ENERJİ VERİMLİLİĞİNİN BELİRLEYİCİLERİ: TÜRKİYE ÖRNEĞİ

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.

Anahtar Kelimeler:

Yapay Sinir Ağları, Tobit, Enerji Etkinliği

THE DETERMINANTS OF ENERGY EFFICIENCY: THE CASE OF TURKEY

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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