Inventory Models Under Carbon Tax and Cap-and-Trade Policies: A Comparative Analysis of Decentralized and Centralized Approaches

Abstract

Due to tougher carbon restrictions and regulations, businesses have been researching approaches to decrease the amount of carbon emissions throughout the inventory supply process and achieve sustainable development. The two most common approaches are (i) decentralized, which involves implementing a carbon tax or cost for emitting carbon, and (ii) centralized, which includes introducing an emissions trading (cap-and-trade) mechanism. Within this research, we optimize a two-stage supply management system under FPH(finite planning horizon) while taking into consideration these two policies. Using a linear time and inventory-dependent demand model, we investigated various techniques within a specific time frame. We created and solved two distinct MINLP (Mixed Integer Non-Linear Programming) approaches for each carbon strategy. These models can assist businesses/firms in determining the minimum overall cost, optimal order quantity, optimal replenishment time, and replenishment cycles. Using mathematical tools, our sensitivity evaluations indicate that organizations can reduce overall projected emissions and costs by making parameter variations under both carbon regimes. We additionally showed that while both approaches optimize the overall supply chain cost, the order quantity and total emissions remain constant.