Machine Learning–Based Framework For Accurate CO₂ Emission Prediction And Environmental Impact Analysis

The rapid increase in carbon dioxide (CO₂) emissions has become a major environmental concern due to its significant contribution to global warming and climate change. Accurate prediction of CO₂ emissions is essential for developing effective environmental policies and implementing sustainable strategies to reduce greenhouse gas emissions. Traditional statistical forecasting methods often struggle to capture complex relationships between multiple environmental and industrial factors that influence carbon emissions. In recent years, machine learning techniques have emerged as powerful tools for analysing environmental data and improving prediction accuracy.This study presents a machine learning–based framework for forecasting CO₂ emissions using historical environmental and fuel consumption data. The proposed system analyses various factors such as fuel consumption patterns, vehicle characteristics, engine size, and other related attributes to estimate future carbon emissions. Several machine learning regression algorithms, including Linear Regression, Gaussian Process Regression, Multilayer Perceptron (MLP), and Sequential Minimal Optimization for Regression (SMOreg), are implemented and evaluated to determine the most accurate prediction model.The dataset used in this research is obtained from a publicly available environmental dataset and undergoes preprocessing steps such as data cleaning, normalization, and outlier detection to improve model performance. The trained models are evaluated using performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), and correlation coefficient.Experimental results indicate that machine learning algorithms can effectively predict CO₂ emissions, with SMOreg demonstrating superior performance compared to other models in terms of prediction accuracy and error reduction. The proposed framework can assist environmental researchers and policymakers in understanding emission trends and making informed decisions for climate change mitigation.