What are the Main Determinants of Renewable Energy Consumption? A Panel Threshold Regression Approach

Abstract

Because of severe environmental impacts associated with the use of conventional energy sources, most of the countries attempt to decarbonize their energy sector by increasing share of renewable energy in their total energy consumption which also reduces their energy import dependency. Therefore, this study aims to understand the determinants of renewable energy consumption for 58 countries over the period from 1990 to 2012. The period and number of countries are determined based on the data availability for all the variables. For example, data on total greenhouse gas emissions is available only up to 2012. Analysis is based on a panel threshold regression model to find out asymmetric behavior of renewable energy consumption. The model explains renewable energy consumption by various economic factors in a nonlinear framework. Different effects of these factors are expected under different economic regimes characterized by income level. Results show that for real GDP per capita lower than 1527.977 US$, signs, statistical significance and magnitude of coefficients on some variables differ tremendously compared to the upper regime verifying the asymmetric relationship. Therefore, one can conclude that determinants of renewable energy consumption differ across countries and time based on the real GDP per capita level.

Factors of Renewable Energy Consumption in the European Countries – the Bayesian Averaging Classical Estimates Approach

“…Another useful and popular characteristic in model averaging is so-called posterior inclusion probability (P.I.P. ), which is defined as the posterior probability that the independent variable is relevant in explaining the dependent variable [46,47]. In our case, the P.I.P.…”

Factors of Renewable Energy Consumption in the European Countries—The Bayesian Averaging Classical Estimates Approach

“…Another useful and popular characteristic in model averaging is so-called posterior inclusion probability (PIP), which is defined as the posterior probability that the independent variable x i is relevant in explaining the dependent variable [38,52]. In our case, the PIP is calculated as the sum of the posterior model probabilities for all of the models that include a specific variable:…”

Does higher income lead to more renewable energy consumption? Evidence from emerging-Asian countries

“…Those studies either focus on a panel of developing countries (21 African countries – Ergun et al [ 8 ], 9 Balkan countries – Akar [ 36 ], 4 ASEAN countries – Kumaran et al [ 37 ], 34 upper-middle-income developing countries – Shahbaz et al [ 38 ]), a single developing country (Ghana – Kwakwa [ 39 ]) or panels of a large number of pre-dominantly developing countries (69 Belt & Road Initiative countries – Khan et al [ 40 ], 102 countries Li et al [ 7 ]). When dividing their set of countries into two groups (high and low income per capita), Akarsu and Gumusoglu [ 5 ] find the effect of income per capita on REC% to be positive in high-income countries. However, it is negative in low-income countries.…”

“…One of REC's most frequently studied drivers is income or Gross Domestic Product (GDP). On the one hand, it is expected that as income per capita increases, REC would increase because increased income enables countries to manage the expenses of developing and using modern renewable energy technologies [ 4 ] as they would have a higher ability to raise the necessary funds [ 5 ]. Moreover, governments of such countries would be better positioned to make sacrifices to encourage REC [ 6 ].…”

“…Especially in lower-income countries, investment in renewable energy is less likely to be a priority. Hence, as income increases, the new investment capabilities are more likely to be channeled to health or education [ 5 ]. In relatively poorer countries, the renewable energy consumed comes from traditional sources.…”