Modeling Electricity Demand in UAE: Revisit of Time Series Analysis

This paper explains the drivers of electricity demand in the UAE by applying the novel Autoregressive Distributive Lag model (ARDL). This model is well-suited for a small sample size, and it has a robust design in terms of lag selection and length, which mitigates issues of endogeneity and multicollinearity. In addition to GDP and real electricity prices, our model accounts for population, and weather as underlying variables affecting electricity demand in UAE. We employ up to date available time-series annual data for the country which covers the period from the year 1985 to 2022. The results suggest that in the short run, except temperature, no other factor seems to have any significant impact on electricity demand. However, in the long run, changes in income, temperature, and population are positively influencing the electricity demand in the UAE. Also, the electricity price appears to be negatively related to the demand for electricity. Overall, our findings show that policymakers' ability to change electricity consumption in the UAE is limited in the short run. Therefore, long-term policies that promote the use of alternative sources of energy and increase consumer awareness through programs that mitigate the impact of climate change factors seem more appropriate.