Greenhouse Gas Emission-Based K-Means and Hierarchical Cluster Analysis : The Case of the G20

What exactly is understood from climate change mitigation? What should be the most appropriate climate indicator to measure the success of the determined goals and targets? What level of the selected climate indicator can keep climate change within acceptable limits? What kind of climate surprises may be encountered, and how can the economic, social and political implications of the selected climate target be harmonized with these factors? These are some of the questions needing to be answered while determining the political aims and objectives of combatting climate change. The international efforts that started with the United Nations Framework Convention on Climate Change in 1992 and concluded with the Paris Agreement in 2015 have made the goal of limiting the increase in global average temperatures to 1.5 degrees C compared to the pre-Industrial level as the global standard of climate change policy. To achieve this goal, total greenhouse gas emissions must be reduced. The purpose of this study is to compare G20 members with each other using two different cluster analysis methods based on different emission criteria. For this purpose, per capita greenhouse gas emissions, per capita income, per capita electricity consumption, emission intensity of electricity production, emission intensity of primary energy supply, and emission intensity of the economy have been selected for use in the k-means cluster and hierarchical cluster analysis methods. In addition to carbon dioxide emissions, other greenhouse gases have also been included in the analysis. While the first three selected variables expressed at the per capita level are scale variables that determine the total amount of greenhouse gas emissions, the intensity variables expressed at the unit activity level are considered technological variables. Although the emissions of developing countries are close to developed countries in terms of the scale variables, differences are seen to occur between developing and developed members in terms of technological variables and different clusters.