Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lakshmi, Elluri Sri; Kavitha, R.; Vinoth, Dalvin Title A name given to the resource Multivariate Forecasting of Co2 Emissions Using Hybrid Machine Learning Models Based on Energy Consumption and Renewable Adoption Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IConSCEPT66142.2025.11436999" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IConSCEPT66142.2025.11436999</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036573475?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036573475?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lakshmi E.S., CHRIST (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India; Kavitha R., CHRIST (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India; Vinoth D., CHRIST (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India Description An account of the resource The study presents a machine learning approach to predict carbon dioxide (CO2) emissions by analysing key factors such as energy consumption, renewable energy adoption, and economic growth (GDP). Traditional forecasting methods struggle to capture the complex and nonlinear patterns of emissions. To overcome the limitations and improve the accuracy, research combines classical statistical models like ARIMA and VAR with advance techniques, including deep learning (LSTM) and ensemble methods (XGBoost, stacking). The models are trained on a global dataset of energy and economic records. The results shows that the hybrid models, particularly the LSTM + XGBoost and stacked approaches, have outperformed the traditional methods by obtaining a lower Root Mean Square Error (RMSE) and a higher coefficient of determination (R2). Apart from advancing environmental data science, the research offers a solid predictive framework to support policy initiatives related to the Sustainable Development Goals, specifically SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). 2025 IEEE. Subject The topic of the resource Affordable and Clean Energy; Climate Action; CO₂ Emission Forecasting; Hybrid Machine Learning; Multivariate Time Series; Renewable Energy Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157120-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online