Determinants of CO2 Emissions in Turkey: An Application with Multilayer Artificial Neural Networks

Abstract

CO2, which is released into the nature as a result of the use of fossil fuels, is one of the most important greenhouse gases that cause environmental problems. Therefore, it is very important to investigate how CO2 emissions change over time and to determine the factors that affect CO2 emissions. In this study, CO2 emission estimation is carried out using Artificial Neural Networks (ANN) method and Garson’s Algorithm is used to evaluate the relative importance of the independent variables. The results showed that ANN models are a successful method that can be used in the estimation of CO2 emissions. As a result of the importance analysis, it is determined that the most important factor affecting the CO2 emission in Turkey is the renewable energy consumption.

Keywords

• Artificial Neural Networks
• CO2 emissions
• Garson’s Algorithm

Türkiye’de CO2 Emisyonlarının Belirleyicileri: Çok Katmanlı Yapay Sinir Ağları ile Bir Uygulama

Abstract

Fosil yakıtların kullanılması sonucu doğaya salınan CO2, çevresel sorunlara neden olan en önemli sera gazlarından birisidir. Dolayısıyla CO2 emisyonlarının zaman içinde nasıl değiştiğinin araştırılması ve CO2 emisyonu üzerinde etkili olan faktörlerin belirlenmesi oldukça önemlidir. Bu çalışmada Yapay Sinir Ağları (YSA) metodu kullanılarak CO2 emisyonu tahmini gerçekleştirilmiş ve çalışmada ele alınan bağımsız değişkenlerin bağıl önemlerini değerlendirmek amacıyla Garson Algoritması kullanılmıştır. Elde edilen tahmin sonuçları, YSA modellerinin CO2 emisyonu tahmininde kullanılabilecek başarılı bir yöntem olduklarını göstermektedir. Yapılan önem analizi sonucunda Türkiye’de CO2 emisyonunu etkileyen en önemli faktörün yenilenebilir enerji tüketimi olduğu belirlenmiştir.

Keywords

• Yapay Sinir Ağları
• CO2 emisyonu
• Garson Algoritması

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