The intelligent transformation of logistics plays a significant role in meeting the diverse needs of customers, improving operational efficiency, and reducing carbon emissions in logistics activities. Therefore, to achieve sustainable development, logistics enterprises need to face the decision-making problem of intelligent logistics transformation. In this paper, we construct a Stackelberg game model between a financially constrained logistics-service provider (LSP) and a well-funded logistics-service integrator (LSI) and discuss the impact of the wholesale price contract, the cost-sharing contract, the revenue-sharing contract, the two-part tariff contract, and the hybrid cost-sharing and revenue-sharing contract on the intelligence level of logistics services, the profits of supply-chain members, and the channel for logistics-service demand. We found that the cost-sharing contract and the revenue-sharing contract cannot achieve Pareto improvement in the profits of supply-chain members. In addition, the increase in bank-loan interest rates would seriously weaken the level of intelligence and market demand for the entire logistics service. However, when consumers do not have high requirements for the intelligence of logistics services, the two-part power–price contract can create a win–win situation for supply-chain members and increase market demand within a certain range; on the contrary, a hybrid contract of cost sharing and revenue sharing is the best choice. Moreover, in the process of contract design for the intelligent transformation of logistics services, it is necessary to pay attention to the influence of the price-sensitivity coefficient on decision-making.

This study addresses the challenge of electric vehicle power battery recycling by proposing a dynamic deposit-refund system (DRS) under the Extended Producer Responsibility (EPR) framework, as an alternative to the conventional static DRS. An evolutionary game model is developed to capture the strategic interactions between local governments and responsible enterprises, incorporating a feedback mechanism where the deposit level is dynamically adjusted based on corporate EPR fulfillment rates. Using system dynamics simulation, the evolutionary paths under both static and dynamic DRS regimes are compared. The results demonstrate that the dynamic DRS effectively eliminates persistent oscillations and guides the system toward a stable equilibrium. Furthermore, by defining an ideal scenario, key factors are identified and prioritized to assist the government in steering the system toward this desired state. These findings offer actionable insights for designing adaptive regulatory mechanisms and fostering a self-sustaining battery recycling ecosystem.