Designing green logistics networks under carbon tax policy: Post-COVID condition

Abstract

This paper proposes a fuzzy-based method for estimating demand in forward and reverse logistics networks (F&RLNs) subject to carbon pricing in the post-COVID era. We investigate the complex relationships between carbon prices, the reverse logistics network (RLN), and the ever-changing product demand by utilizing fuzzy inference systems (FIS) capabilities. The case study from Mexico confirms the effectiveness of the proposed technique. The findings demonstrate the precision and predictive power of our proposed FIS-based method and show how well it can predict the number of items in demand and the number of goods that will be returned and subject to carbon taxation in the post-COVID era. The results illustrate the significant impact that carbon pricing has on the RLN and the associated product demand. For logistics managers seeking to make informed decisions about the establishment and operation of forward and RLNs within the carbon pricing paradigm, this empirical data provides insightful information in the post-COVID era.