A Hybrid Machine Learning Approach to Dynamic Carbon Footprint  Modeling and Predictive Analytics

M. Ramana Kumar

A Hybrid Machine Learning Approach to Dynamic Carbon Footprint Modeling and Predictive Analytics

Authors

M. Ramana Kumar

Keywords:

Carbon Footprint Modeling, Sustainable Environment, Machine Learning, Real-Time Emission Tracking, Predictive Analytics

Abstract

As climate change accelerates, accurate carbon footprint management is vital for achieving globalsustainability goals. Traditionally, carbon accounting has relied on static, standardized emissionfactors and manual data compilation methods established in the late 1990s. While foundational, these approaches often yield generalized estimates

References

International Energy Agency (IEA). CO2 Emissions in 2022; IEA: Paris, France, 2022.

Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022: Mitigation of Climate Change; IPCC: Geneva, Switzerland, 2022.

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Published

2024-05-20

How to Cite

M. Ramana Kumar. (2024). A Hybrid Machine Learning Approach to Dynamic Carbon Footprint Modeling and Predictive Analytics. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 3733–3742. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5614