Walking the Talk: Practical Implementation of Machine Learning Algorithms for Predicting CO2 Emission Footprint and Sustainability

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.