Modeling the Nexus of Renewable and Non-Renewable Energy Consumption and Economic Progress in Southeastern Europe: A Panel Data Analysis

As the fourth industrial revolution drives innovation and economic growth, the energy sector is increasingly recognized for its significant economic contribution. This research aims to investigate the relationship between economic growth and renewable and non-renewable energy consumption in nine southeastern European countries using panel estimation techniques and causal inference. This research employs a unique approach to modeling the energy-growth nexus, incorporating interaction terms to better understand the impact of renewable energy on real GDP growth. The findings are a valuable addendum to the current body of research on the effects of renewable energy consumption on economic growth, and the results contribute to narrowing the empirical research gap in the econometrical field of panel data estimation and endogeneity. This study uses the fully modified OLS (FMOLS) technique for heterogeneous panels to estimate coefficients, while the error correction model (ECM) is used to estimate the cointegration vector between energy variables and GDP. The non-causality test by Dumitrescu and Hurlin (2012) evaluates the causation between energy variables and economic output. Empirical findings indicate that both renewable and non-renewable energy consumption positively affect economic growth. The outcomes of this study hold significant policy ramifications in terms of prioritizing reformation and investment towards specific sectors to foster capital infusion into renewable energy and energy efficiency projects and initiatives, consequently advancing sustainable economic growth.

Automated summary

The study reveals that both renewable and non-renewable energy consumption have a positive impact on economic growth in nine southeastern European countries. The findings are of significant importance in understanding the influence of renewable energy on real GDP growth and contribute to filling the empirical research gap in panel data estimation and endogeneity.