An Intelligent System For Carbon Footprint Prediction Using Ensemble Regression

Carbon dioxide (CO₂) emissions from industrial and organizational operations such as energy consumption, transportation, and operational processes significantly impact environmental sustainability. Accurate carbon footprint prediction is essential for reliable emission analysis and informed reduction planning. However, traditional systems rely on static calculation methods, which fail to capture dynamic operational patterns and complex emission relationships. The proposed system employs a machine learning–based framework to predict carbon footprint in industrial and organizational environments. Activity-based operational data such as electricity consumption, fuel usage, and transportation parameters are first subjected to data preprocessing and feature engineering. The processed data are then utilized in ensemble regression modeling to generate reliable emission predictions. The system predicts total carbon emissions and provides category-wise emission analysis to identify major emission-contributing activities. The proposed solution enables data-driven decision-making for sustainable operational planning and emission reduction, fostering environmentally responsible practices through analytical assessment of carbon emissions.