Modeling determinants of renewable electricity consumption in Iran

Rising concerns about global warming and energy security signal an increasing reliance on renewable energy sources in the future. Recently, renewable energy sources have emerged as a significant component of global energy consumption. However, less information is available addressing the renewable energy consumption determinants. Considering the contribution of renewable energy to future sustainable and reliable energy, its primary factors must be comprehended to derive energy policy consequences. Using Auto Regressive Distributed Lag (ARDL) bounds testing cointegration strategy and the Vector Error Correction Model (VECM) Granger causality test method over the period 2011-2019, the objective of this study is to model the determinants of renewable electricity consumption in Iran empirically. The primary predictors of renewable electricity consumption are combined pollution, per capita Gross Domestic Product (GDP), oil price, and urbanization, according to the investigation results. Long-term and short-term consumption of renewable electricity is positively and negatively affected by combined pollution, oil price, and urbanization, respectively, according to the empirical findings. In the short term, the per capita GDP has a negative and significant effect on renewable electricity consumption, while in the long term, the opposite is true. Finally, the results of Granger causality analysis utilizing the vector error correction model reveal a bidirectional short-term and long-term Granger causality flowing in a positive direction from combined pollution, per capita GDP, and oil price to renewable power usage. Moreover, the positive bilateral short-term Granger causation between the oil price and per capita GDP is verified.

Modeling determinants of renewable electricity consumption in Iran Keywords:

ARDL , Consumption , Iran , Renewable Electricity , VECM