FORECASTING OF CO2 WITH THE EFFECT OF RENEWABLE ENERGY, NON-RENEWABLE ENERGY, GDP AND POPULATION FOR TURKEY: FORECASTING WITH NMGM (1,N) GRAY FORECASTING MODEL

Yıl 2021, , 810 - 828, 21.12.2021

Özlem Karadağ Albayrak

https://doi.org/10.36543/kauiibfd.2021.033

Cited By: 1

Öz

Karbondioksit salınımı çevre üzerinde olumsuz etkisi olan önemli faktörlerden birisidir. Politika yapıcıların yenilenebilir enerji konusunda teşvik politikaları üretmelerinin nedenlerinden biri de CO2 emisyonlarını azaltmayı istemeleridir. Bu noktadan hareketle CO2 emisyonlarının farklı faktörlere bağlı olarak tahmini yapılmalı ve tahmin sonuçlarına göre yeni politikalar geliştirilip uygulanmalıdır. Bu makalede, yenilenebilir enerji tüketimi, yenilenemeyen enerji tüketimi, GSMH ve Nüfus faktörlerinin zaman içinde CO2 emisyonu üzerindeki etkisini ölçmek için gri tahmin modellerinden yeni bir yaklaşım olan NMGM (1, N) tahmin modeli kullanılmıştır. Bu çalışmada 2006-2015 verisi similasyon seti, 2016-2019 verileri test seti olarak kullanılmıştır. Bu yönteme ek olarak GM (1, N) ve çok değişkenli gri tahmin yöntemi olan ekonometrik model ile tahmin yapılmış ve sonuçlar karşılaştırılmıştır. Sonuç olarak NMGM (1, N) tahmin modeli çok düşük sapma değerleri ile oldukça etkili bir tahmin sunmuştur.

Anahtar Kelimeler

Çok değişkenli gri tahmin modeli, NMGM (1, N), ekonometrik modeller, co2 emisyonları, yenilenebilir enerji tüketimi, yenilenemez enerji tüketimi, GSYİH, nüfus.

FORECASTING OF CO2 WITH THE EFFECT OF RENEWABLE ENERGY, NON-RENEWABLE ENERGY, GDP AND POPULATION FOR TURKEY: FORECASTING WITH NMGM (1,N) GRAY FORECASTING MODEL

Öz

Carbon dioxide emission is one of the important factors that have a negative impact on the environment. One of the reasons why policy makers produce incentive policies on renewable energy is that they want to reduce CO2 emissions. From this point of view, prediction of CO2 emissions must be made depending on different factors, and new policies can be developed and implemented according to the prediction results. In this article, a new approach from gray estimation models, NMGM (1, N) forecasting model, is used to measure the impact of renewable energy consumption, non-renewable energy consumption, GDP and Population factors on CO2 emission over time. 2006-2015 data was simulation set and 2016-2019 data was used as a test set. In addition to this method, estimation was made with GM (1, N) and econometric model, which is the multivariate gray estimation method, and the results were compared. As a result, NMGM (1, N) model has become a very effective estimation method with very low deviation values.

Anahtar Kelimeler

Multivariate grey prediction model, NMGM (1, n), econometric models, co2 emissions, renewable energy consumption, non-renewable energy consumption, GDP, population.

Kaynakça

• Asumadu-Sarkodie S., Owusu P. A. (2017). Carbon dioxide emissions, GDP per capita, industrialization and population: An evidence from Rwanda. Environ. Eng. Res. 22(1), 116-124
• Deng, J.L. (1982). Control Problems of Grey Systems. Systems & Control Letters, 1, 288-294.
• Ding S., Dang, Y-G., Li X-M., Wang J-J.& Zhao K. (2017). Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model. Journal of Cleaner Production 162, 1527-1538.
• Jo T. C., (2003,). The effect of virtual term generation on the neural based approaches to time series prediction. In Proceedings of the IEEE fourth conference on control and automation, Montreal (ss.516–520), Canada.
• Kamzaçebi C., Karakurt I. (2015).Forecasting the Energy-related CO2 Emissions of Turkey Using a Grey Prediction Model. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 37, 1023–1031.
• Kayacan, E., Ulutas, B. & Kaynak, O., (2010). Grey system theory-based models in time series prediction. Expert Syst. Appl, 37, 1784-1789.
• Liu. S. and Lin Y. (2010). Grey Information Theory and Practical Applications, Springer-Verlag, Berlin.
• Menyah K. and Wolde-Rufael Y. (2010). Energy consumption, pollutant emissions and economic growth in South Africa, Energy Economics 32, 1374–1382.
• Nathaniel S. P. and Iheonu C. O. (2019). Carbon dioxide abatement in Africa: The role of renewable and non-renewable energy consumption. Science of the Total Environment 679, 337–345.
• Pai, T.Y., Lo, H.M. & Wan, T.J. (2015). Predicting air pollutant emissions from a medical incinerator using grey model and neural network. Appl. Math. Model. 39 (5), 1513-1525.
• 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, 400-409.
• Pao, H.T., Tsai, C.M. (2011). Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy 36 (5), 2450-2458.
• Sharma S. S. (2011). Determinants of carbon dioxide emissions: Empirical evidence from 69 countries, Applied Energy 88, 376–382.
• Shaheen A., Sheng J., Arshad S., Muhammad H. & Salam S. (2020). Forecasting the determinants of environmental degradation: a gray modeling approach. Energy Sources, Part A: Recovery, Utilization, And Environmental Effects Https://Doi.Org/10.1080/15567036.2020.1827090
• Valadkhani A., Smyth R .& Nguyen J. (2019). Effects of primary energy consumption on CO2 emissions under optimal thresholds: Evidence from sixty countries over the last half century. Energy Economics 80, 680–690.
• Xie M.,Yan S.,Wu L., Liu L., Bai Y., Liu L. & Tong Y. (2021). A novel robust reweighted multivariate grey model for forecasting the greenhouse gas emissions. Journal of Cleaner Production 292, 126001.
• Xu Z., Liu L. & Wu, L. (2021). Forecasting the carbon dioxide emissions in 53 countries and regions using a non-equigap grey model. Environmental Science and Pollution Research, 28, 15659–15672.
• Wang, Z.X., Ye, D.J. (2016). Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models. J. Clean. Prod. 142, 600-612.
• Wang, Z.X., Hao, P. (2016). An improved grey multivariable model for predicting industrial energy consumption in China. Applied Mathematical Modelling, 40 (11), 5745-5758.
• Wu L., Liu S., Liu D., Fang Z. & Xu H. (2015). Modelling and forecasting CO2emissions in the BRICS (Brazil, Russia,India, China, and South Africa) countries using a novel multi-variablegrey model. Energy, 79, 489-495490.
• Ye L., Xie N. & Hu A. (2021). A novel time-delay multivariate grey model for impact analysis of CO2 emissions from China’s transportation sectors. Applied Mathematical Modelling 91, 493–507.
• Yilmaz H. and Yilmaz M. (2013). Forecasting the Energy-related CO 2 Emissions of Turkey Using a Grey Prediction Model. Sigma, 31, 141-148.
• Zeng B., Duan H. & Zhou Y. (2019). A new multivariable grey prediction model with structure Compatibility, Applied Mathematical Modelling, 75, 385–397.
• Zeng B., Luo C., Liu S., Bai Y.& Li C. (2016). Development of an optimization method for the GM(1,N) model, Engineering Applications of Artificial Intelligence, 55, 353–362.
• Zeng B., Li H. & Ma X. (2020).A novel multi-variable grey forecasting model and its application in forecasting the grain production in China. Computers & Industrial Engineering, 150, 106915.
• British Petroleum (BP). (2021). https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html. Accessed on 17.06. 2021
• World Bank (WB). (2021) https://databank.worldbank.org/source/world-development-indicators. Accessed on 12.06. 2021
• Turkish Statistical Institute (TSI) (2021). https://tuikweb.tuik.gov.tr/UstMenu.do?metod=temelist. Accessed on 07.08. 2021
• Earth System Science Data (ESSD). (2019). https://www.ucsusa.org/resources/each-countrys-share-co2-emissions. Accessed on 05.05. 2021
• International Energy Agency (IEA) Global Energy Review 2021. (2021). https://www.iea.org/reports/global-energy-review-2021/co2-emissions#abstract. Accessed on 10.04. 2021.