An Analysis of Turkey’s Energy Efficiency with Artificial Neural Networks and ARDL Approach

Year 2018, Volume: 18 Issue: 4, 661 - 670, 23.10.2018

Salih Çam , Çiler Sigeze Esra Ballı

Abstract

This study investigates Turkey’s energy efficiency for the period of 1960-2013 utilizing the ARDL (Autoregressive Distributed Lag) in the context of TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) and Artificial Neural Networks algorithm. In the first stage of the analysis, energy efficiency scores obtained via TOPSIS, then efficiency scores employed as output for the Artificial Neural Networks. Finally, ARDL utilized to estimate the coefficients of the variables both in the short and the long run. The empirical results depict that Turkey’s energy efficiency tends to increase over the years. Besides, according to Artificial Neural Networks results, the most important variable determining energy efficiency is found to be per capita capital stock.

Keywords

Enery efficiency, TOPSIS, Artificial Neural Networks, ARDL

Türkiye’nin Enerji Verimliliğinin Yapay Sinir Ağı ve ARDL Yaklaşımı ile Analizi

Abstract

Bu çalışmada Türkiye’nin enerji verimliliği ARDL yöntemi, TOPSIS yöntemi ve yapay sinir ağları algoritması analizleri çerçevesinde 1960-2013 dönemi için incelenmiştir. Çalışmanın ilk aşamasında TOPSIS yöntemi kullanılarak enerji verimlilik skorları elde edilmiştir, elde edilen skorlar daha sonra Yapay Sinir Ağlarının çıktı verisi olarak kullanılmıştır. Son olarak, değişkenlerin kısa ve uzun dönem katsayıları ARDL yöntemi ile tahmin edilmiştir. Elde edilen sonuçlar Türkiye’nin enerji verimliliğinin yıllara göre artma eğiliminde olduğunu göstermektedir. Bunun yanında, Yapay Sinir Ağları analizi sonuçlarına göre enerji etkinliğini belirleyen en önemli değişken kişi başına düşen sermaye stokudur.

Keywords

Enerji etkinliği, TOPSIS, Yapay Sinir Ağları Algoritması, ARDL

References

• Ang, B. W. (2006). Monitoring changes in economy-wide energy efficiency: From energy–gdp ratio to composite efficiency index. Energy Policy, 34 (5), 574-582.
• Apergis, N., Aye, G. C., Barros, C. P., Gupta, R. ve 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. ve Wanke, P (2018). Energy efficiency drivers in South Africa: 1965–2014. Energy Efficiency, 1-18.
• Azadeh, A., Amalnick, M. S., Ghaderi, S. F. ve Asadzadeh, S. M. (2007). An ıntegrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy ıntensive manufacturing sectors. Energy Policy, 35(7), 3792-3806.
• Balachandra, P., Ravindranath, D. ve Ravindranath, N. H. (2010). Energy efficiency in India: Assessing the policy regimes and their ımpacts. Energy Policy, 38(11), 6428-6438.
• Bian, C., Hu, Y., Ravi, V., Kuznetsova, I. S., Shen, X., Mu, X. ve Qiu, Y. (2016). The Asian arowana (scleropages formosus) genome provides new ınsights into the evolution of an early lineage of teleosts. Scientific Reports, 6, 24501.
• Blomberg, J., Henriksson, E. ve Lundmark, R. (2012) Energy efficiency and policy in swedish pulp and paper mills: A data envelopment analysis approach. Energy Policy, 42, 569-579.
• Chang, T. P. ve 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.
• Chen, A. S. ve Leung, M. T. (2004). Regression neural network for error correction in foreign exchange forecasting and trading. Computers & Operations Research, 31(7), 1049-1068.
• Cui, Q. ve Li, Y. (2014). The evaluation of transportation energy efficiency: an application of three-stage virtual frontier DEA. Transportation Research Part D: Transport And Environment, 29: 1-11.
• Deng, H., Yeh, C. H. ve Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963-973.
• Fang, C. Y., Hu, J. L. ve Lou, T. K. (2013). Environment-adjusted total-factor energy efficiency of Taiwan’s service sectors. Energy Policy, 63, 1160-1168.
• Gómez-Calvet, R., Conesa, D., Gómez-Calvet, A. R. ve 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, 137154.
• Grösche, P. (2009). Measuring residential energy efficiency ımprovements with DEA. Journal Of Productivity Analysis, 31(2), 87-94. Jasic, T. ve Wood, D. (2003). Neural network protocols and model performance. Neurocomputing, 55(3), 747-753.
• Kılıç, S. B., Lopcu, K. ve Paksoy, S. (2014). Artificial neural network models to build an early warning system for Turkish commercial banks before and after the 2001 financial crisis ınternational conference on eurasian economies bildiriler kitabı: 1-10.
• Kılıç, S. B., Lopcu, K. ve Paksoy, S. (2014). Artificial neural network models to build an early warning system for Turkish commercial banks before and after the 2001 financial crisis ınternational conference on eurasian economies bildiriler kitabı: 1-10.
• Khoshnevisan, B., Rafiee, S., Omid, M. ve Mousazadeh, H. (2013). Reduction of co2 emission by ımproving energy use efficiency of greenhouse cucumber production using DEA approach. Energy, 55, 676-682.
• Lee, W. S. ve Lee, K. P. (2009). Benchmarking the performance of building energy management using data envelopment analysis. Applied Thermal Engineering, 29(16), 3269-3273.
• Lee, Y. C., Hu, J. L. ve Kao, C. H. (2011). Efficient saving targets of electricity and energy for regions in China. International Journal of Electrical Power & Energy Systems, 33(6), 1211-1219.
• Leung, M. T., Daouk, H. ve Chen, A. S. (2000). Forecasting stock ındices: a comparison of classification and level estimation models. International Journal of Forecasting, 16(2), 173-190.
• Liu, W. ve Lin, B. (2018) Analysis of energy efficiency and its ınfluencing factors in China’s transport sector. Journal of Cleaner Production, 170, 674-682.
• Lundgren, T., Marklund, P. O. ve Zhang, S. (2016). Industrial energy demand and energy efficiency–evidence from Sweden. Resource and Energy Economics, 43, 130-152.
• Honma, S. ve Hu, J. L. (2008). Total-factor energy efficiency of regions in Japan. Energy Policy, 36(2),821-833.
• Hu, J. L. ve Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206-3217.
• Hu, J. L. ve Kao, C. H. (2007). Efficient energy-saving targets for apec economies. Energy Policy, 35(1), 373-382.
• Hwang, C. L. ve Yoon, K. (1981). Methods for multiple attribute decision making. ın multiple attribute decision making (pp. 58-191). Berlin: Springer.
• Makridou, G., Andriosopoulos, K., Doumpos, M. ve Zopounidis, C. (2016). Measuring the efficiency of energy-ıntensive ındustries across European countries. Energy Policy, 88, 573-583.
• 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, OECD Publishing.
• Peng, L., Zeng, X., Wang, Y. ve Hong, G. B. (2015). Analysis of energy efficiency and carbon dioxide reduction in the Chinese pulp and paper ındustry. Energy Policy, 80, 65-75.
• Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.
• Pesaran MH, Shin Y. (1999). An autoregressive distributed lag ve distributed lag modeling approach to cointegration analysis. Strom, S.(eds.). Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, Cambridge: Cambridge University Press.
• 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.
• Shi, G. M., Bi, J. ve Wang, J. N. (2010). Chinese regional ındustrial energy efficiency evaluation based on a DEA model of fixing non-energy. Inputs Energy Policy, 38(10), 6172-6179.
• Song, M. L., Zhang, L. L., Liu, W. ve Fisher, R. (2013). Bootstrap-DEA analysis of BRICS’energy efficiency based on small sample data. Applied Energy, 112, 1049-1055.
• T.C. Enerji ve Tabii Kaynaklar Bakanlığı (ETKB) (2017). 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. ve 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. ve Fan, Y. (2007). An empirical analysis of energy efficiency in China’s iron and steel sector. Energy, 32(12), 2262-2270.
• Yoon, K. ve Ching-Lai H. (1981). Multiple Attribute Decision Making: Methods And Applications, Verlag Berlin An.: Springer
• Yu, L., Wang, S. ve Lai, K. K. (2010). Foreign-Exchange-Rate Forecasting With Artificial Neural Networks, Springer Science ve Business Media.
• Zhang, X. P., Cheng, X. M., Yuan, J. H. ve Gao, X. J. (2011). Total-factor energy efficiency in developing countries. Energy Policy, 39(2), 644-650.