Modeling Residential Electricity Demand in the GCC Countries: The Importance of Weather and Exogenous Stochastic Trends

Abstract

In a world with increased international focus on energy use, comparing energy consumption behavior across countries can inform decision makers about their country’s relative performance and opportunities for future improvement. In particular, during recent decades, understanding the drivers of residential electricity consumption – and by association the intensity and productivity of residential electricity use – has become a considerable dimension for policy-related international cross-country comparisons. However, the contrasts are arguably more meaningful when comparisons are normalized for uncontrollable exogenous factors, weather being a prime example. Given the rapid development of space climate control technologies the interdependency between climate variation and residential electricity consumption has in all probability increased with space heating and cooling representing the largest share of building energy consumption in many countries (Perez-Lombard et al., 2008). Moreover, analyzing the effect of weather on residential electricity demand is of special relevance to the Gulf Cooperation Council (GCC) countries – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates (UEA) – which, by virtue of being located near the tropics, are characterized by one of the hottest and most arid climates in the world.Furthermore, residential electricity consumption of the GCC countries has been exponentially increasing over recent decades coupled by a steep population growth and economic leap (Squalli, 2007; Reiche 2010). At a time when residential electricity prices have been administered by the state and therefore fixed in nominal terms for a number of years between adjustments. Within this context, this paper attempts to model residential electricity demand for the six GCC countries in order to estimate the GDP, price and population elasticities as well as controlling and quantifying the effect of climate conditions. The model utilized recognizes that electricity is a derived demand based on the demand for energy services such as heating, cooling, and cooking (Hunt and Ryan, 2015). Hence in addition to the key drivers of GDP, prices, population and weather an explicit allowance is made for energy efficiency and other exogenous effects by estimating a stochastic underlying energy demand trend (UEDT), as suggested by Hunt et al. (2003a, b). This paper is divided into five sections as follow: after the introduction, Section 2 discusses the background to the work and relevant previous literature, Section 3 details the methodology adopted, Section 4 discusses the data and estimation results, and Section 5 closes with a summary and conclusion.