The Impact of Economic Growth on the Ecological Environment and Renewable Energy Production: Evidence from Azerbaijan and Hungary

Sugra Ingilab Humbatova, Nargiz Hajiyeva, Monika Garai Fodor, Kiran Sood () and Simon Grima ()
Additional contact information
Sugra Ingilab Humbatova: Economics Department, Azerbaijan State University of Economics (UNEC), Istiglaliyyat Street 6, AZ1001 Baku, Azerbaijan
Nargiz Hajiyeva: Political Scientist, Women Researchers Council, Azerbaijan State University of Economics (UNEC), Istiglaliyyat Street 6, AZ1001 Baku, Azerbaijan
Monika Garai Fodor: Political Scientist, Women Researchers Council, Azerbaijan State University of Economics (UNEC), Istiglaliyyat Street 6, AZ1001 Baku, Azerbaijan
Kiran Sood: Political Scientist, Women Researchers Council, Azerbaijan State University of Economics (UNEC), Istiglaliyyat Street 6, AZ1001 Baku, Azerbaijan
Simon Grima: Department of Insurance and Risk Management, Faculty of Economics, Management and Accountancy, University of Malta, MSD 2080 Msida, Malta

JRFM, 2024, vol. 17, issue 7, 1-26

Abstract: This article reflects on the necessity of employing renewable energy sources in the modern era to mitigate the negative environmental impact caused by traditional energy sources and address environmental pollution. Through research conducted in Azerbaijan and Hungary, it analyses the influence of economic growth on the ecological environment and renewable energy production. Due to limitations in the general dataset, the study considers the period of 1997–2022 for CO 2 emissions causing environmental pollution, 2007–2022 for renewable energy production in Azerbaijan, and 2000–2021 for the same in Hungary. Information regarding wind and solar energy in Azerbaijan has been available since 2013. Temporal sequences have been utilised in the research, employing Augmented Dickey–Fuller and Phillips–Perron (PP) unit root tests to examine the stationarity of the time series. An Autoregressive Distributed Lag (ARDL) model has been constructed, and the credibility of the model has been verified using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) models. The findings reveal that in Azerbaijan, the long-term impact of economic growth on hydro-energy has been negative, while dependence on biomass and waste has been insignificant but positive. The influence on wind and solar energy production has also been negative and insignificant, akin to hydro-energy production. However, energy supply from renewable sources has been positively affected by the aggregate indicator of economic growth, albeit insignificantly. The impact of economic growth on carbon dioxide has been significant in two magnitudes, whereas in other cases, it has been insignificant but positive. In Hungary, economic growth has positively affected renewable energy production. However, the impact on carbon dioxide has been negative, meaning that this indicator has decreased as economic growth has increased. The study concludes that the impact of economic growth on indicators of both countries has been more effective in Hungary, which can be attributed to economic development.

Keywords: economic growth; renewable energy; carbon dioxide (CO 2 ); ARDL model; green economy (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1911-8074/17/7/275/pdf (application/pdf)
https://www.mdpi.com/1911-8074/17/7/275/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:17:y:2024:i:7:p:275-:d:1426473

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().