Optimizing Sustainable Electronics Supply Chains Under Carbon Taxation and Fuzzy Demand: A Multi-Goal Programming Approach

Abstract

The sustainable transformation of electronics supply chains (ESCs) increasingly relies on effective green supply chain planning under carbon pricing and demand uncertainty. However, prior studies often lack an integrated framework that jointly considers carbon taxation, green technology investment, and profitability-environment trade-offs in forward and reverse supply chains. To address this gap, this study proposes a fuzzy multi-goal optimization model using linear goal programming under progressive carbon taxation. The model incorporates fuzzy demand (triangular fuzzy numbers), carbon emissions, carbon taxes, and green investment costs and is converted into a solvable linear form via a defuzzification-based procedure to simultaneously achieve multiple aspiration levels for economic and environmental objectives. A real-world ESC case validates the model. The results show that carbon taxation and green investments can reduce emissions while maintaining profitability, with total cost and emission sensitivity of +/- 10-20% across different policies and demand uncertainty settings. The findings support adaptive, policy-aware planning by guiding green investment intensity and forward-reverse logistics decisions to balance cost efficiency and emissions reduction and provide actionable insights for managers facing progressive carbon pricing regulations.