Carbon Footprint Estimation Using Ensemble Learning Techniques

Abstract

Carbon footprint must be accurately estimated to ensure sustainable practices are encouraged as well as assist environmental decisions making. In recent years, the methods of machine learning have proven useful in predicting and analyzing emissions, and this is due to the growing access to large-scale environmental and transactional data. The paper is an estimation framework of carbon footprint using ensemble learning methods to enhance the accuracy and robustness of the prediction. The approach under proposal incorporates the use of several base learners such as decision trees, random forests, and gradient boosting models to learn complicated nonlinear relationships between energy consumption, transportation patterns, and production activities. To improve the model performance and minimize noise, the feature selection and data preprocessing methods are used. The ensemble model is trained and tested on real world data and the performance of the ensemble model against that of the individual machine learning models is compared through the use of standard evaluation measures like the mean absolute error and the root mean square error. It has been proven by experiments that the ensemble-based approach is more accurate and stable in estimating carbon emissions. The suggested system is applicable successfully to smart cities, industrial surveillance, and e-commerce infrastructures to facilitate a sustainable development and carbon-reduction policy.