ENERJİ VERİMLİLİĞİNİN BELİRLEYİCİLERİ: TÜRKİYE ÖRNEĞİ

Year 2018, 18. EYI Special Issue, 839 - 854, 20.01.2018

Salih Çam , Esra Ballı , Çiler Sigeze

https://doi.org/10.18092/ulikidince.346477

Abstract

Bu çalışmada Türkiye’nin
enerji verimliliğinin belirleyicileri TOPSIS (Technique for Order Preference by
Similarity to Ideal Solution), Tobit model ve yapay sinir ağları (YSA)
algoritması yöntemleri birlikte kullanılarak incelenmiştir.  Çalışma TOPSIS enerji verimlilik skorlarının
hesaplanması ve yapay sinir ağları ve Tobit modelleriyle tahmin olmak üzere iki
aşamada gerçekleştirilmiştir. İlk aşamada karbon emisyonu, Gayri Safi Yurtiçi
Hasıla (GSYİH), yenilenebilir ve yenilenemeyen enerji tüketimi, işgücü miktarı
ve sermaye stoku değişkenleri kullanılarak Türkiye’nin 1960-2013 dönemine ait
yıllık enerji etkinlik skorları hesaplanmıştır. Sonraki aşamada TOPSIS
yöntemiyle elde edilen etkinlik skorları YSA ve Tobit modellerinde bağımlı
değişken olarak kullanılırken, karbon emisyonu, GSYİH, yenilenebilir ve
yenilenemeyen enerji tüketimi, işgücü miktarı, sermaye stoku-işgücü oranı, kriz
yıllarını temsil eden kukla değişkenler, doğrusal trend ve trendin karesi
bağımsız değişkenler olarak kullanılmıştır. Yapılan analizler sonucunda YSA
algoritmasının tahmini değerleri ile TOPSIS enerji verimlilik skorları
arasındaki korelasyon katsayısı 0.998 olarak gerçekleşmiştir.

Keywords

Yapay Sinir Ağları, Tobit, Enerji Etkinliği

THE DETERMINANTS OF ENERGY EFFICIENCY: THE CASE OF TURKEY

Abstract

In
this study, energy efficiency determinants of Turkey were investigated using
TOPSIS (Technique for Order Similarity to Ideal Solution) method, Tobit model
and Artificial Neural Network (ANN) algorithm. The study performed a two
stage analysis: calculation
of TOPSIS energy efficiency scores and estimation with artificial neural
networks and Tobit models. In the first stage, Annual energy efficiency scores
of Turkey for the period 1960-2013 calculated by using  carbon emissions, Gross Domestic Product (GDP),
renewable and non-renewable energy consumption, labor force and capital stock
variables. In the second stage, the efficiency scores obtained by the TOPSIS
method used as a dependent variable in YSA and Tobit models, while carbon
emissions, GDP, renewable and non-renewable energy consumption, labor force,
capital stock/labor ratio, dummy variables representing crisis years, deterministic
trend and square of trend used as independent variables. The results exhibit
that the ANN model can predict the experimental results with high correlation
coefficient, 0.998.

Keywords

TOPSIS, Artificial Neural Network, Tobit, Energy Efficiency

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