Uncovering Carbon Dioxide Emission Patterns: Data-Driven Insights with K-Means Clustering Across 20 Countries

Ariana Yunita; Angelia Regina Dwi Kartika

doi:10.25299/itjrd.2026.25696

Authors

Ariana Yunita Department of Computer Science, Universitas Pertamina
Angelia Regina Dwi Kartika Department of Computer Science, Universitas Pertamina

DOI:

https://doi.org/10.25299/itjrd.2026.25696

Keywords:

Clustering Analysis, Dimensionality Reduction, CO2 emissions, Greenhouse gas, K-Means

Abstract

Greenhouse Gas (GHG) emissions pose a pressing global issue due to their significant environmental impacts, particularly climate change, which disrupts ecosystems and human life. Carbon dioxide emissions are a major contributor to GHGs. This study aims to gain insights into the sectors contributing to CO2 emissions across 20 countries, which is crucial for decision-making in emissions mitigation. The seven sectors investigated include electricity and heat generation, gas fuel consumption, liquid fuel consumption, manufacturing and construction industries, residential and commercial sectors, solid fuel consumption, and transportation. The
methods employed encompass clustering with the K-Means algorithm and dimensionality reduction using Principal Component Analysis (PCA). The clustering results yielded four clusters based on CO2 emission levels across these sectors, with PCA analysis and exploratory data analysis (EDA) findings supporting this grouping. The evaluation of the model uses the Davies-Bouldin Index (DBI), the Elbow Method, and the Silhouette Score to determine the optimal parameter for the K value in K-Means. Insights from these findings are discussed.

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