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BRICS-T Ülkelerinin Kömür Tüketimlerinin Çoklu Regresyon Analizi ile Tahmini

Year 2020, Volume: 5 Issue: 1, 32 - 45, 26.04.2020

Abstract

Bu
çalışmada, BRICS-T ülkelerinin ekonomik ve demografik verileri kullanılarak
regresyon analizi ile kömür tüketimlerinin tahminine yönelik istatistiksel
modeller geliştirilmiştir. Geliştirilen modellerin istatistiksel doğruluğu ve
tahmin performansları, çeşitli yaklaşımlar vasıtasıyla test edilmiştir. Bunun
yanı sıra, geliştirilen modellerde ilgili ülkelerin kömür tüketimini
istatistiksel olarak etkileyen en önemli değişkenler de tespit edilmiştir. Modelleme
çalışmalarına ek olarak, BRICS-T ülkelerine ve bu gruptaki ülkelerin enerji
görünümlerine yönelik bir değerlendirmede sunulmuştur. Çalışma sonuçları,
BRICS-T ülkelerinin küresel enerji denkleminde hatırı sayılır bir yerde
olduğunu ve bu ülkelerin çok yakın bir gelecekte dünya ekonomisine yön verecek
bir pozisyona geleceğini göstermiştir. Sonuçlar ayrıca, geliştirilen/önerilen
modellerin güçlü bir uygulanabilirlik potansiyellerinin olduğunu da ortaya
koymuştur.

References

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  • [11] Lin, B. and Wesseh, PK. 2014. Energy consumption and economic growth in South Africa reexamined: A nonparametric testing approach. Renew. Sust. Energ. Rev. 40: 840-850.
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  • [15] Atıcı, U. and Ersoy, A. 2009. Correlation of specific energy of cutting sawsand drilling bits with rock brittleness and destruction Energy. J. Mater. Process. Technol. 209: 2602–2612.
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  • [17] Sari, R. and Soytas, U. 2004. Disaggregate energy consumption, employment and income in Turkey. Energ. Econ. 26: 335-344.
  • [18] Jinke, L., Hualing, S. and Dianming, G. 2008. Causality relationship between coal consumption and GDP: Difference of major OECD and Non-OECD countries. Appl. Energ. 85: 421-429.
  • [19] Yuan, JH., Kang, J.G., Zhao, CH. and Hu, ZG. 2008. Energy consumption and economic growth: Evidence from China at both aggregated and disaggregated levels. Energ. Econ. 30: 3077- 3094.
  • [20] Jinke, L. and Li, ZA. 2011. A causality analysis of coal consumption and economic growth for China and India. Nat. Resour. 2: 54-60.
  • [21] Li, R. and Leung, GCK. 2012. Coal consumption and economic growth in China. Energy Policy. 40: 438-443.
  • [22] Bloch, H., Rafiq, S. and Salim, R., 2012. Coal consumption, CO2 emission and economic growth in China: Empirical evidence and policy responses. Energ. Econ. 34: 518-528.
  • [23] Michieka, MN. and Fletcher, JJ. 2012. An investigation of the role of China’s urban population on coal consumption. Energy Policy. 48: 668–676.
  • [24] Hao, Y., Zhang, ZY., Liao, H. and Wei, YM. 2015. China’s farewell to coal: A forecast of coal consumption through 2020. Energy Policy, 86, 444–455.
  • [25] Wang, C., Li, BB., Liang, QM. and Wang, JC. 2018. Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models. Energy. 162: 272-281.
  • [26] Bildirici, M.E. and Bakirtas, T., 2014. The relationship among oil, natural gas and coal consumption and economic growth in BRICTS (Brazil, Russian, India, China, Turkey and South Africa) Countries. Energy. 65: 134-144.
  • [27] Sasana, H. and Ghozali, I. 2017. The impact of fossil and renewable energy consumption on the economic growth in Brazil, Russia, India, China and South Africa. International Journal of Energy Economics and Policy. 3: 194-200.
  • [28] Lin, FL., Lotz, RI. and Chang, T. 2018. Revisit coal consumption, CO2 emissions and economic growth nexus in China and India using a newly developed bootstrap ARDL bound test. Energ. Explor. Exploit. 36(3): 450–463.
  • [29] Chang, T., Deale, D., Gupta, R., Hefer, R., Lotz, R.L. and Kengne, B.S. 2017. The causal relationship between coal consumption and economic growth in the BRICS countries: Evidence from Panel-Granger Causality Tests. Energ. Source. Part B. 12(2): 138-146.
  • [30] Ma, M., Su, M., Li, S., Jiang, F. and Li, R. 2018. Predicting coal consumption in South Africa based on linear (Metabolic Grey Model), nonlinear (Non-Linear Grey Model) and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models. Sustainability. 10: 2552; doi:10.3390/su10072552.
  • [31] Enayatollahi, I., Bazzazi, AA. and Asadi, A. 2014. Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech. Rock Eng. 47(2): 799–807.
  • [32] Esmaeili, M., Osanloo, M., Rashinidejad, F., Bazzazi AA. and Taji M. 2014. Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng. Comput. 30(4): 549-558.
  • [33] Yerel, S. and Ersen T. 2013. Prediction of the calorific value of coal deposit using linear regression analysis. Energ. Source. Part A. 35: 976-980.
  • [34] Cohen, J., Cohen, P., West, SG. and Aiken, LS., 2003. Applied multiple regression/correlation analysis for the behavioral sciences. Lawrence Erlbaum Associates. Publishers Mahwah, New Jersey.
  • [35] Kanıt, R. ve Baykan, NU. 2004. Bina yaklaşık maliyetinin çoklu doğrusal regresyon ile belirlenmesi. Politeknik Dergisi. 7(4): 359-367.
  • [36] Karakurt, I., Aydin, G. and Kaya, S. 2015. Modeling of Turkey’s CO2 emissions using economic and demographic variables. 24th. International Mining Congress and Exhibition of Turkey, Antalya-Türkiye, pp. 1474-1479.
  • [37] Kayaalp, TG., Güney, ÇM. ve Cebeci, Z. 2015. Çoklu doğrusal regresyon modelinde değişken seçiminin zootekniye uygulanışı. Çukurova Üniversitesi Çukurova Tarım ve Gıda Bilimleri Dergisi. 30(1): 1 –8.
  • [38] Yavuz, S. 2009. Regresyon analizinde doğrusala dönüştürme yöntemleri ve bir uygulama. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi. 23(1): 165-179.
  • [39] Hamzaçebi, C. and Karakurt, I. 2015. Forecasting the energy-related CO2 emissions of Turkey using grey prediction model. Energ. Source., Part A. 37(9): 1023–1031.
  • [40] Aydin, G., Karakurt, I. and Hamzacebi, C.. 2015. Artificial neural network and regression Mmdels for performance prediction of abrasive waterjet in rock cutting. Int. J. Adv. Manuf. Tech. 75: 1321–1330.
  • [41] Uysal, H. and Karabat, S. 2017. Forecasting and evaluation for raisin export in Turkey. BIO Web of Conferences, 40th. World Congress of Vine and Wine, Sofia, Bulgaria, Article number : 03002.
  • [42] Lewis, C.D. 1982. International and Business Forecasting Methods. Butterworths, London
Year 2020, Volume: 5 Issue: 1, 32 - 45, 26.04.2020

Abstract

References

  • [1] Bianco, V., Manca, O. and Nardini, S., 2009. Electricity consumption forecasting in Italy using linear regression models. Energ. 34: 1413–1421.
  • [2] Mohr, SH., Wang, J., Ellem, G., Ward, J. and Giurco D. 2015. Projection of world fossil fuels by country. Fuel. 141: 120-135.
  • [3] ETB., 2017. Dünya ve Türkiye enerji ve tabi kaynaklar görünümü. https://www.enerji.gov.tr/File/?path=ROOT%2F1%2FDocuments%2FEnerji%20ve%20Tabii%20Kaynaklar%20G%C3%B6r%C3%BCn%C3%BCm%C3%BC%2FSayi_15.pdf, Erişim tarihi Nisan 2019.
  • [4] BP., 2019. British Petroleum statistical review of world energy. https://www.bp.com/, Erişim Tarihi Temmuz 2019.
  • [5] Satti, LS., Hassan, SM., Mahmood, H. and Shahbaz, M. 2014. Coal consumption: An alternate energy resource to fuel economic growth in Pakistan. Econ. Model. 36: 282-287.
  • [6] Chai, J., Du, M., Liang, T., Sun, X.C. and Zhang, Z.G. 2019. Coal consumption in China: How to bend down the curve?. Energ. Econ. 80: 38-47.
  • [7] Şengönül, A. ve Koşaroğlu, ŞM. 2018. Elektrik tüketimi ve ekonomik büyüme arasındaki ilişki: BRICS ülkeleri için bir uygulama. C.Ü. İktisadi ve İdari Bilimler Dergisi. 19(2): 431-447.
  • [8] Bozma, G., Aydın, R. ve Kolçak, M. 2018. BRICS ve MINT ülkelerinde ekonomik büyüme ve enerji tüketimi ilişkisi. Iğdır Üniversitesi Sosyal Bilimler Dergisi. 15: 323-338.
  • [9] Gusarova., S. 2019. Role of China in the development of trade and FDI cooperation with BRICS countries. China Econ. Rev. DOI: https://doi.org/10.1016/j.chieco.2019.01.010.
  • [10] Azevedo, VG., Sartori, S. and Campos, LMS., 2018. CO2 emissions: A quantitative analysis among the BRICS nations. Renew. Sust. Energ. Rev. 81: 107–115.
  • [11] Lin, B. and Wesseh, PK. 2014. Energy consumption and economic growth in South Africa reexamined: A nonparametric testing approach. Renew. Sust. Energ. Rev. 40: 840-850.
  • [12] WBI., 2019. Worldbank Indicators. https://data.worldbank.org/indicator, Erişim Tarihi Temmuz 2019.
  • [13] MME., 2019. Ministry of Mines and Energy of Brazil. BRICS Energy Indicators.
  • [14] Bianco, V., Scarpa, F. and Tagliafico, L.A., 2014. Analysis and future outlook of natural gas consumption in the Italian residential sector. Energ. Convers. Manag. 87: 754–764.
  • [15] Atıcı, U. and Ersoy, A. 2009. Correlation of specific energy of cutting sawsand drilling bits with rock brittleness and destruction Energy. J. Mater. Process. Technol. 209: 2602–2612.
  • [16] Durak, S. 2012. Türkiye sanayi ve konut elektrik enerji talebinin öngörülmesi ve konut elektrik tüketimini etkileyen parametrelerin belirlenmesi. İstanbul Teknik Üniversitesi, Enerji Enstitüsü, Enerji Bilim ve Teknoloji Anabilim Dalı, Yüksek Lisans Tezi, 91s.
  • [17] Sari, R. and Soytas, U. 2004. Disaggregate energy consumption, employment and income in Turkey. Energ. Econ. 26: 335-344.
  • [18] Jinke, L., Hualing, S. and Dianming, G. 2008. Causality relationship between coal consumption and GDP: Difference of major OECD and Non-OECD countries. Appl. Energ. 85: 421-429.
  • [19] Yuan, JH., Kang, J.G., Zhao, CH. and Hu, ZG. 2008. Energy consumption and economic growth: Evidence from China at both aggregated and disaggregated levels. Energ. Econ. 30: 3077- 3094.
  • [20] Jinke, L. and Li, ZA. 2011. A causality analysis of coal consumption and economic growth for China and India. Nat. Resour. 2: 54-60.
  • [21] Li, R. and Leung, GCK. 2012. Coal consumption and economic growth in China. Energy Policy. 40: 438-443.
  • [22] Bloch, H., Rafiq, S. and Salim, R., 2012. Coal consumption, CO2 emission and economic growth in China: Empirical evidence and policy responses. Energ. Econ. 34: 518-528.
  • [23] Michieka, MN. and Fletcher, JJ. 2012. An investigation of the role of China’s urban population on coal consumption. Energy Policy. 48: 668–676.
  • [24] Hao, Y., Zhang, ZY., Liao, H. and Wei, YM. 2015. China’s farewell to coal: A forecast of coal consumption through 2020. Energy Policy, 86, 444–455.
  • [25] Wang, C., Li, BB., Liang, QM. and Wang, JC. 2018. Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models. Energy. 162: 272-281.
  • [26] Bildirici, M.E. and Bakirtas, T., 2014. The relationship among oil, natural gas and coal consumption and economic growth in BRICTS (Brazil, Russian, India, China, Turkey and South Africa) Countries. Energy. 65: 134-144.
  • [27] Sasana, H. and Ghozali, I. 2017. The impact of fossil and renewable energy consumption on the economic growth in Brazil, Russia, India, China and South Africa. International Journal of Energy Economics and Policy. 3: 194-200.
  • [28] Lin, FL., Lotz, RI. and Chang, T. 2018. Revisit coal consumption, CO2 emissions and economic growth nexus in China and India using a newly developed bootstrap ARDL bound test. Energ. Explor. Exploit. 36(3): 450–463.
  • [29] Chang, T., Deale, D., Gupta, R., Hefer, R., Lotz, R.L. and Kengne, B.S. 2017. The causal relationship between coal consumption and economic growth in the BRICS countries: Evidence from Panel-Granger Causality Tests. Energ. Source. Part B. 12(2): 138-146.
  • [30] Ma, M., Su, M., Li, S., Jiang, F. and Li, R. 2018. Predicting coal consumption in South Africa based on linear (Metabolic Grey Model), nonlinear (Non-Linear Grey Model) and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models. Sustainability. 10: 2552; doi:10.3390/su10072552.
  • [31] Enayatollahi, I., Bazzazi, AA. and Asadi, A. 2014. Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech. Rock Eng. 47(2): 799–807.
  • [32] Esmaeili, M., Osanloo, M., Rashinidejad, F., Bazzazi AA. and Taji M. 2014. Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng. Comput. 30(4): 549-558.
  • [33] Yerel, S. and Ersen T. 2013. Prediction of the calorific value of coal deposit using linear regression analysis. Energ. Source. Part A. 35: 976-980.
  • [34] Cohen, J., Cohen, P., West, SG. and Aiken, LS., 2003. Applied multiple regression/correlation analysis for the behavioral sciences. Lawrence Erlbaum Associates. Publishers Mahwah, New Jersey.
  • [35] Kanıt, R. ve Baykan, NU. 2004. Bina yaklaşık maliyetinin çoklu doğrusal regresyon ile belirlenmesi. Politeknik Dergisi. 7(4): 359-367.
  • [36] Karakurt, I., Aydin, G. and Kaya, S. 2015. Modeling of Turkey’s CO2 emissions using economic and demographic variables. 24th. International Mining Congress and Exhibition of Turkey, Antalya-Türkiye, pp. 1474-1479.
  • [37] Kayaalp, TG., Güney, ÇM. ve Cebeci, Z. 2015. Çoklu doğrusal regresyon modelinde değişken seçiminin zootekniye uygulanışı. Çukurova Üniversitesi Çukurova Tarım ve Gıda Bilimleri Dergisi. 30(1): 1 –8.
  • [38] Yavuz, S. 2009. Regresyon analizinde doğrusala dönüştürme yöntemleri ve bir uygulama. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi. 23(1): 165-179.
  • [39] Hamzaçebi, C. and Karakurt, I. 2015. Forecasting the energy-related CO2 emissions of Turkey using grey prediction model. Energ. Source., Part A. 37(9): 1023–1031.
  • [40] Aydin, G., Karakurt, I. and Hamzacebi, C.. 2015. Artificial neural network and regression Mmdels for performance prediction of abrasive waterjet in rock cutting. Int. J. Adv. Manuf. Tech. 75: 1321–1330.
  • [41] Uysal, H. and Karabat, S. 2017. Forecasting and evaluation for raisin export in Turkey. BIO Web of Conferences, 40th. World Congress of Vine and Wine, Sofia, Bulgaria, Article number : 03002.
  • [42] Lewis, C.D. 1982. International and Business Forecasting Methods. Butterworths, London
There are 42 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

İzzet Karakurt 0000-0002-3360-8712

Gökhan Aydın 0000-0002-6670-6458

Mohammad Reza Amırı 0000-0002-1710-6034

Publication Date April 26, 2020
Submission Date October 11, 2019
Acceptance Date March 23, 2020
Published in Issue Year 2020 Volume: 5 Issue: 1

Cite

APA Karakurt, İ., Aydın, G., & Amırı, M. R. (2020). BRICS-T Ülkelerinin Kömür Tüketimlerinin Çoklu Regresyon Analizi ile Tahmini. Harran Üniversitesi Mühendislik Dergisi, 5(1), 32-45.