The vigorous development of new energy vehicles has driven an escalating demand for power batteries and a rapidly growing volume of retired batteries. Low recycling efficiency of retired batteries and mismatched cost-benefit allocation of technology investment have emerged as key issues in the closed-loop supply chain. To address these issues, we investigate AI technology investment and contract mechanisms in the closed-loop supply chain of power batteries, taking into account government subsidies. By considering three scenarios, i.e. an independent AI investment model, a cost-sharing model, and a revenue-cost sharing model, we examine how different collaboration mechanisms influence AI investment and collaboration strategies among the participating companies, and also investigate the effects of government subsidies on supply chain decision. Our results show that both coordinated models achieve higher AI investment than the independent model. Additionally, when government subsidies are within a moderate range, both wholesale price and retail price are lowest under the revenue-cost sharing scenario, moderate under the independent investment scenario, and highest under the cost-sharing scenario. Furthermore, supplier profits rise under both coordinated models. Manufacturer profits increase only when the cost-sharing ratio and revenue-sharing ratio are relatively low. Moreover, coordinated models improve social welfare under moderate subsidy levels by strengthening AI investment incentives. However, deeper coordination and higher subsidies may increase environmental impacts due to scale expansion effects, revealing a trade-off between economic benefits and environmental sustainability. These findings highlight the need for balanced subsidy and coordination strategies to promote AI adoption while ensuring sustainable development in power battery closed-loop supply chains.
Citation: Qi Zheng, Miao Yu. Closed-loop supply chain decisions for power battery with AI technology under government subsidy[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 5001-5038. doi: 10.3934/jimo.2026173
The vigorous development of new energy vehicles has driven an escalating demand for power batteries and a rapidly growing volume of retired batteries. Low recycling efficiency of retired batteries and mismatched cost-benefit allocation of technology investment have emerged as key issues in the closed-loop supply chain. To address these issues, we investigate AI technology investment and contract mechanisms in the closed-loop supply chain of power batteries, taking into account government subsidies. By considering three scenarios, i.e. an independent AI investment model, a cost-sharing model, and a revenue-cost sharing model, we examine how different collaboration mechanisms influence AI investment and collaboration strategies among the participating companies, and also investigate the effects of government subsidies on supply chain decision. Our results show that both coordinated models achieve higher AI investment than the independent model. Additionally, when government subsidies are within a moderate range, both wholesale price and retail price are lowest under the revenue-cost sharing scenario, moderate under the independent investment scenario, and highest under the cost-sharing scenario. Furthermore, supplier profits rise under both coordinated models. Manufacturer profits increase only when the cost-sharing ratio and revenue-sharing ratio are relatively low. Moreover, coordinated models improve social welfare under moderate subsidy levels by strengthening AI investment incentives. However, deeper coordination and higher subsidies may increase environmental impacts due to scale expansion effects, revealing a trade-off between economic benefits and environmental sustainability. These findings highlight the need for balanced subsidy and coordination strategies to promote AI adoption while ensuring sustainable development in power battery closed-loop supply chains.
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Qi Zheng, Miao Yu. Closed-loop supply chain decisions for power battery with AI technology under government subsidy[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 5001-5038. doi: 10.3934/jimo.2026173
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