标签
服装
碳纤维
碳排放税
业务
废物管理
环境经济学
自然资源经济学
计算机科学
温室气体
化学
经济
工程类
政治学
生态学
生物化学
算法
复合数
法学
生物
作者
Honghong Gao,Jianwu Tang,Daqing Gong,Xiaoxia Zhao,Xiaojie Yan
出处
期刊:Ecological Chemistry and Engineering
[De Gruyter]
日期:2024-12-01
卷期号:31 (4): 507-525
标识
DOI:10.2478/eces-2024-0033
摘要
Abstract This paper investigates the trade-off between carbon emissions and profitability in used clothing inventory management under carbon labelling and taxation policies. The study analyses various decision models within a two-tier supply chain comprising a single supplier and retailer, deriving optimal solutions for inventory carbon emission levels, sales prices, wholesale prices, and profit maximisation for each supply chain participant. Our findings reveal several key patterns: For environmentally conscious suppliers, both centralised and decentralised decision-making models show that increasing carbon tax rates lead to decreased inventory carbon emission levels of used clothing. When carbon tax remains constant, heightened consumer awareness results in reduced inventory carbon emission levels. For pollution-intensive suppliers under decentralised decision-making, the inventory carbon emission levels follow a U-shaped trend. Similarly, when carbon tax is fixed, increasing consumer awareness of carbon labels also produces a U-shaped trend in suppliers’ inventory carbon emission levels. Under centralised decision-making, higher carbon tax rates consistently lead to decreased inventory carbon emission levels. Additionally, stronger consumer preferences for low-carbon labels result in reduced used clothing inventory carbon emission levels. When the baseline of low carbon levels is identical, carbon tax and carbon regulation intensity demonstrate similar effects on inventory carbon emission levels, demand, supply chain profits, and coordination mechanisms, while exhibiting opposite effects on sales prices. The study achieves supply chain coordination through an optimal wholesale price contract based on Nash bargaining, with numerical analysis validating the proposed model.
科研通智能强力驱动
Strongly Powered by AbleSci AI