Walking the Talk: Practical Implementation of Machine Learning Algorithms for Predicting CO2 Emission Footprint and Sustainability
Document Type
Article
Publication Title
Signals and Communication Technology
Abstract
The increasing levels of CO2 emissions have become a significant concern worldwide. Accurate prediction of CO2 emission levels plays a crucial role in implementing sustainable practices and driving policy decisions. This study aims to develop a machine learning model for predicting CO2 emission footprints using various socio-economic and environmental factors. The model will facilitate effective planning and decision-making in reducing global carbon emissions. The main objective of this study is to develop a machine learning model that accurately predicts CO2 emissions from fossil fuels and identifies the most important factors that contribute to these emissions. The results of this study could provide insights into the most effective strategies for reducing CO2 emissions and mitigating the impacts of climate change.
First Page
149
Last Page
175
DOI
10.1007/978-3-031-45214-7_8
Publication Date
1-1-2024
Keywords
Algorithms, Artificial intelligence, Climate change, CO emission 2, Machine learning, Sustainability
Recommended Citation
M. Alloghani, "Walking the Talk: Practical Implementation of Machine Learning Algorithms for Predicting CO2 Emission Footprint and Sustainability," Signals and Communication Technology, vol. Part F1802, pp. 149 - 175, Jan 2024.
The definitive version is available at https://doi.org/10.1007/978-3-031-45214-7_8