CLUSTERING ANALYSIS OF TURKEY AND EU COUNTRIES ON THE AXIS OF GREEN GROWTH HEADLINE INDICATORS

Clustering is the classification of objects into different groups, or the division of a data set into

subsets (clusters) so that the data in each subset share some common characteristics. Clustering techniques

basically seeks a structure of natural clusters for a dataset, based on their similarities or dissimilarities [1].

Green growth is a complex and multidimensional concept. While this concept is similar to “sustainable

development”, unlike sustainable development, green growth aims to support economic growth and

development in a way that balances environmental damage concerns with long-term economic growth [2].

Five main categories including approximately 150 sub-criteria developed by the OECD balance out the

central elements of green growth. While a large number of well-designed indicators are potentially required

in economic environmental connectivity to provide background information, they are unlikely to resonate

with policy makers and the wider public. Thus, to synthesize the main messages in a clear and easy

interpretable way, one of the possible standing out approaches is to assemble a small number of headline

indicators. Therefore, in this study, headline indicators are used to cluster the countries. This study was

carried out to classify EU countries and Turkey according to the headline indicators of green growth.

Considering 5 headline indicators of green growth, Turkey and EU countries were grouped using fuzzy C

means clustering with two different tool, MATLAB and R. As a result of the analyses, the countries that

provide homogeneity in terms of green growth headline indicators and show similarities among themselves

were grouped to determine whether Turkey has similar characteristics with other countries in the same

cluster, lastly, the statistical results were evaluated.