A Smart Segregated Emission Analytics Framework For Sustainable Living And Industrial Responsibility

Abstract: Carbon emissions are one of the major contributors to global climate change and environmental degradation. Accurate estimation of carbon footprints is essential for promoting sustainable living and responsible industrial practices. Most existing carbon emission calculation systems rely on generalized regional averages, which often fail to provide accurate insights into emissions produced by individual households and industries. This paper proposes a Smart Segregated Emission Analytics Framework that calculates carbon emissions separately for residential and industrial sectors. The proposed system utilizes a Decision Tree machine learning algorithm to analyze energy consumption and predict emission levels effectively. By separating emission data sources and applying intelligent prediction techniques, the system provides more accurate emission estimates and helps identify major pollution contributors. The framework improves prediction accuracy, reduces computational complexity, and supports better environmental decision-making. The proposed system offers a cost-effective and scalable solution for monitoring and reducing carbon emissions in modern societies.

Keywords: Carbon Footprint, Carbon Emission Prediction, Decision Tree Algorithm, Machine Learning, Environmental Sustainability.