Öz
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.
Anahtar Kelimeler
Artificial neural network co2 emissions R&D expenditure STIRPAT
Kaynakça
- Ang, J. B. (2009). CO2 emissions, research and technology transfer in China. Ecological Economics.
- Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2007). Forecasting electrical consumption by integration of Neural Network, time series and ANOVA. Applied Mathematics and Computation.
- Benkachcha, S., Benhra, j., & El Hassani, H. (2013). Causal Method and Time Series Forecasting model based on Artificial Neural Network . International Journal of Computer Applications.
- Corradini, M., Costantini, V., Mancinelli, S., & Mazzanti, M. (2014). Unveiling the dynamic relation between R&D and emission abatement. Ecological Economics.
- Dietz, T., & Rosa, E. A. (1994). Rethinking the Environmental Impacts of Population, Affluence and Technology. Human Ecology Review.
- Ehrlich, P. R., & Holdren, J. P. (1971). Impact of Population Growth. Science.
- Fargione, J., Hill, J., Tilman, D., Polasky, S., & Hawthorne, P. (2008). Land Clearing and the Biofuel Carbon Debt. Science.
- Figueroa, J. D., Fout, T., Plasynski, S., McIlvried, H., & Srivastava, R. D. (2008). Advances in CO2 capture technology—The U.S. Department of Energy’s Carbon Sequestration Program. international journal of greenhouse gas control.
- Fischer, C., & Newell, R. G. (2008). Environmental and Technology Policies for Climate Mitigation. Journal of environmental economics and management.
- Goulder, L. H., & Mathai, K. (2000). Optimal CO2 Abatement in the Presence of Induced. Journal of Environmental Economics and Management.
- Goulder, L. H., & Schneider, S. H. (1999). Induced technological change and the attractiveness of CO abatement policies. Resource and Energy Economics.
- Hamzaçebi, C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy.
- Hang, G., & Yuan-sheng, J. (2011). The Relationship between CO2 Emissions, Economic Scale,Technology, Income and Population in China. Procedia Environmental Sciences.
- IEA. (2015, 10 28). Excerpt From Energy Balances Of Oecd Countrıes. Excerpt From Energy Balances Of Oecd Countrıes: https://www.iea.org/publications/freepublications/ publication/EnergyBalancesofOECDcountries2015editionexcerpt.pdf
- Işık, N., & Kılınç, E. C. (2014). Ulaştırma Sektöründe CO2 Emisyonu ve Enerji Ar-Ge Harcamaları İlişkisi. Sosyo Ekonomi.
- Kerr, R. A. (2007). Global warming is changing the world. Science.
- Kim, S. (2013). The Analysis on the Relationship between R&D Productivity of Renewable Energy and Emission Trading Scheme; Using OECD Patent Data. Korean Resource Economics Association.
- Lantz, V., & Feng, Q. (2006). Assessing income, population, and technology impacts on CO2 emissions in Canada: Where’s the EKC? Ecological Economics.
- Lindmark, M. (2002). An EKC-pattern in historical perspective: carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870–1997. Ecological Economics.
- Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H. A., & Nielsen, T. S. (2005). Standardizing the Performance Evaluation of ShortTerm Wind Power Prediction Models. Wind Engineering Volume.
- OECD/IEA. (2016, 10 29). International Energy Agency . http://www.iea.org/newsroom/ news/2016/november/world-energy-outlook-2016.html
- Popp, D., Hascic, I., & Medhi, N. (2011). Technology and the diffusion of renewable energy. Energy Economics.
- Rosa, E. A., & Dietz, T. (1998). Climate Change and Society Speculation, Construction and Scientific Investigation. International sociology.
- Srivastava, D. C., & Oyama, T. (2009). Evaluating the emission reduction targets in UNFCCC Kyoto Protocol by applying primary energy data analyses. Journal of Asian Public Policy.
- Tarawneh, B., & Imam, R. (2014). Regression versus artificial neural networks: Predicting pile setup from empirical data. KSCE Journal of Civil Engineering.
- UNFCCC. (2016, 11 03). United Nations Framework Convention on Climate Change: https://unfccc.int/kyoto_protocol/items/2830.php
- Uzlu, E., Akpınar, A., Özturk, H. T., Nacar, S., & Kankal, M. (2014). Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy.
- Weina, D., Gilli, M., Mazzanti, M., & Nicolli, F. (2014). Green inventions and greenhouse gas emission dynamics:A close examination of provincial Italian data. SEEDS Working Paper Series.
- World Bank . (2016, 11 03). The world bank: http://data.worldbank.org/
- York, R., Rosa, E. A., & Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics.
- Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting.
Öz
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
Kaynakça
- Ang, J. B. (2009). CO2 emissions, research and technology transfer in China. Ecological Economics.
- Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2007). Forecasting electrical consumption by integration of Neural Network, time series and ANOVA. Applied Mathematics and Computation.
- Benkachcha, S., Benhra, j., & El Hassani, H. (2013). Causal Method and Time Series Forecasting model based on Artificial Neural Network . International Journal of Computer Applications.
- Corradini, M., Costantini, V., Mancinelli, S., & Mazzanti, M. (2014). Unveiling the dynamic relation between R&D and emission abatement. Ecological Economics.
- Dietz, T., & Rosa, E. A. (1994). Rethinking the Environmental Impacts of Population, Affluence and Technology. Human Ecology Review.
- Ehrlich, P. R., & Holdren, J. P. (1971). Impact of Population Growth. Science.
- Fargione, J., Hill, J., Tilman, D., Polasky, S., & Hawthorne, P. (2008). Land Clearing and the Biofuel Carbon Debt. Science.
- Figueroa, J. D., Fout, T., Plasynski, S., McIlvried, H., & Srivastava, R. D. (2008). Advances in CO2 capture technology—The U.S. Department of Energy’s Carbon Sequestration Program. international journal of greenhouse gas control.
- Fischer, C., & Newell, R. G. (2008). Environmental and Technology Policies for Climate Mitigation. Journal of environmental economics and management.
- Goulder, L. H., & Mathai, K. (2000). Optimal CO2 Abatement in the Presence of Induced. Journal of Environmental Economics and Management.
- Goulder, L. H., & Schneider, S. H. (1999). Induced technological change and the attractiveness of CO abatement policies. Resource and Energy Economics.
- Hamzaçebi, C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy.
- Hang, G., & Yuan-sheng, J. (2011). The Relationship between CO2 Emissions, Economic Scale,Technology, Income and Population in China. Procedia Environmental Sciences.
- IEA. (2015, 10 28). Excerpt From Energy Balances Of Oecd Countrıes. Excerpt From Energy Balances Of Oecd Countrıes: https://www.iea.org/publications/freepublications/ publication/EnergyBalancesofOECDcountries2015editionexcerpt.pdf
- Işık, N., & Kılınç, E. C. (2014). Ulaştırma Sektöründe CO2 Emisyonu ve Enerji Ar-Ge Harcamaları İlişkisi. Sosyo Ekonomi.
- Kerr, R. A. (2007). Global warming is changing the world. Science.
- Kim, S. (2013). The Analysis on the Relationship between R&D Productivity of Renewable Energy and Emission Trading Scheme; Using OECD Patent Data. Korean Resource Economics Association.
- Lantz, V., & Feng, Q. (2006). Assessing income, population, and technology impacts on CO2 emissions in Canada: Where’s the EKC? Ecological Economics.
- Lindmark, M. (2002). An EKC-pattern in historical perspective: carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870–1997. Ecological Economics.
- Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H. A., & Nielsen, T. S. (2005). Standardizing the Performance Evaluation of ShortTerm Wind Power Prediction Models. Wind Engineering Volume.
- OECD/IEA. (2016, 10 29). International Energy Agency . http://www.iea.org/newsroom/ news/2016/november/world-energy-outlook-2016.html
- Popp, D., Hascic, I., & Medhi, N. (2011). Technology and the diffusion of renewable energy. Energy Economics.
- Rosa, E. A., & Dietz, T. (1998). Climate Change and Society Speculation, Construction and Scientific Investigation. International sociology.
- Srivastava, D. C., & Oyama, T. (2009). Evaluating the emission reduction targets in UNFCCC Kyoto Protocol by applying primary energy data analyses. Journal of Asian Public Policy.
- Tarawneh, B., & Imam, R. (2014). Regression versus artificial neural networks: Predicting pile setup from empirical data. KSCE Journal of Civil Engineering.
- UNFCCC. (2016, 11 03). United Nations Framework Convention on Climate Change: https://unfccc.int/kyoto_protocol/items/2830.php
- Uzlu, E., Akpınar, A., Özturk, H. T., Nacar, S., & Kankal, M. (2014). Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy.
- Weina, D., Gilli, M., Mazzanti, M., & Nicolli, F. (2014). Green inventions and greenhouse gas emission dynamics:A close examination of provincial Italian data. SEEDS Working Paper Series.
- World Bank . (2016, 11 03). The world bank: http://data.worldbank.org/
- York, R., Rosa, E. A., & Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics.
- Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting.
Ayrıntılar
| Bölüm | Makaleler |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 6 Kasım 2016 |
| Yayımlandığı Sayı | Yıl 2016 ICEBSS Özel Sayısı |
Kaynak Göster
Dergimiz EBSCOhost, ULAKBİM/Sosyal Bilimler Veri Tabanında, SOBİAD ve Türk Eğitim İndeksi'nde yer alan uluslararası hakemli bir dergidir.