Akademik Veri Yönetim Sistemi
XLVIII International SYmposium on operational Research, Belgrade, Sırbistan, 20 - 23 Eylül 2021, ss.483-488
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.