The role of financial institutions in the green energy transition: International Panel Study 1960 - 2017

Published: 3 May 2022| Version 2 | DOI: 10.17632/gfdnw4y887.2
Marinko Skare,


Detecting the impact and role of financial institutions in each country/region on their renewable or green energy transition is the main research objective of this paper. For this purpose, the study uses a global sample of 214 countries/regions for the period 1960 to 2017. The list of selected countries/regions can be found in Appendix Table A1. The data availability of the global sample results that this is an unbalanced panel dataset. In addition, we categorize the 214 country/region sample into three groups according to the World Bank’s country income level classification, namely high-income countries/regions, upper-middle income countries/regions, and low and lower-middle income countries/regions.


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This study seeks to provide evidence that national/regional financial institutions are driving the local green energy transition. In focusing on the benchmark of financial institutions, Cihák et al. [31] highlighted that financial institutions come in different shapes and sizes. Put differently, it is essential to measure and assess each characteristic of the financial institution. Thus, several measures of four characteristics of financial institutions were developed and presented building on a review of the empirical literature on financial institutions. These four dimensions include (1) the size of financial institutions (financial depth), (2) the degree to which individuals can and do use financial institutions (financial access), (3) the efficiency of financial institutions in providing financial services (financial efficiency), and (4) the stability of financial institutions (financial stability). Following Cihák et al. [31], we identify appropriate proxies based on these dimensions. Since each dimension also contains several measures, only variables suggested for the benchmarking exercise for each dimension are chosen. This is done to control the number of independent variables while improving the reliability of the empirical results. As a result, four core explanatory variables are finally selected, namely (1) private credit by deposit money banks to GDP (%) (financial depth), (2) number of listed companies per 10k population (financial access), (3) net interest margin (%) (financial efficiency) and (4) bank z-score (financial stability) [32, 33].


Sveuciliste Jurja Dobrile u Puli


Panel Data, Panel Data Model