Government Dynamic Incentive-Penalty Mechanisms and Agricultural Supply Chain Joint Emission Reduction: An Evolutionary Game Study

Under the “dual-carbon” strategic background, the low-carbon transformation of agricultural product supply chains will inevitably enter a critical phase of collaborative emission reduction across the entire industry chain. Currently, this transformation faces prominent challenges, including insufficient coordination among supply chain segments and a lack of dynamic adjustment mechanisms for policy tools. From the perspective of evolutionary game theory, we construct a tripartite low-carbon collaboration model for agricultural product supply chains, focusing on internal stakeholder interactions and endogenous motivation mechanisms for collaborative emission reduction. By introducing decision sensitivity parameters and designing dynamic carbon tax and subsidy incentive mechanisms, the research investigates the impact of policy tool combinations on low-carbon collaboration evolution through theoretical analysis and numerical simulation. The results reveal the non-linear effects of key parameters such as carbon tax rates, subsidy intensity, and decision sensitivity on evolutionary strategies. Carbon tax and subsidy policies jointly influence decision evolution: the former imposes transition pressure through marginal cost constraints, while the latter lowers participation barriers by incentivizing revenue functions. Their combination establishes a graded incentive system. Model simulations identify the most efficient carbon subsidy combinations, providing a theoretical basis for governments to formulate differentiated policies for low-carbon collaboration in agricultural product supply chains. This study offers practical insights for building collaborative innovation mechanisms among supply chain entities and promoting industry-wide emission reduction coordination.