Application of a Principal Component Analysis within the Economic Circularity Indicators Framework
Abstract
In this article, it has been tested the correlation relationships between the normalized values of main circular economy indicators for EU27 countries, for the period 2010-2023. Thus, it has been applied a principal component analysis, (PCA), in EViews 9 to check the eigenvalues, also eigenvectors loadings of the correlation matrix. The goal of this methodology was to identify the degree of correlation between the chosen variables and to reduce the dimension of variation between the variables by eliminating the factors. It has been found that in terms of dimensionality reduction, factors 1, 2, and 3 have eigenvalues greater than 1. More exactly factor 1 has a value of 3.854 and factor 2 has a value of 1.629, and factor 3 has a value of 1.162. Thus, the factors retained are three. Concerning, eigenvalues figures, we have found that the proportion for factor 1 is 38.55%, for factor 2 is 16.29%, and for factor 3 is 11.62% of the total variance. The first three components namely account for 66.46% of the total variation.
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