Towards Greener Logistics: AI-Driven Carbon Footprint Optimization for Smart Cities

Abstract

The rapid growth of logistics and computational infrastructures has substantially contributed to global carbon emissions, emphasizing the need for sustainable optimization solutions. This paper proposes an AI-powered Carbon Footprint Optimization (CFO) framework that minimizes CO₂ emissions in supply chain logistics through intelligent route and resource planning. The system employs an XGBoost regression model trained on segment-level parameters such as distance, slope, cargo weight, traffic density, and weather conditions to accurately predict fuel consumption and emission levels. These predictions are integrated into a Vehicle Routing Problem (VRP), solved using Google OR-Tools, where the optimization objective focuses on minimizing carbon emissions rather than distance or time.

The framework also incorporates real-time traffic and weather data, ensuring adaptive and efficient route recommendations, while results are visualized through an interactive Folium-based map. Additionally, the study integrates concepts from Green Algorithms to quantify computational carbon footprints and ECO-CHIP methodologies to promote sustainable computing practices. Experimental results demonstrate that the proposed system significantly reduces emissions and enhances route efficiency compared to traditional distance-based approaches. This work establishes a scalable foundation for sustainable logistics, with potential extensions toward multi-vehicle optimization, reinforcement learning-based decision systems, and real-time carbon aware routing.