Machine Learning Approaches for Real-time Carbon Emission Prediction and Mitigation

The escalating global concern surrounding carbon dioxide (CO2) emissions and their consequential impact on climate change necessitates advanced methodologies to predict and mitigate these emissions. This research delves into the application of various machine learning models to predict CO2 emissions based on a comprehensive dataset spanning from 1960 to the present day, encompassing all nations globally. The dataset, sourced from authoritative entities like the United Nations Framework Convention on Climate Change (UNFCCC) and the International Energy Agency (IEA), provides a granular view of each country's historical and current CO2 emissions landscape. In this study, we employed multiple machine learning algorithms, including Random Forest, Support Vector Machine, Gradient Boosting, MLP Classifier, KNN, and Decision Tree, to forecast CO2 emissions. Preliminary results indicate that the Random Forest and Gradient Boosting models achieved an impeccable accuracy of 100%, showcasing their robust predictive capabilities. In contrast, the MLP Classifier presented challenges in distinguishing certain classes, underscoring the need for further optimization. Beyond mere prediction, the research underscores the potential of these models in real-time mitigation strategies. By understanding the emission patterns and their contributing factors, policymakers and stakeholders can devise more effective, data-driven strategies to reduce the carbon footprint. Moreover, the study serves as a testament to the power of machine learning in addressing some of the most pressing environmental challenges of our time. The fusion of historical data with advanced machine learning techniques offers promising avenues for not only understanding the trajectory of CO2 emissions but also for devising strategies to combat the looming climate crisis. This research stands as a beacon for future endeavors aiming to harness technology in the fight against climate change.