Convergence in renewable energy sources and the dynamics of their determinants: An insight from a club clustering algorithm

In this article, the convergence in renewable energy sources is investigated for 183 countries spanning the years 2000 to 2018. We apply the convergence algorithm proposed by Philips and Sul, and use two measures of renewable energy sources, namely, renewable electricity output (% of total electricity output) and renewable energy consumption (% of total final energy consumption). We also investigate the dynamics of the determinants/factors possibly driving the convergence clubs of the countries via multinomial logit regression. The determinants were divided into macroeconomics, socio-economic, and institutional quality factors for the purpose of knowing the roles they play in the convergence process. Previous studies have failed to use the two measures of renewable energy sources concurrently, identify the determinants of the convergence process, and utilise the econometric approaches applied to achieve the objectives of this study. Hence, the contribution of this study. At the global level, the panel convergence results reveal that there is nonconvergence in renewable energy sources. Given the discrepancy in renewable energy sources and the energy challenges that many countries face around the world, this finding is unsurprising. The determinants that played a significant role in the likelihood of a country belonging to a final convergence club include agricultural value added, FDI, trade openness, land, ICT, population, and institutional quality. Overall, this study found that the process of renewable energy sources convergence process is yet to echo desirable emanations of energy sectors/policies sharing/moving in similar directions, but the narrative appears to be different when the algorithm form clubs.