Multivariate Forecasting of Co2 Emissions Using Hybrid Machine Learning Models Based on Energy Consumption and Renewable Adoption
- Title
- Multivariate Forecasting of Co2 Emissions Using Hybrid Machine Learning Models Based on Energy Consumption and Renewable Adoption
- Creator
- Lakshmi, Elluri Sri; Kavitha, R.; Vinoth, Dalvin
- Description
- 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.
- Source
- Proceedings of 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Affordable and Clean Energy; Climate Action; CO2 Emission Forecasting; Hybrid Machine Learning; Multivariate Time Series; Renewable Energy
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833157120-7;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Lakshmi, Elluri Sri; Kavitha, R.; Vinoth, Dalvin, “Multivariate Forecasting of Co2 Emissions Using Hybrid Machine Learning Models Based on Energy Consumption and Renewable Adoption,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26075.