Reseach Article
Predicting Carbon Dioxide Emissions using Machine Learning Models
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 2 - Number 4 |
| Year of Publication: 2026 |
| Authors: Aarav Chhabra |
10.5120/jaai202657
|
Aarav Chhabra . Predicting Carbon Dioxide Emissions using Machine Learning Models. Journal of Advanced Artificial Intelligence. 2, 4 ( Jan 2026), 13-19. DOI=10.5120/jaai202657
Abstract
In India, carbon dioxide (CO2) concentrations have been progressively rising, with a striking increase of 40% over the past decade, outpacing the global average. The per capita CO2 emissions in India is equal to 0.41 tons per person, which is an increase by 1.49 above the figure of 1.90 tons per person in 2022. This concerning development is largely attributed to swift industrial growth, urban expansion, and rising energy demands. The demand for precise forecasting and reduction of CO2 emissions has become more urgent, as elevated CO2 levels are contributing significantly to changing climate. While the shift to renewable energy sources is vital for decreasing global CO2 emissions, India's dependence on non-renewable persists. In relevance to this, it is proposed for a novel approach to forecast CO2 levels in India using machine learning models. This study utilized a variety of machine learning models, comprising of support vector machines, linear regressions, and polynomial regressions, for analyzing historical data concerning CO2 emissions and energy usage. This study's findings indicate that the threshold for critical CO2 levels, set at 5000 ppm, is projected to be reached by 2082. This study’s results show that these models can effectively predict CO2 levels in India with high accuracy, providing valuable insights for future policy changes. By identifying patterns and trends in CO2 emissions, this study can develop strategies aimed at mitigating climate change and fostering sustainable energy practices. This research highlights the importance of machine learning-based forecasting in supporting India's shift to a carbon neutral economy and achieving its ambitious carbon reduction goals.
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