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Analyzing the Temporal Interplay and Contribution of Socioeconomic, CO2 Related Industry, and Education to the Year-on-Year Change in CO2 Emissions: An In-Depth Analysis Using Machine Learning Approach
Mukendi, C.M.; Choi, H.; Jung, S.; Kim, Y.-S. Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics. Sustainability2024, 16, 4242.
Mukendi, C.M.; Choi, H.; Jung, S.; Kim, Y.-S. Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics. Sustainability 2024, 16, 4242.
Mukendi, C.M.; Choi, H.; Jung, S.; Kim, Y.-S. Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics. Sustainability2024, 16, 4242.
Mukendi, C.M.; Choi, H.; Jung, S.; Kim, Y.-S. Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics. Sustainability 2024, 16, 4242.
Abstract
To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change
Keywords
Absolute change in CO2 emissions; Short-term trend analysis; Machine learning
Subject
Environmental and Earth Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
