Optimizing multinational manufacturing supply chains under diverse carbon policies using carbon gaming

Abstract

In response to the environmental impact of human activities, governments worldwide have introduced various carbon policies. However, decision-making for multinational companies with different carbon policies has become a challenge for global supply chains (GSCs). To address the gap in the literature on the equitable allocation of carbon allowances and the operational management of GSCs under heterogeneous regulations, we propose a two-stage framework. First, we introduce the “Carbon Game”—a multiplayer Nash framework with a bi-level nested structure, where asymmetric manufacturers strategically determine their pricing, production, and carbon allowance allocation under diverse carbon taxes and subsidies. We then embed these equilibrium strategies into a multi-objective mixed integer linear programming (MILP) model to optimize GSC decisions, including fleet composition and network configuration. By integrating game-theoretic principles with multi-objective optimization, our framework provides new management insights. Numerical experiments show that carbon-efficient manufacturers should prioritize green technology investments to consolidate their advantage and use carbon labelling to capture environmentally conscious markets, while carbon-inefficient manufacturers should focus on cost-saving strategies, delaying green investments under stringent policies such as high taxes or low subsidies. Carbon-efficient manufacturers are also more responsive to policy changes. From a policy perspective, while both carbon taxes and subsidies generally incentivize green technology adoption, subsidies prove more effective and result in greater emission reductions. Across all tested scenarios, our method achieves a 15.07% profit increase and a 1.77% emission reduction compared to the Grandfathering approach. These findings inform multinational firms’ competitive strategies and help policymakers balance subsidies and taxes.