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

Year 2016, ICEBSS Special Issue, 136 - 150, 06.11.2016

Sema Behdioğlu , Fadime Çelik

Abstract

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

AR-GE HARCAMALAERI VE EMİSYONU: YAPAY SİNİR AĞI YAKLAŞIMI

Abstract

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.

Keywords

AR-GE harcamaları, co2 emisyonunu, STIRPAT

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