Carbon Footprint Tracking in Logistics System: A Survey of AI-Based Emission Monitoring Approaches

Abstract: The logistics and transportation industry is one of the leading contributors to global carbon dioxide (CO₂) emissions. With the rapid growth of e-commerce and supply chain operations, accurately monitoring and reducing carbon footprints in logistics has become a critical environmental and operational challenge. This paper presents a survey of existing research on AI-driven carbon emission monitoring, route optimization, green logistics, and machine learning- based prediction systems. Six key studies are analyzed and compared across dimensions such as methodology, dataset, emission parameters, AI models, and limitations. Based on this survey, we propose a comprehensive web-based Carbon Footprint Tracking System for logistics operations that integrates machine learning-based emission prediction, AI- powered route optimization, shipment-wise carbon tracking, and a centralized analytics dashboard. The proposed system aims to address key gaps in existing approaches by combining real-time tracking, future emission forecasting, and actionable carbon reduction recommendations in a unified platform.

Keywords: Carbon Footprint, Logistics Emissions, Green Logistics, Route Optimization, Machine Learning, CO₂ Prediction, Sustainable Transportation, Emission Tracking, Artificial Intelligence, Smart Logistics