Seçilmiş OECD Ülkelerinde Yenilenebilir Enerji Tüketiminin Makro Ekonomik Belirleyicileri

Year 2021, Volume: 11 Issue: 1, 267 - 289, 30.06.2021

Uğur Yıldırım , Muhammed Kaya

https://doi.org/10.18074/ckuiibfd.845859

Cited By: 3

Abstract

Bu çalışma, seçilmiş OECD ülkelerinde yenilenebilir enerji kullanımını belirleyen faktörleri incelemektedir. Ayrıca yenilenebilir enerji kullanımının bu değişkenler üzerindeki etkileri de araştırılmıştır. Çalışmada altı farklı model kurulmuştur ve 1996 ile 2017 yıllarını arası dönem panel ARDL yöntemi kullanılarak incelenmiştir. Elde edilen bulgular, salınımının yenilenebilir enerji kullanımının en güçlü belirleyicisi olduğunu göstermiştir. Aynı zamanda cari işlemler dengesi, ekonomik büyüme, enflasyon ve petrol fiyatları da yenilenebilir enerji kullanımını etkilemektedir. Elde edilen bir başka sonuç ise yenilenebilir enerji kullanımının diğer değişkenleri güçlü biçimde etkileyecek düzeyde olmadığıdır.

Keywords

Yenilenebilir Enerji, OECD Ülkeleri, Panel ARDL

References

• Al-Mulali, U., Ozturk, I., ve Lea, H.H. (2015). The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe. Nat. Hazards, 79, 621- 644.
• Anderson, T. W., ve Hsiao, C. (1982). Formulation and estimation of dynamic models using panel data. Journal of econometrics, 18(1), 47-82.
• Anderson, T. W., ve Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598-606.
• Apergis, N., Payne J.E., Menyah, K., ve Wolde-Rufael, Y. (2010). On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecological Economics, 69, 2255-2260.
• Arellano, M. (1989). A note on the Anderson-Hsiao estimator for panel data. Economics Letters, 31(4), 337-341.
• Arellano, M., ve Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1), 29-51. Baum, C. F., Schaffer, M.E., ve Stillman S. (2003). Instrumental variables and GMM: Estimation and testing. The Stata Journal, 3(1), 1-31.
• Bhattacharya, M., Churchill, S., Awaworyi, P., ve Sudharshan, R. (2017). The dynamic impact of renewable energy and institutions on economic output and 〖CO〗_2 emissions across regions. Renewable Energy, 111, 157-167.
• Bird, L., Bolingerb , M., Gaglianoc, T., Wiserb, R., Brown, M., ve Parsons, B. (2005). Policies and market factors driving wind power development in the United States. Energy Policy, 33, 1397-1407.
• Bloch, H., Rafiq, S., ve Salim, R. (2015). Economic growth with coal, oil and renewable energy consumption in China: Prospects for fuel substitution. Economic Modelling, 44, 104-115.
• Bölük, G., ve Mert, M. (2014). Fossil & renewable energy consumption, GHGs (greenhouse gases) and economic growth: evidence from a panel of EU (European Union) countries. Energy, 74, 439-446.
• BP Veri Tabanı, 〖CO〗_2 Emisyonu ve Ham Petrol Fiyatları https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html (Erişim Tarihi: 10.01.2020)
• Breitung, J., ve Das, S. (2005). Panel unit root tests under cross‐sectional dependence. Statistica Neerlandica, 59(4), 414-433.
• Chang, T.H., Huang , C.M., ve Lee, M.C. (2009). Threshold effect of the economic growth rate on the renewable energy development from a change in energy price: evidence from OECD countries. Energy Policy, 37, 5796-5802.
• Dünya Bankası Veri Tabanı, Cari İşlemler Dengesi, https://data.worldbank.org/indicator/ BN.CAB.XOKA.CD (Erişim Tarihi: 25.12.2019)
• Dünya Bankası Veri Tabanı, Ekonomik Büyüme, https://data.worldbank.org/indicator/ NY.GDP.MKTP.KD, (Erişim Tarihi: 29.12.2019)
• Dünya Bankası Veri Tabanı, Enflasyon Oranı, https://data.worldbank.org/indicator/ FP.CPI.TOTL.ZG, (Erişim Tarihi: 29.12.2019)
• Dünya Bankası Veri Tabanı, İşsizlik Oranı, https://data.worldbank.org/indicator/SL.UEM. TOTL.ZS (Erişim Tarihi: 10.01.2020)
• Dvořáka, P., Martinátb, S., Horstc, D.V., Frantáld B., ve Turečková K. (2017). Renewable energy investment and job creation; a cross-sectoral assessment for the Czech Republic with reference to EU benchmarks. Renewable and Sustainable Energy Reviews, 69, 360-368.
• Hillebrand, B., Buttermann, H.G., Behringer, J.M., ve Bleuel M. (2006). The expansion of renewable energies and employment effects in Germany. Energy Policy. 34, 3484-3494.
• Ibrahiem, D.M. (2015). Renewable electricity consumption, foreign direct investment and economic growth in Egypt: An ARDL approach. Procedia Economics and Finance, 30, 313- 323.
• Igliński, B., Iglińska, A., Anna, C., Marcin, K.W. ve Buczkowski, R. (2016). Renewable energy production in the Lodzkie Voivodeship. The PEST analysis of the RES in the voivodeship and in Poland. Renewable and Sustainable Energy Reviews, 58,737-750
• Inglesi-Lotz, R. (2016). The impact of revewable energy consumption to economic growth: A Panel data application. Energy Economics, 53, 58-63.
• Ito, K. (2017). 〖CO〗_2 emissions, renewable and non-renewable energy consumption, and economic growth: evidence from panel data for developing countries. International Economics ,151,1-6.
• Jebli, M.B., Farhani, S., ve Guesmi, K. (2020). Renewable energy, 〖CO〗_2 emissions and value added: Empirical evidence from countries with different income levels. Structural Change and Economic Dynamics , 53, 402-410.
• Khattak, S. I., Ahmad M., Khan Z.U. ve Khan, A. (2020). Exploring the impact of innovation, renewable energy consumption, and income on 〖CO〗_2 emissions: new evidence from the BRICS economies. Environmental Science and Pollution Research , 27, 13866-13881.
• Menyah, K., ve Wolde-Rufael, Y. (2010). 〖CO〗_2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38, 2911-2915.
• OECD Veri Tabanı, Bütçe Dengesi, https://data.oecd.org/gga/general-government-deficit.htm, (Erişim Tarihi: 25.12.2019)
• OECD Veri Tabanı, Yenilenebilir Enerji, https://data.oecd.org/energy/renewable-energy.htm, (Erişim Tarihi: 10.01.2020)
• Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653-670.
• 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(3), 597-625.
• Pesaran, M. H., Shin, Y., ve Smith R.P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621-634.
• Pesaran, M. H., ve Smith R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of econometrics, 68(1), 79-113.
• Peters, G. P., Marland, G., Le Quéré, C., Boden, T., Canadell J.G., ve Raupach M.R. (2012). Rapid growth in 〖CO〗_2 emissions after the 2008–2009 global financial crisis. Nature Climate Change, 2(1), 1-3
• Sadorsky, P. (2009). Renewable energy consumption and income in emerging economies. Energy Policy; 37, 4021-4028
• Shah, I. H., Hiles, C., ve Morley, B. (2018). How do oil prices, macroeconomic factors and policies affect the market for renewable energy? Applied Energy, 215, 87-97 Vaona, A. (2016). The effect of renewable energy generation on import demand. Renewable Energy, 86, 354-359.
• Wei, M., Patadia, S. ve Kammen, D.M. (2010). Putting renewables and energy efficiency to work: How many jobs can the clean energy industry generate in the US? Energy Policy 38, 919–931
• Westerlund, J. (2005). New simple tests for panel cointegration. Econometric Reviews, 24(3), 297-316.