HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDLY LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION

Abstract

The increasing need for climate change mitigation has led the way towards making logistics and supply chain management greener. While global trade and transportation are growing, the pollution factor, such as carbon footprints and the use of energy, has taken center stage as a matter of concern. With this, Hybrid AI Models and Sustainable Machine Learning are being incorporated into green logistics for reducing carbon emissions and creating sustainable green supply chains. These AI-based solutions employ techniques such as deep learning, optimization algorithms, and neural networks to optimize route transportation, improve vehicle performance, and optimize resource allocation. With the use of these technologies, carbon footprint minimization has been able to register improvements of up to 30% in certain situations, and improvements in resource efficiency have been witnessed at 25%. The convergence of these technologies not only enables the reduction of carbon footprints but also increases sustainability in supply chain management. The ability of Hybrid AI models to spur sustainability is discussed in this paper through more efficient logistics functions, maximizing resource utilization, and enabling green supply chain practices while creating a mechanism to attain long-term environmental objectives.