YENİLENEBİLİR ENERJİ VE YENİLENEMEYEN ENERJİ KULLANIMININ EKOLOJİK AYAK İZİ ÜZERİNDEKİ ETKİSİ: BİR PANEL ÇALIŞMASI

Öz

Bu çalışma gelişmekte olan ekonomiler için yenilenebilir olmayan enerji kullanımı, yenilenebilir enerji kullanımı ve ekonomik büyüme ile ekolojik ayak izi arasındaki ilişkiyi incelemektedir. Yani, burada çevresel kirliliğin sürükleyicileri ekolojik ayak izi modeli bağlamında tahmin edilmektedir. Ampirik açıdan ilk olarak; yatay kesit bağımlılığı (YKB) ve eğim heterojenliği araştırılmaktadır. İkinci olarak sabitli ve sabitli-trendli CADF testi yardımıyla serilerin birim kök analizi yapılmaktadır. Seriler arasındaki uzun dönem ilişkisini tahmin etmek için Kao, Pedroni ve Westerlund panel eşbütünleşme yaklaşımlarından istifade edilmektedir. Uzun dönem tahminleri için CCE-MG panel tahmincisine başvurulmaktadır. Son olarak Dumitrescu-Hurlin tekniği ile serilerin nedensellik araştırması üzerinde durulmaktadır. YKB ve eğim heterojenliğinin varlığının yanı sıra serilerin birinci farklarında durağan oldukları tespit edilir. Çalışmada ekonomik büyüme, yenilenemeyen enerji kullanımı, yenilenebilir enerji kullanımı ile ekolojik ayak izi arasında uzun dönemli bir ilişkiye rastlanır. Ekonomik büyüme ve yenilenemeyen enerji kullanımı ekolojik ayak izini teşvik ederken, yenilenebilir enerji kullanımı ise ekolojik ayak izini engellemektedir. Dolayısıyla ilk iki değişken çevre kirliliğini körükleyici son değişken ise çevre kirliliğini önleyici bir etkide bulunur. Çalışmanın politika yapıcılarına en önemli tavsiyesi çevresel bozulmanın önlenmesinde yenilenebilir enerji sektörünün desteklenmesi ve bu sektörün teşviklerde ön plana çıkarılmasıdır.

Anahtar Kelimeler

• Yenilenebilir Enerji
• Yenilenemeyen Enerji
• Ekolojik Ayak İzi
• Panel Veri Analizi

THE IMPACT OF RENEWABLE ENERGY USE AND NONRENEWABLE ENERGY USE ON ECOLOGICAL FOOTPRINT: A PANEL STUDY

Öz

This study examines the relationship between non-renewable energy consumption, renewable energy consumption, economic growth, and the ecological footprint in developing economies. In this context, the drivers of environmental degradation are estimated using the ecological footprint model. Empirically, the analysis first investigates cross-sectional dependence (CSD) and slope heterogeneity. Second, unit root properties of the variables are examined using the CADF test with both constant and constant–trend specifications. To estimate the long-run relationships among the variables, the study employs the panel cointegration approaches proposed by Kao, Pedroni, and Westerlund. Long-run coefficients are obtained using the CCE-MG estimator. Finally, causal relationships among the variables are analyzed using the Dumitrescu–Hurlin panel causality test. The results confirm the presence of cross-sectional dependence and slope heterogeneity, and indicate that all variables are stationary in their first differences. The findings reveal a long-run cointegrating relationship between economic growth, non-renewable energy consumption, renewable energy consumption, and the ecological footprint. Economic growth and non-renewable energy consumption are found to increase the ecological footprint, whereas renewable energy consumption significantly reduces it. Thus, while the former two variables exacerbate environmental degradation, the latter plays a mitigating role in environmental pressure. From a policy perspective, the main implication of this study is that promoting and supporting the renewable energy sector should be a priority instrument for policymakers aiming to prevent environmental degradation and achieve sustainable development.

Anahtar Kelimeler

• Renewable Energy
• Nonrenewable Energy
• Ecological Footprint:
• Panel Data Analysis

Kaynakça

1. Asane-Otoo, E. (2015). Carbon footprint and emission determinants in Africa. Energy, 82, 426–435. https://doi.org/10.1016/j.energy.2015.01.053
2. British Petrol (2025). Global energy data, https://www.bp.com/ adresinden 9 Kasım 2025 tarihinde alınmıştır. Blomquist, J. ve Westerlund, J. (2013). Testing slope homogeneity in large panels with serial correlation. Economics Letters, 121(3), 374–378. https://doi.org/10.1016/j.econlet.2013.09.012
3. Demirbaş, A. (2003). Energy and environmental issues relating to greenhouse gas emissions in Turkey. Energy Conversion and Management, 44(1), 203–213. https://doi.org/10.1016/S0196-8904(02)00056-0
4. Do, T.T.P., Hong, H.T.H. and Thi, D. (2025). Green finance, natural resources, and environmental quality in Asia: the moderating role of institutional quality, International Journal of Sustainable Energy, 44(1), https://doi.org/10.1080/14786451.2025.2553082
5. Dogan, M., Georgescu, I., Ǹstepe, H., Sarıgül, S.S. and Tatar, H.E. (2025). Renewable energy consumption and the ecological footprint in Denmark: Assessing the influence of financial development and agricultural contribution. Agriculture, 15, 835. https://doi.org/10.3390/agriculture15080835
6. Global Footprint Network (2025). Global footprint data, https://data.footprintnetwork.org/ adresinden 10 Kasım 2025 tarihinde alınmıştır.
7. Guloglu, B., Caglar, A. E. and Pata, U. K. (2023). Analyzing the determinants of the load capacity factor in OECD countries: Evidence from advanced quantile panel data methods. Gondwana Research, 118, 92–104. https://doi.org/10.1016/j.gr.2023.02.013
8. Huang, Z. and Ren, X. (2024). Impact of natural resources, resilient economic growth, and energy consumption on CO2 emissions. Resources Policy, 90, https://doi.org/10.1016/j.resourpol.2024.104714

9. Jinapor, J. A., Abor, J. Y. and Graham, M. (2024). FDI, industrialisation and environmental quality in SSA—the role of institutional quality towards environmental sustainability. Humanities and Social Sciences Communications, 11, 1484. https://doi.org/10.1057/s41599-024-04000-6
10. Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90, 1–44.
11. Kaya, F., Mia, B., Aktar, M., Hossain, M. D., Hassan, M. M., Rahaman, M. A. and Voumik, L. C. (2025). Impact of environmental taxes, hydroelectricity consumption, economic globalization, and gross domestic product (GDP) on the load capacity factor in the selected European Union (EU) member countries. Regional Sustainability, 6(2). https://doi.org/10.1016/j.regsus.2025.100210
12. Kaygusuz, K. (2002). Environmental impacts of energy utilisation and renewable energy policies in Turkey. Energy Policy, 30(8), 689–698. https://doi.org/10.1016/S0301-4215(02)00032-0
13. Midttun, A. and Chander, I. (1998). The political economy of energy use and pollution: The environmental effects of East-European transition to market economy. Energy Policy, 26(13), 1017–1029. https://doi.org/10.1016/S0301-4215(98)00048-2
14. Munir, K. and Ameer, A. (2020). Nonlinear effect of FDI, economic growth, and industrialization on environmental quality: Evidence from Pakistan. Management of Environmental Quality: An International Journal, 31(1), 223-234. https://doi.org/10.1108/MEQ-10-2018-0186
15. Pata, U. K. (2025). Towards a sustainable future with renewable energy and load capacity factor: Institutions, technology, energy and climate policy uncertainties. Renewable Energy, 254, https://doi.org/10.1016/j.renene.2025.123710
16. Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20, 597–625.
17. Pesaran, M. H. (2004). General diagnostic test for cross section dependence in panels. Cambridge Working Papers in Economics 0435, Faculty of Economics, University of Cambridge. https://ideas.repec.org/p/cam/camdae/0435.html adresinden 15 Ekim 2025 tarihinde alınmıştır.
18. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012.
19. Pesaran, M. H. (2007). A simple panel unit root test in the presence of crosssection dependence. Journal of Applied Econometrics, 22(2), 265–312. https://doi.org/10.1002/jae.951
20. Raghutla, C. and Kolati, Y. (2023). Does renewable energy improve environmental quality? Evidence from RECAI countries. Environmental Science and Pollution Research, 30, 100717–100730. https://doi.org/10.1007/s11356-023-29402-y
21. Saboori, B., Mahdavian, S.M. and Radmehr, R. (2024). Assessing the environmental impact of institutional quality at aggregate and disaggregate levels: The role of renewable and non-renewable energy consumption and trade in MENA countries, Sustainable Environment, 10(1), https://doi.org/10.1080/27658511.2024.2426833
22. Samour, A., Radmehr, R., Ofori, E. K., Ali, E. B., Ivani, J., Sov, P., Shayanmehr, S., Babo, M., Kacaniova, M. and Dimnwobi, S. K. (2024). The role of financial inclusion and technological innovation in stimulating environmental sustainability in the European countries: A new perspective based on load capacity factor. Heliyon, 10, https://doi.org/10.1016/j.heliyon.2024.e39970
23. Shahbaz, M., Salah Uddin, G., Ur Rehman, I. and Imran, K. (2014). Industrialization, electricity consumption and CO2 emissions in Bangladesh. Renewable and Sustainable Energy Review, 31, 575–586. https://doi.org/10.1016/j.rser.2013.12.028
24. Sikder, M., Wang, C., Yao, X., Huai, X., Wu, L., Yeboah, F. K., Wood, J., Zhao, Y. and Dou, X. (2022). The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: Evidence from the panel ARDL approach. Science of The Total Environment, 837, https://doi.org/10.1016/j.scitotenv.2022.155795
25. United Nations, (2026). Paris Anlaşması, https://www.un.org/en/climatechange/paris-agreement adresinden 09 Haziran 2026 tarihinde alınmıştır.
26. Wang, Z., Rasool, Y., Zhang, B., Ahmed, Z. and Wang, B. (2020). Dynamic linkage among industrialisation, urbanisation, and CO2 emissions in APEC realms: Evidence based on DSUR estimation. Structural Change and Economic Dynamics, 52, 382–389. https://doi.org/10.1016/j.strueco.2019.12.001
27. Westerlund, J. (2005). A panel CUSUM test of the null of cointegration. Oxford Bulletin of Economics and Statistics, 67(2), 231–262. https://doi.org/10.1111/j.1468-0084.2004.00118.x
28. Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709-748. https://doi.org/10.1111/j.1468-0084.2007.00477.x
29. World Bank. (2025). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators adresinden Kasım 2025 tarihinde alınmıştır.
30. Xu, L., Wang, X. and Guo, W. (2022). Does renewable energy adaptation, globalization, and financial development matter for environmental quality and economic progress? Evidence from panel of big five (B5) economies. Renewable Energy, 192, 631–640. https://doi.org/10.1016/j.renene.2022.05.004