Reduction of carbon emissions under sustainable supply chain management with uncertain human learning

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Abstract

Customers’ growing concern for environmentally friendly goods and services has created a competitive and environmentally responsible business scenario. This global awareness of a green environment has motivated several researchers and companies to work on reducing carbon emissions and sustainable supply chain management. This study explores a sustainable supply chain system in the context of an imperfect flexible production system with a single manufacturer and multiple competitive retailers. It aims to reduce the carbon footprints of the developed system through uncertain human learning. Three carbon regulation policies are designed to control carbon emissions caused by various supply chain activities. Despite the retailers being competitive in nature, the smart production system with a sustainable supply chain and two-level screening reduces carbon emissions effectively with maximum profit. Obtained results explore the significance of uncertain human learning, and the total profit of the system increases to 0.039% and 2.23%, respectively. A comparative study of the model under different carbon regulatory policies shows a successful reduction in carbon emissions (beyond 20%), which meets the motive of this research.

Keywords

supply chain management / smart production / deteriorating products / emission reduction / uncertain human learning

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