Araştırma Makalesi
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Macro economic determinants of renewable energy consumption in the OECD countries: Wind energy model

Yıl 2019, Sayı: 53, 325 - 342, 30.06.2019
https://doi.org/10.18070/erciyesiibd.535681

Öz

In this study, macroeconomic determinants of renewable energy consumption for OECD countries are examined for wind energy. With the panel data analysis from 2000 to 2015, general government final consumption expenditures, government efficiency, per capita energy use, per capita GDP were tested in determining the determinants of wind energy. According to the results of Emirmahmutoğlu and Köse (2011) causality analysis, no significant result was obtained for the public size variable measured by final consumption expenditures. In addition to this significant findings were obtained from government effectiveness to wind energy for Australia, France, Japan and Turkey economies. Also in Australia, Canada, Turkey, Denmark and Germany for per capita energy use is a significant determiner. The findings of Ozturk's (2010) study were evaluated for the case of testing the economic growth which is one of the basic macroeconomic determinants. According to the results, while the conservation hypothesis is valid for Australia and the United Kingdom, the growth hypothesis is valid for Germany. Expressing the presence of bilateral relations "feedback hypothesis" is valid for Turkey and the Netherlands.

Kaynakça

  • Abolhosseini, S., Heshmati, A. ve Altmann, J. (2014). A review of renewable energy supply and energy efficiency technologies, IZA Discussion Paper No. 8145, 1-35.
  • Apergis, N. ve Danuletiu, D. C. (2014). Renewable energy and economic growth: evidence from the sign of panel long-run causality. International Journal of Energy Economics and Policy, 4(4), 578-587.
  • Breusch, T. S. ve Pagan, A. R. (1980). The lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47, 239-253.
  • Cadoret, I. ve Padovano, F. (2016). The political drivers of renewable energies policies. Energy Economics, 56, 261–269. Çalışkan, Ş. (2009). Türkiye’nin enerjide dışa bağımlılık ve enerji arz güvenliği sorunu. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (25), 297-310.
  • Çetin, M., Ecevit, E. ve Yücel, A. G. (2018). The impact of economic growth, energy consumption, trade openness, and financial development on carbon emissions: empirical evidence from Turkey. Environmental Science and Pollution Research, 25(36), 36589-36603.
  • Eberhardt, M. ve Teal, F. (2010). Aggregation versus heterogeneity in cross-country growth empirics. CSAE WPS, 32, 1-28. Emirmahmutoğlu, F. ve Köse, N. (2011). Testing for granger causality in heterogeneous mixed panels. Economic Modelling, 28, 870-876.
  • International Energy Agency (IEA). (2017). World Energy Outlook, 9 rue de la Fédération 75739 Paris Cedex 15, France.
  • Karakaş, E. (1994). Devlet ekonomi ilişkileri üzerine bazı düşünceler, Maliye Araştırma Merkezi Konferansları. Lucas, J. N. V., Francés, G. E. ve González, E. S. M. (2016). Energy security and renewable energy deployment in the EU: liaisons dangereuses or virtuous circle?. Renewable and Sustainable Energy Reviews, 62, 1032-1046.
  • Marques, A. C., Fuinhas, J. A. ve Manso, J. P. (2010). Motivations driving renewable energy in European countries: A panel data approach. Energy Policy, 38, 6877-6885.
  • Marques, A. C., Fuinhas, J. A. ve Manso, J. P. (2011). A quantile approach to ıdentify factors promoting renewable energy in european countries. Environmental and Resource Economics, 49, 351-366.
  • Öztürk, İ. (2010). A literature survey on energy–growth nexus. Energy Policy, 38, 340–349. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. University of Cambridge & USC, 1-39. 342 Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 53, Haziran 2019, ss.325-342
  • Pesaran, M. H. (2006). Estimatıon and inference in large heterogeneous panels with a multıfactor error structure. Econometrica, 74, 967-1012. Pesaran, M. H. (2007). A simple panel unit root test ın the presence of cross-section dependence. Wiley InterScience, 22, 265-312.
  • Pesaran, M. H. Ve Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142, 50-93. Pesaran, M. H., Ullah, A. ve Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. Econometrics Journal, 11, 105-127.
  • Polzin, F., Migendt, M., Täube, F. A. ve Flotow, P. V. (2015). Public policy influence on renewable energy investments—A panel data study across OECD countries. Energy Policy, 80, 98-111.
  • Popp, D., Hascic, I. ve Medhi, M. (2011). Technology and the diffusion of renewable energy. Energy Economics, 33, 648-662. Sadorsky, P. (2009). Renewable energy consumption and income in emerging economies. Energy Policy, 37, 4021-4028.
  • Schumacher, K. ve Yang, Z. (2018). The determinants of wind energy growth in the United States: Drivers and barriers to state-level development. Renewable and Sustainable Energy Reviews, 97, 1-13.
  • Stadelmann, M. ve Castro, P. (2014). Climate policy innovation in the South–Domestic and international determinants of renewable energy policies in developing and emerging countries. Global Environmental Change, 29, 413-423.
  • Thapar, S., Sharma, S. ve Verma, A. (2018). Key determinants of wind energy growth in India: Analysis of policy and non-policy factors. Energy Policy, 122, 622-638.
  • U.S. Energy Information Administration (EIA), (2016) International Energy Outlook 2016 With Projections to 2040, Office of Energy Analysis U.S. Department of Energy, Washington.
  • Westerlund, J. ve Edgerton, D. L. (2007). A Panel Bootstrap Cointegration Test. Economics Letters, 97, 185-190.

OECD ülkelerinde yenilenebilir enerji tüketiminin makro ekonomik belirleyicileri: Rüzgâr enerjisi modeli

Yıl 2019, Sayı: 53, 325 - 342, 30.06.2019
https://doi.org/10.18070/erciyesiibd.535681

Öz

Çalışmada OECD ülkeleri için yenilenebilir enerji tüketiminin makro ekonomik belirleyicileri, rüzgar enerjisi için incelenmektedir. 2000-2015 yıllarını kapsayan panel veri analizinde rüzgar enerjisinin belirleyicileri genel hükümet nihai tüketim harcamaları, hükümet etkinliği, kişi başına düşen enerji kullanımı ve kişi başına düşen Gayri Safi Yurtiçi Hasıla (GSYH) değişkenleri ile sınanmıştır. Uygulanan Emirmahmutoğlu ve Köse (2011) nedensellik analizi sonuçlarına göre nihai tüketim harcamaları ile ölçülen kamu büyüklüğü değişkeni için anlamlı bir sonuç elde edilememiştir. Bunun yanında Avustralya, Fransa, Japonya ve Türkiye için hükümet etkinliğinden rüzgar enerjisine doğru anlamlı bulgular elde edilmiştir. Ayrıca Avustralya, Kanada, Türkiye, Danimarka ve Almanya için kişi başına düşen enerji kullanımı anlamlı bir belirleyicidir. Temel makroekonomik belirleyicilerden biri olan ekonomik büyümenin sınanması olgusu için ise Öztürk’ün (2010) çalışması ile ortaya koyduğu bulgular değerlendirilmiştir. Elde edilen sonuçlara göre Avustralya ve Birleşik Krallık için “koruma hipotezi” geçerli iken, Almanya için “büyüme hipotezi” geçerlidir. Karşılıklı ilişki varlığını ifade eden “geri bildirim hipotezi” ise Türkiye ve Hollanda için geçerli bulunmuştur.

Kaynakça

  • Abolhosseini, S., Heshmati, A. ve Altmann, J. (2014). A review of renewable energy supply and energy efficiency technologies, IZA Discussion Paper No. 8145, 1-35.
  • Apergis, N. ve Danuletiu, D. C. (2014). Renewable energy and economic growth: evidence from the sign of panel long-run causality. International Journal of Energy Economics and Policy, 4(4), 578-587.
  • Breusch, T. S. ve Pagan, A. R. (1980). The lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47, 239-253.
  • Cadoret, I. ve Padovano, F. (2016). The political drivers of renewable energies policies. Energy Economics, 56, 261–269. Çalışkan, Ş. (2009). Türkiye’nin enerjide dışa bağımlılık ve enerji arz güvenliği sorunu. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (25), 297-310.
  • Çetin, M., Ecevit, E. ve Yücel, A. G. (2018). The impact of economic growth, energy consumption, trade openness, and financial development on carbon emissions: empirical evidence from Turkey. Environmental Science and Pollution Research, 25(36), 36589-36603.
  • Eberhardt, M. ve Teal, F. (2010). Aggregation versus heterogeneity in cross-country growth empirics. CSAE WPS, 32, 1-28. Emirmahmutoğlu, F. ve Köse, N. (2011). Testing for granger causality in heterogeneous mixed panels. Economic Modelling, 28, 870-876.
  • International Energy Agency (IEA). (2017). World Energy Outlook, 9 rue de la Fédération 75739 Paris Cedex 15, France.
  • Karakaş, E. (1994). Devlet ekonomi ilişkileri üzerine bazı düşünceler, Maliye Araştırma Merkezi Konferansları. Lucas, J. N. V., Francés, G. E. ve González, E. S. M. (2016). Energy security and renewable energy deployment in the EU: liaisons dangereuses or virtuous circle?. Renewable and Sustainable Energy Reviews, 62, 1032-1046.
  • Marques, A. C., Fuinhas, J. A. ve Manso, J. P. (2010). Motivations driving renewable energy in European countries: A panel data approach. Energy Policy, 38, 6877-6885.
  • Marques, A. C., Fuinhas, J. A. ve Manso, J. P. (2011). A quantile approach to ıdentify factors promoting renewable energy in european countries. Environmental and Resource Economics, 49, 351-366.
  • Öztürk, İ. (2010). A literature survey on energy–growth nexus. Energy Policy, 38, 340–349. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. University of Cambridge & USC, 1-39. 342 Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 53, Haziran 2019, ss.325-342
  • Pesaran, M. H. (2006). Estimatıon and inference in large heterogeneous panels with a multıfactor error structure. Econometrica, 74, 967-1012. Pesaran, M. H. (2007). A simple panel unit root test ın the presence of cross-section dependence. Wiley InterScience, 22, 265-312.
  • Pesaran, M. H. Ve Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142, 50-93. Pesaran, M. H., Ullah, A. ve Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. Econometrics Journal, 11, 105-127.
  • Polzin, F., Migendt, M., Täube, F. A. ve Flotow, P. V. (2015). Public policy influence on renewable energy investments—A panel data study across OECD countries. Energy Policy, 80, 98-111.
  • Popp, D., Hascic, I. ve Medhi, M. (2011). Technology and the diffusion of renewable energy. Energy Economics, 33, 648-662. Sadorsky, P. (2009). Renewable energy consumption and income in emerging economies. Energy Policy, 37, 4021-4028.
  • Schumacher, K. ve Yang, Z. (2018). The determinants of wind energy growth in the United States: Drivers and barriers to state-level development. Renewable and Sustainable Energy Reviews, 97, 1-13.
  • Stadelmann, M. ve Castro, P. (2014). Climate policy innovation in the South–Domestic and international determinants of renewable energy policies in developing and emerging countries. Global Environmental Change, 29, 413-423.
  • Thapar, S., Sharma, S. ve Verma, A. (2018). Key determinants of wind energy growth in India: Analysis of policy and non-policy factors. Energy Policy, 122, 622-638.
  • U.S. Energy Information Administration (EIA), (2016) International Energy Outlook 2016 With Projections to 2040, Office of Energy Analysis U.S. Department of Energy, Washington.
  • Westerlund, J. ve Edgerton, D. L. (2007). A Panel Bootstrap Cointegration Test. Economics Letters, 97, 185-190.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Esma Gültekin 0000-0001-5897-0462

Ahmet Uğur 0000-0002-5735-7428

Yayımlanma Tarihi 30 Haziran 2019
Kabul Tarihi 22 Mart 2019
Yayımlandığı Sayı Yıl 2019 Sayı: 53

Kaynak Göster

APA Gültekin, E., & Uğur, A. (2019). OECD ülkelerinde yenilenebilir enerji tüketiminin makro ekonomik belirleyicileri: Rüzgâr enerjisi modeli. Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi(53), 325-342. https://doi.org/10.18070/erciyesiibd.535681

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