Analysing the Industrial Electricity Demand for Turkey

Yıl 2020, , 187 - 218, 28.12.2020

İsmail Kavaz

https://doi.org/10.33399/biibfad.761687

Cited By: 1

Öz

This study analyses the effects of some selected parameters on Turkey’s industrial electricity demand by using annual data between 1978 and 2018. In this regard, the Autoregressive Distributed Lag (ARDL) Bounds Testing method is utilized for establishing the models. The variables that used in the models of this study are electricity consumption, industrial value added, price of electricity, urbanization rate and average air temperature. According to the empirical findings, the price elasticities are estimated negative as expected which are -0.14 and -0.18 for the short and long term, respectively. On the other hand, the income elasticities have positive signs and computed as statistically significant. The short and long run income elasticities of industrial electricity demand are found as 0,15 and 0,35, respectively. Additionally, the urbanization rate and air temperature positively affect the industrial electricity demand of Turkey. These results indicate that the estimated price and income elasticities for the Turkish industrial electricity demand are very low and smaller than 1 in absolute terms. Therefore, it can be said that an increase and/or decrease in price and income as percentage is more than increase in electricity consumption for the industrial sector. In addition, these results imply that since the electricity usage in Turkey’s industrial sector is a necessity, consumers are not changing their consumption behaviour easily with respect to the price and income movements.

Anahtar Kelimeler

Elasticity Estimates, Industrial Electricity Demand, ARDL Bounds Testing, Turkey

Analysing the Industrial Electricity Demand for Turkey

Öz

Bu çalışma 1978 ile 2018 arasındaki yıllık verileri kullanarak Türkiye’nin sanayi sektörü elektrik talebine etki eden bazı parametreleri analiz etmektedir. Bu bağlamda, modelleri oluşturmak için ARDL Sınır Testi yaklaşımı kullanılmıştır. Çalışmanın modellerinde kullanılan değişkenler elektrik tüketimi, sanayi katma değeri, elektriğin fiyatı, şehirleşme oranı ve ortalama hava sıcaklığıdır. Ampirik bulgulara göre kısa ve uzun dönem fiyat esneklikleri beklendiği üzere negatif ve kısa dönem için -0.14, uzun dönem için ise -0.18 olarak hesaplanmıştır. Diğer taraftan, gelir esneklikleri pozitif işarete sahip ve anlamlı olarak tahmin edilmiştir. Sanayi sektörü elektrik talebinin kısa ve uzun dönem gelir esneklikleri sırasıyla 0,15 ve 0,35 olarak bulunmuşlardır. Buna ek olarak, şehirleşme oranı ve hava sıcaklığı değişkenleri Türkiye’nin sanayi sektörü elektrik talebini pozitif bir şekilde etkilemektedir. Bu sonuçlar, Türkiye için hesaplanan sanayi sektörü elektrik talebi fiyat ve gelir esnekliklerinin oldukça küçük ve mutlak değerce 1’den az olduğuna işaret etmektedir. Dolayısıyla, sanayi sektörü için fiyat ve gelirde yaşanan artışın ve/veya azalışın yüzdesel olarak elektrik tüketiminde yaşanandan daha fazla olduğu söylenebilir. Ayrıca, bu sonuçlar Türkiye’nin sanayi sektöründeki elektrik kullanımı zorunlu olduğu için tüketicilerin tüketim alışkanlıklarını fiyat ve gelirde meydana gelen hareketler karşısında kolayca değiştirmedikleri anlamına gelmektedir.

Anahtar Kelimeler

Esneklik Hesaplamaları, Sanayi Sektörü Elektrik Talebi, ARDL Sınır Testi, Türkiye

Kaynakça

• Akay, D., & Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.
• Arisoy, I., & Ozturk, I. (2014). Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach. Energy, 66, 959-964.
• Beenstock M., Goldin E., & Nabot D. (1999). The demand for electricity in Israel. Energy Economics, 21(2), 168-183.
• Bilgili, M., Sahin, B., Yasar, A., & Simsek, E. (2012). Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews, 16(1), 404-414.
• Campbell, A. (2018). Price and income elasticities of electricity demand: Evidence from Jamaica. Energy Economics, 69, 9-32.
• Cialani, C., & Mortazavi, R. (2018). Household and industrial electricity demand in Europe. Energy Policy, 122, 592-600.
• Dilaver, Z., & Hunt, L. C. (2011a). Industrial electricity demand for Turkey: A structural time series analysis. Energy Economics, 33(3), 426-436.
• Dilaver, Z., & Hunt, L. C. (2011b). Modelling and forecasting Turkish residential electricity demand. Energy Policy, 39(6), 3117-3127.
• Dilaver, Z., & Hunt, L. C. (2011c). Turkish aggregate electricity demand: An outlook to 2020. Energy, 36(11), 6686-6696.
• Ediger, V. S., & Tatlidil, H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473-487.
• El-Shazly, A. (2013). Electricity demand analysis and forecasting: A panel cointegration approach. Energy Economics, 40, 251-258.
• Engle, R., & Granger C. (1987). Cointegration and Error Correction Representation: Estimation and Testing. Econometrica, 55, 251-276.
• Ercan, Y., Durmaz, A., & Sivrioglu, M. (1988). EFOM-12C Enerji arz modelinin Türkiye’ye uygulanması [Application of the Energy Supply Model EFOM-I2C for Turkey]. Project Report, Gazi University, Ankara.
• Fisher, F. M., & Kaysen, C. (1962). A Study in Econometrics: The Demand for Electricity in the United States. North-Holland.
• Gujarati, D. N. (2003). Basic Econometrics, 4th ed. New York: McGraw-Hill.
• IEA: International Energy Agency. (2019a). Electricity information, International Energy Agency, France.
• IEA: International Energy Agency. (2019b). Energy Prices and Taxes for OECD Countries, International Energy Agency, France.
• Kadilar, C. (2000). Uygulamalı Çok Değişkenli Zaman Serileri Analizi. [Applied Multivariate Time Series Analysis]. Ankara: Büro Basımevi.
• Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of Econometrics, 54(1-3), 159-178.
• MacKinnon, J. J. (1991). Critical Values for Cointegration Tests in Long-Run Economic Relationships, In. R. F. Engle and C. W. Granger (Eds), Readings in Cointegration, Oxford University Press, Oxford, 267-76.
• Ministry of Energy and Natural Resources (MENR), (2014). 2015-2019 Strategic Plan (in Turkish translated by author). Retrieved on 12 March 2020 from www.enerji.gov.tr/tr-TR/Stratejik-Plan.
• Ministry of Energy and Natural Resources (MENR), (2017). National Energy Efficiency Action Plan 2017-2023 (in Turkish translated by author). Retrieved on 10 June 2020 from http://www.resmigazete.gov.tr/eskiler/2018/01/20180102M1-1-1.pdf.
• Narayan, P. K. (2005). The saving and investment nexus for China: evidence from cointegration tests. Applied Economics, 37(17), 1979-1990.
• Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationship. Journal of Applied Econometrics, 16(3), 289-326.
• Schwert, G. W. (1989). Tests for unit roots: A monte carlo investigation. Journal of Business & Economic Statistics, 7(2), 147-159.
• Taban, S., & Aktar, İ. (2008). An empirical examination of the export led-growth hypothesis in Turkey. Journal of Yasar University, 3(11), 1535-1551.
• TSMS: Turkish State Methodological Service (2019). Official Statistics-Turkey Average Temperature [Resmi İstatistikler-Türkiye Ortalama Sıcaklık], Retrieved on 2 July 2020 from https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=parametrelerinTurkiyeAnalizi.
• Turkish Electricity Transmission Company (TEİAŞ), (2020). Electricity Statistics (in Turkish translated by author). Retrieved on 30 March 2020 from https://www.teias.gov.tr/tr-TR/turkiye-elektrik-uretim-iletim-istatistikleri.
• Turkish Statistical Institute (TurkStat). Energy Statistics (in Turkish translated by author). Retrieved on 2 April 2020 from http://www.tuik.gov.tr/PreTablo.do?alt_id=1029.
• Verbeek, M. (2004). A Guide to Modern Econometrics. 2nd ed. New York: John Wiley and Sons.
• World Bank, (2020). World Development Indicators. Retrieved on 9 April 2020 from http://databank.worldbank.org/data/reports.aspx?source=2&country=TUR.
• Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.