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N -11 Ülkelerinin Birincil Enerji Tüketimine Yönelik Toplam Nüfusa Dayalı Tahmin Modelleri

Year 2025, Volume: 30 Issue: 2, 719 - 736, 31.08.2025
https://doi.org/10.53433/yyufbed.1668438

Abstract

Bu çalışma, trend analizi (TA) ile toplam nüfusa (TP) dayalı olarak gelecek on bir (N-11) ülkelerinin birincil enerji tüketimine (PEC) yönelik tahmin modelleri oluşturmayı amaçlamaktadır. Bu bağlamda, N-11 ülkelerinin 1985'ten 2023'e kadar olan TP ve PEC verileri tahmin modellerinin oluşturulması için kullanılmıştır. Kurulan modeller daha sonra çeşitli istatistiksel göstergelerle doğrulanmıştır. Ayrıca, kurulan modellerin tahmin doğrulukları, ortalama mutlak sapma (MAD), ortalama karekök hatası (RMSE), bağıl ortalama karekök hatası (RRMSE), %95'te belirsizlik (U95), maksimum mutlak bağıl hata (erMAX) ve ortalama mutlak yüzde hatası (MAPE) gibi çeşitli hata endeksleriyle ölçülmüştür. Ek olarak, kurulan modeller ile örnek ülkelerinin PEC'leri 2025 yılından itibaren on yıllık bir dönem için tahmin edilmiştir. Kurulan modellerin N-11 ülkelerinin PEC’lerini başarılı bir şekilde tahmin edebildiği tespit edilmiştir. Ayrıca, tahmin sonuçları, mevcut çalışmanın koşulları dikkate alındığında, yakın gelecekte ilgili ülkeler için önemli artışların beklendiğini açıkça göstermiştir.

References

  • Aydin, G. (2014). Production modeling in the oil and natural gas industry: An application of trend analysis. Petroleum Science and Technology, 32(5), 555-564. https://doi.org/10.1080/10916466.2013.825271
  • Aydin, G. (2015). Forecasting natural gas production using various regression models. Petroleum Science and Technology, 33(15-16), 1486-1492. https://doi.org/10.1080/10916466.2015.1076842
  • Aydin, G., Kaya, S., & Karakurt, I. (2015, April). Modeling of energy consumption based on population: The case of Turkey. Proceedings of 24th International Mining Congress and Exhibition of Turkey, Antalya, Türkiye, pp. 88-92.
  • Azadeh, A., Saberi, M., Asadzadeh, S. M., & Khakestani, M. (2011). A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA, Canada, Singapore, Pakistan and Iran. Energy, 36(12), 6981-6992. https://doi.org/10.1016/j.energy.2011.07.016
  • Azevedo, V. G., Sartori, S., & Campos, L. M. S. (2018). CO2 emissions: A quantitative analysis among the BRICS nations. Renewable and Sustainable Energy Reviews, 81, 107-115. https://doi.org/10.1016/j.rser.2017.07.027
  • Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. https://doi.org/10.1016/j.energy.2009.06.034
  • 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. https://doi.org/10.1016/j.enconman.2014.07.081
  • BP. (2020). Technical report on BP energy outlook 2035. Accessed on 15.02.2025. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2015.pdf
  • BP. (2023). British Petroleum Energy Outlook – 2022, Insights from the Accelerated, Net Zero and New Momentum scenarios – Brazil. Accesed on 10.3.2025. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2024-country-insight-brazil.pdf
  • Byrne, R. F. (2012). Beyond traditional time-series: Using demand sensing to improve forecasts in volatile times. Journal of Business Forecasting, 31(2), 13-20.
  • Cambazoğlu, B. (2021). Comparative macroeconomic analysis of CIVETS Countries. Turkish Studies - Social Sciences, 15(2), 77-91. https://dx.doi.org/10.29228/TurkishStudies.40165
  • Celiker, M., Yukseler, U., & Dursun, Ö. F. (2021). Trend analyses for discharge-recharge of Tacin karstic spring (Kayseri, Turkey). Journal of African Earth Sciences, 184, 104344. https://doi.org/10.1016/j.jafrearsci.2021.104344
  • Chang, C. L., & Fang, M. (2022). Renewable energy-led growth hypothesis: New insights from BRICS and N-11 economies. Renewable Energy, 188, 788-800. https://doi.org/10.1016/j.renene.2022.02.052
  • Chiu, Y. B., & Lee, C. C. (2020). Effects of financial development on energy consumption: The role of country risks. Energy Economics, 90, 104833. https://doi.org/10.1016/j.eneco.2020.104833
  • Demircioglu, M., & Esiyok, S. (2022). Energy consumption forecast of Turkey using artificial neural networks from a sustainability perspective. International Journal of Sustainable Energy, 41(8), 1127-1141. https://doi.org/10.1080/14786451.2022.2026357
  • Despotovic, M., Nedic, V., Despotovic, D., & Cvetanovic, S. (2015). Review and statistical analysis of different global solar radiation sunshine models. Renewable and Sustainable Energy Reviews, 52, 1869-1880. https://dx.doi.org/10.1016/j.rser.2015.08.035
  • Do, T. M., & Sharma, D. (2011). Vietnam's energy sector: a review of current energy policies and strategies. Energy Policy, 39(10), 5770-5777. https://doi.org/10.1016/j.enpol.2011.08.010
  • Ehigiamusoe, K. U., & Dogan, E. (2022). The role of interaction effect between renewable energy consumption and real income in carbon emissions: Evidence from low-income countries. Renewable and Sustainable Energy Reviews, 154, 111883. https://doi.org/10.1016/j.rser.2021.111883
  • EI. (2024). Statistical review of world energy 2024. Accesed on 12.12.2024. https://www.energyinst.org/statistical-review
  • Erba, S., & Beydoğan, H. O. (2017). Attitudes of educators towards educational research. Kırsehir Journal of the Faculty of Education, 18(3), 246-260. https://dergipark.org.tr/tr/download/article-file/1487175
  • Ergezer, E. C. (2024). BRICS expansion and the rising voice of the global south. Turkish Economic Policy Research Foundation, an evaluation note. Accessed on 10.12.2024. https://tepav.s3.eu-west-1.amazonaws.com/upload/mce/2024/notlar/brics_genislemesi_ve_kuresel_guneyin_yukselen_sesi.pdf
  • Fareed, Z., & Pata, U. K. (2022). Renewable, non-renewable energy consumption and income in top ten renewable energy-consuming countries: Advanced Fourier based panel data approaches. Renewable Energy, 194, 805-821. https://doi.org/10.1016/j.renene.2022.05.156
  • Gazder, U. (2016, December). Energy consumption trends in energy scarce and rich countries: comparative study for Pakistan and Saudi Arabia. World Renewable Energy Congress-17, Bahrain, pp. 1 – 8. https://doi.org/10.1051/e3sconf/20172307002
  • Gusarova, S. (2019). Role of China in the development of trade and FDI cooperation with BRICS countries. China Economic Review, 57, 101271. https://doi.org/10.1016/j.chieco.2019.01.010
  • Hartono, D., Yusuf, A. A., Hastuti, S. H., Saputri, N. K., & Syaifudin, N. (2021). Effect of COVID-19 on energy consumption and carbon dioxide emissions in Indonesia. Sustainable Production and Consumption, 28, 391-404. https://doi.org/10.1016/j.spc.2021.06.003
  • IEA. (2021). World energy outlook 2021. Accessed on 12.12.2024. https://www.iea.org/reports/world-energy-outlook-2021
  • IMF. (2023). World economic outlook database. Accessed on 10.12.2024. https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases#sort=%40imfdate%20descending
  • IPCC. (2013). The physical science basis, Contribution of working group I to the fifth 468 assessment report of the intergovernmental panel on climate change. Cambridge (UK), NY 469 (USA): Cambridge University Press.
  • Islam, M. M., Irfan, M., Shahbaz, M., & Vo, X. V. (2022). Renewable and non-renewable energy consumption in Bangladesh: The relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality. Renewable Energy, 184, 1130-1149. https://doi.org/10.1016/j.renene.2021.12.020
  • Kankal, M., Akpınar, A., Komurcu, I. M., & Ozsahin, S. T. (2011). Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927-1939. https://doi.org/10.1016/j.apenergy.2010.12.005
  • Karakurt, I. (2020). Energy consumption modelling using socio-economic indicators: Evidence from the BRICS-T countries. Journal of the Southern African Institute of Mining and Metallurgy, 120(7), 425-43. https://doi.org/10.17159/2411-9717/901/2020
  • Karakurt, I. (2021). Modelling and forecasting the oil consumptions of the BRICS-T countries. Energy, 220, 119720. https://doi.org/10.1016/j.energy.2020.119720
  • Kartal, M. T. (2022). The role of consumption of energy, fossil sources, nuclear energy, and renewable energy on environmental degradation in top-five carbon producing countries. Renewable Energy, 184, 871-880. https://doi.org/10.1016/j.renene.2021.12.022
  • Kavaklıoğlu, K., Ceylan, H., Ozturk, K. H., & Canyurt, E. O. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion Managemenet, 50(11), 2719–2727. https://doi.org/10.1016/j.enconman.2009.06.016
  • Khan, A. M., & Osinska, M. (2021). How to predict energy consumption in BRICS countries?. Energies, 14(10), 2749. https://doi.org/10.3390/en14102749
  • Khan, M. A., Rehan, R., Chhapra, I. U., & Bai, A. (2022). Inspecting energy consumption, capital formation and economic growth nexus in Pakistan. Sustainable Energy Technologies and Assessments, 50, 101845. https://doi.org/10.1016/j.seta.2021.101845
  • Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669-679. https://doi.org/10.1016/j.ijforecast.2015.12.003
  • Kok, B., & Benli, H. (2017). Energy diversity and nuclear energy for sustainable development in Turkey. Renewable Energy, 111, 870-877. https://doi.org/10.1016/j.renene.2017.05.001
  • Kone, A. Ç., & Buke, T. (2010). Forecasting of CO2 emissions from fuel combustion using trend analysis. Renewable and Sustainable Energy Reviews, 14(9), 2906-2915. https://doi.org/10.1016/j.rser.2010.06.006
  • Konuk, F., Zeren, F., Akpınar, S., & Yıldız, S. (2021). Biomass energy consumption and economic growth: Further evidence from NEXT-11 countries. Energy Reports, 7, 4825-4832. https://doi.org/10.1016/j.egyr.2021.07.070
  • Li, M. F., Tang, X. P., Wu, W., & Liu, H. B. (2013). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management, 70, 139-148. http://dx.doi.org/10.1016/j.enconman.2013.03.004
  • Lorente, D. B., Driha, O. M., Halkos, G., & Mishra, S. (2022). Influence of growth and urbanization on CO2 emissions: The moderating effect of foreign direct investment on energy use in BRICS. Sustainable Development, 30(1), 227-240. https://doi.org/10.1002/sd.2240
  • Mensah, I. A., Sun, M., Gao, C., Omari-Sasu, A. Y., Zhu, D., Ampimah, B. C., & Quarcoo, A. (2019). Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach. Journal of Cleaner Production, 228, 161-174. https://doi.org/10.1016/j.jclepro.2019.04.281
  • Oseni, M. O. (2012). Improving households’ access to electricity and energy consumption pattern in Nigeria: Renewable energy alternative. Renewable and Sustainable Energy Reviews, 16(6), 3967-3974. https://doi.org/10.1016/j.rser.2012.03.010
  • Ostrom, C. W. (1978). Time series analysis - Regression techniques. 1st ed. The USA, Sage Publications, p.82
  • Ozturk, S., & Ozturk, F. (2018). Forecasting energy consumption of Turkey by ARIMA model. Journal of Asian Scientific Research, 8(2), 52-60. https://doi.org/10.18488/journal.2.2018.82.52.60
  • Pao, H. T., & Tsai, C. M. (2021). 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. https://doi.org/10.1016/j.energy.2010.09.041
  • PwC. (2024). The world in 2050. Accessed on 15.03.2025. https://www.pwc.com/gx/en/world-2050/assets/pwc-the-world-in-2050-full-report-feb-2017.pdf
  • Simba, A. H. M., & Oztek, M. F. (2020). Empirical analysis of energy consumption and economic growth in Tanzania: based on Engel and Granger test. Journal of Economics, Finance and Accounting, 7(3), 250-262. http://doi.org/10.17261/Pressacademia.2020.1292
  • Somoye, O. A., Ozdeser, H., & Seraj, M. (2022). Modeling the determinants of renewable energy consumption in Nigeria: Evidence from Autoregressive Distributed Lagged in error correction approach. Renewable Energy, 190, 606-616. https://doi.org/10.1016/j.renene.2022.03.143
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Sixth ed. Boston: Pearson, p.1018.
  • Terzi, H., & Pata, U. K. (2016). The effect of oil consumption on economic growth in Turkey. Dogus University Journal, 17(2), 225-240.
  • Tuzemen, O. B., & Tuzemen, S. (2022). The impact of foreign direct investment and biomass energy consumption on pollution in BRICS countries: A panel data analysis. Global Journal of Emerging Market Economies, 14(1), 76-92. https://doi.org/10.1177/09749101211067092
  • Um, J., Yun, H., Jeong, C. S., & Heo, J. H. (2011). Factor analysis and multiple regression between topography and precipitation on Jeju Island Korea. Journal of Hydrology, 410(3-4), 189-203. https://doi.org/10.1016/j.jhydrol.2011.09.016
  • UN. (2018). United Nations, Department of Economic and Social Affairs, Population Division, World Urbanization Prospects: The 2018 Revision. Accessed on 20.03.2025. https://population.un.org/wup/downloads
  • Wang, M., Wang, M., & Wu, L. (2022a). Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China. Energy, 243, 123024. https://doi.org/10.1016/j.energy.2021.123024
  • Wang, Z., Pham, T. L. H., Sun, K., Wang, B., Bui, O., & Hashemizadeh, A. (2022b). The moderating role of financial development in the renewable energy consumption - CO2 emissions linkage: The case study of Next-11 countries. Energy, 254, 124386. https://doi.org/10.1016/j.energy.2022.124386
  • WBI. (2025). Worldbank indicators on total population. Accessed on 20.03.2025. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS
  • Yildirim, D. C., Yildirim, S., & Demirtas, I. (2019). Investigating energy consumption and economic growth for BRICS-T countries. World Journal of Science, Technology and Sustainable Development, 16(4), 84-195. http://doi.org/10.1108/WJSTSD-12-2018-0063
  • Zafar, M. V., Zaidi, A. H., Sinha, A., Gedikli, A., & Hou, F. (2019). The role of stock market and banking sector development, and renewable energy consumption in carbon emissions: Insights from G-7 and N-11 countries. Resources Policy, 62, 427-436. https://doi.org/10.1016/j.resourpol.2019.05.003
  • 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. https://doi.org/10.1016/S2212-5671(15)00890-4
  • Zeeshan, M., Han, J., Rehman, A., Ullah, I., Afridi, F. E. A., & Fareed, Z. (2022). Comparative analysis of trade liberalization, CO2 emissions, energy consumption and economic growth in Southeast Asian and Latin American regions: A structural equation modeling approach. Frontiers in Environmental Science, 10, 854590. https://doi.org/10.3389/fenvs.2022.854590

Forecasting Models Based on Total Population for Primary Energy Consumption of the N – 11 Countries

Year 2025, Volume: 30 Issue: 2, 719 - 736, 31.08.2025
https://doi.org/10.53433/yyufbed.1668438

Abstract

This study aims to establish forecasting models for primary energy consumption (PEC) of next eleven (N–11) countries based on total population (TP) using trend analysis (TA). In this regards, the data of TP and PEC spanning from 1985 to 2023 were utilized for the N – 11 countries to establish forecasting models. The established models were then validated by several statistical indicators. In addition, forecasting accuracies of the established models were measured by various error indices such as mean absolute deviation (MAD), root mean square error (RMSE), relative root mean square error (RRMSE), uncertainty at 95% (U95), maximum absolute relative error (erMAX) and mean absolute percentage error (MAPE). Moreover, the PECs of case countries were forecasted for period of ten years starting at 2025 by the established models. It is determined that the established models are able to successfully forecast the PECs of N – 11 countries. Furthermore, the forecasting results show evidently that significant increases are expected for the related countries in near future when taking into account the current studies’ conditions.

References

  • Aydin, G. (2014). Production modeling in the oil and natural gas industry: An application of trend analysis. Petroleum Science and Technology, 32(5), 555-564. https://doi.org/10.1080/10916466.2013.825271
  • Aydin, G. (2015). Forecasting natural gas production using various regression models. Petroleum Science and Technology, 33(15-16), 1486-1492. https://doi.org/10.1080/10916466.2015.1076842
  • Aydin, G., Kaya, S., & Karakurt, I. (2015, April). Modeling of energy consumption based on population: The case of Turkey. Proceedings of 24th International Mining Congress and Exhibition of Turkey, Antalya, Türkiye, pp. 88-92.
  • Azadeh, A., Saberi, M., Asadzadeh, S. M., & Khakestani, M. (2011). A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA, Canada, Singapore, Pakistan and Iran. Energy, 36(12), 6981-6992. https://doi.org/10.1016/j.energy.2011.07.016
  • Azevedo, V. G., Sartori, S., & Campos, L. M. S. (2018). CO2 emissions: A quantitative analysis among the BRICS nations. Renewable and Sustainable Energy Reviews, 81, 107-115. https://doi.org/10.1016/j.rser.2017.07.027
  • Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. https://doi.org/10.1016/j.energy.2009.06.034
  • 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. https://doi.org/10.1016/j.enconman.2014.07.081
  • BP. (2020). Technical report on BP energy outlook 2035. Accessed on 15.02.2025. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2015.pdf
  • BP. (2023). British Petroleum Energy Outlook – 2022, Insights from the Accelerated, Net Zero and New Momentum scenarios – Brazil. Accesed on 10.3.2025. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2024-country-insight-brazil.pdf
  • Byrne, R. F. (2012). Beyond traditional time-series: Using demand sensing to improve forecasts in volatile times. Journal of Business Forecasting, 31(2), 13-20.
  • Cambazoğlu, B. (2021). Comparative macroeconomic analysis of CIVETS Countries. Turkish Studies - Social Sciences, 15(2), 77-91. https://dx.doi.org/10.29228/TurkishStudies.40165
  • Celiker, M., Yukseler, U., & Dursun, Ö. F. (2021). Trend analyses for discharge-recharge of Tacin karstic spring (Kayseri, Turkey). Journal of African Earth Sciences, 184, 104344. https://doi.org/10.1016/j.jafrearsci.2021.104344
  • Chang, C. L., & Fang, M. (2022). Renewable energy-led growth hypothesis: New insights from BRICS and N-11 economies. Renewable Energy, 188, 788-800. https://doi.org/10.1016/j.renene.2022.02.052
  • Chiu, Y. B., & Lee, C. C. (2020). Effects of financial development on energy consumption: The role of country risks. Energy Economics, 90, 104833. https://doi.org/10.1016/j.eneco.2020.104833
  • Demircioglu, M., & Esiyok, S. (2022). Energy consumption forecast of Turkey using artificial neural networks from a sustainability perspective. International Journal of Sustainable Energy, 41(8), 1127-1141. https://doi.org/10.1080/14786451.2022.2026357
  • Despotovic, M., Nedic, V., Despotovic, D., & Cvetanovic, S. (2015). Review and statistical analysis of different global solar radiation sunshine models. Renewable and Sustainable Energy Reviews, 52, 1869-1880. https://dx.doi.org/10.1016/j.rser.2015.08.035
  • Do, T. M., & Sharma, D. (2011). Vietnam's energy sector: a review of current energy policies and strategies. Energy Policy, 39(10), 5770-5777. https://doi.org/10.1016/j.enpol.2011.08.010
  • Ehigiamusoe, K. U., & Dogan, E. (2022). The role of interaction effect between renewable energy consumption and real income in carbon emissions: Evidence from low-income countries. Renewable and Sustainable Energy Reviews, 154, 111883. https://doi.org/10.1016/j.rser.2021.111883
  • EI. (2024). Statistical review of world energy 2024. Accesed on 12.12.2024. https://www.energyinst.org/statistical-review
  • Erba, S., & Beydoğan, H. O. (2017). Attitudes of educators towards educational research. Kırsehir Journal of the Faculty of Education, 18(3), 246-260. https://dergipark.org.tr/tr/download/article-file/1487175
  • Ergezer, E. C. (2024). BRICS expansion and the rising voice of the global south. Turkish Economic Policy Research Foundation, an evaluation note. Accessed on 10.12.2024. https://tepav.s3.eu-west-1.amazonaws.com/upload/mce/2024/notlar/brics_genislemesi_ve_kuresel_guneyin_yukselen_sesi.pdf
  • Fareed, Z., & Pata, U. K. (2022). Renewable, non-renewable energy consumption and income in top ten renewable energy-consuming countries: Advanced Fourier based panel data approaches. Renewable Energy, 194, 805-821. https://doi.org/10.1016/j.renene.2022.05.156
  • Gazder, U. (2016, December). Energy consumption trends in energy scarce and rich countries: comparative study for Pakistan and Saudi Arabia. World Renewable Energy Congress-17, Bahrain, pp. 1 – 8. https://doi.org/10.1051/e3sconf/20172307002
  • Gusarova, S. (2019). Role of China in the development of trade and FDI cooperation with BRICS countries. China Economic Review, 57, 101271. https://doi.org/10.1016/j.chieco.2019.01.010
  • Hartono, D., Yusuf, A. A., Hastuti, S. H., Saputri, N. K., & Syaifudin, N. (2021). Effect of COVID-19 on energy consumption and carbon dioxide emissions in Indonesia. Sustainable Production and Consumption, 28, 391-404. https://doi.org/10.1016/j.spc.2021.06.003
  • IEA. (2021). World energy outlook 2021. Accessed on 12.12.2024. https://www.iea.org/reports/world-energy-outlook-2021
  • IMF. (2023). World economic outlook database. Accessed on 10.12.2024. https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases#sort=%40imfdate%20descending
  • IPCC. (2013). The physical science basis, Contribution of working group I to the fifth 468 assessment report of the intergovernmental panel on climate change. Cambridge (UK), NY 469 (USA): Cambridge University Press.
  • Islam, M. M., Irfan, M., Shahbaz, M., & Vo, X. V. (2022). Renewable and non-renewable energy consumption in Bangladesh: The relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality. Renewable Energy, 184, 1130-1149. https://doi.org/10.1016/j.renene.2021.12.020
  • Kankal, M., Akpınar, A., Komurcu, I. M., & Ozsahin, S. T. (2011). Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927-1939. https://doi.org/10.1016/j.apenergy.2010.12.005
  • Karakurt, I. (2020). Energy consumption modelling using socio-economic indicators: Evidence from the BRICS-T countries. Journal of the Southern African Institute of Mining and Metallurgy, 120(7), 425-43. https://doi.org/10.17159/2411-9717/901/2020
  • Karakurt, I. (2021). Modelling and forecasting the oil consumptions of the BRICS-T countries. Energy, 220, 119720. https://doi.org/10.1016/j.energy.2020.119720
  • Kartal, M. T. (2022). The role of consumption of energy, fossil sources, nuclear energy, and renewable energy on environmental degradation in top-five carbon producing countries. Renewable Energy, 184, 871-880. https://doi.org/10.1016/j.renene.2021.12.022
  • Kavaklıoğlu, K., Ceylan, H., Ozturk, K. H., & Canyurt, E. O. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion Managemenet, 50(11), 2719–2727. https://doi.org/10.1016/j.enconman.2009.06.016
  • Khan, A. M., & Osinska, M. (2021). How to predict energy consumption in BRICS countries?. Energies, 14(10), 2749. https://doi.org/10.3390/en14102749
  • Khan, M. A., Rehan, R., Chhapra, I. U., & Bai, A. (2022). Inspecting energy consumption, capital formation and economic growth nexus in Pakistan. Sustainable Energy Technologies and Assessments, 50, 101845. https://doi.org/10.1016/j.seta.2021.101845
  • Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669-679. https://doi.org/10.1016/j.ijforecast.2015.12.003
  • Kok, B., & Benli, H. (2017). Energy diversity and nuclear energy for sustainable development in Turkey. Renewable Energy, 111, 870-877. https://doi.org/10.1016/j.renene.2017.05.001
  • Kone, A. Ç., & Buke, T. (2010). Forecasting of CO2 emissions from fuel combustion using trend analysis. Renewable and Sustainable Energy Reviews, 14(9), 2906-2915. https://doi.org/10.1016/j.rser.2010.06.006
  • Konuk, F., Zeren, F., Akpınar, S., & Yıldız, S. (2021). Biomass energy consumption and economic growth: Further evidence from NEXT-11 countries. Energy Reports, 7, 4825-4832. https://doi.org/10.1016/j.egyr.2021.07.070
  • Li, M. F., Tang, X. P., Wu, W., & Liu, H. B. (2013). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management, 70, 139-148. http://dx.doi.org/10.1016/j.enconman.2013.03.004
  • Lorente, D. B., Driha, O. M., Halkos, G., & Mishra, S. (2022). Influence of growth and urbanization on CO2 emissions: The moderating effect of foreign direct investment on energy use in BRICS. Sustainable Development, 30(1), 227-240. https://doi.org/10.1002/sd.2240
  • Mensah, I. A., Sun, M., Gao, C., Omari-Sasu, A. Y., Zhu, D., Ampimah, B. C., & Quarcoo, A. (2019). Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach. Journal of Cleaner Production, 228, 161-174. https://doi.org/10.1016/j.jclepro.2019.04.281
  • Oseni, M. O. (2012). Improving households’ access to electricity and energy consumption pattern in Nigeria: Renewable energy alternative. Renewable and Sustainable Energy Reviews, 16(6), 3967-3974. https://doi.org/10.1016/j.rser.2012.03.010
  • Ostrom, C. W. (1978). Time series analysis - Regression techniques. 1st ed. The USA, Sage Publications, p.82
  • Ozturk, S., & Ozturk, F. (2018). Forecasting energy consumption of Turkey by ARIMA model. Journal of Asian Scientific Research, 8(2), 52-60. https://doi.org/10.18488/journal.2.2018.82.52.60
  • Pao, H. T., & Tsai, C. M. (2021). 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. https://doi.org/10.1016/j.energy.2010.09.041
  • PwC. (2024). The world in 2050. Accessed on 15.03.2025. https://www.pwc.com/gx/en/world-2050/assets/pwc-the-world-in-2050-full-report-feb-2017.pdf
  • Simba, A. H. M., & Oztek, M. F. (2020). Empirical analysis of energy consumption and economic growth in Tanzania: based on Engel and Granger test. Journal of Economics, Finance and Accounting, 7(3), 250-262. http://doi.org/10.17261/Pressacademia.2020.1292
  • Somoye, O. A., Ozdeser, H., & Seraj, M. (2022). Modeling the determinants of renewable energy consumption in Nigeria: Evidence from Autoregressive Distributed Lagged in error correction approach. Renewable Energy, 190, 606-616. https://doi.org/10.1016/j.renene.2022.03.143
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Sixth ed. Boston: Pearson, p.1018.
  • Terzi, H., & Pata, U. K. (2016). The effect of oil consumption on economic growth in Turkey. Dogus University Journal, 17(2), 225-240.
  • Tuzemen, O. B., & Tuzemen, S. (2022). The impact of foreign direct investment and biomass energy consumption on pollution in BRICS countries: A panel data analysis. Global Journal of Emerging Market Economies, 14(1), 76-92. https://doi.org/10.1177/09749101211067092
  • Um, J., Yun, H., Jeong, C. S., & Heo, J. H. (2011). Factor analysis and multiple regression between topography and precipitation on Jeju Island Korea. Journal of Hydrology, 410(3-4), 189-203. https://doi.org/10.1016/j.jhydrol.2011.09.016
  • UN. (2018). United Nations, Department of Economic and Social Affairs, Population Division, World Urbanization Prospects: The 2018 Revision. Accessed on 20.03.2025. https://population.un.org/wup/downloads
  • Wang, M., Wang, M., & Wu, L. (2022a). Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China. Energy, 243, 123024. https://doi.org/10.1016/j.energy.2021.123024
  • Wang, Z., Pham, T. L. H., Sun, K., Wang, B., Bui, O., & Hashemizadeh, A. (2022b). The moderating role of financial development in the renewable energy consumption - CO2 emissions linkage: The case study of Next-11 countries. Energy, 254, 124386. https://doi.org/10.1016/j.energy.2022.124386
  • WBI. (2025). Worldbank indicators on total population. Accessed on 20.03.2025. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS
  • Yildirim, D. C., Yildirim, S., & Demirtas, I. (2019). Investigating energy consumption and economic growth for BRICS-T countries. World Journal of Science, Technology and Sustainable Development, 16(4), 84-195. http://doi.org/10.1108/WJSTSD-12-2018-0063
  • Zafar, M. V., Zaidi, A. H., Sinha, A., Gedikli, A., & Hou, F. (2019). The role of stock market and banking sector development, and renewable energy consumption in carbon emissions: Insights from G-7 and N-11 countries. Resources Policy, 62, 427-436. https://doi.org/10.1016/j.resourpol.2019.05.003
  • 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. https://doi.org/10.1016/S2212-5671(15)00890-4
  • Zeeshan, M., Han, J., Rehman, A., Ullah, I., Afridi, F. E. A., & Fareed, Z. (2022). Comparative analysis of trade liberalization, CO2 emissions, energy consumption and economic growth in Southeast Asian and Latin American regions: A structural equation modeling approach. Frontiers in Environmental Science, 10, 854590. https://doi.org/10.3389/fenvs.2022.854590
There are 62 citations in total.

Details

Primary Language English
Subjects Energy, Coal
Journal Section Engineering and Architecture / Mühendislik ve Mimarlık
Authors

Büşra Demir Avci 0009-0001-1023-1318

İzzet Karakurt 0000-0002-3360-8712

Gökhan Aydın 0000-0002-6670-6458

Publication Date August 31, 2025
Submission Date March 31, 2025
Acceptance Date July 10, 2025
Published in Issue Year 2025 Volume: 30 Issue: 2

Cite

APA Demir Avci, B., Karakurt, İ., & Aydın, G. (2025). Forecasting Models Based on Total Population for Primary Energy Consumption of the N – 11 Countries. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 30(2), 719-736. https://doi.org/10.53433/yyufbed.1668438