GREEN LOGISTICS AND SUSTAINABLE TRANSPORTATION: AI-BASED ROUTE OPTIMIZATION, CARBON FOOTPRINT REDUCTION, AND THE FUTURE OF ECO-FRIENDLY SUPPLY CHAINS

Abstract

The rapid expansion of global supply chains has led to increased carbon emissions and environmental concerns, necessitating the adoption of sustainable logistics solutions. This study explores the role of artificial intelligence (AI) in optimizing transportation routes, minimizing fuel consumption, and reducing the carbon footprint of supply chains. AI-powered route optimization integrates real-time traffic data, weather conditions, and vehicle efficiency to enhance last-mile delivery and freight management. Machine learning algorithms further contribute to predictive maintenance, fleet electrification strategies, and demand forecasting, ensuring operational sustainability. This research also examines green logistics practices, including the use of electric and hydrogen-powered vehicles, multimodal transportation networks, and circular economy models to minimize environmental impact. Blockchain-enabled carbon tracking and AI-driven sustainability metrics offer improved transparency in carbon footprint reporting. Additionally, the study highlights regulatory frameworks and industry initiatives promoting low-emission transportation and smart logistics hubs. Findings suggest that AI-driven logistics solutions can significantly improve efficiency while meeting sustainability goals. However, challenges such as high implementation costs, data privacy concerns, and infrastructure limitations must be addressed. Future research should focus on integrating AI with IoT and blockchain for enhanced traceability and decision-making in sustainable supply chains. The study concludes that AI-powered green logistics can revolutionize transportation, offering a viable path toward carbon-neutral and cost-effective global supply chains.