AI-Driven Carbon Footprint Tracking and Emission Reduction in Logistics Networks
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P106Keywords:
Artificial Intelligence, Carbon Footprint, Emission Reduction, Logistics, Supply Chain, Sustainability, Machine Learning, Smart Logistics, Predictive AnalyticsAbstract
Climate change and environmental conservation have been globalised and turned into global imperatives. The logistics industry, a big contributor to Greenhouse Gas (GHG) emissions, is under increasing pressure to turn greener. Artificial Intelligence (AI) has been known to be a tool for transformation towards data-driven environmental strategies. This paper is an in-depth overview of AI-powered carbon tracking and calibration in logistics networks. We review different AI approaches (machine learning, deep learning, and reinforcement learning) and their implementation in transportation, warehousing, and supply chain optimization. Our approach combines the collection of real-time data, predictive analytics, decision-making models, monitoring of emissions, detection of inefficiencies, and recommendation of low-carbon alternatives. Using simulated case studies, we assess AI applications’ impact, comparing traditional approaches and AI-driven processes. The given results also show that AI can greatly contribute to the decrease in emissions, increase route optimization and fuel efficiency, and promote sustainable warehousing. In addition, we address such challenges as data availability, complexity of integration, and ethical implications. This paper thus adds to this emerging literature by proposing a realistic architectural approach, methods, and a way forward to sustainable logistics through AI
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