Cezayir’de Enerji Tüketimi, Karbon Emisyonları ve Ekonomik Büyüme: ARDL ve NARDL Modellerinden Koşullu Bulgular (1990–2022)

Öz

Bu çalışma, hidrokarbona bağımlı bir ekonomi olan Cezayir’de 1990 ila 2022 döneminde gelir–emisyon ilişkisinin Çevresel Kuznets Eğrisi (ÇKE) ile açıklanabilirliğini incelemektedir. Kişi başına CO₂ emisyonları; kişi başına gelir, gelirin karesi, enerji kullanımı ve ticarî açıklığın bir fonksiyonu olarak yalın bir koşullu ARDL belirtimiyle modellenmekte olup simetri testleri ve bütünleşme derecesine duyarlı olmayan nedensellik testleri destekleyici kanıt sunmaktadır. Üç bulgu öne çıkmaktadır. Birincisi, istikrarlı bir uzun dönem ilişkisine ilişkin kanıt zayıftır: sınır testi yalnızca %10 düzeyinde marjinaldir ve %5 düzeyinde anlamlı değildir; Johansen testleri de eş bütünleşmeyi desteklememektedir. Bu nedenle düzey tahminleri uzun dönem esneklikleri değil, koşullu betimlemeler olarak sunulmaktadır. İkincisi, geleneksel ters-U biçimli ÇKE desteklenmemektedir; tahmin edilen düzey terimleri U-biçimli bir profil ima etmekle birlikte, gelir ile enerji kullanımı arasındaki güçlü eş doğrusallık ve anlamlı fonksiyonel biçim testi, ima edilen dönüm noktasının yapısal ya da politika eşiği olarak yorumlanmasını engellemektedir. Üçüncüsü, enerji kullanımı emisyonlarla en güçlü kısa dönem ilişkisine ve öngörü hatası varyansında en büyük paya sahiptir; bu değişkenin modelden çıkarılması gelir katsayılarını istikrarsızlaştırmaktadır. Dolayısıyla Cezayir için politika bakımından anlamlı marj, emisyonların kendiliğinden azalacağı bir gelir eşiği değil, enerji arzı ve kullanımının karbon yoğunluğudur. Tüm sonuçlar belirtime ve 33 gözlemli örnekleme koşulludur; bu örneklemin tanımlama sınırları varsayımla geçiştirilmek yerine açıkça raporlanmaktadır.

Anahtar Kelimeler

• CO₂ emisyonları
• Çevresel Kuznets Eğrisi
• NARDL
• QARDL
• FMOLS/DOLS
• enerji tüketimi
• Granger/Toda–Yamamoto nedenselliği
• Cezayir

Energy Consumption, Carbon Emissions and Economic Growth in Algeria: Conditional Evidence from ARDL and NARDL Models (1990–2022)

Öz

This study asks whether the Environmental Kuznets Curve (EKC) describes the income–emissions relationship in Algeria, a hydrocarbon-dependent economy, over 1990–2022. Per-capita CO₂ emissions are modelled as a function of per-capita income, its square, energy use and trade openness using a parsimonious conditional ARDL specification, with symmetry and integration-robust causality tests as supporting evidence. Three findings emerge. First, the evidence for a stable long-run relationship is weak: the bounds test is marginal at the 10% level and insignificant at 5%, and the Johansen tests do not support cointegration, so the level estimates are reported as conditional descriptions rather than long-run elasticities. Second, the conventional inverted-U EKC is not supported; the fitted level terms imply a U-shaped profile, but severe collinearity between income and energy use, together with a significant functional-form test, precludes any structural or policy-threshold reading of the implied turning point. Third, energy use has the strongest short-run association with emissions and the largest share of forecast-error variance, and its omission destabilizes the income coefficients. For Algeria the policy-relevant margin is therefore the carbon intensity of energy supply and use rather than an income threshold at which emissions would fall automatically. All conclusions are conditional on the specification and on a 33-observation sample, whose identification limits are reported rather than assumed away.

Anahtar Kelimeler

• CO₂ emissions
• Environmental Kuznets Curve
• NARDL
• QARDL
• FMOLS/DOLS
• ; energy consumption
• Granger/Toda–Yamamoto causality
• Algeria

Kaynakça

1. Adebayo, T. S., & Kirikkaleli, D. (2021). Impact of renewable energy consumption, globalization, and technological innovation on environmental degradation in Japan: Application of wavelet tools. Environment, Development and Sustainability, 23(11), 16057–16082. https://doi.org/10.1007/s10668-021-01322-2
2. Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659
3. Bergougui, B. (2024). Moving toward environmental mitigation in Algeria: Asymmetric impact of fossil fuel energy, renewable energy and technological innovation on CO2 emissions. Energy Strategy Reviews, 51, 101281. https://doi.org/10.1016/j.esr.2023.101281
4. Bouznit, M., & Pablo-Romero, M. D. P. (2016). CO₂ emission and economic growth in Algeria. Energy Policy, 96, 93–104. https://doi.org/10.1016/j.enpol.2016.05.036
5. CEIC. (2023). Algeria: Renewable energy consumption (% of total final energy consumption), 1990–2021 [Data set]. CEIC Data. Retrieved 15 June 2024.
6. Charfeddine, L. (2017). The impact of energy consumption and economic development on ecological footprint and CO₂ emissions: Evidence from a Markov switching equilibrium correction model. Energy Economics, 65, 355–374. https://doi.org/10.1016/j.eneco.2017.05.009
7. Charfeddine, L., & Kahia, M. (2019). Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: A panel vector autoregressive (PVAR) analysis. Renewable Energy, 139, 198–213. https://doi.org/10.1016/j.renene.2019.01.010
8. Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49(4), 431–455. https://doi.org/10.1016/j.ecolecon.2004.02.011

9. Dogan, E., & Seker, F. (2016). Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy. Renewable Energy, 94, 429–439. https://doi.org/10.1016/j.renene.2016.03.078
10. Elbadri, M., Bsikre, S., Alamari, O., & Balcilar, M. (2023). Nexus between renewable energy consumption, economic growth, and CO2 emissions in Algeria: New evidence from the Fourier-Bootstrap ARDL approach. Natural Resources Forum, 47(3), 393–412. https://doi.org/10.1111/1477-8947.12292
11. Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey–Fuller type unit root tests. Economics Letters, 117(1), 196–199. https://doi.org/10.1016/j.econlet.2012.04.081
12. Energy Institute. (2024). Statistical review of world energy 2024 (73rd ed.). Energy Institute. https://www.energyinst.org/statistical-review
13. Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99–126. https://doi.org/10.1016/0304-4076(96)01468-7
14. Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American Free Trade Agreement (NBER Working Paper No. 3914). National Bureau of Economic Research. https://doi.org/10.3386/w3914
15. Halicioglu, F. (2009). An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy, 37(3), 1156–1164. https://doi.org/10.1016/j.enpol.2008.11.012
16. Jalil, A., & Mahmud, S. F. (2009). Environment Kuznets curve for CO2 emissions: A cointegration analysis for China. Energy Policy, 37(12), 5167–5172. https://doi.org/10.1016/j.enpol.2009.07.044
17. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580. https://doi.org/10.2307/2938278
18. Kahia, M., Aïssa, M. S. B., & Lanouar, C. (2017). Renewable and non-renewable energy use–economic growth nexus: The case of MENA net oil importing countries. Renewable and Sustainable Energy Reviews, 71, 127–140. https://doi.org/10.1016/j.rser.2017.01.010
19. Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089. https://doi.org/10.1162/003465303772815961
20. McNown, R., Sam, C. Y., & Goh, S. K. (2018). Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50(13), 1509–1521. https://doi.org/10.1080/00036846.2017.1366643
21. Narayan, P. K. (2005). The saving and investment nexus for China: Evidence from cointegration tests. Applied Economics, 37(17), 1979–1990. https://doi.org/10.1080/00036840500278103
22. Ozturk, I., & Acaravci, A. (2013). The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey. Energy Economics, 36, 262–267. https://doi.org/10.1016/j.eneco.2012.08.025
23. Pata, U. K. (2021). Renewable and non-renewable energy consumption, economic complexity, CO2 emissions, and ecological footprint in the USA: Testing the EKC hypothesis with a structural break. Environmental Science and Pollution Research, 28(1), 846–861. https://doi.org/10.1007/s11356-020-10446-3
24. Pata, U. K., & Caglar, A. E. (2021). Investigating the EKC hypothesis with renewable energy consumption, human capital, globalization and trade openness for China: Evidence from augmented ARDL approach with a structural break. Energy, 216, 119220. https://doi.org/10.1016/j.energy.2020.119220
25. Pesaran, M. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
26. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
27. Phillips, P. C. B., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I(1) processes. The Review of Economic Studies, 57(1), 99–125. https://doi.org/10.2307/2297545
28. Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
29. Shahbaz, M., Hye, Q. M. A., Tiwari, A. K., & Leitão, N. C. (2013). Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renewable and Sustainable Energy Reviews, 25, 109–121. https://doi.org/10.1016/j.rser.2013.04.009
30. Sharif, A., Baris-Tuzemen, O., Uzuner, G., Ozturk, I., & Sinha, A. (2020). Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from the quantile ARDL approach. Sustainable Cities and Society, 57, 102138. https://doi.org/10.1016/j.scs.2020.102138
31. Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in honor of Peter Schmidt: Econometric methods and applications (pp. 281–314). Springer. https://doi.org/10.1007/978-1-4899-8008-3_9
32. Stern, D. I. (2004). The rise and fall of the environmental Kuznets curve. World Development, 32(8), 1419–1439. https://doi.org/10.1016/j.worlddev.2004.03.004
33. Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica, 61(4), 783–820. https://doi.org/10.2307/2951763
34. Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. https://doi.org/10.1016/0304-4076(94)01616-8
35. World Bank. (2021). The changing wealth of nations 2021: Managing assets for the future. World Bank.
36. World Bank. (2024). World development indicators [Data set]. Indicators EN.GHG.CO2.PC.CE.AR5, NY.GDP.PCAP.KD, EG.USE.PCAP.KG.OE, NE.TRD.GNFS.ZS. Retrieved 15 June 2024, from https://databank.worldbank.org/source/world-development-indicators
37. Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. https://doi.org/10.1080/07350015.1992.10509904