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Factor Analysis Of Energy Security: Net Import Dependency

Journal: SocioEconomic Challenges (SEC) (Vol.6, No. 2)

Publication Date:

Authors : ;

Page : 138-146

Keywords : energy dependency; energy policy; energy security; natural gas security; net import dependency;

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Abstract

This paper analyzes various aspects of energy dependency and identifies the hidden factors behind national energy security. Based on a review of the scientific literature, the factors that reduce energy security in the consumption of natural gas, oil, and nuclear energy generation were identified. One such factor is import dependency on energy resources. Import dependency on energy is a crucial characteristic of energy security and can exacerbate the effects of external aggression, non-competitive behavior, and pressure. Given the energy crisis of 2022, most countries around the world have revised their energy security policies to reduce import dependency by diversifying supplies and reducing dependence on Russian energy resources. The factor analysis of energy dependency ratios, 1990-2020, was used to study the energy security of the EU27 countries. The factor analysis was performed for annual indices calculated using the Eurostat database of “Simplified energy balances.” The paper hypothesized the existence of latent relationships between energy security variables for the twenty-seven EU countries. The hypothesis was verified using Bartlett's sphericity test and Kaiser-Meyer-Olkin criterion. The results of hypothesis testing showed its statistical significance (the p-value < 0.05) and the possibility of factor analysis. Correspondence indices also indicated the adequacy of possible prediction of a set of variables. The factor analysis was executed in Python using the FactorAnalyzer module (the release 0.4.0). The principal factor extraction and the varimax rotation model were used to obtain the initial solution, preserving the orthogonality of the loading matrix. The factor structure of the model was confirmed for four factors consisting of the twenty-eight elements. The received four factors model allowed us to describe around 80% of the cumulative variance. It was found that each factor separately explained 46.89%, 15.80%, 10.91%, and 6.39% of the variance, respectively.

Last modified: 2022-07-13 15:37:09