A dynamic perspective of government intervention in a competitive closed-loop supply chain

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Abstract

Changes in environmental burden are inherently dynamic because a steady state cannot be instantaneously reached, and the current state is associated with the previous state and continuously evolves. In this study, we examine a dynamic closed-loop supply chain (CLSC) consisting of an original equipment manufacturer (OEM) selling new products and an independent remanufacturer (IR) selling recovered products generated from end-of-life (EOL) products. The government intervenes in the CLSC by imposing taxes or subsidies to reduce the environmental burden. We propose a baseline model without government intervention and six different policies: a fixed tax or subsidy on the firm side, a unit tax or subsidy on the firm side, and a unit tax or subsidy on the consumer side. We derive the firms’ closed-form instantaneous and steady-state decisions at the subgame perfect Nash equilibrium. Finally, we conduct a comparative study to explore the effects of the policies on the environmental burden, firm profits (producer surpluses), consumer surplus, and social welfare. We find that the proposed policies can effectively reduce the environmental burden and mitigate the intensity of price competition, except for the policy with the fixed subsidy to the IR. The policies are all profitable for the IR and, thus, effective in encouraging recovery; however, the policies are not necessarily profitable for the OEM. The policies with a unit tax for consumers and fixed subsidy for the OEM are more conducive to social welfare. Moreover, the degree of dynamics improves the effectiveness of the policies in reducing the environmental burden.

Original languageEnglish
JournalEuropean Journal of Operational Research
DOIs
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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