Promoting sustainable production and consumption practices in the construction machinery industry is crucial for achieving energy savings and reducing carbon emissions. However, there is a lack of targeted studies addressing the challenge of scaling up leasing and recycling while maximizing economic benefits for enterprises. To fill this gap, this paper presents a lease-oriented closed-loop supply chain model that incorporates a carbon tax policy to investigate the impact of the carbon tax rate and consumer preferences for remanufactured products on the supply chain and introduces a leasing compensation–cost apportioning combined contract to achieve supply chain coordination in the construction machinery sector. The model considers differential selling and leasing prices for new and remanufactured products, as well as the recovery rate, under both centralized and decentralized decision-making approaches. The study explores the interrelationships between various parameters through sensitivity analysis and numerical simulation. The results demonstrate that within a certain range of the cost apportioning proportional coefficient and leasing compensation proportional coefficient, the combined contract can lead the supply chain to achieve Pareto optimality. As the carbon tax rate increases, it was observed that the profits for all parties in the supply chain tend to decrease. However, due to the increased demand for remanufactured products, the product recovery rate improves, resulting in a reduction in total carbon emissions in the closed-loop supply chain of construction machinery. Moreover, the profits of all parties and the total supply chain profits initially decrease and then increase with an increasing preference coefficient for remanufactured products among consumers. By leveraging these factors and adopting effective strategies, such as enhancing consumer recognition of remanufactured products and optimizing pricing and cost allocation, it is indeed possible for the profits of all parties and the total profits in the supply chain to surpass the initial values, even in the face of increasing carbon tax rates. This demonstrates the potential for aligning sustainability objectives with economic benefits in the construction machinery industry.

Construction machinery operations are intrinsically linked to critical societal challenges, including safety risks and carbon emissions. In response to the high incidence of fatal accidents during installation and dismantling phases, the Chinese government has officially promoted integrated installation–dismantling services to enhance construction safety since 2023. However, the economic viability of this policy for leasing companies remains largely underexplored. To address this gap, this paper develops a leasing-oriented closed-loop construction machinery supply chain model that incorporates integrated installation–dismantling services under an industrial internet platform. The study first compares and analyzes the product leasing demand, installation and dismantling demand, and supply chain profits under both centralized and decentralized decision-making scenarios. Based on these analyses, a profit–cost sharing joint contract is designed to coordinate the supply chain. Furthermore, the interrelationships among key parameters are examined through a sensitivity analysis and numerical simulation. The results reveal that enhancing leasing information services increases both the demand for construction machinery and the platform’s operating costs. These costs are positively correlated with the product’s selling price, leading to higher purchasing costs for lessees. Similarly, improving information services for installation and dismantling raises the platform’s operating costs and enhances service levels, which in turn increases installation and dismantling costs for lessees. The findings demonstrate that within a certain range of cost-sharing and leasing-sharing proportional coefficients, the joint contract enables the supply chain to achieve Pareto optimization. This approach simultaneously alleviates economic pressure on lessees, improves construction safety, and promotes the integration of installation and dismantling services.

Leasing and recycling are important methods for developing the low-carbon sustainability of the construction machinery industry. However, there are some dilemmas caused by the extreme limitations of product service life. There has been little research on construction machinery leasing thus far and a lack of description of its unique characteristics in previous models. Focusing on the problem of pricing and coordination, a two-stage model of a leasing closed-loop supply chain based on the practical application of an Industrial Internet platform was proposed. Under centralized and decentralized decision-making scenarios, the prices of leasing and selling, maintenance effort, and recovery rate were calculated, respectively. By using the Shapley value method to optimize the selling price, and the two-part pricing contract to calculate the compensation fee, global profits can be reasonably distributed, while the supply chain can be improved. The effects of different contracts on the improvement of supply chain profit were investigated. Additionally, the correlation among parameters was explored via sensitivity analysis and numerical simulation. The findings show that the maintenance and recycling of construction machinery can be improved by using an Industrial Internet platform while achieving supply chain coordination via contracts. The enhancement of maintenance effort can decrease remanufacturing costs, which are positively correlated with the selling price and leasing price of products, further promoting the recovery rate of used products. The findings of this paper show that manufacturers can take advantage of the Industrial Internet platform to improve recycling efficiency as well as to decrease product prices and remanufacturing costs by promoting remanufacturing technologies. On the other hand, leasers need to enhance the maintenance of construction machinery. Strengthening cooperation via contracts can jointly promote leasing and recycling in the construction machinery industry and help achieve low-carbon sustainable development.

Leasing and recycling are important issues to achieve low-carbon sustainable development of construction machinery industry, however there are some dilemmas bothering which is caused by excessive restriction of product service life. Focusing on the problem of pricing and coordination, a two-stage model of leasing closed-loop supply chain based on the Industrial Internet platform practical application was proposed. Under the centralized and decentralized decision-making scenarios, the price of leasing and selling, maintenance effort and recovery rate are calculated respectively. By using the Shapley value method to optimize the selling price, and the two-part pricing contract to calculate the compensation fee, the global profit is reasonably distributed, while the supply chain achieving Pareto optimality. The performance of different contracts on the improvement of supply chain profit was investigated. And the correlation among parameters was also explored through sensitivity analysis and numerical simulation. The findings showed that maintenance and recycling of construction machinery can be improved with the advantage of the Industrial Internet platform, while achieving the supply chain coordination by contracts. The enhancement of maintenance effort can decrease the remanufacturing cost which is positively correlated to the selling price and leasing price of product, furthermore promoting the recovery rate of used products. In the future, reasonable control of platform cost can help improve the recycling of construction machinery.

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed.

This paper proposes an integrated framework for maintenance, spare parts ordering, and quality control planning within a multi-state production system that is subject to two competing modes of failure: hard failure and soft failure. Hard failures occur suddenly and without any prior warning, whereas soft failures develop following the onset of a defect. The quality of the product is significantly influenced by the state of the system, as a defective system typically leads to an increase in non-conforming products. To address this, periodic inspections are conducted to detect defects early, and preventive replacements (PR) are scheduled upon identification of a defective state, which acts as a crucial quality control indicator. Corrective replacements (CR) become necessary when either a hard or soft failure occurs. The timeliness of these replacements is contingent upon spare part availability; thus, we implement a hybrid spare ordering policy to enhance responsiveness. PR and CR are executed promptly when time-based ordered spares are accessible, while a condition-based order is triggered in the event of shortages in time-based orders. To determine the optimal inspection interval and time-based spare ordering point, we analyze the renewal scenarios of the system with the objective of minimizing the expected cost rate. We propose an optimization algorithm and a simulation procedure to validate the effectiveness of our policy through numerical experiments. The results highlight the significant potential of our approach in reducing unit losses related to non-conforming products while effectively minimizing the expected cost rate.