Kömür Kaynaklı CO2 Emisyonlarının Tahminine Yönelik Model Geliştirilmesi: BRICS-T Ülkeleri Örneği

Year 2020, Volume: 10 Issue: 1, 214 - 229, 15.06.2020

İzzet Karakurt , Gökhan Aydın

https://doi.org/10.31466/kfbd.611850

Cited By: 1

Abstract

Bu çalışmada BRICS-T ülkelerinin kömür kaynaklı karbondioksit (CO2) emisyonlarının tahminine yönelik istatistiksel modeller geliştirilmiştir. Gruptaki ülkelerin ekonomik ve demografik verileri kullanılarak çoklu regresyon yöntemi ile kömür kaynaklı CO2 emisyonları modellenmiştir. 1971–2016 dönemine ait verilerin kullanıldığı çalışmada, seçilen dönemlere ait veriler iki gruba ayrılmıştır. 1971–2010 yıları arasındaki ilk grup istatistiksel modellerin geliştirilmesinde kullanılırken, 2011–2016 yılları arasındaki ikinci grup ise geliştirilen modellerin performans ölçümlerinin yapılmasında kullanılmıştır. Ayrıca geliştirilen modellerin istatistiksel geçerliliği, çeşitli yaklaşımlar ile test edilmiştir. Ek olarak, kömür kaynaklı CO2 emisyonlarının istatistiksel olarak etkileyen en önemli değişkenler de tespit edilmiştir. Modelleme çalışmalarının yanı sıra, BRICS-T ülkelerinin enerji ve CO2 emisyonlarına yönelik bir değerlendirme de sunulmuştur.

Keywords

BRICS-T Ülkeleri, Kömür, CO2 Emisyonu, Regresyon Analizi

Supporting Institution

-

Project Number

-

Thanks

-

Development of Models for the Estimation of Coal-related CO2 Emissions: The Case of BRICS-T Countries

Abstract

Statistical models were developed for the estimation of coal-related carbon dioxide (CO2) emissions from the BRICS-T countries in this study. Coal-related CO2 emissions were modeled by multiple regression method using the economic and demographic data of the countries in the group. In the study in which the annual data over the period of 1971-2016 was used, the selected data was divided into two groups. While the first group from 1971 to 2010 was used for developing the models, the second group from 2011 to 2016 was used for performance measurement of the developed models. Additionally, the proposed models were statistically verified by various approaches. Furthermore, the significant variables statistically affecting the coal-related CO2 emissions were determined. Besides modelling studies, an assessment of energy and CO2 emissions from the BRICS-T countries was also presented.

Keywords

Coal, CO2 emission, Regression Analysis, BRICS-T Countries

Project Number

-

References

• Abas, N., Kalair, A., Khan, N.(2015). Review of fossil fuels and future energy technologies. Futures, 69, 31–49.
• Atıcı, U., Ersoy, A. (2009). Correlation of specific energy of cutting sawsand drilling bits with rock brittleness and destruction energy. Journal of Materials Processing Technology, 209, 2602–2612.
• Aydin, G., Karakurt, I., Hamzacebi, C. (2015). Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting. The International Journal of Advanced Manufacturing Technology, 75, 1321–1330.
• Aydin, G., Karakurt, I., Hamzaçebi, C. (2015). Performance prediction of diamond sawblades using artificial neural network and regression analysis. Arabian Journal for Science and Engineering, 40(7), 2003–2012.
• Azevedo, VG., Sartori, S., Campos, L.M.S. (2018). CO2 emissions: A quantitative analysis among the BRICS nations. Renewable and Sustainable Energy Reviews, 81, 107–115.
• Bianco, V., Manca, O., Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34, 1413–1421.
• Bianco, V., Scarpa, F., Tagliafico, L.A. (2014). Analysis and future outlook of natural gas consumption in the Italian residential sector. Energy Conversion and Management, 87, 754–764.
• Bozma, G., Aydın, R., Kolçak, M. (2018). BRICS ve MINT ülkelerinde ekonomik büyüme ve enerji tüketimi ilişkisi. Iğdır Üniversitesi, Sosyal Bilimler Dergisi, 15, 323–338.
• BP. (2019). British Petroleum, https://www.bp.com/, Erişim Tarihi Mart 2019.
• Cohen, J., Cohen, P., West, S.G., Aiken, L.S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences, Lawrence Erlbaum Associates, Publishers Mahwah, New Jersey.
• Cowan, W.N., Chang, T., Lotza, R.I., Gupta, R. (2014). The nexus of electricity consumption, economic growth and CO2 emissions in the BRICS countries. Energy Policy, 66, 359-368.
• Enayatollahi, I., Bazzazi, A.A., Asadi, A. (2014). Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mechanics and Rock Engineering, 47(2), 799–807.
• Esmaeili, M., Osanloo, M., Rashinidejad, F., Bazzazi, A.A., Taji, M. (2014). Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with Computers, 30(4), 549-558.
• Hamzacebi, C., Karakurt, I. (2015). Forecasting the energy-related CO2 emissions of Turkey using grey prediction model. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 37(9), 1023–1031.
• Huaman, R.N.E., Jun, T.X. (2014). Energy related CO2 emissions and the progress on CCS projects: a review. Renewable and Sustainable Energy Reviews, 31, 368–85.
• IEA.(2019). CO2 emissions from fuel combustion 2018 highlights. https://webstore.iea.org/co2-emissions-from-fuel-combustion-2018-highlights, Erişim tarihi Mart 2019.
• Kanıt, R., Baykan, N.U. (2004). Bina yaklaşık maliyetinin çoklu doğrusal regresyon ile belirlenmesi. Politeknik Dergisi, 7(4), 359-367.
• Karakurt, I., Aydin, G., 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.
• Kasman, A., Duman, S.Y. (2015). CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Economic Modelling, 44, 97–103.
• Kayaalp, T.G., Güney, Ç.M., Cebeci, Z. (2015). Çoklu doğrusal regresyon modelinde değişken seçiminin zootekniye uygulanışı. Çukurova Üniversitesi, Çukurova Tarım ve Gıda Bilimleri Dergisi, 30(1), 1 –8.
• Lewis, C.D., 1982. International and business forecasting methods. Butterworths, London.
• Lin, B., Wesseh, P.K. (2014). Energy consumption and economic growth in South Africa reexamined: A nonparametric testing approach. Renewable and Sustainable Energy Reviews, 40, 840-850.
• Lin, F.L., Lotz, R.I., Chang, T. (2018). Revisit coal consumption, CO2 emissions and economic growth nexus in China and India using a newly developed bootstrap ARDL bound test. Energy Exploration & Exploitation, 36(3), 450–463.
• Mardani, A., Streimikiene, D., Cavallaro, F., Loganathan, N., Khoshnoudi, M. (2019). Carbon dioxide (CO2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017. Science of the Total Environment, 649, 31–49.
• Maryam, J., Mittal, A., Sharma, V. (2017). CO2 emissions, energy consumption and economic growth in BRICS: An empirical analysis. IOSR Journal Of Humanities And Social Science, 22(2), 53-58.
• Menyah, K., Rufael, Y.W. (2010). Energy consumption, pollutant emissions and economic growth in South Africa. Energy Economics, 32(6), 1374-1382.
• Ozturk, I., Acaravci, A. (2010). CO2 emissions, energy consumption and economic growth in Turkey. Renewable and Sustainable Energy Reviews, 14(9), 3220-3225.
• Pao, H.T., Tsai, C.M. (2010). CO2 emissions, energy consumption and economic growth in BRIC countries. Energy Policy, 38, 7850–60.
• Pao, H.T., Tsai, C.M,( 2011a). Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy, 36(1), 685-693.
• Pao, H.T., Tsai, C.M. (2011b). Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy, 36(5), 2450-2458.
• Pao, H.T., Fu, H.C., Tseng, C.L. (2012). Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy, 40(1), 400-409.
• Pao, HT., Yu, HC., Yang, YH. (2011). Modeling the CO2 emissions, energy use, and economic growth in Russia. Energy, 36(8), 5094-5100.
• Şengönül, A., Koşaroğlu, Ş.M. (2018). Elektrik tüketimi ve ekonomik büyüme arasındaki ilişki: BRICS ülkeleri için bir uygulama. C.Ü. İktisadi ve İdari Bilimler Dergisi, 19(2), 431-447.
• Srinivasan, P., Ravindra, I.S. (2015). Causality among energy consumption, CO2 emission, economic growth and trade: A case of India. Foreign Trade Review, 50(3), 168–189.
• Uysal, H., Karabat, S. (2017). Forecasting and evaluation for raisin export in Turkey. BIO Web of Conferences, 40th. World Congress of Vine and Wine, Sofia, Bulgaria, 9: Article number : 03002.
• Wang, S.S., Zhou, D.Q., Zhou, P., Wang, Q.W. (2011). CO2 emissions, energy consumption and economic growth in China: A panel data analysis. Energy Policy, 39(9), 4870–4875.
• WBI. (2019). Worldbank indicators. https://data.worldbank.org/indicator, Erişim Tarihi Mart 2019.
• Wu, L., Liu, S., Liu, D., Fang, Z., Xu, H. (2015). Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model. Energy, 79(1), 489-495.
• Yavuz, S. (2009). Regresyon analizinde doğrusala dönüştürme Yöntemleri ve bir uygulama. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(1), 165-179.
• Yerel, S., Ersen, T. (2013). Prediction of the calorific value of coal deposit using linear regression analysis. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 35, 976-980.
• Zakarya, G.Y., Mostefa, B., Abbes, S.M., Seghir, G.M. (2015). Factors affecting CO2 emissions in the BRICS countries: A panel data analysis. Procedia Economics and Finance, 26, 114 – 125.