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Smart Carbon Emission Monitoring and Forecasting for Sustainable Logistics
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Author(s): S. D. Lalitha (R.M.K. Engineering College, India), C. Shanmugapriya (R.M.K. Engineering College, India), M. S. Sujitha (R.M.K. Engineering College, India)and K. Sowmiya (R.M.K. Engineering College, India)
Copyright: 2026
Pages: 26
Source title:
Leveraging Technology and Renewable Energy for Sustainable Supply Chains
Source Author(s)/Editor(s): Tharwa Najar (Gafsa University, Tunisia), Adel Aloui (ISTEC Business School, France)and Moacir Godinho Filho (EM Normandie Business School, France)
DOI: 10.4018/979-8-3373-3790-6.ch010
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Abstract
Sustainable logistics demands efficient and scalable systems for monitoring and forecasting carbon emissions. This work presents a Smart Carbon Emission Monitoring and Forecasting System (SCEMFS) for real-time detection of gases like CO2, CH4, NH3, and VOCs using MQ-series sensors (MQ-2, MQ-7, MQ-9, MQ-135) integrated with a microcontroller. The collected data is used to train machine learning models—decision trees, random forests, SVM, and neural networks—for accurate prediction and classification of gas concentrations. Real-time analytics enables early intervention and improved environmental compliance. The system is scalable and suitable for various logistics settings, contributing to reduced carbon footprints and promoting sustainability in supply chain operations.
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