AR-GE HARCAMALAERI VE EMİSYONU: YAPAY SİNİR AĞI YAKLAŞIMI

Günümüzde artan sanayileşme, nüfus, ekonomik büyüme ve teknolojideki gelişmeler ile enerji ihtiyacı giderek artmaktadır. Artan enerji ihtiyacını karşılamak için ülkeler daha fazla enerji üretme veya ithal etme gereksinimi duymaktadır. Karbon emisyonunun büyük bir bölümü birincil enerji kaynaklarının kullanımından kaynaklanmaktadır. Birincil enerji kaynaklarından en çok karbon emisyonuna sebep olan kaynak fosil yakıtlardır. Dünyanın enerji ihtiyacının büyük bir bölümü de fosil yakıtlardan karşılanmaktadır. Bunun sonucunda da enerji tüketimi arttıkça sera gazı emisyonlarında özellikleemisyonunda artış olmaktadır. Son yıllarda emisyonunun çevreye verdiği zararlar önemli boyutlara ulaşmıştır. emisyonundaki artış dünyanın dengesini bozmuş ve küresel ısınmaya sebep olmuştur. Bu nedenle teknolojik gelişmeler ve Ar-Ge faaliyetleri gibi zehirli gazların emisyonlarının azaltılmasına yönelik olmaya başlamıştır. emisyonu ve AR-GE harcamaları arasındaki ilişkinin belirlenmesi politikacılar ve uygulamacılar açısından önem arz etmektedir Bu bağlamda çalışmada 1996-2013 yılları arasında OECD ülkelerinin AR-GE harcamaları ile emisyonu arasındaki ilişki STIRPAT modeli çerçevesinde yapay sinir ağları kullanılarak analiz edilmiştir. Analiz sonuçlarına göre beklentilere uygun olarak OECD ülkelerinde Ar-Ge harcamaları emisyonunu negatif yönde etkilemiştir.

Anahtar Kelimeler:

AR-GE harcamaları, co2 emisyonunu, STIRPAT

R&D EXPENDITURE AND EMISSION: ARTIFICIAL NEURAL NETWORK BASED APPROACH

Nowadays, energy demand increases with advanced in technology, economic growth, population and growing industrialization. The countries need more energy to produce or to import for meet to energy need. A big part of carbon emission arises from use of primary energy source. Fossil fuels are most cause carbon emissions in primary energy source. A large part of the world’s energy need is met by fossil fuels. Consequently, as energy consumption increases, greenhouse gas emissions especially have increase. In recent years, environmental damage caused by carbon emissions upset balance of world and caused global warming. Therefore; technological development and R&D activites have become for reducing toxic gases such as . It is important to determine the relationship between emission and R&D expenditure negatively has affected carbon emission and R&D expenditure for policymaker and practitions. In this regard, this study, the relationship between OECD countries R&D expenditure and emission for 1996-2013 was examined using artificial neural network within the framework STIRPAT model. According to the analysis results in accordance with expectations R&D expenditure negatively has affected carbon emissions in the OECD countries.

Keywords:

Artificial neural network co2 emissions, R&D expenditure, STIRPAT,

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