Exploring Greenhouse Gas Emissions and socio-economic factors for climate change mitigation: A worldwide clustering analysis

Sammanfattning: In response to the pressing need to address climate change and reduce global greenhouse gas (GHG) emissions, this study implemented Gaussian Mixture Models Clustering to detect the levels of GHG emissions and related socio-economic factors in 174 countries. To handle the panel data, Principal Component Analysis was conducted to achieve dimension reduction. Based on the algorithm, countries were grouped into four clusters according to the development of similar features from 2001 to 2018. The algorithm performed well, yielding an average silhouette value of 0.54, indicating a clear assignment of data points to reference clusters with minimal uncertainty. With the exception of one cluster containing only seven countries, all countries were equally divided among the clusters, allowing for the capture of potential peculiarities. The clusters were arranged in order of GHG emissions per capita, from the highest to the lowest. The results reveal that the cluster with the highest GHG emissions per capita also exhibited the highest levels of GDP per capita, consumption of fossil fuels, imports and exports, internet usage, and urbanization. Conversely, high GHG emissions per capita coincided with low renewable electricity and energy outputs, as well as male and female unemployment rates. Identifying patterns and similar socio-economic structures among countries enables collaboration and the implementation of unified measures and regulations, making climate change mitigation more efficient and effective.