Low-carbon supply chain management: manufacturer capital constraints and bankruptcy risk

Purpose This paper builds a decision-making model for supply chains under manufacturer capital constraint and bankruptcy risk, to investigate the supply chain members' optimal decisions under different carbon asset financing schemes, providing insights for capital-constrained enterprises. Design/methodology/approach A supplier-dominated Stackelberg game model, based on the newsvendor framework, is developed to analyze different carbon asset financing schemes and optimal decisions of supply chain participants. Furthermore, numerical simulations using real data from the cement industry are conducted in MATLAB R2024a to validate the analytical findings of the model. Findings The bankruptcy risk is not always detrimental to the capital-constrained manufacturer in the supply chain. An aggressive product quantity strategy with limited bankruptcy responsibility may enable the manufacturer to obtain higher profits. Higher financing costs do not necessarily mean increased bankruptcy risk, as it depends on the manufacturer's initial capital. Pledge financing always provides steady revenue growth for the capital-constrained manufacturer, while sell-buyback financing offers the potential for maximizing revenue with associated risks. The impact of carbon price volatility on the manufacturer varies across different financing schemes. An increase in carbon price will reduce the bankruptcy risk under pledge financing, but can contribute to lower profits under sell-buyback financing. Originality/value The contributions of this paper are as follows: Firstly, this paper addresses the capital constraints faced by the manufacturer in the low-carbon supply chain context by introducing carbon asset financing. Secondly, we conduct a comparative analysis of different carbon asset financing schemes, examining the relationship of optimal decisions and profits. Finally, carbon price volatility, market demand uncertainty and bankruptcy risk are incorporated into our model.