Determinants of Yearly CO 2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics

Christian Mulomba Mukendi (), Hyebong Choi, Suhui Jung and Yun-Seon Kim
Additional contact information
Christian Mulomba Mukendi: Department of Advanced Convergence, Handong Global University, Pohang 37554, Republic of Korea
Hyebong Choi: School of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang 37554, Republic of Korea
Suhui Jung: School of Management and Economics, Convergence, Handong Global University, Pohang 37554, Republic of Korea
Yun-Seon Kim: Department of Global Development and Entrepreneurship, Convergence, Handong Global University, Pohang 37554, Republic of Korea

Sustainability, 2024, vol. 16, issue 10, 1-28

Abstract: In order to understand the dynamics in climate change, inform policy decisions and prompt timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of the year-on-year CO 2 emission changes across ten countries, considering a broad range of factors including socioeconomic factors, CO 2 -related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO 2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of the variations in the target. To capture the emissions trend variation, we categorized countries by the change in CO 2 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, CO 2 emissions from coal, the three-year average change in CO 2 emissions, GDP, CO 2 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 CO 2 emissions changes, contributing to the global efforts to address climate change.

Keywords: absolute change in CO 2 emissions; short-term trend analysis; machine learning modeling; categorization; explainable machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/10/4242/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/10/4242/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:10:p:4242-:d:1396929

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().