Does the level of financial development affect renewable energy? Evidence from developed countries with system generalized method of moments (System‐GMM) and cross‐sectionally augmented autoregressive distributed lag (CS‐ARDL)

• Published in: Sustainable development (Bradford, West Yorkshire, England) Vol. 30; no. 5; pp. 1326 - 1342
• Main Authors: Saygın, Oğuz, İskenderoğlu, Ömer
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.10.2022; Wiley Periodicals Inc
• Subjects: Alternative energy; Augmentation; Banking; CS‐ARDL; Developed countries; Economic research; Energy consumption; Energy development; Estimators; Finance; financial development; Generalized method of moments; Industrialized nations; Method of moments; Renewable energy; renewable energy consumption; Renewable resources; Research subjects; Securities markets; Statistical analysis; Stock exchanges; Sustainable development; system GMM
• ISSN: 0968-0802; 1099-1719
• DOI: 10.1002/sd.2319

The relationship between financial development and energy consumption is the most frequently research subject in economy and finance. The main objective of conducting this paper is to answer does financial development have an impact on renewable energy in developed countries? Many papers performed in energy literature, the findings were pointing to the existence of this kind of relationship. In order to examine the relationship between financial development and renewable energy consumption, a total of 23 developed countries, benefited from annual frequency data between 1990 and 2015. In addition to the widely used system generalized method of moments estimation method, models are also estimated with modern estimators using the cross‐sectionally augmented autoregressive distributed lag. As a result of the analysis performed indicates that there is a strong relationship between financial development and renewable energy consumption in developed countries when financial development is measured using banking variables. Otherwise, the estimated coefficients on stock market variables are negative and none of them found statistically significant in all models. These findings are consistent for both estimators.