constructs a real-time accounting and policy simulation platform for global supply chain carbon footprint that integrates multi-intelligent body system and digital twin technology to realize the dynamic perception and intelligent response to the multi-node and whole process of carbon emission. The platform integrates distributed IoT collection, edge computing, cognitive prediction model and policy gaming mechanism at the system architecture level, and verifies its significant improvement in data updating frequency, prediction accuracy and event response speed by comparing experiments with static LCA and IoT semi-real-time solutions. Further, through the simulation of four types of policies, namely carbon tax, carbon trading, subsidy and technology standard, the system shows strong policy adaptability and emission reduction control ability, and it is found that the optimal combination of medium-intensity carbon tax, cap-and-trade and targeted subsidy is the optimal combination, which takes into account the environmental benefits and the economic cost control. The study shows that the platform can be used as an important technology path to support carbon governance in global supply chains, and promote the paradigm shift from after-the-fact accounting to real-time sensing and forward-looking governance.