MODELING GREEN GROWTH IN SUSTAINABLE DEVELOPMENT THROUGH CLASSICAL AND ROBUST DISCRIMINANT ANALYSES

Ergül B., Altın Yavuz A.

3. BİLSEL INTERNATIONAL KIBYRA SCIENTIFIC RESEARCHES CONGRESS 19-20 OCTOBER, 2024/ BURDUR/ TÜRKİYE, Burdur, Türkiye, 19 - 20 Ekim 2024, cilt.1, sa.1, ss.331-337

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 1
  • Basıldığı Şehir: Burdur
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.331-337
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Green growth, prioritizing environmental sustainability and aiming to achieve economic development within these
principles. This concept promotes the efficient use of natural resources, reduction of carbon emissions, and
conservation of ecosystems. Green growth strategies focus on areas such as renewable energy, energy efficiency,
sustainable agriculture, and clean technologies to combine economic growth with environmental goals. This
approach also plays a significant role in combating climate change.
Classical Discriminant Analysis is a statistical technique frequently used in classification problems. Its purpose is
to understand the differences between variables that separate observation units into specific categories or classes.
Classical Discriminant Analysis creates a linear function that divides data into two or more classes, allowing
predictions about which class new observations belong to. Robust Discriminant Analysis was developed due to
the sensitivity of classical discriminant analysis to outliers. The classical method can yield incorrect classification
rates due to outliers present in the dataset. In robust discriminant analysis, the aim is to minimize the impact of
these outliers and obtain more reliable results.
In this study, countries were evaluated using green growth index data through classical and robust discriminant
analyses. The correct classification rates of both classical and robust discriminant analyses were compared.