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ENERJİ VERİMLİLİĞİNİN BELİRLEYİCİLERİ: TÜRKİYE ÖRNEĞİ

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

References

  • Apergis, N., Aye, G. C., Barros, C. P., Gupta, R., & Wanke, P. (2015). Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs. Energy Economics, 51, 45-53.
  • Aye, G. C., Gupta, R., & Wanke, P. (2015). Energy Efficiency Drivers in South Africa: 1965-2014 (No. 201571).
  • Azadeh, A., Amalnick, M. S., Ghaderi, S. F., & Asadzadeh, S. M. (2007). An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors. Energy Policy, 35(7), 3792-3806.
  • Bian, Y., Hu, M., Wang, Y., & Xu, H. (2016). Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach. Renewable and Sustainable Energy Reviews, 55, 990-998.
  • Carbon Dioxide Information Analysis Center. http://cdiac.ornl.gov/
  • Chang, T. P., & Hu, J. L. (2010). Total-factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Applied Energy, 87(10), 3262-3270.
  • Çengel, Y. A. (2011). Energy efficiency as an inexhaustible energy resource with perspectives from the US and Turkey. International Journal of Energy Research, 35(2), 153-161.
  • Deng, H., Yeh, C. H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963-973.
  • Energy Information Agency (EIA) (2011), https://www.iea.org/newsroom/news/2011/march/2011-03-08-.html
  • Energy Information Agency (EIA) (2017), Energy Technology. Perspectives 2017. Catalysing Energy Technology Transformations. https://www.iea.org/publications/freepublications/publication/EnergyTechnologyPerspectives2017ExecutiveSummaryEnglishversion.pdf
  • Gómez-Calvet, R., Conesa, D., Gómez-Calvet, A. R., & Tortosa-Ausina, E. (2014). Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?. Applied Energy, 132, 137-154.
  • Greene, W. H. (2003). Econometric analysis. Pearson Education India.
  • Grösche, P. (2009). Measuring residential energy efficiency improvements with DEA. Journal of Productivity Analysis, 31(2), 87-94.
  • Honma, S., & Hu, J. L. (2008). Total-factor energy efficiency of regions in Japan. Energy Policy, 36(2), 821-833.
  • Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy policy, 34(17), 3206-3217.
  • Hu, J. L., & Kao, C. H. (2007). Efficient energy-saving targets for APEC economies. Energy policy, 35(1), 373-382.
  • Yoon, K., & Hwang, C. L. (1981). TOPSIS (technique for order preference by similarity to ideal solution)–a multiple attribute decision making, w: Multiple attribute decision making–methods and applications, a state-of-the-at survey.
  • Jebali, E., Essid, H., & Khraief, N. (2017). The analysis of energy efficiency of the Mediterranean countries: A two-stage double bootstrap DEA approach. Energy.991-1000.
  • Mukherjee, K. (2008a). Energy use efficiency in US manufacturing: a nonparametric analysis. Energy Economics, 30(1), 76-96.
  • Mukherjee, K. (2008b). Energy use efficiency in the Indian manufacturing sector: an interstate analysis. Energy policy, 36(2), 662-672.
  • OECD (2011). Towards green growth. https://www.oecd.org/greengrowth/48012345.pdf
  • Özkara, Y., & Atak, M. (2015). Regional total-factor energy efficiency and electricity saving potential of manufacturing industry in Turkey. Energy, 93, 495-510.
  • Pusnik, M., Al-Mansour, F., Sucic, B., & Cesen, M. (2016). Trends and prospects of energy efficiency development in Slovenian industry. Energy.
  • Ramanathan, R. (2000). A holistic approach to compare energy efficiencies of different transport modes. Energy Policy, 28(11), 743-747.
  • Ramanathan, R. (2005). An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa. Energy, 30(15), 2831-2842.
  • Song, M. L., Zhang, L. L., Liu, W., & Fisher, R. (2013). Bootstrap-DEA analysis of BRICS’energy efficiency based on small sample data. Applied energy, 112, 1049-1055.
  • Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
  • T.C. Enerji ve Tabii Kaynaklar Bakanlığı http://www.enerji.gov.tr/File/?path=ROOT%2F1%2FDocuments%2FFaaliyet%20Raporu%2Fetkb_fr_ds_225x300mm_bask%C3%B0_d.pdf, (02.08.2017).
  • Wang, K., Lu, B., & Wei, Y. M. (2013). China’s regional energy and environmental efficiency: a range-adjusted measure based analysis. Applied energy, 112, 1403-1415.
  • Wei, Y. M., Liao, H., & Fan, Y. (2007). An empirical analysis of energy efficiency in China's iron and steel sector. Energy, 32(12), 2262-2270.
  • Yu, L., Wang, S., & Lai, K. K. (2010). Foreign-exchange-rate forecasting with artificial neural networks (Vol. 107). Springer Science & Business Media.
  • Zhang, X. P., Cheng, X. M., Yuan, J. H., & Gao, X. J. (2011). Total-factor energy efficiency in developing countries. Energy Policy, 39(2), 644-650.

THE DETERMINANTS OF ENERGY EFFICIENCY: THE CASE OF TURKEY

Year 2018, 18. EYI Special Issue, 839 - 854, 20.01.2018
https://doi.org/10.18092/ulikidince.346477

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.

References

  • Apergis, N., Aye, G. C., Barros, C. P., Gupta, R., & Wanke, P. (2015). Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs. Energy Economics, 51, 45-53.
  • Aye, G. C., Gupta, R., & Wanke, P. (2015). Energy Efficiency Drivers in South Africa: 1965-2014 (No. 201571).
  • Azadeh, A., Amalnick, M. S., Ghaderi, S. F., & Asadzadeh, S. M. (2007). An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors. Energy Policy, 35(7), 3792-3806.
  • Bian, Y., Hu, M., Wang, Y., & Xu, H. (2016). Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach. Renewable and Sustainable Energy Reviews, 55, 990-998.
  • Carbon Dioxide Information Analysis Center. http://cdiac.ornl.gov/
  • Chang, T. P., & Hu, J. L. (2010). Total-factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Applied Energy, 87(10), 3262-3270.
  • Çengel, Y. A. (2011). Energy efficiency as an inexhaustible energy resource with perspectives from the US and Turkey. International Journal of Energy Research, 35(2), 153-161.
  • Deng, H., Yeh, C. H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963-973.
  • Energy Information Agency (EIA) (2011), https://www.iea.org/newsroom/news/2011/march/2011-03-08-.html
  • Energy Information Agency (EIA) (2017), Energy Technology. Perspectives 2017. Catalysing Energy Technology Transformations. https://www.iea.org/publications/freepublications/publication/EnergyTechnologyPerspectives2017ExecutiveSummaryEnglishversion.pdf
  • Gómez-Calvet, R., Conesa, D., Gómez-Calvet, A. R., & Tortosa-Ausina, E. (2014). Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?. Applied Energy, 132, 137-154.
  • Greene, W. H. (2003). Econometric analysis. Pearson Education India.
  • Grösche, P. (2009). Measuring residential energy efficiency improvements with DEA. Journal of Productivity Analysis, 31(2), 87-94.
  • Honma, S., & Hu, J. L. (2008). Total-factor energy efficiency of regions in Japan. Energy Policy, 36(2), 821-833.
  • Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy policy, 34(17), 3206-3217.
  • Hu, J. L., & Kao, C. H. (2007). Efficient energy-saving targets for APEC economies. Energy policy, 35(1), 373-382.
  • Yoon, K., & Hwang, C. L. (1981). TOPSIS (technique for order preference by similarity to ideal solution)–a multiple attribute decision making, w: Multiple attribute decision making–methods and applications, a state-of-the-at survey.
  • Jebali, E., Essid, H., & Khraief, N. (2017). The analysis of energy efficiency of the Mediterranean countries: A two-stage double bootstrap DEA approach. Energy.991-1000.
  • Mukherjee, K. (2008a). Energy use efficiency in US manufacturing: a nonparametric analysis. Energy Economics, 30(1), 76-96.
  • Mukherjee, K. (2008b). Energy use efficiency in the Indian manufacturing sector: an interstate analysis. Energy policy, 36(2), 662-672.
  • OECD (2011). Towards green growth. https://www.oecd.org/greengrowth/48012345.pdf
  • Özkara, Y., & Atak, M. (2015). Regional total-factor energy efficiency and electricity saving potential of manufacturing industry in Turkey. Energy, 93, 495-510.
  • Pusnik, M., Al-Mansour, F., Sucic, B., & Cesen, M. (2016). Trends and prospects of energy efficiency development in Slovenian industry. Energy.
  • Ramanathan, R. (2000). A holistic approach to compare energy efficiencies of different transport modes. Energy Policy, 28(11), 743-747.
  • Ramanathan, R. (2005). An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa. Energy, 30(15), 2831-2842.
  • Song, M. L., Zhang, L. L., Liu, W., & Fisher, R. (2013). Bootstrap-DEA analysis of BRICS’energy efficiency based on small sample data. Applied energy, 112, 1049-1055.
  • Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
  • T.C. Enerji ve Tabii Kaynaklar Bakanlığı http://www.enerji.gov.tr/File/?path=ROOT%2F1%2FDocuments%2FFaaliyet%20Raporu%2Fetkb_fr_ds_225x300mm_bask%C3%B0_d.pdf, (02.08.2017).
  • Wang, K., Lu, B., & Wei, Y. M. (2013). China’s regional energy and environmental efficiency: a range-adjusted measure based analysis. Applied energy, 112, 1403-1415.
  • Wei, Y. M., Liao, H., & Fan, Y. (2007). An empirical analysis of energy efficiency in China's iron and steel sector. Energy, 32(12), 2262-2270.
  • Yu, L., Wang, S., & Lai, K. K. (2010). Foreign-exchange-rate forecasting with artificial neural networks (Vol. 107). Springer Science & Business Media.
  • Zhang, X. P., Cheng, X. M., Yuan, J. H., & Gao, X. J. (2011). Total-factor energy efficiency in developing countries. Energy Policy, 39(2), 644-650.
There are 32 citations in total.

Details

Journal Section Articles
Authors

Salih Çam

Esra Ballı

Çiler Sigeze This is me

Publication Date January 20, 2018
Published in Issue Year 2018 18. EYI Special Issue

Cite

APA Çam, S., Ballı, E., & Sigeze, Ç. (2018). ENERJİ VERİMLİLİĞİNİN BELİRLEYİCİLERİ: TÜRKİYE ÖRNEĞİ. Uluslararası İktisadi Ve İdari İncelemeler Dergisi839-854. https://doi.org/10.18092/ulikidince.346477

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