Industrial electricity demand for Turkey: A structural time series analysis

Z. Dilaver Zafer, Lester C. Hunt

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This research investigates the relationship between Turkish industrial electricity consumption, industrial value added and electricity prices in order to forecast future Turkish industrial electricity demand. To achieve this, an industrial electricity demand function for Turkey is estimated by applying the structural time series technique to annual data over the period 1960 to 2008. In addition to identifying the size and significance of the price and industrial value added (output) elasticities, this technique also uncovers the electricity Underlying Energy Demand Trend (UEDT) for the Turkish industrial sector and is, as far as is known, the first attempt to do this. The results suggest that output and real electricity prices and a UEDT all have an important role to play in driving Turkish industrial electricity demand. Consequently, they should all be incorporated when modelling Turkish industrial electricity demand and the estimated UEDT should arguably be considered in future energy policy decisions concerning the Turkish electricity industry. The output and price elasticities are estimated to be 0.15 and - 0.16 respectively, with an increasing (but at a decreasing rate) UEDT and based on the estimated equation, and different forecast assumptions, it is predicted that Turkish industrial electricity demand will be somewhere between 97 and 148 TWh by 2020.

    Original languageEnglish
    Pages (from-to)426-436
    Number of pages11
    JournalEnergy Economics
    Volume33
    Issue number3
    DOIs
    Publication statusPublished - May 2011

    Keywords

    • Energy Demand Modelling and Forecasting
    • Future scenarios
    • Structural Time Series Model (STSM)
    • Turkish Industrial Electricity Demand

    Fingerprint

    Dive into the research topics of 'Industrial electricity demand for Turkey: A structural time series analysis'. Together they form a unique fingerprint.

    Cite this