再制造
补贴
激励
业务
产业组织
频道(广播)
微观经济学
经济
计算机科学
工程类
制造工程
市场经济
电信
作者
Baozhuang Niu,Yiyuan Ruan,Fanzhuo Zeng
标识
DOI:10.1016/j.tre.2022.102880
摘要
• Subsidy and environmental tax regulations are considered to study the strategies of the OEM and the supplier selling remanufactured components (supplier R). • Supplier R prefers to develop its self-brand business when the government subsidy is high. • Opportunities for the long-existing co-opetition relationship between the OEM and the competitive supplier R are identified. • Incentive alignment opportunities for the competitive supplier R, the OEM and the government are identified. Recently, many governments have levied environment tax on production while subsidizing the output of remanufactured components to promote resource-saving and pollution reduction. Considering government’s regulations, this paper investigates whether the supplier selling remanufactured components (Supplier R) should develop a self-brand and whether the original equipment manufacturer (OEM) should keep sourcing from this competitive Supplier R or source from a supplier selling new components (Supplier N). We develop a channel system comprising of Supplier R, Supplier N, and OEM, based on which we analyze their equilibrium decisions in three typical structures (i.e., Base Scenario, Co-opetitive Scenario, Chain-to-chain Scenario). We find the OEM prefers to purchase remanufactured components when (1) its brand image advantage is significant; or (2) its brand image advantage is limited but the environment tax rate is high; or (3) its brand image advantage is limited, the environment tax rate is low, but the subsidy to supplier is high, even if Supplier R has self-brand and competes with the OEM. Our results are both theoretically interesting and practically relevant because we build a unifying model to study the government’s regulation optimization and show a full map for both the component suppliers and the OEMs to decide market encroachment or channel structure configuration.
科研通智能强力驱动
Strongly Powered by AbleSci AI