AI-based Carbon Footprint Tracking and Reduction

Industrial sectors contribute significantly to global carbon emissions, driving the urgent need for smarter energy management solutions. Real-time monitoring and predictive analytics are essential for promoting environmental sustainability. Traditional carbon credit trading systems are often centralized, lack transparency, and are prone to inefficiencies and manipulation. Furthermore, they provide limited real-time insights into emissions management. Conventional methods rely on manual reporting or centralized authorities, leading to delayed emission detection and inaccurate carbon trading. These approaches struggle to deliver the accuracy and scalability required for modern sustainability efforts. This work proposes an AI-driven integrated platform that combines IoT-based real-time monitoring, machine learning prediction, automated load optimization, and a dynamic carbon credit trading marketplace. The Random Forest Classifier is employed to predict emissions accurately and optimize energy consumption patterns. Simulations were conducted by collecting real-time industrial energy data and training the model to forecast carbon emissions. The system dynamically adjusts loads and facilitates carbon trading based on the predicted emission levels. The system was developed using Python AI modeling, Node.js web platform, and LoRaWAN technology for secure IoT communication. The results of the tests showed that the training accuracy is 100 percent while validation accuracy is 96 percent. Further, emissions were predicted to be 750.0 tons which is less than 1000 tons. Energy load management kept consumption below 500 kWh consumption, demonstrating effective optimization in real-time. Ultimately, our proposed platform provides accurate emissions forecasting, efficient energy management, and transparent carbon trading, giving the industry a scalable solution to meet sustainability and compliance demands.