Drivers of carbon dioxide emissions: an empirical investigation using hierarchical and non-hierarchical clustering methods

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Abstract

The mitigation of CO2 emissions requires a global effort with common but differentiated responsibilities. In this paper, we identify clusters of CO2 emissions across 72 countries. First, using the stochastic version of the IPAT and employing the dynamic common correlated effects technique, we identify three key determinants affecting CO2 emissions (non-renewables, population, and real GDP). In the second step, both hierarchical and non-hierarchical clustering methods are considered to identify the optimal number of clusters. We identify two to four clusters with different member countries, and in particular establish that in most cases, a 2-cluster solution appears to be optimal. The contents of clusters vary slightly according to the clustering methods for each period. The clustering results from using only the overall CO2 emissions indicate that the countries we consider form three clusters, with China and the USA each within a single member cluster. The remaining 70 countries form the third cluster. Our findings reflect the prominent roles of China and the USA in overall CO2 emissions. Analyses with sub-period and largest emitters reflect a different clustering structure. Some policy recommendations in setting emission reductions are made, considering different clusters across countries.

Original languageEnglish
Pages (from-to)1-40
Number of pages40
JournalEnvironmental and Ecological Statistics
Volume27
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • Cluster analysis
  • CO emissions
  • Key determinants
  • Stochastic version of IPAT

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