Towards green logistics: An innovative decision support model for zero-emission transportation modes development

Abstract

The transition towards sustainable and eco-friendly transportation modes is a critical step in alleviating the adverse impacts of climate change and reducing carbon emissions. In this aspect, the zero-emission transportation modes development (ZETMD) has received growing interest from policymakers and researchers across the globe. However, developing zero-emission transportation modes is a complex task, as numerous challenges exist in real-life scenarios that must be addressed. Additionally, the identification and modeling of challenges related to ZETMD considering large-scale uncertainty are absent in the literature. Therefore, this study uniquely contributes to the existing literature by identifying and analyzing the critical challenges of ZETMD by offering an integrated and novel decision support method that combines Interval Type-2 Trapezoidal Fuzzy Set (IT2TrFS) Pareto and a generalized IT2TrFS weighted averaging (GIT2TrFSWA) operator-based IT2TrFS-Best-Worst Method (BWM) with lower defuzzification (LD). This integrated model is robust and capable of handling a broader scale of uncertainty during the subjective judgment of challenges. The IT2TrFS-Pareto analysis helps identify the most critical challenges, while GIT2TrFSWA operator-based IT2TrFS-BWM with LD aids in determining the importance of these challenges. The IT2TrFS-Pareto analysis identifies nine challenges as critical challenges from an initially selected sixteen challenges. The findings of the IT2TrFS-BWM-LD analysis suggest that the “High cost of clean technologies development for transport” is the most critical challenge for ZETMD carrying a normalized IT2TrFS defuzzified weight of 0.26067. Further, comprehensive sensitivity, comparative, and statistical correlation analyses confirm the robustness and reliability of the proposed model. The study findings can serve as a benchmark for policymakers and decision-makers to overcome these challenges by formulating and implementing appropriate strategic policies in real-life scenarios that can further mitigate carbon emissions.