Gelişmekte olan ülkelerde enerji ve kalkınma arasındaki ilişki: volatilite dinamikleri üzerine istatistiksel bir bakış açısı

Yıl 2025, Cilt: 15 Sayı: 4, 1194 - 1208, 15.12.2025

Michael Sunday Olayemi , Atabek Movlyanov , Oluwamayowa Opeyimika Olajide

https://doi.org/10.17714/gumusfenbil.1753096

Öz

Bu çalışma, enerji piyasası oynaklığı ile kalkınma göstergeleri arasındaki ilişkiyi altı gelişmekte olan ekonomi—Nijerya, Gana, Kenya, Hindistan, Pakistan ve Bangladeş—üzerinde incelemektedir. Analiz, 2000–2020 dönemini kapsayan yıllık zaman serisi verilerine (126 ülke-yıl gözlemi) dayanmaktadır. Bu ülkeler, benzer enerji bağımlılığı yapıları, kalkınma zorlukları ve veri erişilebilirliği dikkate alınarak seçilmiştir. Beş temel gösterge kullanılmıştır: kişi başına enerji tüketimi, ham petrol fiyatı, kişi başına düşen gayri safi yurt içi hasıla (GSYİH), sanayi üretim endeksi ve İnsani Gelişme Endeksi (İGE). Bu göstergeler, ekonomik performans ve toplumsal refahın farklı boyutlarını temsil etmektedir. Enerji değişkenlerindeki oynaklık, Genelleştirilmiş Otoregresif Koşullu Değişen Varyans (GARCH) modeli ve asimetrik uzantıları olan EGARCH ve TGARCH modelleri kullanılarak tahmin edilmiştir. Sonuçlar, tüm ülkelerde yüksek oynaklık kalıcılığı olduğunu göstermektedir (α + β ≈ 0.95, p < 0.01); bu da enerji şoklarının uzun vadeli etkiler yarattığını ortaya koymaktadır. Nijerya, Hindistan ve Pakistan için anlamlı asimetri katsayıları (γ > 0, p < 0.05), olumsuz şokların (örneğin fiyat artışları veya arz kesintileri) oynaklık üzerinde daha güçlü etkiler yarattığını göstermektedir. Regresyon sonuçları ayrıca enerji oynaklığının İGE üzerinde negatif ve anlamlı bir etkisi olduğunu (β = –0.042, p < 0.01), buna karşın enerji tüketimi (β = 0.675, p < 0.001) ve sanayi üretiminin (β = 0.314, p < 0.05) ekonomik büyüme ve refaha olumlu katkı sağladığını ortaya koymaktadır. Yıllık verilerin kullanımı, gelişmekte olan ekonomilerde enerji–kalkınma ilişkilerinin yapısal ve politika temelli doğasını yansıtmakta ve yüksek frekanslı veri eksikliğini gidermektedir. Bulgular, enerji piyasası oynaklığının önemli bir kalkınma engeli olduğunu ve enerji çeşitlendirmesi, yönetişim reformu ve istatistiksel kapasite geliştirme gerekliliğini vurgulamaktadır. Bu çalışma, enerji ve kalkınma planlamasında oynaklık yönetiminin entegrasyonuna yönelik ampirik kanıtlar sunarak Sürdürülebilir Kalkınma Amaçları 7 (Erişilebilir ve Temiz Enerji) ve 8 (İnsana Yakışır İş ve Ekonomik Büyüme) hedeflerine katkı sağlamaktadır.

Anahtar Kelimeler

Enerji oynaklığı , GARCH modelleri , Gelişmekte olan ülkeler , İnsani Gelişme Endeksi , Enerji çeşitlendirmesi , Sürdürülebilir kalkınma

The relationship between energy and development in developing countries: A statistical perspective on volatility dynamics

Öz

This study investigates the relationship between energy market volatility and development outcomes across six developing economies—Nigeria, Ghana, Kenya, India, Pakistan, and Bangladesh—using annual time-series data (2000–2020), totaling 126 country–year observations. These countries were selected based on comparable energy dependency, developmental challenges, and data availability across Sub-Saharan Africa and South Asia. Five key indicators were analyzed: energy consumption per capita, crude oil prices, GDP per capita, industrial output index, and the Human Development Index (HDI)—each representing economic performance and social welfare dimensions. Volatility was modeled using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework and its asymmetric extensions (EGARCH and TGARCH) to capture persistence and shock asymmetry in energy variables. Results show high volatility persistence across all countries (α + β ≈ 0.95, p < 0.01), implying that energy shocks have long-lasting effects. Significant asymmetry coefficients (γ > 0, p < 0.05) in Nigeria, India, and Pakistan indicate that negative shocks—such as price surges or supply disruptions—produce stronger volatility responses than positive shocks. Regression results further reveal that energy volatility negatively and significantly affects HDI (β = –0.042, p < 0.01), while energy consumption (β = 0.675, p < 0.001) and industrial output (β = 0.314, p < 0.05) contribute positively to economic growth and welfare. The use of annual data is justified by the macro-structural nature of energy–development linkages in developing economies, where high-frequency data are scarce and policy effects unfold over multi-year periods. The findings demonstrate that volatility is a major development constraint and underscore the importance of energy diversification, governance reform, and statistical capacity building. These results provide empirical support for integrating volatility management into national energy and development planning, contributing toward achieving Sustainable Development Goal 7 (Affordable and Clean Energy) and Goal 8 (Decent Work and Economic Growth).

Anahtar Kelimeler

Energy volatility , GARCH models , Developing countries , Human Development Index , Energy diversification , Sustainable development

Kaynakça

• Aastveit, K. A., Bjørnland, H. C., & Thorsrud, L. A. (2019). The world is not enough! Small open economies and regional dependence in the world oil market. Energy Economics, 81, 821–834. https://doi.org/10.1016/j.eneco.2019.04.023
• Adom, P. K., & Bekoe, W. (2020). Energy efficiency, industrial production and CO₂ emissions in Sub-Saharan Africa. Energy Policy, 140, 111360. https://doi.org/10.1016/j.enpol.2019.111360
• Ahid, R. M. A., Khurshid, M., Waheed, M., & Sanni, T. (2022). Impact of environmental fluctuations on stock markets: Empirical evidence from South Asia. Journal of Environmental and Public Health, 2022, 7692086. https://doi.org/10.1155/2022/7692086
• Akinyele, D. O., & Rayudu, R. K. (2016). Strategy for developing energy systems for remote communities: Insights to best practices and sustainability. Sustainable Energy Technologies and Assessments, 16, 106–127. https://doi.org/10.1016/j.seta.2016.05.001
• Arroyo Marioli, F., Fatas, A., & Vasishtha, G. (2024). Fiscal policy volatility and growth in emerging markets and developing economies. International Review of Economics & Finance, 92, 758–777. https://doi.org/10.1016/j.iref.2024.01.041
• Bank, W. (2021). World Development Indicators https://databank.worldbank.org/source/world-development-indicators
• Bhattacharyya, S. C. (2019). Energy access and governance in developing countries. Energy Policy, 129, 600–607. https://doi.org/10.1016/j.enpol.2019.02.046
• Bildirici, M., & Ersin, Ö. (2018). ARDL-GARCH modelling of energy prices and macroeconomic uncertainty. Energy, 144, 1020–1032. https://doi.org/10.1016/j.energy.2017.12.118
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
• Chung, S. (2024). Modelling and forecasting energy market volatility using GARCH and machine learning approaches. Energy Economics, 121, 107217. https://doi.org/10.48550/arXiv.2405.19849
• Akinyele, D. O., & Rayudu, R. K. (2016). Strategy for developing energy systems for remote communities: Insights to best practices and sustainability. Sustainable Energy Technologies and Assessments, 16, 106-127. https://doi.org/https://doi.org/10.1016/j.seta.2016.05.001
• Dong, K., Sun, R., Hochman, G., & Zhang, S. (2022). Energy transition and inclusive growth: Evidence from developing economies. Energy Policy, 165, 112948. https://doi.org/10.1016/j.enpol.2022.112948
• Engle, R. (2001). GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics. Journal of Economic Perspectives, 15(4), 157–168. https://doi.org/10.1257/jep.15.4.157
• Erdogdu, E. (2020). Political instability, regulatory quality and renewable energy investments. Energy Economics, 86, 104666. https://doi.org/10.1016/j.eneco.2019.104666
• Es, H. A. (2020). Gri Tahmin Modelleri ile Toplam Enerji Talep Tahmini: Türkiye Örneği [Forecasting Total Energy Demand with Grey Prediction Models: The Case of Turkey]. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 10(3), 771-782. https://doi.org/10.17714/gumusfenbil.676909
• Gupta, S., Gupta, E., & Sarangi, G. K. (2020). Household energy poverty index for India: An analysis of inter-state differences. Energy Policy, 144, 111592. https://doi.org/10.1016/j.enpol.2020.111592
• International Energy Agency. (2022). World Energy Outlook 2022. https://www.iea.org/reports/world-energy-outlook-2022
• International Energy Agency. (2024). World Energy Outlook 2024. https://www.iea.org/reports/world-energy-outlook-2024
• Jenkins, K., McCauley, D., Heffron, R., Stephan, H., & Rehner, R. (2016). Energy justice: A conceptual review. Energy Research & Social Science, 11, 174-182. https://doi.org/https://doi.org/10.1016/j.erss.2015.10.004
• Karekezi, S., & Kimani, J. (2002). Status of power sector reform in Africa: impact on the poor. Energy Policy, 30(11), 923-945. https://doi.org/https://doi.org/10.1016/S0301-4215(02)00048-4
• Kim, Y., & Nguyen, T. D. (2023). Asymmetric responses of energy volatility to global shocks: Evidence from emerging markets. Energy Research & Social Science, 99, 103074. https://doi.org/10.1016/j.erss.2023.103074
• Le, Y., Wen, J., Wu, Y., Liu, J., & Zhu, Y. (2024). Investigating factors influencing oil volatility: A GARCH-MIDAS model analysis. Frontiers in Energy Research, 12, 1392905. https://doi.org/10.3389/fenrg.2024.1392905
• Mahalik, M. K., Babu, M. S., & Loganathan, N. (2021). Energy consumption, financial development and economic growth nexus in emerging economies: A panel GMM approach. Energy Reports, 7, 6351–6362. https://doi.org/10.1016/j.egyr.2021.09.024
• Mensah, J. T., Boachie, M. K., & Sobiesuo, P. (2021). Renewable energy consumption and human capital development in ECOWAS countries. Renewable Energy, 172, 1053–1064. https://doi.org/10.1016/j.renene.2021.03.067
• Narayan, P. K., & Smyth, R. (2009). Multivariate Granger causality between electricity consumption, exports and GDP: Evidence from a panel of Middle Eastern countries. Energy Policy, 37(1), 229–236. https://doi.org/10.1016/j.enpol.2008.08.020
• Nasreen, S., & Anwar, S. (2022). Volatility spillovers from global oil markets to Asian energy markets: A multivariate GARCH approach. Energy Economics, 111, 106040. https://doi.org/10.1016/j.eneco.2022.106040
• Ouedraogo, N. S. (2013). Energy consumption and economic growth: Evidence from the economic community of West African states (ECOWAS). Energy Economics, 36, 637–647. https://doi.org/10.1016/j.eneco.2012.11.011
• Özkan, O., Abosedra, S., Sharif, A., & Alola, A. A. (2024). Dynamic volatility among fossil energy, clean energy and major assets: Evidence from a novel DCC-GARCH approach. Economic Change and Restructuring, 57(3), 98–112. https://doi.org/10.1007/s10644-024-09696-9
• Paramati, S. R., Apergis, N., & Ummalla, M. (2017). Renewable energy consumption and economic growth in South Asia. Renewable Energy, 110, 32–42. https://doi.org/10.1016/j.renene.2016.10.006
• Payne, J. E. (2010). Survey of the international evidence on the causal relationship between energy consumption and growth. Journal of Economic Studies, 37(1), 53–95. https://doi.org/10.1108/01443581011012261
• Sachs, J. D., Schmidt-Traub, G., Kroll, C., Lafortune, G., & Fuller, G. (2019). Sustainable Development Report 2019. Cambridge University Press.
• Salisu, A. A., & Isah, K. O. (2017). Revisiting the role of GARCH models in forecasting oil market volatility. Energy Economics, 66, 1–14. https://doi.org/10.1016/j.eneco.2017.05.009
• Sen, A., & Jamasb, T. (2022). Regulatory transparency, institutional reforms and electricity market performance. Energy Research & Social Science, 85, 102409. https://doi.org/10.1016/j.erss.2021.102409
• Shittu, I., Saqib, A., Abdul Latiff, A. R., & Baharudin, S. A. (2024). Energy subsidies and energy access in developing countries: Does institutional quality matter? SAGE Open, 14(3), 21582440241271118. https://doi.org/10.1177/21582440241271118
• Sovacool, B. K., & Dworkin, M. H. (2015). Energy justice: Conceptual insights and practical applications. Applied Energy, 142, 435–444. https://doi.org/10.1016/j.apenergy.2015.01.002
• Sovacool, B. K., & Mukherjee, I. (2011). Conceptualizing and measuring energy security: A synthesized approach. Energy, 36(8), 5343–5355. https://doi.org/10.1016/j.energy.2011.06.043
• Todaro, M. P., & Smith, S. C. (2020). Economic development (13th ed.). Pearson.
• UNCTAD. (2023). Trade and Development Report 2022. United Nations. https://doi.org/https://doi.org/10.18356/9789210021623
• UNDP. (2020). Human Development Report 2020. UNDP (United Nations Development Programme). http://report2020.archive.s3-website-us-east-1.amazonaws.com/
• UNDP. (2024). Human Development Report 2023-24. UNDP (United Nations Development Programme). http://report2023-24.hdr.undp.org.s3-website-us-east-1.amazonaws.com/
• UNDP. (2025). Human Development Report 2025. UNDP (United Nations Development Programme). https://report.hdr.undp.org
• United Nations Conference on Trade and Development. (2023). Trade and Development Report 2022.
• United Nations Development Programme. (2024). Human Development Report 2023/24. https://report2023-24.hdr.undp.org
• World Bank. (2021). World Development Indicators.
• Zahid, R. M. A., Khurshid, M., Waheed, M., & Sanni, T. (2022). Impact of Environmental Fluctuations on Stock Markets: Empirical Evidence from South Asia. Journal of Environmental and Public Health, 2022(1), 7692086. https://doi.org/https://doi.org/10.1155/2022/7692086